In this episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, speaks with Kailin Noivo, President and Co-Founder of Noibu, and Rohit Nathany, Chief Product and Technology Officer at Mejuri. Together, they unpack what is holding ecommerce teams back from sustained revenue and conversion growth and what actually works in practice.

Ecommerce Revenue Growth in a High-Cost, High-Expectation Market

Today’s ecommerce environment is shaped by rising acquisition costs, operational sprawl, and customers who expect speed, relevance, and reliability by default. Rohit points to macroeconomic pressure, tariffs, and shifting buying behaviour as forces that are squeezing margins while raising the bar for customer experience.

At the same time, brands are struggling to connect the dots between marketing spend, on-site behaviour, and conversion outcomes. Personalisation is widely discussed, but execution often breaks down when teams cannot see how customer interactions move from ad click to checkout. Kailin describes this as a “perfect storm”, explaining that: “infrastructure scaled rapidly during the pandemic, and now needs consolidation, optimisation, and clearer ownership.”

Customer Experience, Team Alignment, and the Practical Use of AI

Improving customer experience at scale requires more than simply adopting new technology. Organisations also need the right data, processes, and operational alignment to turn those tools into meaningful customer outcomes. It requires teams to work from the same signals and trust the same data. Both Kailin and Rohit stress that AI and automation only deliver value when they remove friction from day-to-day operations rather than adding another layer of complexity.

Used well, AI can support data analytics by automating routine monitoring, surfacing patterns that matter, and freeing teams to focus on higher-value work. Used poorly, it becomes just another disconnected tool. The difference comes down to team alignment and culture, like clear ownership, shared goals, and a willingness to continuously refine how decisions are made.

For ecommerce leaders, this is less about digital transformation as a slogan and more about operational discipline. Simplifying the stack, aligning teams around outcomes, and treating customer experience as a measurable business driver are what sustain revenue growth when conditions are uncertain.

If you would like to find out more, visit: https://www.noibu.com/

Takeaways

• Building resilience in revenue and conversion growth is crucial.
• Ecommerce leaders face a perfect storm of challenges.
• AI is central to enhancing customer experience in ecommerce.
• Data-rich environments often lead to insight-poor outcomes.
• Connecting the dots between data and decisions is essential.
• A strong culture of experimentation fosters innovation.
• Tool consolidation can streamline operations and reduce costs.
• Visibility in data access is critical for effective decision-making.
• Speed of action is influenced by organisational culture.
• Establishing a KPI tree helps unify team efforts.

Chapters

00:00 Introduction to Ecommerce Challenges

06:04 Real-World Applications of Ecommerce Analytics & Monitoring

11:50 The Role of AI in Ecommerce

17:57 Data Utilisation and Decision Making

24:13 Culture and Team Alignment in Ecommerce

29:56 Practical Strategies for Ecommerce Leaders

Instead, revenue is earned continuously through product usage, introducing new challenges for finance teams around billing accuracy, revenue visibility, forecasting, and managing increasingly complex cost structures driven by AI-powered products.

In the latest episode of Tech Transformed, host Dana Gardner speaks with Lee Greene, Vice President of Sales at Vayu, about how AI and usage-based pricing are reshaping the economics of SaaS and why many companies are discovering that their pricing strategy is only as strong as the infrastructure behind it.

One idea from the conversation

“Pricing strategy is only as strong as the infrastructure behind it.”

What you will learn in this episode

• Why usage-based pricing exposes hidden revenue leakage in many SaaS companies
• • How AI-driven products introduce unpredictable cost structures and margin pressure
• • Why disconnected CRM, product, and ERP systems break revenue visibility
• • What finance and revenue teams need to support scalable usage-based billing and forecasting

Why SaaS Economics Are Breaking Away From Fixed Subscriptions

Greene argues that usage-based pricing isn’t simply an emerging trend. It is a response to assumptions that no longer hold true.

Traditional SaaS subscription models were built around predictable costs and relatively stable product usage. AI-driven products have fundamentally changed that equation. Each interaction with an AI-powered system can create variable cost, making static pricing models increasingly difficult to sustain.

This shift is also changing buyer expectations. Customers increasingly resist flat pricing structures and instead prefer models that reflect the value they actually receive. Usage-based pricing aligns economic benefit with real consumption, allowing buyers to justify spend internally while pushing vendors to be accountable for measurable outcomes rather than bundled feature sets.

AI’s Double Role

The conversation also highlights how AI is introducing a structural challenge for SaaS finance and revenue teams.

Usage-based pricing generates enormous volumes of data across product usage, customer behaviour, and cost inputs. Traditional billing systems were not designed to process this level of complexity.

At the same time, AI is also becoming the only scalable way to manage it. Automated usage tracking, dynamic pricing logic, and real-time billing reconciliation are increasingly necessary to maintain operational accuracy and financial control.

Treating AI solely as a product capability, rather than embedding it into revenue operations, can leave organizations exposed to billing errors, misaligned pricing models, and revenue leakage.

Revenue Management Shifts From Contracts to Operations

One of Greene’s key observations is that usage-based pricing does not necessarily create revenue leakage. Instead, it reveals problems that already existed.

The difference is visibility.

In traditional SaaS models, revenue was largely secured at the moment of contract signature. In usage-based models, revenue must be earned continuously through product consumption. This means billing accuracy, system integration, and data flow directly influence financial performance.

Disconnected CRM, product, and ERP systems can create gaps that lead to misbilling, delayed revenue recognition, and customer disputes. As a result, the infrastructure supporting revenue operations becomes inseparable from pricing strategy itself.

What SaaS Leaders Must Build to Stay Economically Viable

The discussion concludes with a broader perspective on how SaaS companies must evolve to support this new economic model.

The future belongs to organizations that design their pricing and revenue systems for variability. Pricing models must adapt to changing demand, and the systems behind them must support that flexibility without relying on heavy manual processes.

Automation and no-code AI tools are increasingly enabling finance and revenue teams to adjust pricing models as usage patterns evolve. This agility is not simply about speed. It is about maintaining control in an environment where AI-driven cost structures and product usage can shift rapidly.

Usage-based pricing is doing more than changing how SaaS products are sold. It is reshaping how companies think about value, risk, and revenue itself, making flexibility, intelligent automation, and data-driven decision making central to long-term success.

About Vayu

Vayu helps SaaS companies manage complex usage-based and hybrid revenue models by connecting product usage data, billing systems, and finance infrastructure.

Learn more at:https://www.withvayu.com/

Takeaways

• The shift from fixed subscription models to usage-based pricing driven by AI
• How AI is both creating and solving new pricing and billing challenges
• Why revenue infrastructure plays a critical role in preventing revenue leakage
• The importance of flexible pricing models that adapt to demand and usage patterns
• The growing role of automation and AI in modern revenue operations

Chapters

00:00 – Introduction

02:30 – The economic shift in SaaS: Moving toward usage-based models

05:00 – The role of AI in transforming SaaS pricing and revenue streams

06:47 – Buyer preferences and evolving value quantification

08:38 – Infrastructure's role in supporting flexible billing models

11:49 – How finance teams can shape technology to control revenue

14:24 – Process reengineering and AI-driven automation

17:15 – Adaptable SaaS infrastructure and market signals

20:30 – Preparing for the unknown: sandboxing and scenario modeling

24:49 – Opportunities in connecting SaaS apps and managing data flow

28:54 – Building automated, scalable billing and integration flow

Understanding the Digital Thread

The digital thread represents the traceability of all decisions and information regarding a product from its inception and throughout its lifecycle. According to Laura Beckwith, the digital thread allows manufacturers to trace decisions made during the requirements stage through to engineering and ultimately to manufacturing and service. This traceability is not just about having data; it’s also about ensuring that various teams and systems can access the right information to facilitate informed decision-making.

Challenges in Implementing the Digital Thread

Despite the promise that digital threads hold, manufacturers face significant challenges in connecting data from multiple systems. Beckwith highlights the example of a smartphone, which undergoes various phases from design to manufacturing. Each phase involves distinct software systems—like CAD for design and ERP for manufacturing—many of which do not communicate well with one another. This lack of integration often leads to inefficiencies, such as manual data entry and miscommunication between teams.

The Impact of Customisation on Complexity

As customisation becomes the norm, the complexity of managing product data increases exponentially. Beckwith notes that while smartphones may have limited customisations, products like cars offer vast configurability. For instance, when configuring a car, consumers can choose from an extensive array of options. Behind the scenes, however, manufacturers must manage numerous engineering constraints and compliance regulations. This is where the digital thread becomes essential, enabling manufacturers to track and manage these complex configurations effectively.

The Role of Configuration Lifecycle Management (CLM)

The upcoming CLM Summit 2026 will focus on mastering customisation complexity and building a reliable data foundation for configurable products. Beckwith explains that a scalable CLM approach is crucial for establishing a reliable digital thread. It ensures that all product configurations, such as the combination of seat heating and memory seats in a car, are tracked accurately. This not only aids in the manufacturing process but also enhances customer service by allowing manufacturers to address issues based on specific configurations.

More broadly, the digital thread provides manufacturers with a framework for managing the growing complexity of modern product development. By enabling seamless communication between data systems and implementing effective CLM practices, organisations can better align engineering, manufacturing, and service functions. 

For more information visit: https://configit.com/

Takeaways

• The digital thread provides traceability of product decisions.
• Manufacturers face challenges due to siloed data systems.
• Customisation complexity is increasing in manufacturing.
• Digital threads are crucial for configurable products like cars.
• CLM helps bridge the gap between engineering and marketing.
• Starting small can lead to the successful implementation of digital threads.
• Data alignment is essential for effective communication.
• Real-world examples illustrate the benefits of digital threads.
• A strong digital thread enhances customer experience.
• AI can leverage data from digital threads for predictive maintenance.

Chapters

00:00 Introduction to Digital Threads in Manufacturing

02:14 Understanding the Digital Thread

06:47 Challenges in Connecting Data Systems

11:12 Customisation, Complexity, and Digital Threads

15:43 The Role of Configuration Lifecycle Management (CLM)

20:23 Real-World Use Case: Implementing Digital Threads

23:42 Guidance for Early Adopters of Digital Threads

In this episode of the Tech Transformed, Shubhangi Dua speaks with Camden Swita, Head of AI, New Relic, about why AI observability has become a critical requirement for modern enterprises, particularly as agentic AI and AI-driven operations take on increasingly autonomous roles.

The discussion explores how traditional observability models fall short when applied to probabilistic systems, why many AI ops initiatives stall at proof-of-concept, and what security and IT leaders must prioritize to safely scale AI in production.

Be the first to see how intelligent observability takes you beyond dashboards to agentic AI with business impact at New Relic Advance, February 24, 2026.

Why AI Adoption Is Outpacing Operational Readiness

While AI adoption is accelerating rapidly, most organizations still lack visibility into what their AI systems are actually doing once deployed. Generative AI is already widely used for natural language querying, coding assistants, customer support bots, and increasingly within IT operations and SRE workflows.

As these systems move into production, new challenges emerge around cost control, governance, performance quality, and trust. Leaders recognize AI’s potential value, but without deep observability, they struggle to determine whether AI-enabled systems are delivering consistent outcomes or introducing hidden operational and security risks.

How Observability Must Evolve for Agentic AI and AI Ops

The episode then examines how observability itself must evolve to support agentic and autonomous AI systems. While core observability principles still apply, AI introduces a new layer of complexity that requires visibility into model behavior, agent decision-making, and multi-step workflows.

Modern AI observability extends traditional application performance monitoring by capturing telemetry from LLM interactions, agent orchestration layers, and automated evaluations of output quality against intended use cases.

Without this visibility, teams are effectively operating blind, unable to diagnose failures, validate compliance, or confidently deploy AI at scale. At the same time, AI is increasingly being embedded into observability platforms to reduce noise, accelerate root cause analysis, and improve incident response.

Making Agentic AI Work in Practice

Successful adoption starts with low-risk, high-friction tasks such as incident triage, dashboard interpretation, and runbook summarization, rather than fully autonomous remediation. These use cases deliver immediate productivity gains while preserving human oversight. Over time, stronger feedback loops, better context management, and human-in-the-loop learning allow agents to become more reliable and useful. Looking ahead, Camden predicts that 2026 will be a turning point for agentic AI in production, driven by maturing AI observability platforms, richer semantic data, and knowledge graphs that connect technical telemetry to real business outcomes.

Listen to Are “Vibe-Coded” Systems the Next Big Risk to Enterprise Stability?

When Vibe Code Breaks Ops

AI-generated code is pushing prototypes into production faster than ops can cope. How observability becomes the gatekeeper for enterprise resilience.

Key Takeaways

1. AI adoption is accelerating in enterprise environments.
2. Organizations face complexities in productionizing AI features.
3. Natural language querying is a common AI application.
4. AI agents are increasingly used in IT operations.
5. Observability is crucial for understanding AI systems.
6. Traditional observability solutions are evolving to include AI monitoring.
7. Incident response teams struggle with alert noise and context gathering.
8. AI can assist in incident management and root cause analysis.
9. Future trends include more reliable AI agents and monitoring solutions.
10. Organizations need to invest in AI observability to succeed.

Chapters

1. 01:20 The Current State of AI Adoption
2. 02:28 Purposeful AI Usage in Organizations
3. 04:40 Observability in the Age of AI
4. 08:05 Evolving Observability Solutions
5. 11:36 Challenges in Incident Response
6. 16:04 Integrating AI in Operations
7. 23:33 Future Trends in AI Monitoring
8. 30:29 Investment Strategies for AI Solutions

#ArtificialIntelligence #EnterpriseAI #GenerativeAI #AgenticAI #AIAgents #AIObservability #AIInProduction #AIOps #AISecurity #AIGovernance #ModelMonitoring #LLMOps #ITOperations #SRE #DevOps #IncidentResponse #RootCauseAnalysis #DigitalTransformation #Automation #FutureOfAI

In this episode of Tech Transformed, host Jon Arnold and Ben Chodor, Chief Executive Officer of CallRevu, about how AI is reshaping the way dealerships handle calls, manage repair orders, and engage with customers throughout their journey. They explore the role of real-time analytics in improving interactions, the importance of answering every incoming call, and why AI has become essential in modern dealership operations.

Customer Experience Has Changed

The customer journey is no longer a simple transaction. Today, it spans pre-purchase research, purchasing, and post-purchase support. Chodor highlights that every interaction matters; customers now expect engagement and guidance at every stage, not just information. 

Competition in automotive sales is fierce, and customers expect fast responses. Chodor notes that dealerships leveraging AI can provide updates on service times, answer inquiries promptly, and ensure no customer engagement is lost. Real-time insights also empower managers to make better operational decisions and improve the overall customer experience.

AI in Automotive Dealerships

AI technology is changing the way dealerships operate. Chodor discusses how CallRevu’s technology listens to every sales and service call, providing real-time analytics to dealerships. This capability allows managers to intervene in calls, ensuring that customer concerns are addressed promptly. For instance, if a call goes unanswered, the system can alert management, enabling them to engage with the customer immediately, thus reducing missed opportunities.

The integration of AI and analytics in automotive dealerships is not just about improving sales; it's about transforming the entire customer experience. From ensuring every call is answered to providing real-time insights for better decision-making, technology is reshaping how dealerships engage with customers. As the automotive industry continues to evolve, those who prioritise customer experience through innovative solutions will undoubtedly lead the way.

If you would like to find out more information, go to https://www.callrevu.com/

Takeaways

• AI enhances customer engagement in automotive dealerships.
• Real-time analytics can significantly improve communication.
• Every call to a dealership is crucial for sales.
• AI helps reduce the number of calls going to voicemail.
• Dealerships must adapt to a more competitive landscape.
• Customer experience is more than just selling cars.
• AI can provide instant responses to customer inquiries.
• Training tools powered by AI can improve sales techniques.
• The automotive industry is shifting towards data-driven decisions.
• AI is essential for modern dealership operations.

Chapters

00:00 Introduction to Customer Experience in Automotive Dealerships

05:00 The Role of AI in Enhancing Communication

10:08 Transforming Customer Engagement with Real-Time Analytics

15:03 The Importance of Incoming Calls and Tracking

19:55 AI's Impact on the Automotive Industry

24:49 Future Trends in Automotive Technology

Guest: Manesh Tailor, EMEA Field CTO, New Relic

Host: Shubhangi Dua, B2B Tech Journalist, EM360Tech

AI-driven development has become obsessive recently, with vibe-coding becoming more common and accelerating innovation at an unprecedented rate. This, however, is also leading to a substantial increase in costly outages. Many organisations do not fully grasp the repercussions until their customers are affected.

In this episode of the Tech Transformed Podcast, EM360Tech’s Podcast Producer and B2B Tech Journalist, Shubhangi Dua, spoke with Manesh Tailor, EMEA Field CTO at New Relic, about why AI-generated code, also called vibe-coding, rapid prototyping, and a focus on speed create dangerous gaps. They also talked about why full-stack observability is now crucial for operational resilience in 2026 and beyond.

AI Vibe Code Prioritising Speed over Stability

AI has changed how software is built. Problems are solved faster, prototypes are created in hours, and proofs-of-concept (POC) swiftly reach production. But this speed comes with drawbacks.

“These prototypes, these POCs, make it to production very readily,” Tailor explained. “Because they work—and they work very quickly.”

In the past, the time needed to design and implement a solution served as a natural filter. However, the barrier has now disappeared.

Tailor tells Dua: “The problem occurs, the solution is quick, and these things get out into production super, super fast. Now you’ve got something that wasn’t necessarily designed well.”

The outcome is that the new systems work but do not scale. They lack operational resilience and greatly increase the cognitive load on engineering teams.

New Relic's research indicates that in EMEA alone:

1. The annual median cost of high-impact IT outages for EMEA businesses is $102 million per year
2. Downtime costs EMEA businesses an average of $2 million per hour
3. More than a third (37%) of EMEA businesses experience high-impact outages weekly or more often.

Essentially, AI-driven development heightens risks and increases blind spots. “There are unrealised problems that take longer to solve—and they occur more often,” Tailor noted. This is because many AI-generated solutions overlook operability, scaling, or long-term maintenance.

Modern architectures were already complex before AI came along. Microservices, SaaS dependencies, and distributed systems scatter visibility across the stack.

“We’ve got more solutions, more technology, more unknowns, all moving faster,” he tells Dua. “That’s generated more data, more noise—and more blind spots.”

Traditional monitoring tools were built for known issues—predefined components, predictable dependencies, and static systems. “Monitoring was about what you already understood,” Tailor explained. “Observability is about the unknown unknowns.”

AI-generated code complicates the situation because teams often lack detailed knowledge of how that code was created, how components interact, or how dependencies change over time.

This is where full-stack observability becomes essential—not as a standalone tool, but as a coordinated capability that connects signals across applications, infrastructure, data, and AI systems in real time.

Also Watch: How Do AI and Observability Redefine Application Performance?

Reactive to Proactive: The Role of AI in Observability

Ironically, the same AI that increases complexity is also necessary to manage it. According to New Relic data, 96 per cent of organisations plan to adopt AI monitoring and 84 per cent plan to implement AIOps by 2028.

However, Tailor stresses that success relies on using AI to enhance—rather than replace—human expertise. “We have to leverage AI to establish baselines much faster,” he said. “But humans still bring experience and judgment that machines don’t have.”

AI allows teams to shift from responding to known patterns to proactively spotting anomalies before they turn into customer-facing incidents.

Beyond uptime and performance, observability is becoming a regulatory requirement. “If it’s not observed, then it’s rogue,” Tailor warned.

New regulations like the EU AI Act and ISO 42001 will require organisations to show visibility into AI systems, decision-making processes, and operational behaviour. “You won’t be allowed to operate AI solutions without the right level of observability,” he added.

The 2026 Takeaway: Observability is Essential for AI

As AI-driven development becomes the norm, Tailor’s message to CIOs, CTOs, and CDOs is: “Observability isn’t an option. Without it, your AI strategy simply won’t work.”

Organisations that neglect to invest in centralised, full-stack observability risk more than outages—they risk compliance failures, security issues, and rising operational costs.

“Otherwise,” Tailor stated, “you will limit the ability to benefit from your AI strategy.”

To learn more, visit NewRelic.com or listen to the full episode of the Tech Transformed podcast at EM360Tech.com.

Also Watch: How Can AI Bridge the Gap from Observability to Understandability?

Takeaways

1. If you don't get your observability house in order, all the grand plans with AI may be at risk.
2. Speed has been favoured over good governance and engineering standards.
3. Observability is about understanding the relationship between components, not just monitoring known issues.
4. AI can help establish baselines faster in a rapidly changing environment.
5. Without observability, you can't make your AI strategy work.

Chapters

1. 00:00 Introduction to AI and Observability
2. 01:11 The Risks of Rapid Software Development
3. 04:21 Understanding the Cost of Outages
4. 06:30 Blind Spots in AI-Driven Systems
5. 11:29 Transitioning to Full-Stack Observability
6. 13:58 Moving from Reactive to Proactive Monitoring
7. 18:54 Real-World Applications of AI Monitoring
8. 19:51 The Future of AI and Observability

#Observability #AIOps #AIDrivenDevelopment #FullStackObservability #ITOutages #VibeCoding #AIinProduction #DevOps #NewRelic #TechPodcast

The First Mile and Climate Pressures

Horn’s perspective comes from a mix of experience: growing up on a farm, studying physics, and working in investment banking. That combination gives him a lens on both the natural systems that underpin agriculture and the data-driven tools that help manage risk.

Extreme weather patterns like droughts, heavy rainfall, and hurricanes are putting pressure on crops such as cocoa, coffee, wheat, and soy. The consequences ripple outward: production costs rise, commodity prices fluctuate, and supply chains become less predictable. A simple example illustrates this clearly: certain chocolate biscuits in the UK have moved from being chocolate-filled to chocolate-flavoured, reflecting disruptions in cocoa production in West Africa caused by extreme weather and disease. These changes are not isolated; they affect global markets and everyday products.

Turning Data into Actionable Insights

AI can help make sense of the complexity. Treefera, for instance, combines satellite imagery, sensor data, and other datasets to provide insights on crop yields, supply risks, and climate impacts. Horn describes it like a car dashboard: “You don’t need to know every technical detail to understand what’s happening and act accordingly.”

The value of AI lies not in flashy algorithms but in its ability to translate raw data into practical decision-making tools. By analysing multiple signals from weather events to agricultural output, AI can highlight trends, flag potential disruptions, and support planning for traders, insurers, or supply chain managers. The goal is clarity and action, not simply more information.

Data, Regulation, and Responsible Use

Alongside operational complexity, organisations face questions about data governance. Emerging regulations such as the EU AI Act aim to ensure AI is used responsibly, and companies need to maintain control over proprietary information while leveraging technology effectively. Horn stresses the importance of frugal, transparent AI applications that produce meaningful insights without unnecessary complexity.

In practice, this means balancing innovation with compliance: using AI to understand risks, improve planning, and support sustainability without overstating its capabilities or creating new vulnerabilities. The conversation underlines a key point: the impact of AI is most tangible when it’s applied thoughtfully, in service of real-world decisions.

In short, AI is helping organisations navigate the increasingly unpredictable intersection of climate, risk, and supply chain complexity. The first mile, long a blind spot, is becoming visible not through hype or marketing claims, but through practical, data-driven insight that helps people respond to the world as it is, not as we wish it to be.

Takeaways

• AI can significantly improve the management of supply chains.
• Climate change is causing more extreme weather patterns, affecting agriculture.
• Data sovereignty is crucial for companies to maintain control over their proprietary data.
• AI native businesses leverage AI as a core component of their operations.
• The EU AI Act aims to create a framework for responsible AI use.
• AI can help simplify complex information into actionable insights.
• Frugal AI usage can lead to more efficient operations.
• The evolution of AI technologies includes advancements in large language models.
• Understanding the risks associated with climate change is essential for supply chain management.
• Companies must balance compliance with innovation in AI applications.

Chapters

00:00 Introduction to AI in Supply Chains

04:40 Navigating Climate Challenges with AI

09:40 AI-Native Business Models

13:57 The Evolution of AI Technologies

18:16 Understanding Data Sovereignty

21:54 Balancing AI Regulation and Innovation

25:48 Future of AI in Sustainability

About John Horn

Jonathan Horn is the founder of Treefera, an AI platform delivering accurate, auditable data to support carbon offsetting and nature-positive initiatives for landowners, investors, governments, NGOs, scientists, and marketplace participants. An innovative thinker and problem solver, he holds a PhD in Theoretical Fluid Dynamics and has extensive experience designing and implementing AI and data analytics solutions at scale. Jonathan has also applied data mesh principles to enable distributed, data-driven organisations, with a background in optimising banking operations through advanced data and AI systems.

In a recent episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, spoke with Stella d’Ambrumenil, Product Manager at Configit, about the operational realities and future potential of generative AI technology in manufacturing.

The Challenge of Complexity

Modern manufacturers often operate somewhere between make-to-order and assemble-to-order models. While these approaches allow flexibility, they also expose companies to a major problem, such as fragmented configuration processes. Sales teams, engineers, and manufacturing units may all handle different aspects of customisation separately, relying on spreadsheets or outdated product documentation. The result is inefficiency, errors, and an inability to scale effectively.

“The problem isn’t just that you have lots of options,” Stella explains. “It’s that the knowledge about those options is scattered. If configuration is handled differently across departments, you inevitably get mistakes and lost time.”

Configit Ace® Prompt: Bridging the Gap

Enter Configit Ace® Prompt, the latest tool designed to tackle this very problem. At its core, Configit Ace® Prompt converts unstructured data into structured configuration logic that can be used across all departments. Formalising configuration knowledge ensures that customisation is accurate, repeatable, and manageable.

This approach not only reduces errors but also democratizes access to critical product information. Engineers, product managers, and sales teams no longer need to interpret fragmented data manually — they can work from a single source of truth. Early adopters report significant time savings, fewer mistakes, and smoother collaboration.

Why Configuration Lifecycle Management Matters

Configit Ace® Prompt is a key enabler of Configuration Lifecycle Management (CLM). CLM is an approach to maintaining consistent data and processes across the entire product lifecycle — from design and engineering to manufacturing and service. This is crucial for companies seeking to scale customisation without creating chaos in operations.

By adding generative AI technology, manufacturers can implement a CLM approach faster to automate logic creation, catch configuration errors early, and ensure that complex products are delivered efficiently.

Looking Ahead: CLM Summit 2026

For professionals interested in deepening their understanding of configuration management, Configit’s CLM Summit 2026 — an online event scheduled for May 6 & 7 - will provide insights into best practices, advanced strategies, and tools like Configit Ace® Prompt. It’s an opportunity to see how companies can leverage configuration management to stay competitive in a world of growing product complexity.

For more insights, visit: configit.com

Takeaways

• Manufacturers face increasing challenges with product complexity and customisation demands.
• Configit Ace® Prompt helps convert unstructured product knowledge into usable configuration logic.
• Configuration Lifecycle Management (CLM) is crucial for establishing and maintaining a shared source of truth.
• Product data fragmentation leads to inefficiencies in manufacturing processes.
• AI can assist in catching errors in configuration data.
• The tool aims to lower the barrier to entry for data consolidation.
• Excel remains a popular tool, but Configit Ace® Prompt offers a familiar interface.
• Early beta testers have reported significant time savings with Configit Ace® Prompt.
• Generative AI has potential applications in guided configuration and data analysis.
• The upcoming CLM Summit will provide insights into product configuration management.

Chapters

00:00 Introduction to Tech Transformed and Configit

02:48 Understanding Product Complexity in Manufacturing

05:54 The Role of Configit Ace® Prompt in Configuration Management

08:53 Configuration Lifecycle Management Explained

11:52 The Importance of Data Consistency and Cleanup

15:14 User Experience and Adoption of Configit Ace® Prompt

17:54 Generative AI and Its Future Applications

21:07 Conclusion and Future Events

About Configit

At Configit, we help our customers globally to master the challenges of getting configurable products to market faster, with higher quality and engineered at lower costs. As a pioneer of Configuration Lifecycle Management (CLM), we have been instrumental in driving the adoption of CLM solutions globally. Trusted by the world’s largest manufacturing companies for their mission-critical functions, our advanced configuration platform built on patented Virtual Tabulation® technology handles the most complex products on the market. Our customers include ABB, Jaguar Land Rover, John Deere, Grundfos, Vestas, Siemens, Danfoss amongst others.

In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Gary Eimerman, Chief Learning Officer at Multiverse, about the pressing challenge of closing the AI and data skills gap in the workforce. They explore how organisations can build an AI-ready workforce, focusing on non-technical employees and the importance of a skills-first approach to learning.

The Skills-First Approach

Multiverse champions a skills-first approach to upskilling employees in AI and data, asserting that this targeted training drives measurable business impact, including increased productivity, revenue growth, and time savings. This strategy moves beyond general AI literacy to focus on practical, applied learning. By diagnosing both organisational needs and individual skill levels, the approach identifies gaps and prescribes tailored, project-based learning experiences. Employees don’t just complete modules in isolation; they work on real-world projects that apply the skills they are learning from day one, reinforcing retention and ensuring that training contributes to tangible outcomes.

Learning in the AI Era

Gary explains that learning in the AI era is not simply about providing tools or access to content; it’s about driving behaviour change, aligning learning with business outcomes, and embedding a culture of continuous skill development. As AI reshapes both the work we do and the way we learn, organisations that invest in people-first strategies position themselves to thrive rather than merely adapt. This conversation demonstrates that the future of work is always on learning, and that meaningful investment in AI and data skills is no longer optional; it’s a critical driver of business success.

Unlocking Workforce Potential

By combining practical, applied training with ongoing support and measurable outcomes, companies can not only close the AI skills gap but also unlock the full potential of their workforce in an era defined by rapid technological change.

Takeaways

• Technology alone is never enough; people must be invested in.
• Reskilling is a necessity due to technological disruption.
• Organisations must focus on human behaviour change, not just software deployment.
• A skills-first approach is critical for effective learning.
• Learning should be project-based and applied immediately.
• Non-technical roles are increasingly adopting AI tools.
• Creating time and space for learning is essential.
• Highlighting success stories builds confidence in using AI.
• Measuring impact through metrics like revenue per employee is vital.
• The future of work requires a cultural shift towards continuous learning.

Chapters

00:00 Closing the AI and Data Skills Gap

02:02 Challenges in Building an AI-Ready Workforce

06:06 The Skills First Approach to Learning

10:04 Supporting Non-Technical Employees in AI

13:46 Measuring the Impact of AI Skills Investment

18:13 The Evolution of Learning in the AI Era

22:59 Preparing for the Future of Work

About Multiverse

Multiverse is the upskilling platform for AI and tech adoption. Multiverse has partnered with over 1,500 companies to deliver a new kind of learning that’s transforming the workforce through tech skills.

Multiverse apprenticeships are for people of any age or career stage and focus on critical AI, data and tech skills. Multiverse learners have driven $2bn + ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.

For more information, visit www.multiverse.io

The Evolution of Dealership Communication

Communication has always been at the heart of dealership operations. The phone system was once the primary lifeline between customers and dealerships, giving managers the visibility needed to ensure interactions were handled correctly. Today, communication extends far beyond the phone. SMS, MMS, instant messaging, and other channels allow customers to engage in multiple ways.

Sean explains how integrating these channels into a single technology platform provides managers with a clear view of all interactions, ensuring employees follow policies and customers receive the attention they deserve. This approach strengthens trust and improves the overall customer experience.

Compliance and Data Privacy in Automotive Communication

Alongside multi-channel communication, compliance and data privacy are critical. Regulations like GDPR and UN R155 require dealerships to protect customer data while maintaining seamless communication. Transparent practices, combined with adherence to regional rules, help build trust and protect both customers and the dealership’s reputation. Observing patterns in customer interactions also allows dealerships to make informed decisions, improve processes, and enhance service quality. Using these data insights, dealerships can make communication more effective and meaningful for every customer.

Infrastructure That Keeps Dealerships Operational

Reliable infrastructure underpins all communication efforts. Sean shares how dealerships can prepare for unexpected disruptions with geo-redundant systems, cloud-based platforms, and layered internet backups, including options like Starlink or fibre connections. These measures ensure dealerships stay operational, customers can reach them without interruption, and business continuity is maintained.

Preparing for Emerging Communication Channels

As new channels emerge, proactive preparation is key. Dealerships that view communication as an investment, rather than a cost, position themselves for long-term success. Monitoring trends, adapting quickly, and fostering transparency help maintain strong customer relationships even as expectations evolve.

Training and Staff Development

Staff development is a critical component of a communication strategy. By using insights from technology platforms, dealerships can guide employee training, build accountability, and create a culture of learning. Confident, well-trained teams contribute to consistent, high-quality interactions that enhance customer trust.

Success in automotive communication isn’t just about adopting the latest tools—it’s about building systems and practices that protect customers, support employees, and foster trust at every touchpoint. Sean Barrett’s insights provide a roadmap for dealerships aiming to elevate communication strategies, improve customer satisfaction, and maintain resilience in an increasingly connected world.

Learn more: callrevu.com

Takeaways

1. Trust and transparency are essential for customer relationships.
2. The evolution of communication systems has introduced multi-channel interactions.
3. Compliance challenges vary across regions and regulations.
4. AI can provide deeper insights into customer interactions.
5. Training with AI can enhance employee performance and accountability.
6. Dealerships should view communication as an investment, not an expense.
7. Data insights are crucial for improving customer experience.
8. Emerging communication channels require proactive preparation.
9. A risk-free training environment can foster employee growth.
10. Customer lifetime value is realised through effective data utilisation.

Chapters

00:00 Introduction to Trust and Transparency in Automotive Communication

03:06 Evolution of Dealership Communication Systems

05:02 Building Reliable Communication Infrastructure

07:13 Navigating Compliance Challenges in Automotive

09:12 Preparing for Emerging Communication Channels

11:15 The Role of AI in Automotive Communication

14:10 Bridging the Gap Between AI and Human Interaction

16:35 Success Stories and Training Innovations

18:22 Practical Actions for Dealerships

22:14 Conclusion and Key Takeaways

About CallRevu

CallRevu is the leading communication intelligence platform built for automotive retail, empowering dealerships to take control of every conversation, from the first ring to the final result. Its unified solution combines a hosted phone system, call monitoring, performance training, and reputation management fueled by AI-powered analytics that turn every customer interaction into actionable intelligence.

Founded in a dealership in 2008, CallRevu was created by the industry, for the industry. We deliver the tools dealerships need to drive revenue, improve operations, and deliver exceptional customer experiences.

Moving Away from Monolithic CDPs

Organisations are moving away from rigid, all-in-one CDPs as regulations around data privacy, consent, and cross-border data flows intensify. Joe explains that companies can no longer rely on systems that lock them into a single architecture or make compliance retrofitting difficult. Data governance, consent management, and data sovereignty have become critical considerations in every technology decision, forcing leaders to rethink the underlying structure of their CDPs.

Challenges in Composable Systems

While composable CDPs offer flexibility, they introduce new challenges. Organisations must define ownership and accountability within modular systems to prevent fragmentation and ensure consistent data quality. Leadership must consider how compute, storage, and access are distributed across modules while maintaining compliance and security standards. Joe notes that without clarity on ownership, organisations risk operational inefficiency and weakened governance.

Flexibility and Modularity in Data Management

The core advantage of composable architectures lies in modularity. By decoupling components from data ingestion to activation, organisations gain the freedom to innovate without being constrained by a monolithic platform. Joe emphasises: “You need flexibility in where data lives, how compute happens, ultimately doubling down on sovereignty, security, and that composable idea that initially started with data.” This approach allows teams to adopt new tools, scale selectively, and respond to changing business or regulatory requirements with agility.

Embracing First-Party Data Strategies

The shift to first-party data strategies is essential in today’s marketing landscape. With third-party cookies being phased out and privacy regulations tightening, companies must rely on direct, trusted data from their customers. Composable CDPs provide the framework to centralise first-party data while giving teams the ability to personalise experiences, maintain compliance, and safeguard trust. Joe highlights that organisations need to view data not just as an asset, but as a responsibility, balancing customer value with ethical management.

Here are what leaders can do:

1. Rethink data architecture: Move from monolithic to composable systems to gain flexibility, scalability, and regulatory alignment.
2. Prioritise governance: Define ownership, consent management, and security practices across modular components.
3. Focus on first-party data: Build direct customer relationships and leverage trusted data responsibly.
4. Embrace modularity: Enable innovation, adaptability, and resilience in data management through composable design.

This episode offers practical insights for leaders navigating the transition from traditional CDPs to composable architectures. It highlights how thoughtful design, governance, and first-party data strategies empower organisations to act with agility, comply with regulations, and deliver better customer experiences.

For more information, book a demo with Uniphore.

Takeaways

• Organisations are moving away from rigid monolithic CDPs due to regulatory pressures.
• Composable architectures offer flexibility and modularity in data management.
• The shift to first-party data strategies is essential in the current landscape.
• Data governance and consent management are critical in modern marketing.
• Organisations face challenges in defining ownership within composable systems.
• Uniphore supports hybrid deployment options for data management.
• AI integration is crucial for enhancing CDP functionalities.
• Flexibility in architecture helps avoid vendor lock-in.
• People and processes are as important as technology in CDP implementation.
• Clear alignment on objectives is necessary for successful transitions.

Chapters

00:00 The Shift from Monolithic CDPs to Composable Architectures

08:18 Understanding the Limitations of Monolithic CDPs

10:59 Rethinking First-Party Data Strategies

17:44 Challenges in Implementing Composable CDPs

21:18 Uniphore Role in Composable Marketing Intelligence

25:28 Future Considerations for CDP Ecosystems

About Uniphore

Uniphore is a B2B artificial intelligence (AI) company that provides a full-stack Business AI Cloud platform for enterprises to manage customer interactions, sales, marketing, and internal operations. Founded in 2008 and incubated at IIT Madras, Uniphore has dual headquarters in Palo Alto, California, and Chennai, India, and reached unicorn status with a $2.5 billion valuation in 2022. The platform is designed to be sovereign, composable, and secure, allowing businesses to connect diverse data sources, leverage AI models, and deploy AI agents across the enterprise. Uniphore’s offerings include Customer Service AI for agent guidance and analytics, Sales AI for real-time insights, Marketing AI with CDP capabilities, and People AI for HR automation.

In the recent episode of the Tech Transformed podcast, Dialpad Lead Product Manager Calvin Hohener sits down with host Jon Arnold, Principal at J Arnold & Associates. They discuss the transition from legacy chatbots to more autonomous agents capable of completing tasks and improving customer interactions.

The conversation highlights the importance of understanding the technology's impact on enterprise architecture, the need for clean data, and the strategic implications for C-level executives. Hohener emphasises the importance of starting with clear use cases and working closely with vendors to maximise the potential of AI in business operations.

From Legacy Chatbots to Agentic AI

Most people have used chatbots and found them lacking. Hohener explains why: earlier conversational AI was based on retrieval-augmented generation (RAG). These systems could take user input, search a knowledge base or the internet, and provide an answer. This was helpful for customer service queries, but limited.

“Previous AI models could retrieve and return information, but now we’re moving into a new phase with agentic AI.” Agentic AI can take action rather than just providing information. 

For AI agents to succeed, organisations must first organise their data. “How your internal knowledge is structured is crucial. Even if the data is unorganised, you need to know its location and ensure it’s clean,” stated Hohener.

Agentic systems depend on internal knowledge, including knowledge base articles, CRM notes, and process documentation. If this foundation is disordered, the agent’s output will not be reliable. 

This isn’t about achieving ideal data cleanliness from the start; it’s about knowing what information exists, where it is, and whether it can be trusted. If an AI agent bases its decisions on outdated, conflicting, or incomplete content, it will struggle to perform tasks aptly, regardless of how sophisticated the model is. Enterprises need at least basic clarity about which systems hold which knowledge, who is responsible for them, and whether there is consistency across sources.

Hohener noted that organisations often overlook how quickly conflicting information can undermine an otherwise well-designed agent. A single outdated procedure or mismatched policy in a knowledge repository can lead an AI to produce incorrect results or halt during workflow execution. 

Keeping internal content clean, deduplicated, and consistent gives the agent a reliable, valid source. This reliability becomes crucial when AI starts taking meaningful actions, not just providing answers.

By focusing on data readiness early, enterprises not only reduce deployment obstacles but also set the stage for scaling agentic AI across more complex processes. In many ways, preparing data isn’t just a technical task; it’s an organisational one. 

How Human Agents Work with AI Agents?

The Dialpad Lead Product Manager noted that human roles, too, will evolve with agentic AI entering the contact centre. For instance, human agents will take on more of an advisory role—reviewing conversation traces and helping adjust the models.”

Instead of just resolving customer issues, they will help refine and oversee AI workflows. This includes reviewing conversation logs, noting where an AI agent may have misinterpreted intent, and providing feedback to improve the models.

This mentor-mentee relationship between human and digital agents becomes crucial as organisations increase automation. Human agents bring domain knowledge, contextual judgment, and the ability to handle unique situations, all of which help the AI improve over time. In return, digital agents reduce the repetitive workload and allow humans to engage in higher-level thinking. 

Takeaways

• The time is now for adopting agentic AI solutions.
• Agentic AI represents a significant shift in customer experience technology.
• Legacy chatbots have limitations that agentic AI can overcome.
• Real-world applications of agentic AI include mundane tasks like scheduling and verification.
• Quantifying time savings is crucial for measuring ROI with AI.
• AI agents can improve customer satisfaction metrics when implemented correctly.
• Data organisation is essential for effective AI deployment.
• Human agents will focus on more complex cases as AI handles routine tasks.
• C-level executives should view AI as a strategic investment.
• Collaboration with reputable vendors is key to successful AI implementation.

Chapters

• 00:00 Introduction to Agentic AI
• 05:10 Evolution Beyond Legacy Chatbots
• 11:44 Real-World Applications of Agentic AI
• 15:38 Impact on Enterprise Architecture
• 21:41 Strategic Considerations for C-Level Executives

AI isn’t just a buzzword here; it’s a practical lever. It can cut through the repetitive tasks that slow teams down, surface the information they need instantly, and free them to focus on high-value work. The companies winning aren’t replacing staff; they’re removing the obstacles that keep people from doing their best. In a world where speed and accuracy matter more than ever, ignoring that shift isn’t optional.

In the latest episode of Tech Transformed, hosted by Christina Stathopolus, founder of Dare to Data, Daniel Mackey, CEO of Teamwork.com, discussed how AI is reshaping the daily operations of client service teams. From automating repetitive admin tasks to surfacing critical information faster, AI is giving teams the bandwidth to focus on the work that truly drives value for clients. 

AI and Business Transformation in Practice

During the conversation, Mackey highlighted how AI is reshaping business operations, emphasising efficiency and productivity rather than job displacement. “AI has transformed our company,” he noted, pointing to tangible improvements across workflow and project management. Teams are now able to focus on strategic initiatives, leaving repetitive tasks to intelligent systems.

The Teamwork.com CEO also shared a recent example from a government agency that integrated AI into its processes. By automating routine administrative work, the agency experienced better resource allocation and improved project outcomes. “They’re more efficient, higher quality,” Mackey said. “AI allows them to focus on the bigger parts of the business.”

Rethinking Productivity and Client Delivery

One of the challenges in the industry is that most AI features are added onto existing tools that weren’t designed for client services. Mackey discussed how TeamworkAI addresses this gap. Built into a platform designed specifically for managing client services end-to-end, TeamworkAI connects projects, people, and profits in one system.

By integrating AI directly into client delivery workflows, organisations can streamline project management, reduce manual reporting, and ensure that technology enhances rather than disrupts service delivery. This approach allows businesses to use technology strategically, rather than simply automating isolated tasks.

Technology and the Future of Work

The discussion also touched on the broader impact of AI on traditional business models. Organisations that adopt AI thoughtfully can improve their internal processes, freeing employees from repetitive tasks and enabling them to contribute to higher-value projects. Mackey emphasised that the goal isn’t just automation, it’s profitable client delivery. AI can unlock both time and insight, allowing businesses to prioritise the most impactful work.

AI is redefining how businesses allocate resources, manage projects, and deliver value to clients. By eliminating repetitive work and connecting projects, people, and profits, technologies like TeamworkAI show how AI can drive efficiency, productivity, and business transformation without sacrificing the human element.

To discover how your team can leverage TeamworkAI to deliver seamless projects, smarter resourcing, and stronger profits, visit teamwork.com

Takeaways

• Profitable client delivery now depends on AI – learn what actually works before investing in the wrong tools.
• Efficiency gains from AI lead directly to better performance, improving output and reducing wasted effort.
• AI and automation free up resources, allowing organisations to focus on strategic priorities.
• Technology adoption reshapes business models, ensuring organisations remain competitive in a rapidly changing landscape.
• AI integration is critical for competitiveness; without it, margins and business outcomes could be at risk.

Chapters

-00:00 Introduction to Client Services and AI

- 3:00 The Role of AI in Streamlining Workflows

- 9:00 Centralising Data for Efficiency

- 15:00 Maintaining Human-Centred Client Relationships

- 21:00 Future of Technology in Client Services

- 27:00 Takeaways for CTOs and CISOs

About Teamwork.com

Teamwork.com is an AI-powered project and resource management platform designed to keep client projects running smoothly, simplify resource planning, and help businesses protect their margins.

Headquartered in Cork, Ireland, Teamwork.com employs more than 200 people worldwide, with additional hubs in Denver, Gdańsk, and Belfast. The company supports teams across industries with tools that make collaboration clearer, workflows more efficient, and delivery more predictable.

“I view it not just as a business opportunity, but we are here to solve a problem that has existed as long as commerce has,” Gozzo said. He emphasised that AI's goal isn’t just efficiency. It is about building trust and clearly understanding customer needs to allow productive interactions.

Artificial intelligence, he noted, “has really enabled what used to be much more costly to happen at scale.” The Ada Chief Product and Technology Officer pointed out that the best customer experiences are highly personalised. Comparing it to arriving at a luxury hotel where the staff already knows your name, even on your first visit. He noted that modern AI aims to make such experiences, which were once only for a select few, common for everyone.

Looking to the future, Gozzo tells Stathopoulos he believes generative AI will foster more engagement between customers and brands. “If I consider the trend, I think we will have much more natural, personalised, and effortless interactions than ever before because of this technology.”

Gen AI’s impact on Customer Data 

When discussing operational challenges, especially regarding customer data management, the guest speaker stressed quality over quantity. Gozzo explained that in most AI set-ups, “the real value lies not in the data you’ve collected, but in the understanding of how your business runs, operates, and the people doing the tasks you want to automate.”

Governance, Human Orchestration & the Future of AI

Beyond personalisation, AI should be implemented responsibly and monitored closely. “The first thing with any AI deployment is to avoid thinking of it as software you buy, deploy, and forget. They need ongoing monitoring, engagement, and maintenance,” Gozzo tells Stathopoulos. 

He suggested thorough testing processes and collaboration with specialised companies like AIUC, which verify AI systems against common risks. “These tests need to happen quarterly or yearly because the underlying models change so rapidly,” he added.

In addition to regularly conducting AI checks, the human element is also critical. AI might automate up to 80% of routine tasks, but humans will still play a vital role. Gozzo described the human role as that of an orchestrator, managing teams that include both humans and AI systems and effectively delegating tasks between them.

Finally, Gozzo talked about AI's immediate impact on customer experience. “Our leading customers’ AI agents are outperforming humans. They deliver higher-quality customer service experiences, and customers prefer interacting with their AI.” The key measure, he said, is the positive effect on business growth and customer lifetime value.

The chief technology officer’s parting advice to IT decision makers is: “The people on your team know how to make AI work. Capture their insights. Don’t treat this as a technology project. The technologist will not dominate the next decade. This is about business leaders and experts doing the heavy lifting.”

At the core of generative and agentic AI, Gozzo reminded listeners, are humans—the operational leaders and domain experts who embed knowledge into AI systems and drive real change.

Takeaways

• AI can transform customer experience from reactive to proactive.
• Quality data is more important than large data sets.
• Human insight is crucial for effective AI implementation.
• Governance and continuous monitoring are essential for AI systems.
• Collaboration between humans and AI is the future of work.
• The role of human agents will shift from execution to management and delegation.
• The most mature companies are moving beyond metrics like CSAT to measure AI’s impact of outcomes like customer lifetime value and retention.
• Testing and validation processes are key to successful AI deployment.
• AI can and is delivering better-than-human customer service.
• The success of AI initiatives relies on operational leaders' insights.

Chapters

• 00:00 Transforming Customer Experience with AI
• 02:51 The Role of Data in AI Solutions
• 05:48 Governance and Security in AI Deployment
• 09:03 Human-AI Collaboration in Customer Service
• 12:01 The Future of AI in Customer Experience

About Ada

Ada is the trusted AI-native customer service company, built to transform how enterprises engage with customers. Powered by the Ada ACX operating model—which unifies technology, methodology, and expertise—Ada deploys high-performing AI agents that deliver personalized, efficient interactions across every channel and language. 

Since 2016, Ada has powered over 5.5 billion interactions for leading brands like Square, Peloton, Canva, and monday.com, delivering extraordinary experiences at scale through higher-quality, more efficient customer service. 

With enterprise-grade trust, security, and compliance (SOC 2, GDPR, HIPAA, AIUC-1), Ada enables organizations to reduce cost-to-serve, elevate CSAT, and confidently scale AI-powered customer experience. Learn more at ada.cx.

In this episode of Tech Transformed, Trisha Pillay speaks with Jana Vidis, Business Development Manager at IFB, about the worldwide 3G sunset, what it means for enterprises, and how proactive planning can prevent costly disruptions. They explore the reasons behind the transition to 4G and 5G, the impact on various industries, and the strategies organisations can implement to assess their reliance on legacy devices. 

Why the 3G Sunset Matters

3G networks have powered connectivity for decades, offering wide coverage and reliability. But as global operators move to 4G and 5G, maintaining 3G is no longer sustainable. Carriers are discontinuing services, and support is dwindling, leaving legacy devices vulnerable to:

• Operational downtime
• Inconsistent performance
• Increased security risks

Jana emphasises:

“Have a good understanding of what devices you have. Work with IT partners to prepare for future changes. Plan your transition and act before disruption hits.”

Jana also stressed the importance of understanding current technology deployments, planning for transitions, and future-proofing investments to avoid disruptions. The conversation highlights the need for proactive measures in adapting to technological advancements and ensuring operational continuity.

A Global Timeline

The transition is already well underway across multiple regions:

• North America: AT&T, Verizon, and T-Mobile 3G networks discontinued in February 2022; Canada’s shutdown begins in early 2025.
• Europe: Most countries, including the UK, Germany, Hungary, and Greece, will complete shutdowns by the end of 2025.
• Asia: Japan phased out 3G in 2022, Singapore in July 2024, and India plans completion by the end of 2025.
• Africa: South Africa started in July 2025; other countries are slowing the transition.
• South America: Providers like Telefonica, Entel, and Claro completed shutdowns in 2022–2023.
• Middle East: Oman started shutting down in July 2024; Zain Bahrain in Q4 2022; Kuwait, Iran, and Jordan are following.

Industrial devices still using 3G must transition now to avoid operational disruption. From smart infrastructure to remote IoT systems, legacy devices left unaddressed can cause downtime, inconsistent performance, and increased security risks.

Takeaways

• 3G networks are being phased out to enable 4G and 5G development.
• Businesses must assess their reliance on 3G devices before shutdowns.
• Legacy devices can cause operational disruptions if not addressed.
• Understanding current technology deployments is crucial for businesses.
• Proactive planning can mitigate risks associated with network changes.
• Investing in infrastructure now can save costs in the future.
• Collaboration with IT partners is essential for smooth transitions.
• Testing new devices before rollout is a best practice.
• The demand for faster connectivity is driving technological advancements.
• Future-proofing technology investments is key to long-term success.

Chapters

00:00 The Global Shift from 3G Networks

02:48 Understanding the Impact on Businesses

05:44 Assessing Reliance on 3G Devices

08:57 Operational Changes and Disruptions

12:02 Strategies for Transitioning to New Technologies

14:55 Future-Proofing Connectivity Decisions

17:44 Key Takeaways and Final Thoughts

About Jana Vidis

Jana Vidis is a cybersecurity and technology expert passionate about helping businesses protect their operations and make technology work smarter. At IFB, she delivers tailored solutions, supports clients, and drives growth through strategic business development.

A champion for women in tech, Jana leads Women in Tech Aberdeen, promoting diversity, inclusion, and empowerment in the North East of Scotland. Her expertise spans cybersecurity, disaster recovery, resilience planning, connectivity, hosted voice, and cloud solutions.

In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability.

The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. 

What’s Beyond MELT?

Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications.

“Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added.

“What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.”

The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don’t explain why.

“Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don’t see the network, you can’t find the root cause.”

The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps.

Elastiflow’s approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. 

Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks.

“Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.”

Isolated Monitoring to Unified Experience 

Cowart believes that observability can’t just be about green lights and red lights, or whether a switch is on or off. It has to stress the experience, not only the user’s but also how one application interacts with another.

“The biggest shift organizations need to make,” the ElastiFlow CEO advises, “is to move from infrastructure health to usage experience. What really counts is whether the service being delivered is performing as it should.”

A unified experience can be achieved if leaders disband conventional barriers between teams. Imagine a Venn diagram where DevOps, NetOps, and SecOps overlap—each relying on network data as a common source of truth. The network becomes the central point that connects performance, reliability, and security around shared goals as per Cowart. 

This philosophy supports ElastiFlow’s latest innovation – integrating network flow data directly into OpenTelemetry traces. Rather than treating network analytics as a separate console, ElastiFlow now includes connectivity information as part of the same trace that developers and SREs use to monitor applications.

“It’s the first time anyone has done this,” Cowart points out. “An engineer can now see within their observability platform not just an HTTP call but also the related network session, latency, and context—all in one view.”

By embedding the “fifth pillar” of observability—network flows— ElastiFlow aims to prepare organizations for future challenges. With Kubernetes, hybrid cloud, and microservices making the network more abstract yet increasingly essential, visibility at this level is no longer optional; it’s strategic.

“As infrastructure becomes self-healing and redundant,” Cowart concludes, “leaders need to concentrate less on isolated device health and more on the experience being delivered. That’s where the business value lies, and that’s where the network finally becomes clear.”

Takeaways

• Legacy observability tools often overlook network connectivity.
• The MELT framework leaves a critical gap – network flow data.
• Network blind spots lead to extended troubleshooting cycles.
• XOps (NetOps, DevOps, SecOps) data siloshinder effective problem resolution.
• Security threats exploit the lack of network visibility, especially in east-west traffic.
• Network flow data is essential for comprehensive observability.
• Integrating application context enhances network data utility.
• Collaboration between IT teams is necessary for effective observability.

Chapters

• 00:00 Introduction to Network Observability
• 06:04 The Blind Spot in Traditional Observability
• 12:08 Security Implications of Network Observability
• 18:03 Integrating Network Data with Application Context
• 24:08 Future of Comprehensive Observability

In this episode of Tech Transformed, analyst and host Dana Gardner speaks with two leaders from across the IBM portfolio: Maria Bracho, CTO for the Americas at Red Hat, and Tyler Lynch, Field CTO for the HashiCorp product suite. 

They discuss how organisations can move from scattered cloud operations to a unified, automated model that supports AI securely and at scale. The conversation covers the pressures leaders face today, the role of automation, and the skills and operating model changes required as AI becomes core to enterprise strategy. 

What you’ll learn 

• Why tool sprawl and shrinking teams are increasing operational risk 
• How AI amplifies gaps in data, security, and processes 
• What skills and operating model changes CIOs must prioritise 
• Why hybrid cloud is essential for multi-model AI workloads 
• The growing importance of automation in cloud and AI delivery 
• How poor data hygiene can rapidly increase AI costs 
• Practical steps for building secure, reliable AI operations 

Key insights from the discussion 

Cloud complexity is accelerating 

Most organisations now run “a sprawl of tool sets and environments,” Bracho notes, often without the people or standardized processes to manage them. While cloud created opportunities, the operational overhead has increased. 

AI raises the stakes 

Training, tuning, and inference often run in different environments, each with separate performance and security requirements. Bracho describes AI as “the killer workload,” reinforcing the need for robust hybrid architectures. 

Skills gaps slow progress 

Lynch highlights the disconnect between AI teams and production engineering teams. Without alignment, model deployment becomes slow and risky — echoing findings from the HashiCorp 2025 Cloud Complexity Report, where most organizations say platform and security teams are not working in sync. 

AI exposes underlying weaknesses 

“AI is not going to solve complexity; it will amplify what you already have,” Bracho says. But with structured processes and automation, AI can reduce operator workload and help teams adopt best practices faster. 

Automation is becoming essential 

The Cloud Complexity Report shows that more than half of enterprises see automation as key to unlocking cloud innovation. With the foundations already laid, AI can accelerate progress by improving consistency and reducing manual effort. 

Modernization is continuous 

Both guests emphasise that AI success depends on long-term investment in people, operating rhythms, and security. Consulting can help organizations start strong, but lasting results come from internal alignment and disciplined execution. 

Episode chapters 

00:00 Navigating cloud complexity

08:11 Skills and operating model challenges

15:13 Automation for cloud and AI productivity

21:48 How consulting accelerates AI readiness

24:10 Final guidance for CIOs 

About the guests 

Maria Bracho 

CTO, Americas at Red Hat. Maria advises enterprise customers on hybrid cloud architectures, platform strategy, and AI-enabled operations across large-scale environments.

Connect on LinkedIn 

Tyler Lynch 

Field CTO for the HashiCorp product suite within the IBM portfolio. Tyler works with organisations to modernise infrastructure, security, and automation practices that support cloud and AI workloads. 

Connect on LinkedIn 

About HashiCorp 

HashiCorp is the Infrastructure Cloud company, helping organisations automate hybrid and multicloud environments with Infrastructure Lifecycle Management and Security Lifecycle Management. Offerings include managed services on the HashiCorp Cloud Platform (HCP), self-hosted enterprise products, and source-available tools.

Visit hashicorp.com 

About Red Hat 

Red Hat delivers enterprise open-source solutions, including Red Hat OpenShift and Red Hat Enterprise Linux, enabling organisations to build and manage hybrid cloud environments with speed, reliability, and consistency.

Visit redhat.com 

About IBM 

IBM provides hybrid cloud, AI, and consulting expertise that brings together Red Hat, HashiCorp, and other portfolio capabilities. IBM helps organisations modernise applications, streamline operations, strengthen security, and prepare for AI at scale.

Vist ibm.com 

How AI Is Changing EDA and Chip Design

In the latest episode of Tech Transformed, host John Santaferraro speaks with Dr. Thomas Andersen, Vice President of AI and Silicon Innovation at Synopsys, about the real-world impact of AI in chip design. Together, they explore how AI and automation are redefining EDA, how generative AI is accelerating design efficiency, and what the Synopsys acquisition of Ansys means for the future of simulation and system-level integration.

As Dr. Andersen explains, “AI is transforming EDA. Synopsys leads in silicon design, and the Ansys acquisition expands our capabilities across multiphysics simulation and system optimization.”

From Silicon to Systems

The integration of complex hardware and software has become one of the greatest challenges in semiconductor and OEM innovation. Traditional sequential development, where software waits for hardware, often causes delays and missed targets. Advances in EDA tools and virtual prototyping now enable engineers to initiate software design months before silicon is finalised, thereby accelerating bring-up and enhancing collaboration across the supply chain.

“Generative AI enables more efficient design,” says Andersen. “AI reshapes engineering workflows, but human expertise remains essential.”

The result is faster time-to-market, enhanced design verification, and greater overall system reliability.

Listen to the full conversation on the Tech Transformed podcast to discover how Synopsys is advancing electronic design automation, improving engineering workflows and chip design from silicon to systems.

For more insights follow Synopsys:

• X: @Synopsys
• Instagram: @synopsyslife
• Facebook: https://www.facebook.com/Synopsys/
• LinkedIn: https://www.linkedin.com/company/synopsys/

Takeaways

• AI is transforming EDA and chip design by automating complex processes.
• Synopsys is a leader in silicon-to-systems design, providing critical software for chipmakers.
• The acquisition of Ansys expands Synopsys' capabilities beyond EDA.
• Generative AI is enabling more efficient and adaptable chip design.
• AI-powered observability is reshaping engineering workflows.
• The complexity of chip design has increased, requiring advanced tools and automation.
• Human expertise remains essential in chip design, despite advances in automation.
• EDA tools simulate chip designs, reducing the need for physical prototypes.
• Agent engineers are the future, automating tasks traditionally done by humans.
• The integration of AI in EDA is making the process faster, smarter, and more collaborative.

Chapters

• 00:00:00 Introduction to EDA and AI
• 00:03:00 Synopsys and Its Role in Chip Design
• 00:09:00 The Impact of AI on EDA
• 00:15:00 Generative AI and Automation
• 00:21:00 Ansys Acquisition and Future Prospects

About Synopsys

Synopsys, Inc. (Nasdaq: SNPS) is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. Learn more at www.synopsys.com.

Follow EM360Tech for more insights:

Website: www.em360tech.com

X: @EM360Tech

LinkedIn: EM360Tech

In this episode of Tech Transformed, host Dana Gardner joins Arpad Kun, VP of Engineering and Infrastructure at Bitrise, to explore how solid CI/CD foundations can drive innovation and enable enterprises to harness AI in more practical, impactful ways. Drawing on findings from the Bitrise Mobile DevOps Insights Report, Kun shares how teams are optimising mobile delivery pipelines to accelerate development and support intelligent automation at scale.

Complexity of Continuous Integration

“Continuous integration pipelines are becoming more complex,” says Kun. “Build times are decreasing despite increasing complexity.” Faster compute and caching solutions are helping offset these pressures, but only when integrated into a cohesive CI/CD platform that can handle the rising demands of modern software delivery.

A mature CI/CD environment creates stability and predictability. When developers trust their pipelines, they iterate faster and with less friction. As Kun notes, “A robust CI/CD platform reduces anxiety around releases.” Frequent, smaller iterations deliver faster feedback, shorten release cycles, and often improve app ratings—especially in the fast-paced world of mobile and cross-platform development.

AI Ambitions with Engineering Reality

It’s easy to become swept up in the potential of AI without considering whether existing foundations can support it. Many development environments are not yet equipped to handle the iterative, data-intensive nature of AI-powered software engineering. Without scalable CI/CD pipelines, teams risk encountering bottlenecks that can cancel out the potential benefits of AI.

To truly drive innovation, enterprises must align their AI ambitions with robust automation, strong observability, and disciplined engineering practices. A well-designed CI/CD platform allows teams to integrate AI responsibly, accelerating testing, improving deployment accuracy, and maintaining agility even as complexity grows.

Takeaways

• Continuous integration pipelines are becoming more complex.
• Build times are decreasing despite increasing complexity.
• Faster computing and caching are key to improving delivery speed.
• Flaky tests have increased significantly, causing inefficiencies.
• Monitoring and isolating flaky tests can improve build success rates.
• Maintaining flow for engineers is crucial for productivity.
• A robust CI/CD platform reduces anxiety around releases.
• Frequent iterations lead to faster feedback and improved app ratings.
• Cross-platform development is on the rise, especially with React Native.
• The future of software development will be influenced by AI.

For more insights, follow Bitrise:

X: @bitrise

Instagram: @bitrise.io

Facebook: facebook.com/bitrise.io

LinkedIn: linkedin.com/company/bitrise

Chapters

00:00 Introduction to Tech Transformed Podcast

01:08 The Complexity of Continuous Integration

04:24 Challenges of Increased Speed in Development

08:09 The Importance of Continuous Integration and Deployment

10:32 Building a Robust CI/CD Platform

14:25 The Impact of Release Frequency on Business

16:52 Types of Applications and Development Trends

18:47 Aligning Development with Business Goals

24:15 Preparing for the Future of Software Development

About Bitrise

Bitrise is a top mobile CI/CD platform, streamlining build, test, and deployment for mobile apps. It offers a user-friendly interface, robust integrations, and scalable infrastructure to simplify development and ensure efficient delivery of high-quality apps.

Follow EM360Tech for more insights:

Website: www.em360tech.com

X: @EM360Tech

LinkedIn: EM360Tech

This shift forms the core of a recent Tech Transformed podcast episode between host Dana Gardner and Pejman Tabassomi, Field CTO for EMEA at Datadog. Together, they explore how observability has changed into what Tabassomi calls the “nervous system of AI”, a framework that allows enterprises to translate complexity into clarity and automation into measurable outcomes.

Building AI Literacy

AI models make decisions that can affect everything from customer experiences to financial forecasting. It's important to understand that without observability, those decisions remain obscure.

“Visibility into how models behave is crucial,” Tabassomi notes. True observability allows teams to see beyond outputs and into the reasoning of their systems, even if a model is drifting, automation is adapting effectively, and results align with strategic goals. This transparency builds trust. It also ensures accountability, giving organizations the confidence to scale AI responsibly without losing sight of the outcomes that matter most.

Observability 

Observability is not merely about monitoring; it is about decision-making. It provides the insight required to manage complex systems, optimize outcomes, and act with agility. For organizations relying on AI and automation, observability becomes the differentiator between being merely efficient and achieving a sustainable competitive edge. In short, observability is no longer optional; it is central to translating technology into strategy and strategy into advantage.

For more insights follow Datadog:

• X: @datadoghq 
• Instagram: @datadoghq 
• Facebook: facebook.com/datadoghq facebook.com
• LinkedIn: linkedin.com/company/datadog

Takeaways

• Observability has evolved from cost efficiency to a strategic role in AI.
• Integration with automation ensures explainable and predictable outcomes.
• Standards like OpenTelemetry unify observability efforts.
• Observability enhances security and governance in AI applications.
• Real-world examples show observability's impact on business processes.
• Strategic steps for leaders to leverage observability are provided.
• Observability provides visibility and governance for AI-driven businesses.
• Automation and observability integration are crucial for business success.
• Observability aids in enhancing security across AI applications.

Chapters

00:00:00 Introduction to Observability and AI

00:03:00 The Evolution of Observability

00:06:00 Observability as a Governance Tool

00:09:00 Automation and Observability

00:12:00 Standardization and Open Telemetry

00:15:00 Security and Observability

00:18:00 Practical Applications and Case Studies

00:21:00 Strategic Steps for Leaders

00:24:00 Conclusion and Future Outlook

About Datadog

Datadog is a SaaS platform that integrates and automates infrastructure monitoring, application performance monitoring, log management, real-user monitoring, and more—delivering unified, real-time observability and security across the entire technology stack. Used by organizations of all sizes and industries, Datadog enables digital transformation and cloud migration, fosters collaboration among development, operations, security, and business teams, accelerates time to market, reduces problem resolution time, and provides insight into user behavior and business performance.

Follow EM360Tech for more insights:

Website: www.em360tech.com

X: @EM360Tech

LinkedIn: EM360Tech

While artificial intelligence (AI) has hastened development and made enterprises more efficient, it also comes with more deadlines. The deadlines often merge into after-hours messaging. Burnout has become a default result of productivity, especially in the tech industry. 

In this episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host, Producer and B2B Tech Journalist, speaks with Kate Visconti, Founder and CEO of Five to Flow, about the critical issues of burnout and disengagement in the workplace. They discuss the five core elements of change management, the financial implications of employee wellness, and strategies for enhancing productivity through flow optimisation. 

Also Watch: Fixing the Gender Gap in STEM

The Wellness Wave Diagnostic to Help Fix Profit Leaks

Visconti stresses the importance of creating a supportive work environment and implementing effective change management practices to improve organisational performance. The conversation also highlights the role of technology in productivity and the need for leaders to prioritise employee well-being to drive business success.

With an ambition to change the way organisations define true performance, VIsconti developed a system – a data-driven framework called The Wellness Wave. As per the official Five to Flow website, The Wellness Wave is “a proprietary diagnostic that measures sentiment and business performance across five core elements.”

Visconti sheds light on the original framework of the company. She says, “The original was adopted when we first kicked off as part of our consulting, and it's called the Wellness Wave diagnostic. It’s literally looking across the five core elements — people, culture, process, technology, and analytics.”

This framework helps companies identify and fix their profit leaks, which are the hidden financial losses caused by employee burnout, disengagement, and distraction. 

In her conversation with Dua, host of the Tech Transformed podcast episode, Visconti shares how understanding human behaviour can lead to significant improvements in business performance.

According to Five to Flow’s global diagnostics, only 13 per cent of flow triggers work at their best. For tech leaders, that means most teams are functioning well below their potential.

Kate’s top tip is to create flow blocks. “It’s about designing uninterrupted time for peak focus. This is when your brain isn’t in a stress state. For me, it’s mornings with my coffee. For others, it might be in the afternoon. Communicate those times to your team and protect them like meetings.”

These flow blocks aren’t just productivity tricks; they show that focus is more important than frantic multitasking. “Multitasking is a fallacy,” Kate says. “You’re just rapidly switching tasks and burning through mental energy.”

This episode of the Tech Transformed podcast is a must-listen for CEOs, CIOs, and IT decision-makers who want to regain performance from burnout.

Tune in for a discussion that redefines productivity, from managing time to mastering flow.

Listen at em360tech.com/podcasts

Explore more at fivetoflow.com

Follow the conversation on X and LinkedIn: @EM360Tech

Takeaways

• Burnout and disengagement are significant profit leaks for organisations.
• Understanding the five core elements of change management is crucial for success.
• Employee wellness directly impacts productivity and organisational performance.
• Distraction management is a key area needing improvement in workplaces.
• Creating flow blocks can enhance focus and productivity.
• Multitasking is ineffective; task switching reduces overall efficiency.
• Technology should be leveraged to support, not hinder, productivity.
• The financial cost of burnout is staggering, reaching $8.9 trillion globally.
• Quality of work is more important than quantity; focus on meaningful productivity.
• Leaders must prioritise their own well-being to effectively support their teams.

Chapters

• 00:00 Introduction to Tech Transformed Podcast
• 01:05 Understanding Burnout and Disengagement
• 02:33 The Five Core Elements of Change Management
• 04:52 The Business Value of Employee Wellness
• 08:05 Assessing Financial Impact of Burnout
• 10:55 Strategies for Enhancing Focus and Productivity
• 14:56 Flow Optimisation vs. Time Management
• 19:08 Creating Flow Blocks for Peak Performance
• 22:58 The Role of Technology in Productivity
• 26:58 Implementing Change for Organisational Success
• 30:12 Case Studies and Real-World Applications
• 35:49 Key Takeaways for Leaders

Every business in this day and age lives in the cloud. Our operations, data, and collaboration tools are powered by servers located invisibly around the world. But here’s the question we often overlook: what happens when the cloud falters?

In this episode of Tech Transformed, Trisha Pillay sits down with Jan Ursi, Vice President of Global Channels at Keepit, to uncover the real meaning of cyber resilience in a cloud-first world. Are you putting all your trust in hyperscale cloud providers? Think again. Trisha and Jan explore why relying solely on giants like Microsoft or Amazon can put your data at risk and how independent infrastructure gives organisations control, faster recovery, and true digital sovereignty.

Takeaways:

• The importance of cyber resilience in a cloud-first world
• How independent cloud infrastructure protects your SaaS applications
• Common shared responsibility misconceptions that can cost organisations data
• Strategies for quick recovery from ransomware and cyberattacks
• Why digital sovereignty ensures control and compliance

Chapters:

00:00 – Introduction to Cyber Resilience and Cloud Strategy

05:00 – The Importance of Independent Infrastructure

10:00 – Shared Responsibility and Misconceptions

15:00 – Digital Sovereignty and Compliance

20:00 – Practical Tips for CISOs and CIOs

22:00 – Conclusion

About Jan Ursi:

Jan Ursi leads Keepit’s global partnerships, helping organisations embrace the AI-powered cyber resilience era. Keepit is the world’s only independent cloud dedicated to SaaS data protection, security, and recovery. Jan has previously built and scaled businesses at Rubrik, UiPath, Nutanix, Infoblox, and Juniper, shaping the future of enterprise cloud, hyper-automation, and data protection.

Follow EM360Tech for more insights:

• Website: www.em360tech.com
• X: @EM360Tech
• LinkedIn: EM360Tech
• YouTube: EM360Tech

In the recent episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host, Producer, and Tech Journalist at EM360Tech, speaks to Shenbagaraman about 5G and edge computing and how they power private networks for various industries, from manufacturing, national security to space.

The Radisys’ Head of Standards believes in the idea of combining 5G with edge computing for transformative enterprise connectivity. If you’re a CEO, CIO, CTO, or CISO facing challenges of keeping up the pace with capacity, security and quality, this episode is for you. The speakers provide a guide on how to achieve next-gen private networks and prepare for the 6G future.

Real-Time Control

The growing need for real-time applications, such as high-quality live video streams and small industrial sensors with instant responses, demands data processing to occur closer to the source than ever before. Alluding to the technical solution that provides near-zero latency and ensures data security, Shenbagaraman says:

"By placing the 5G User Plane Function (UPF) next to local radios, we achieve near-zero latency between wireless and application processing. This keeps sensitive data secure within the enterprise network."

Such a strategy has now become imperative in handling both high-volume and mission-critical low-latency data all at the same time. Radisys addresses key compliance and confidentiality issues by storing the data within a private network. Essentially, they create a safe security framework that yields near-zero latency to guarantee utmost data security.

Powering Edge Computing Applications

The real-world benefit of this zero-latency setup is the power it gives to edge computing applications. As the user plane function is the network's final data exit point, positioning the processing application near it assures prompt perspicuity and action.

"The devices could be sending very domain-specific data,” said Shenbagaraman. “The user plane function immediately transfers it to the application, the edge application, where it can be processed in real time."

It reduces errors and improves the efficiency of tasks through the Radisys platform, with the results meeting all essential requirements, including compliance needs.

One such successful use case spotlighted in the podcast is the Radisys work with Lockheed Martin’s defence applications. "We enabled sophisticated use cases for Lockheed Martin by leveraging the underlying flexibility of 5G,” the Radisys speaker exemplified.

Radisys team customised 5G connectivity for the US defence sector. It incorporated temporary, ad-hoc networks in challenging terrains using Internet Access Backhaul. It also covered isolated, permanent private networks for locations such as maintenance hangars.

Intelligence comes from the RAN Intelligent Controller (RIC), Shenbagaraman explained on the podcast. These small, AI and machine learning-enabled network applications run directly on the RIC. The smart applications allow functions like prioritising mission-critical traffic in real time.

Takeaways

• 5G enables low latency and high bandwidth for various applications.
• Edge computing co-located with 5G can achieve near-zero latency.
• Private networks ensure data security and compliance for enterprises.
• RADIS provides solutions compatible with multiple hardware platforms.
• Lockheed Martin's 5G MIL stack showcases practical applications of edge computing.
• 5G technology is evolving and will continue into 6G.
• RADISYS is positioned to play a key role in future connectivity solutions.
• 5G can address the needs of industries like manufacturing and national security.
• The integration of satellite communication with 5G expands connectivity options.
• Enterprises can leverage RADISYS for secure and differentiated connectivity solutions.

Chapters

• 00:00 Introduction to 5G and Edge Computing
• 01:20 The Role of 5G in Various Industries
• 04:26 Understanding Edge Computing and Zero Latency
• 08:16 Private Networks and Their Importance
• 12:19 Making Software Compatible with Hardware
• 15:53 Use Case: Lockheed Martin and 5G MIL Stack
• 20:19 Future of 5G, Satellite Communication, and Edge Computing
• 24:52 Looking Ahead: The Role of RADISYS in 6G 

To help face and overcome some of these challenges, Temporal introduced the concept of “durable execution.” This concept has quickly become an integral part of building AI systems that are not just scalable but also reliable, observable and manageable.

In this episode of the Tech Transformed podcast, host Kevin Petrie, VP of Research at BARC, sits down with Samar Abbas, Co-founder and CEO of Temporal Technologies. They talk about durable execution and its critical role in driving AI innovation within enterprises. 

They discuss Abbas’s extensive background in software resilience, the development of application architectures, and the importance of managing state and reliability in AI workflows. The conversation also touches on the collaboration between developers, data teams, and data scientists, emphasising how durable execution can enhance productivity and governance in AI initiatives.

Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?

Chatbots to Autonomous Agents

“AI agents are going to get more and more mission critical, more and more longer lived, and more asynchronous," Abbas tells Petrie. “They’ll require more human interaction, and you need a very stable foundation to build these kinds of application architectures.”

AI not just fuels chatbots today. Enterprises are increasingly experimenting with agentic workflows—autonomous AI agents that carry out complex background tasks independently. For example, agents can assign, solve, and submit software issues using GitHub pull requests. 

Such a setup isn’t just a distant vision; the Temporal co-founder pointed to OpenAI’s Codex as a real-world case. With this approach, AI becomes a system that can handle hundreds of tasks at once, potentially achieving "100x orders of magnitude velocity," as Abbas described.

However, there are some architectural difficulties to stay mindful of. The AI agents are non-deterministic by nature and often depend on large language models (LLMs) like OpenAI’s GPT, Anthropic’s Claude, or Google’s Gemini. They reason based on probabilities, and they improvise. They often make decisions that are hard to trace or manage.

AI workflows as simple code

This is where Temporal comes in. It becomes the executioner that keeps the system cohesive and in alignment. “What we are trying to solve with Temporal and durable execution more generally is that we tackle challenging distributed systems problems," said Abbas.

Rather than developers stressing over queues, retries, or building their own reliability layers, Temporal allows them to write their AI workflows as simple code. Temporal takes care of everything else—reliable state management, retrying failed tasks, orchestrating asynchronous services, and ensuring uptime regardless of what fails below the surface.

As agent-based architectures become more common, the demand for this kind of system-level orchestration will only increase.

Listen to the full conversation on the Tech Transformed podcast, and discover how Temporal is shaping the next era of durable, autonomous, AI-powered applications.

Learn More at Temporal.io

Takeaways

• Durable execution is essential for maintaining software uptime.
• Samar Abbas has over 25 years of experience in software development.
• AI agents are becoming increasingly mission-critical in applications.
• Organisations need a stable foundation for AI architectures.
• Durable execution provides full visibility into AI decision-making.
• The future of AI involves more autonomous agents performing tasks.
• Context is crucial for maximising the value of AI models.
• Durable execution helps manage scalability and reliability in AI workflows.
• Cross-functional collaboration is key to successful AI initiatives.
• Investing in durable execution can unlock higher productivity and quality.

Chapters

• 00:00 Introduction to Durable Execution and AI Innovation
• 07:06 Samar Abbas's Journey and Insights on Software Resilience
• 12:54 The Role of Durable Execution in AI Workflows
• 18:52 Cross-Functional Collaboration in AI Initiatives
• 24:38 The Importance of Durable Execution for Business Leaders

In this episode of Tech Transformed, Trisha Pillay talks with Anshuman Singh, CEO of HGS UK, about AI in the workplace. They discuss how AI can support employees, improve customer service, and require careful oversight. Singh also shares insights on preparing organisations for AI integration and trends leaders should watch in the coming years.

Questions or comments? Email info@em360tech.com or follow us on YouTube, Instagram, and Twitter @EM360Tech.

Takeaways

• AI is reshaping workforce needs, not just replacing jobs.
• Routine tasks are increasingly being automated by AI.
• AI can free up capacity for more meaningful work.
• The narrative around productivity is changing with AI.
• AI will create new job opportunities, often better-paying.
• Human oversight is crucial in AI decision-making.
• AI can assist in customer service, enhancing empathy.
• Organisations should not wait for perfect AI solutions.
• Training and hands-on experience with AI are essential.
• A psychological safety net is necessary for AI experimentation.

Chapters

00:00 Introduction to AI and Human Element

03:03 AI's Impact on Workforce Dynamics

08:29 The Role of Human Oversight in AI

10:46 AI Innovations in Customer Service

16:34 Positioning for Growth in Business Process Management

20:01 Preparing the Workforce for AI Integration

25:35 Emerging Trends in AI and Workforce

29:19 Final Thoughts on AI and Ethics

As hybrid and remote work become the standard, organizations are rethinking how teams brainstorm, align, and innovate. Traditional whiteboards and digital tools often fall short in keeping pace with today’s complex business challenges. This is where AI-powered canvases are transforming visual collaboration.

In this episode of Tech Transformed, Kevin Petrie, VP of Research at BARC, joins Elaina O’Mahoney, Chief Product Officer at Mural, to explore how AI collaboration tools are reshaping teamwork in off-site locations. From customer journey mapping to process design, AI-powered canvases give teams the ability to visualize ideas, surface insights faster, and make better decisions—while keeping human creativity at the centre.

AI-Powered Canvases, Visuals, and Collaboration

A central theme in the conversation is the distinction between automation and augmentation. While AI can recommend activities, map processes, and identify participation patterns, decision-making remains a human responsibility.

As O’Mahoney explains:

“In the Mural canvas experience, we’re looking to draw out the ability of a skilled facilitator and give it to participants without them having to learn that skill over the years.”

This balance ensures that while AI-powered canvases streamline collaboration, teams still rely on human judgment, creativity, and contextual knowledge. One of the most powerful contributions is in AI-driven visuals, which can translate raw data or unstructured input into clear diagrams, journey maps, or process flows. These visuals not only accelerate understanding but also help teams spot gaps and opportunities more effectively.

For example:

• In customer journey mapping, AI can quickly generate visual flows that highlight pain points and opportunities that would take much longer to uncover manually.
• In manufacturing, AI-powered canvases can create dynamic visuals of workflows, showing how new technologies might disrupt established processes.

The Role of Visual Tools in Hybrid Work

In blended work environments, teams often lack the in-person cues that guide effective collaboration. Visual canvases bring those cues into the digital workspace, showing where ideas are concentrated, highlighting gaps in participation, and enabling alignment across dispersed teams. By combining intuitive design with AI-driven support, platforms like Mural help organisations adapt to the demands of hybrid work while keeping human creativity at the centre.

Takeaways

• AI is reshaping visual collaboration in distributed teams.
• Visual elements enhance understanding and decision-making.
• AI can augment workflows but requires human oversight.
• There is no universal playbook for AI integration in businesses.
• Hybrid work necessitates effective digital collaboration tools.
• AI can help visualize complex customer experiences.
• Human intuition and creativity remain essential in AI applications.
• Training and guidance are crucial for effective AI use.
• Collaboration tools must adapt to diverse work environments.
• AI should be seen as a partner in the creative process.

Chapters

00:00 The Evolution of Visual Collaboration

05:15 Augmenting vs Automating: The Role of AI

10:36 Navigating AI Integration in Business

12:44 Hybrid Work and Team Collaboration

16:08 AI's Role in Visualizing Customer Experiences

17:30 Emphasizing Human-AI Collaboration

About Mural

Mural is a visual collaboration platform that enhances teamwork and productivity. It provides an interactive space for teams to ideate, align, and execute strategies. The platform combines intuitive visual tools with strong security measures to help companies increase efficiency and tackle challenges effectively. Mural offers a variety of features, including a visual work platform with tools like tables, mind maps, and diagrams. Its AI tools assist in structuring working sessions, generating ideas, and automating tasks such as clustering sticky notes. 

“Simply, there is no single view of the truth that exists. With governance and data quality checks, these are often inconsistent, AI systems end up consuming incomplete or conflicting signals,” he added, setting the stage for the podcast.

In this episode of the Don't Panic, It's Just Data podcast, Stathopoulos speaks with Jandreski about the vital role of unified streaming data platforms in facilitating real-time AI. 

They discuss the difficulties businesses encounter when implementing AI, the significance of going beyond batch processing, and the skills necessary for a successful streaming data platform. Applications in the real world, especially in e-commerce and fraud detection, show how real-time data can revolutionise AI strategies.

Your AI Could Be a Step Behind 

Jandreski says that most organisations continue to be engineered on batch-first data systems. That means, they still process information in chunks—often hours or even days later. “It's fine for reporting, but it means your AI is always going to be one step behind.”

However, “the unified streaming platform flips that model from data at rest to data in motion.” A unified platform will “continuously capture the pulse” of the business and feed it directly to AI for automated real-time decision making. 

Challenges of Agentic AI 

Considering that the world is moving toward the era of agentic AI, there are some key challenges that still need to be addressed. Agentic AI means autonomous agents make real-time decisions, maintain memory, use tools and collaborate among themselves. Because they act on their own decisions, regulating them is necessary. 

Building agents is not the main challenge, but the real challenge is “actually giving them the right infrastructure.” Jandreski highlights. Alluding to an example of AI prototyping frameworks such as Longchain or Lama Index, he further explained that those frameworks work for demos. 

In reality, however, they can’t support a long-running system trigger workflows that demand high availability, fault tolerance, and deep integration with the enterprise data. This is because enterprises have multiple systems, and many of them are not connected. This way, the data forms into silos. 

When data is in silos, a unified streaming data platform becomes the key solution. “It provides a real-time event-driven contextual runtime where AI agents need to move from the lab experiments to production reality.”

Takeaways

• Unified streaming data platforms are essential for real-time AI.
• Batch processing creates lag, hindering AI effectiveness.
• Data fragmentation leads to unreliable AI decisions.
• A unified platform ensures data is fresh and trustworthy.
• Real-time AI requires a robust data infrastructure.
• Organisations must move beyond legacy batch systems.
• Governance and data quality are critical for AI success.
• Real-world applications demonstrate the value of streaming data.
• E-commerce and finance are key industries for real-time AI.
• AI strategies need a solid foundation to scale.

Chapters

• 00:00 Introduction to Unified Streaming Data Platforms
• 03:10 Key Blockers in AI Implementation
• 06:13 The Importance of Unified Streaming Data Platforms
• 08:48 Capabilities of a Unified Streaming Data Platform
• 11:54 Real-World Applications of Real-Time AI
• 15:04 Final Takeaways for IT Decision Makers

About Ververica

Ververica, the original creators of Apache Flink®, empowers businesses with high-performance data streaming and processing solutions. Streamlining operations, developer efficiency, and enabling customers to solve real-time use cases reliably and securely. Ververica’s advanced Streaming Data Platform, powered by its cloud native VERA engine, revolutionises Apache Flink®, making it easy for organisations to harness data insights at scale. With Ververica, customers can meet any business SLA, leveraging advanced data streaming and processing capabilities in real-time or on the lakehouse. Ververica enables businesses to connect, process, govern, and analyse data across infinite use cases, with flexible deployment options, including public cloud, private cloud, or on-premise environments. Discover more at ververica.com.

In this episode of the Don't Panic It's Just Data podcast, host Christina Stathopoulos, the Founder of Dare to Data, speaks with Benders about where observability is headed in IT systems. They discuss how AI is transforming observability into a more comprehensive understanding of complex systems, moving beyond traditional monitoring to achieve true understandability. 

Benders explained the importance of merging various data types to provide a complete picture of system performance and user experience. He believes AI can bridge the gap between mere observation of systems and a deeper understanding of their functionality. This could ultimately lead to enhanced incident response and operational efficiency. 

With maturing technology, complexity is expected to grow, too. The straightforward act of “observing” those complexities is like watching a green light on a machine. This is not enough. The major challenge is to “understand” the inside operations of the machine. This is the difference between simply seeing the data and knowing the "why."

Observability to Understandability

As per Benders, the term observability "leaves a lot to be desired." While it’s the industry’s common label, it only describes seeing a system. The real goal, he argues, is to understand it.

Alluding to an analogy, the technical strategist asks Stathopoulos to imagine a nuclear power plant full of a million blinking lights and screens. “You can have all the observability available, but if you're not an expert, you won't grasp what’s actually happening,” says Benders. 

Typically, software has been developed by a single person who knows every inch of it. However, today, technology has become more perplexing. AI, alongside teamwork and collaboration, provides the tools to solve this problem. An engineer might manage code they didn’t write, making a dashboard full of charts unhelpful. Understandability means moving beyond raw data to give context and meaning.

Ultimately, Benders advises IT leaders to embrace change. The tech industry is constantly changing and advancing. Instead of fearing new tools, organizations should focus on what they need to grasp the unknown. As he puts it, "a lot of unknown is coming over the next few decades."

Takeaways

• Observability is not enough; understanding is crucial.
• AI can enhance the understanding of complex systems.
• The shift from observing to understanding is essential for modern IT.
• AI presents both challenges and opportunities in software development.
• New interfaces powered by AI can improve user interaction with data.
• AI can help reduce incident response times significantly.
• Collaboration with AI is becoming the norm in software development.
• Real-world applications show measurable benefits of AI in observability.
• IT decision-makers must prepare for ongoing changes in technology.
• Understanding the unknown is key to navigating future challenges.

Chapters

• 00:00 Introduction to Observability and Understandability
• 05:00 The Role of AI in Modern IT
• 10:02 AI's Impact on Observability Solutions
• 14:58 Real-World Applications of AI in Observability
• 19:52 Key Takeaways for IT Decision Makers

About New Relic 

The New Relic Intelligent Observability Platform helps businesses eliminate interruptions in digital experiences. New Relic is the only AI-strengthened platform to unify and pair telemetry data to provide clarity over your entire digital estate. We move your problem-solving past proactive to predictive by processing the right data at the right time to maximize value and control costs.

That’s why businesses around the world—including Adidas Runtastic, American Red Cross, Domino’s, GoTo Group, Ryanair, Topgolf, and William Hill—run on New Relic to drive innovation, improve reliability, and deliver exceptional customer experiences to fuel growth. Visit: www.newrelic.com.

In this episode of Tech Transformed, Dare To Data Founder Christina Stathopoulos explores the rapid rise of AI agents with Ben Gilman, CEO of Dualboot Partners. Together, they unpack how AI agents differ from traditional automation and what this shift means for software development, enterprise operations, and the future of productivity.

AI Agents vs. Traditional Automation

Unlike traditional automation, which follows strict, deterministic rules, AI agents can adapt to changing inputs, analyze complex data sets, and make autonomous decisions within defined parameters. This allows them to tackle tasks that were previously too intricate or time-consuming for automated systems. Dualboot Partners helps organizations harness these AI agents, integrating them into workflows to deliver real business value through a combination of product, design, and engineering expertise.

“The biggest difference with an AI agent, between a standard tool, is that the agent can perceive information and reason about it, providing context and insights you don’t normally get in an algorithm.” — Ben Gilman, CEO, Dual Boot Partners.

The Future of AI in Enterprise

Organisations face several hurdles when integrating AI agents, including defining clear use cases, understanding the probabilistic nature of AI reasoning, and incorporating agents into existing processes and workflows. Despite the challenges, the potential payoff is substantial. AI agents can boost productivity, improve decision-making, and make enterprises more agile. As these systems mature, humans and AI are increasingly collaborating as true partners, reshaping what the workplace and work itself look like.

Takeaways:

• AI Agents vs. Traditional Automation: AI agents can perceive and reason, offering more context and adaptability compared to deterministic systems.
• Real-World Applications: Examples include virtual vet agents and data analytics tools that enhance productivity and decision-making.
• Challenges in Adoption: Organizations face hurdles in defining specific use cases and integrating AI agents effectively.
• Future of AI in Tech: AI agents are expected to significantly boost productivity and innovation in software development and enterprise operations, with AI-first approaches like Dualboot's "DB90" driving structured adoption and accelerating modernization.

Chapters

0:00 - 3:00: Introduction to AI Agents

3:01 - 6:00: Differences from Traditional Automation

6:01 - 12:00: Real-World Applications and Examples

12:01 - 18:00: Challenges in Adoption

18:01 - 22:00: Future Impact on Tech and Operations

22:01 - 24:00: Conclusion and Final Thoughts

About Dualboot Partners

Dualboot Partners is a product-led digital transformation company that builds powerful software designed to drive business results. With a proven AI-driven process, Dualboot combines product strategy, design, and engineering to help organizations modernize systems, accelerate delivery, and create scalable solutions. The company’s diverse team partners with clients across healthcare, financial services, technology, manufacturing, consumer markets, and more. Dualboot is recognized for technical excellence, clear communication, and a focus on outcomes that strengthen the bottom line.

The conversation dives into how latency is the driving force behind edge adoption. Applications like autonomous vehicles and real-time analytics cannot afford to wait on a round trip to a centralised data centre. They need to compute where the data is generated.

Rather than viewing edge as a rival to the cloud, the discussion highlights it as a natural extension. Edge environments bring speed, resilience and data control, all necessary capabilities for modern applications. 

Adopting Edge Computing

For organisations looking to adopt edge computing, this episode lays out a practical step-by-step approach. The skills necessary in multi-cloud environments – automation, infrastructure as code, and observability – translate well to edge deployments. These capabilities are essential for managing the unique challenges of edge devices, which may be disconnected, have lower power, or be located in hard-to-reach areas. Without this level of operational maturity, Panenkov warns of a "zombie apocalypse" of unmanaged devices.

Simplifying Complexity

Managing different APIs, SDKs, and vendor lock-ins across a distributed network can be a challenging task, and this is where platforms like emma become crucial.

Alluding to emma’s mission, Panenkov explains, "We're building a unified platform that simplifies the way people interact with different cloud and computer environments, whether these are in a public setting or private data centres or even at the edge."

Overall, emma creates a unified API layer and user interface, which simplifies the complexity. It helps businesses manage, automate, and scale their workloads from a singular perspective and reduces the burden on IT teams. They also reduce the need for a large team of highly skilled professionals leads to substantial cost savings. 

emma’s customers have experienced that their cloud bills went down significantly and updates could be rolled out much faster using the platform.

Takeaways

• Edge computing is becoming a reality for more organisations.
• Latency-sensitive applications drive the need for edge computing.
• Real-time analytics and industry automation benefit from edge computing.
• Edge computing enhances resilience, cost efficiency, and data sovereignty.
• Integrating edge into cloud strategies requires automation and observability.
• Maturity in operational practices, like automation and observability, is essential for edge readiness.
• Standardisation of APIs and devices is crucial for future edge solutions.
• The market demand will drive the evolution of edge computing platforms.

Chapters

• 00:00 Introduction to Edge Computing
• 02:28 Driving Forces Behind Edge Computing
• 04:30 Integrating Edge into Cloud Strategies
• 06:41 The Role of Android in Edge Computing
• 08:38 Preparing for Edge Computing Challenges
• 11:30 Elevator Pitch for Emma
• 13:09 Managing Complexities in Edge Computing
• 15:30 Building Operational Maturity for Edge
• 17:54 Use Case: Autonomous Vehicles
• 21:07 Future of Platforms in Edge Computing
• 23:44 Edge Computing in Different Scenarios

In a world that has moved from mass manufacturing to mass customisation, makers of complex products like cars and medical devices face a hidden problem. For more than a century, since the time of Henry Ford, manufacturers have worked in a separate, mass-production mindset. This method in the recent industrial scenario has caused a lot of friction and frustration.

In this episode of the Tech Transformed podcast, Christina Stathopoulos, Dare To Data Founder, talks with Daniel Joseph Barry, VP of Product Marketing at Configit. They talk about Configuration Lifecycle Management (CLM) and its importance in tackling the challenges that manufacturers of complex products face recurrently.

The speakers discuss the move from mass manufacturing to mass customisation, the various choices available to consumers, and the need to connect sales and engineering teams. Barry emphasises the value of working together to tackle these challenges. He points out that using CLM can make processes easier and enhance customer experiences (CX).

What is Configuration Lifecycle Management (CLM)

According to Barry, Configuration Lifecycle Management (CLM) is an approach that involves managing product configurations throughout their lifecycle. He describes it as an extension of Product Lifecycle Management (PLM) that focuses specifically on configurations. 

In today's highly bespoke world, customers are buying configurations of products instead of just the products themselves. The answer isn't to work harder within existing teams but to adopt a new, collaborative approach. This is where Configuration Lifecycle Management (CLM) comes in. 

CLM creates a single, shared source of truth for all product configuration information. It combines data from engineering, sales, and manufacturing. Configit’s patented Virtual Tabulation® (VT™) technology pre-computes all the different options, so there’s no longer a need for slow, real-time calculations. 

Barry says, "It's just a lookup, so it's lightning fast.” This represents a prominent shift that removes the delays and dead ends, frustrating customers and sales staff. Such a centralised system makes sure that every department uses the same, verified information, stopping errors from happening later on. 

One such company, and Configit’s customer, Vestas, a wind power company, automated its configuration process for complex wind turbines that have 160,000 options. By adopting a CLM approach, they cut the time to configure a solution from 60 minutes to just five.

Tune into the podcast for more information on the transformational impact of Configuration Lifecycle Management (CLM). 

Takeaways

• Manufacturers are transitioning from mass manufacturing to mass customisation.
• Customisation leads to complexity and challenges in manufacturing.
• Siloed systems create inefficiencies and reliance on experienced employees.
• Configuration Lifecycle Management (CLM) can automate and streamline processes.
• Aligning sales and engineering is crucial for successful product delivery.
• Digital threads and AI are becoming essential in manufacturing.
• A cross-functional approach is necessary to tackle modern manufacturing challenges.
• Understanding product configurations is key to meeting customer demands.
• Companies must adapt to avoid being left behind in the industry.
• The future of manufacturing relies on integrated solutions and data management.

Chapters

• 00:00 Introduction to Configuration Lifecycle Management
• 02:47 Challenges in Manufacturing Complex Products
• 07:55 The Impact of Customisation on Manufacturing
• 14:33 The Role of Heroes in Manufacturing
• 18:05 Aligning Sales and Engineering
• 26:11 The Benefits of Configuration Lifecycle Management
• 35:49 Conclusion and Key Takeaways

About Configit

Configit builds configuration solutions for manufacturing companies to master the challenges of getting configurable products to market faster, and to sell, manufacture, and service them more effectively.

Trusted by the world’s largest manufacturing companies for their mission-critical functions, Configit’s advanced configuration platform, built on Virtual Tabulation® technology, handles the most complex products in the world.

In this episode of Tech Transformed, Em360Tech host Trisha Pillay talks with Akanksha Sharma, Senior Director at the GSMA Foundation, about how these emerging technologies are creating tangible value, especially for small and medium-sized enterprises (SMEs) and industries with legacy systems like utilities. 

IOT and AI

Sharma highlights that the 2020s will be remembered as the decade when IoT experiences exponential growth, supported by data from GSMA Intelligence projecting over 37 billion IoT connections worldwide by 2030, more than doubling the number recorded in 2021. She notes that, unlike previous technological waves, AI adoption is accelerating rapidly, moving from niche awareness to mainstream use within just a few years.

When discussing climate action and carbon markets, Sharma stresses the need for transparent, data-backed verification mechanisms. She warns against superficial greenwashing practices and advocates for AI systems that prioritise accuracy and ethical standards to ensure genuine environmental benefits.

Takeaways

• Data-driven infrastructure can turn sustainability into reality.
• AI and IoT are set to scale in the 2020s.
• Small and medium enterprises face unique operational challenges.
• Digital solutions can enhance the accuracy of carbon credits.
• Greenwashing misleads consumers about environmental benefits.
• Digital literacy is a major barrier to technology adoption.
• Start with the 'why' when adopting new technologies.
• Ethics in AI must be prioritised to avoid negative consequences.
• The world is changing due to climate change and technology.
• Collaboration is key to effective climate action.

Chapters:

00:00 – Transforming Sustainability with Data-Driven Infrastructure

03:05 – The Role of AI and IoT in Enterprises

09:10 – Challenges in Operational Efficiency and Sustainability

13:42 – Real-World Impact of AI and IoT

16:57 – Carbon Markets and Digital Solutions

21:08 – Understanding Greenwashing

23:30 – Barriers to Technology Adoption

26:17 – Key Takeaways and Predictions

About Akanksha Sharma

Akanksha Sharma leads the ClimateTech and Digital Utilities programmes at GSMA, where she drives innovation at the intersection of mobile technology and sustainability. Since joining GSMA in 2012, she has specialised in leveraging mobile and digital solutions for social impact, with extensive experience in research and data-driven insights through her roles at GSMA Intelligence and Mobile for Development’s Central Insights Unit.

Before GSMA, Akanksha worked as a Power and Utilities Analyst at GlobalData in India, bringing deep sector knowledge to her current focus on transforming utility services through digital technologies. She holds an MBA from IBS Hyderabad and a Bachelor of Science degree in Psychology from Rajasthan University, India.

In this episode of Tech Transformed, EM360Tech’s Trisha Pillay chats with Greg Freeman, the founder of Data Literacy Academy about why knowing data and AI matters for anyone building a digital strategy.

Data and AI Literacy

Freeman points out that many data strategies end up as technical documents rather than actionable roadmaps. He explains that organisations often spend heavily on infrastructure, expecting better tools to solve their problems but without employees who understand how to work with data and why it matters, these investments rarely deliver results.

Freeman explains that data strategies often fail because only a small portion of employees less than 20 per cent are truly enthusiastic about data. Most strategies are designed with this minority in mind, creating an echo chamber that leaves the majority behind. As a result, data stays siloed, and business decisions don’t improve. 

The Data Literacy Academy founder stresses that unless organisations engage the 80 per cent of employees who aren’t already invested, their strategies are unlikely to succeed. When the focus is on tools rather than people, adoption falls behind.

Takeaways

• Data and AI literacy are key to turning strategy into value.
• Tools alone don’t work; people need confidence and context.
• Focus on engaging the data-hesitant majority, not just the enthusiasts.
• Cultural change, not just technical change, is what drives ROI

Chapters

• 00:00 – Introduction
• 02:07 – Beyond the Tech Stack
• 04:41 – Why Strategies Fail
• 08:41 – Literacy Barriers
• 12:08 – Success in the Real World
• 17:17 – Building Lasting Literacy
• 22:20 – AI Needs Literacy Too
• 26:33 – Final Takeaways

About Greg Freeman

Greg Freeman is the founder and CEO of Data Literacy Academy, where he works with CDOs, CIOs, and business leaders to drive real cultural change around data. His mission is to help organisations tackle data illiteracy by building confidence and capability from the ground up, especially for employees who feel disengaged or anxious about data.

With a background in sales leadership and tech startups, Greg brings both strategic insight and real-world experience.

In this episode of the Tech Transformed podcast, Shubhangi Dua, the Host and Podcast Producer at EM360Tech, talks with Rishi Rana, the CEO of Cyara, about how agentic AI is changing customer experience (CX). 

They look at how AI has developed from simple chatbots to advanced systems that can understand and predict customer needs. Rana spotlights the need for ongoing testing and monitoring to make sure AI solutions work well and follow the regulations. 

They also discuss the obstacles businesses encounter when implementing AI, the importance of good data, and the future of AI agents in improving customer interactions.

Agentic AI Transforming Customer Experience (CX)

Customer experience (CX) is changing quickly and significantly, thanks to the rise of agentic AI. These advanced systems go beyond the basic chatbots of the past. 

While such a change may offer a future equipped with a smart, proactive customer journey, it doesn't come without its challenges. These obstacles require organisations to thoughtfully plan and carefully execute strategies.

For years, chatbots provided a basic type of automated customer support. However, Rana explains that the evolution of AI is pushing boundaries. "AI in customer experience (CX) is changing from a basic level of chatbots that have been present for the last five or 10 years. Now they are turning into fully agentic systems that operate across voice, digital and human-assisted channels," said Rana. 

Moving Beyond Basic Chatbots

Chatbots’ lucrative development lies in the strengths of Large Language Models (LLMs) like Google's Gemini, Meta's Llama, and OpenAI's ChatGPT. This is because the AI-backing models are facilitating "voice bots" and other AI agents to move beyond simple response automation to intelligent orchestration. 

Intelligent orchestration results in anticipating user needs, adjusting in real-time, and guiding customers to hybrid solutions where AI and human agents work together. Ultimately, the goal is to greatly improve the customer experience (CX). Studies suggest that 86 per cent of people are willing to pay more for the same service, no matter what it is, when the customer experience is better.

Advancements don’t come without a price. Rana believes the lack of proper guardrails is a cause for concern. "AI is great, but you need to have guardrails and ensure the intent behind the questions and the objective behind the customer interaction is getting answered." 

This requires ongoing testing and monitoring across all channels to ensure consistency and avoid problems like hallucinations, misuse, or bias. These issues can result in major financial losses and damage to reputation. For instance, Rishi Rana mentioned that over "$10 billion in violations and liabilities due to incorrect information given to customers" occurred in 2024 alone.

To successfully execute agentic AI, enterprises must shift left with AI by starting testing of AI agents early. This means focusing on continuous testing and observability instead of relying on one-time quality checks. This proactive approach is key to identifying AI misbehaviour quickly, before customers notice it.

Cyara, with its AI trust suite, leads this effort by validating LLM-based bots and spotting issues like hallucinations and misuse. Their Pulse 360 provides real-time customer experience visibility across 120+ countries and over 360+ carriers, showcasing a wide reach.

Takeaways

• Agentic AI is revolutionising customer interactions across various channels.
• The evolution from basic chatbots to intelligent systems is crucial for better CX.
• Continuous testing and monitoring are essential for AI success.
• Enterprises must ensure compliance with regulations while implementing AI.
• Understanding customer intent is key to effective AI solutions.
• Friction points in customer interactions can lead to dissatisfaction.
• AI must provide a seamless experience across all channels.
• Data quality is critical in the customer experience lifecycle.
• Companies need to proactively address AI misbehaviour to prevent issues.
• The future of AI in CX involves integrating with existing workflows and systems.

Chapters

• 00:00 Transforming Customer Experience with Agentic AI
• 01:43 Evolving Beyond Basic Chatbots
• 05:04 Key Elements of a Great CX Platform
• 06:41 Understanding Customer and Enterprise Perspectives
• 08:25 Identifying Friction Points in Customer Interactions
• 10:14 Implementing Guardrails for AI in CX
• 11:02 The Importance of Continuous Testing and Monitoring
• 13:51 Ensuring Scalability and Compliance in AI Solutions
• 15:45 The Future of AI Agents in Customer Experience
• 18:18 Integrating AI into Existing Workflows
• 22:22 Key Takeaways for CIOs and IT Decision Makers

About Cyara

Cyara is the global leader in AI-powered customer experience assurance, committed to eradicating bad CX. As the only unified platform for continuous testing and monitoring across voice, digital, messaging, and conversational AI channels, Cyara empowers hundreds of the world’s leading brands to optimise more than a quarter of a billion customer interactions every year. From full customer journey visibility to AI governance and compliance, Cyara ensures every touchpoint works flawlessly, helping businesses deliver secure, friction-free, and high-quality CX at scale.

In a recent episode of the Tech Transformed podcast, Fateev explored the concept of durable execution. This approach not only improves software reliability but is also becoming essential for the next generation of AI agents and orchestration.

What is Durable Execution?

Durable execution, a concept trademarked by Temporal Technologies, changes how developers build reliable applications. "The idea is simple–we preserve the full state of code execution all the time," he explained. 

Imagine a function that makes a series of API calls. If the process running that function crashes, even days later, "we can bring that function back in exactly the same state, still blocked on the same API call with all the variables and state there, and deliver the response. Then it will continue to the next line of code."

From a programming point of view, the function actually “never crashed. It just seamlessly waited for three days, blocked on that API call, and then continued execution," says Fateev.

This ability to provide "crashless execution" changes how developers approach building reliable software. It allows functions to run for long periods, even a year, without interruption or data loss.

Temporal's Role in OpenAI's Image Generation 

Alluding to Temporal’s use case, Fateev referenced their contribution to OpenAI’s image generation code. He stated, "Every time you generate the image using OpenAI, it uses Temporal behind the scenes." 

"Image generation is orchestration. It's not just like one API call. There are a lot of API calls which need to happen to generate an image, and Temporal’s tech guarantees execution." 

While a strong tool for AI, durable execution has many uses beyond that. Fateev notes that Temporal has also been used for "driving large-scale operations with an added productivity advantage, and it’s also for a startup with two people with a small-scale solution.”

From infrastructure automation, like HashiCorp Cloud, and data handling to key business tasks such as customer onboarding and instant payments, including UPI in India and similar systems in Brazil, Temporal shows its worth in various industries. 

"Every Snapchat story is an important workflow,” Fateev tells Dua. Leading AI companies like Replit, Abridge, and OpenAI are using Temporal to power their workflows.” The main idea stays the same: "Nearly every time you need to run any business logic reliably, it works well."

Takeaways

• Durable execution is a new paradigm that enhances software reliability.
• It can significantly increase developer productivity by 5X to 10X.
• The concept allows for crashless execution of business applications.
• AI agents face reliability challenges that durable execution can address.
• Temporal's architecture ensures data is encrypted and secure.
• Open-source solutions provide a competitive advantage and prevent vendor lock-in.
• Temporal is the only production-ready solution for durable execution.
• The technology is used by major companies like OpenAI, with potential updates like Netflix, Snapchat 
• Temporal can orchestrate complex workflows, including AI and business processes.
• The system is applicable in various industries, including financial services, retail, logistics, and more.

Chapters

00:00 Introduction to Durable Execution

03:07 The Concept of Durable Execution

06:00 AI Agents and Reliability Challenges

09:07 Cybersecurity and Data Protection

11:53 Open Source Advantage of Temporal

14:57 Temporal's Unique Position in the Market

18:12 Real-World Applications of Temporal

About Temporal Technologies

Temporal is changing how modern software is built through its open-source Durable Execution platform. By guaranteeing the execution of complex workflows even in the face of system failures, Temporal allows developers to focus entirely on business logic rather than infrastructure complexities—increasing developer velocity. Its polyglot capabilities allow seamless orchestration across multiple programming languages, making it ideal for both traditional enterprise applications and next-generation AI workloads. Temporal Cloud, the company's managed service backed by the originators of the project, has been adopted by thousands of leading enterprises. Learn more at www.temporal.io.

In this episode of Tech Transformed, EM360Tech’s Trisha Pillay sits down with Jay Mishra, Chief Product and Technology Officer at Astera, to explore why agentic systems powered by autonomous AI agents, Large Language Models (LLMs), and semantic search are rapidly being recognised as the next generation of enterprise data architecture.

The conversation explores the drivers behind this shift, real-world applications, the impact on data professionals, challenges faced by agentic platforms, and the future of data stacks. Jay emphasises the importance of starting small and measuring ROI to successfully implement agentic solutions.

What is Agentic Data Management?

At its core, agentic data management is the application of intelligent, autonomous agents that can perceive, decide, and act across complex data environments. Unlike traditional automation, which follows predefined scripts, agentic AI is adaptive and self-directed. These agents are capable of learning from user behaviour, integrating with different systems, and adjusting to changes in context, all without human prompts.

As Jay explains, "An agentic system is one that has the agency to make decisions, solve problems, and orchestrate actions based on real-time data and context, not just on training data.

Takeaways

• Agentic data management is the next evolutionary step in data architecture.
• Agents are autonomous and can make decisions on the fly.
• The demand for agentic solutions is increasing due to data volume and AI strategy needs.
• Maturity of foundation models enables near-human reasoning capabilities.
• Real-world applications of agentic AI include insurance claim processing.
• Data engineers will focus on policy and guardrail creation rather than coding.
• Governance, debt and hallucinations are significant challenges in agentic platforms.
• The future of data stacks will include declarative control plans and enhanced memory layers.
• Analysts will play a crucial role in defining policies for agentic systems.
• Starting small and demonstrating ROI is key to successful agentic implementation.

Chapters

00:00 Introduction to Agentic Data Management

02:58 Understanding Agentic Data Management

06:58 Drivers of Change in Data Management

10:03 Real-World Applications of Agentic AI

14:15 Impact on Data Engineers and Analysts

16:43 Challenges and Limitations of Agentic Data Platforms

20:03 Future of Data Stacks

23:31 Final Thoughts on Agentic Data Management

About Jay Mishra

Jay Mishra is the Chief Product and Technology Officer at Astera Software, with over two decades of experience in data architecture and data-centric software innovation. He has led the design and development of transformative solutions for major enterprises, including Wells Fargo, Raymond James, and Farmers Mutual. 

Known for his strategic insight, technical leadership, and passion for empowering organisations, Jay has consistently delivered intelligent, scalable solutions that drive operational excellence and financial success.

For more information, please visit: https://em360tech.com/. Follow our LinkedIn for daily tech insights and YouTube ⁨‪@EM360TechInterviews‬ 

In this episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host and Producer at EM360Tech, talks to Paulsen about the constantly advancing role of AI agents in development environments. 

Paulsen explains how AI agents can help developers by handling simpler tasks, almost like having assistants or junior developers to assist them. Not only would this boost productivity and time efficiency, but the technology will also ensure human oversight. 

The conversation further explores how AI fits into cloud development environments, especially in regulated areas like finance, where security and scalability matter most. Paulsen stresses the value of internal AI models and points out Coder's unique role in offering infrastructure-neutral solutions that meet various enterprise needs.

AI Agents Are More Than Just Code Writers

When people hear "agentic AI" or "coding agents," there's often a misconception about fully autonomous coders. However, Paulsen clarifies, "That's a far stretch from where we currently have been, which is with just AI-assisted IDE extensions such as GitHub, Copilot, Amazon Q Developer and systems of that nature." 

Coder focuses on agentic solutions that have a human developer in the loop, emphasising Paulsen. “Think of an AI agent as a junior engineer working alongside you,” Paulsen explains. 

"If anything, it’s improving the output of the human engineer by having an autonomous or artificial or AI process. In the same development environment, working on other tasks that might not necessarily be as complex," he adds. This means developers can offload simple tasks like bug fixes or dependency updates, freeing them to focus on more complex features.

How to Scale AI Agents Securely in Enterprises?

For large financial institutions that have hundreds and even thousands of software engineers, deploying AI agents at scale requires a consistent and secure approach. Cloud development environments provide the best way to deliver and package these agents for developers.

The main concern for enterprises is ensuring data security in addition to stopping AI agents from "running wild on a laptop." Paulsen stresses the need for agents to work within an "isolated compute," with "boundaries around those agents inside of that isolated compute." 

Such a secure environment provides guardrails to synchronise and boost productivity between humans and AI while preventing sensitive data breaches or "hallucinations" from the AI.

Additionally, financial institutions are now increasingly developing their own internal AI models. Paulsen mentions, "What these institutions need is an AI agent that is trained on the internal dataset and internal LLM that is built within the firm so that it can make those decisions and return the relevant output to the data scientist or software engineer." This move towards self-hosted LLMs and internal AI infrastructure is essential for adopting enterprise-grade AI.

The ultimate message is that cloud development environments should provide the framework where AI agents are running inside an enterprise’s infrastructure. “AI agents have access to the data, and they're observed and governed by a set of security standards that you have internally,” says the EMEA Field CTO at Coder.

Takeaways

AI agents can assist developers by handling simpler tasks.

Human oversight is essential in AI-assisted coding.

Cloud development environments enable high-performance workloads.

Security is critical for AI agents in regulated sectors.

Internal AI models are necessary for financial services.

Coder offers infrastructure-neutral solutions for enterprises.

AI agents can automate maintenance tasks for platform engineers.

Scalability of AI agents is vital for large organisations.

Enterprises need to consider their internal data for AI agents.

Deployment of AI agents must be secure and efficient.

Chapters

00:00 Introduction to AI Agents in Development

02:12 Understanding Agentic AI and Its Role

06:24 Cloud Development Environments and AI Integration

08:47 Ensuring Security and Productivity with AI Agents

11:12 Scaling AI Agents in Regulated Environments

13:10 The Importance of High-Performance Developments

15:52 Internal AI Models in Financial Services

17:47 Coder's Unique Position in the Market

19:27 Key Takeaway for Decision Makers

For more information, please visit: https://em360tech.com/ Follow our LinkedIn for daily tech insights: https://www.linkedin.com/company/2403360/admin/dashboard/ and YouTube  ⁨@EM360TechInterviews⁩ 

Advancements in artificial intelligence are rapidly transforming the way industries operate and influencing the future of society as a whole. AI has become a force behind breakthrough technologies such as big data analytics, robotics, and the Internet of Things (IoT). The rise of generative AI has only accelerated its adoption and broadened its impact across multiple sectors. 

Navigating the Displacement Dilemma

In this episode of Tech Transformed, host Trisha Pillay at EM360Tech sits down with Nigel Cannings, author and AI expert, to explore one of the most pressing questions of our time: what happens to human expertise in the age of rapid AI advancement?

Nigel Cannings warns that while technology promises efficiency and faster results, it also encourages dependency. Our patience has run thin, and in our rush for instant answers, we may be undermining the very systems that develop human expertise. “I’m kind of fascinated by the change we’ve seen in how we process information,” Cannings reflects.

He describes the displacement dilemma as the idea that tools meant to democratise knowledge could actually erode the skills and pathways that build true mastery. He worries about people losing jobs or being too dependent on technology to even start careers. “I’m really interested to talk to people who’ve been affected by the displacement dilemma, people who are losing their jobs, people who think they’re going to lose their jobs, people who can see the erosion of expertise and skills,” Cannings explains.

The Future of AI

As artificial intelligence evolves at breakneck speed, we face a harsh reality: the gap between human and AI intelligence could become so wide that we might not even understand the systems we build. Worse still, AI itself may have no incentive to help us understand it. At that point, it stops being just a tool and becomes an autonomous entity with its private reality.

In 2025, Chief AI Officers report an average AI ROI of 14 per cent, as many AI programs move beyond pilot programs to larger implementations at scale. This is proof that as AI continues to evolve at an unprecedented pace, understanding its implications is important, both for industries navigating these changes and for society adapting to a new technological landscape.

Takeaways

• AI tools meant to democratize knowledge may erode human expertise.
• The displacement dilemma highlights the need for future experts.
• Information consumption is changing due to AI algorithms.
• AI relies heavily on large GPU cards for processing.
• Current AI models are prediction machines, not truly intelligent.
• Future AI may have limited intelligence in specific areas.
• The race in AI development is driven by financial incentives.
• Legislation is crucial for addressing AI's potential harms.
• Humans will always be needed in certain job roles.

Chapters

00:00 Introduction to AI and Human Expertise

04:06 The Displacement Dilemma: Erosion of Expertise

06:59 Changing Information Consumption in the AI Era

10:07 Technical Aspects of AI: Data Centres and Encryption

15:42 Limitations of AI in Scientific Discovery

20:22 The Future of Superhuman AI

23:23 The Race in AI Development

28:18 Navigating the Future of AI Leadership

About Nigel Cannings

Nigel Cannings is a leading expert in speech technology and AI, specialising in applications within the legal and financial sectors. With over 20 years of experience, he holds more than a dozen patents in Natural Language Processing, Cryptography, AI, and Speech Processing. A former city lawyer, Nigel shifted his focus to technology, pioneering innovations such as the integration of GPU technology into speech recognition and advancing work in Explainable AI, AI Ethics, and Secure Search.

Based in London, Nigel is a respected thought leader exploring the evolving relationship between AI and human expertise.

In this episode of the Tech Transformed podcast, Tom Croll, a Cybersecurity Industry Analyst and Tech Advisor at Lionfish, speaks to Panenkov. They talk about the intricacies of powering multi-cloud systems with AI, offering valuable insights for businesses aiming to tap into the full potential of both.

They also discuss data fragmentation, interoperability issues, and security concerns. 

AI Adoption in Multi-Cloud

Addressing the key challenges of AI adoption in multi-cloud environments, Panenkov spotlights one of the most prominent issues – data fragmentation. 

“AI thrives on unified data sets. But multi-cloud setups often lead to data silos across the different platforms,” the founder of emma, the cloud management platform, explained. 

Data silos creates a disconnect which makes it increasingly challenging for AI models. It makes it harder for AI models to access and process the huge amounts of data needed to function efficiently. 

Instead, Panenkov stresses the potential of AI to drive multi-cloud adoption by optimising workloads and automating policies. 

In addition to data fragmentation, the lack of interoperability and tooling presents another challenge when integrating AI with multi-cloud. This is where Inconsistent APIs, a lack of standardisation, and variations in cloud-native tools create major friction. The difference is evident when building AI pipelines across diverse environments. 

Panenkov also pointed out the impact of latency and performance. He says, "Even Kubernetes is sensitive to latency. When we talk about AI and inference, and I'm not even talking about the training, I'm saying that inference is also sensitive." 

Without proper networking solutions, running AI workloads effectively in multi-cloud environments becomes next to impossible.

Of course, security and compliance are a looming challenge for all enterprises across varying industries. Managing data protection in different jurisdictions and environments adds layers of legal and operational complexity.

Despite these challenges, AI has significant advantages in multi-cloud systems that well surpass any challenges. 

Intelligent Orchestration is the Key to Successful Multi-Cloud Adoption

The main topic of the conversation was how AI can actually help overcome the complexities of multi-cloud adoption. As the founder of a cloud management platform, Panenkov believes that AI may be both the driver and the remedy for multi-cloud adoption.

For example, Panenkov describes how AI can orchestrate workload placement and resource allocation. “We can look into and predict the behaviour of the workload, and we can optimise the infrastructure for these workloads, and allocate resources, helping our customers to start scaling.” 

The promise of portability offered by platforms like Kubernetes is often "undercut by vendor-specific customisations," noted Panenkov, and AI can address this through intelligent orchestration.

AI can enhance workload placement, resource allocation, and even latency routing. 

Additionally, AI can automate policies, enforcing security, compliance, and cost rules across various clouds. This "simplification" is critical. Panenkov drew a parallel to the way unified interfaces abstract complexity for users, stating, "All these AI agents have to abstract, evade the complexity for the developers and operators alike."

The impact on business is significant: faster product delivery, accelerated innovation, and the ability to leverage "best of breed" tools without heavy vendor lock-in. This leads to higher agility in scaling and adapting applications to market needs more quickly.

Takeaways

• AI thrives on unified data sets.
• Data fragmentation leads to silos in multi-cloud environments.
• Kubernetes can complicate multi-cloud strategies due to latency issues.
• Edge computing adds complexity but offers low-latency benefits.
• AI can optimise workload placement and resource allocation.
• AI helps enforce security policies across multiple clouds.
• A multi-cloud strategy can reshape how organisations innovate.
• Organisations can mitigate risks by avoiding vendor lock-in.
• Building intelligent abstraction layers is crucial for flexibility.
• Leaders should focus on orchestration and not force-fit clouds. 

Chapters

00:00 Introduction to AI and Multi-Cloud Challenges

02:31 Key Challenges in Multi-Cloud AI Adoption

11:43 AI as a Driver for Multi-Cloud Adoption

16:33 Impact of Multi-Cloud on Innovation and Risk Management

23:00 Final Thoughts and Advice for Leaders

About Emma

emma helps organisations use real multi-cloud options and unlimited computing power to get the most from their cloud and AI efforts. With support from various providers, including hyperscale and EU-regional, and environments such as on-prem, private, and public, emma enables organisations to reach their business goals now and in the future.

Unlike solutions that focus on just certain parts, emma takes a complete approach to cloud management. By combining AI-driven performance, cost, and network management with tools for optimisation and governance, emma delivers a smart platform that makes cloud operations easier and boosts efficiency.

The speakers discuss the current AI scenario's impact on data centers, high-density computing, and cloud infrastructure. This is where Flexential comes in. Mallory stresses the importance of trust and verification in AI deployment, especially regarding security and data privacy, which Flexential has established a reputation for.

“How to adapt to the AI boom?” is one question everyone’s asking, Mallory says. From a service provider perspective, it's a "proverbial gold rush" for powered land. This is essential for building the relevant AI infrastructure that will serve as early entry points. 

Flexential's survey reveals that a staggering "90% of people surveyed are contemplating an AI strategy." The number spotlights the widespread interest and impending demand. “This isn't a short-term trend,” says Mallory. He also projects a "12-year" development cycle for AI infrastructure, emphasizing the long-term commitment required from the industry.

Scaling Up for AI

The unprecedented growth in AI demands specialized infrastructure, especially concerning the sustainable use of AI and running data centers, and strong strategies for scalability, reliability, and cost-effectiveness. 

Flexential is uniquely positioned to meet this challenge. "We've been developing high-performance compute facilities for over 10 years," he states. Their "Gen 4 and Gen 5 sites can cool 50 kilowatts per cabinet air-cooled." 

This information has allowed them to readily support the requirements of H100 and H200 type deployments, not just for service providers, but also for ramping deployments in the healthcare and financial sectors.

Looking ahead, the data center industry is preparing for even higher-density racks and the widespread adoption of liquid cooling. While "all of our sites are liquid-cooled ready," Mallory says, thorough airflow studies and CFD analysis show liquid cooling is genuinely necessary. 

Flexential’s air-cooled solutions are already handling "high-dense pods for some of the companies that have recently gone public and other companies that are out there that you hear about in this AI service provider realm,” Mallory added. 

Takeaways

• 90% of surveyed companies are considering an AI strategy.
• The AI industry is experiencing a gold rush for infrastructure.
• Data centers must adapt to high-density computing demands.
• Liquid cooling is essential for high-performance AI deployments.
• AI regulations are shaping how data centers operate.
• Trust but verify is crucial for AI deployment.
• AI democratization is vital for businesses of all sizes.
• Flexential is focused on providing scalable AI infrastructure.
• Security policies are essential for protecting sensitive data.
• AI can enhance productivity, but it requires human oversight.

Chapters

00:00 The Impact of AI on Data Centers

02:51 Infrastructure Challenges and Solutions

05:59 Navigating AI Regulations and Security

08:59 Democratization of AI for Businesses

12:03 Key Takeaways for CIOs

About Flexential

Flexential enhances organizational operations by providing customized hybrid IT solutions through its FlexAnywhere platform. This platform offers integrated colocation, cloud, connectivity, data protection, and managed and professional services. Backed by three million square feet of data center space across 19 highly connected markets, Flexential helps customers navigate complex IT journeys.

The FlexAnywhere solutions empower customers to address their most intricate hybrid IT infrastructure needs, including reliability, performance, agility, scalability, and seamless application and user interconnection.

• #AI #analysts are crucial for integrating AI into business processes.
• Organisations need to rethink their data management strategies for AI.
• AI #data clearinghouse concepts help manage data access and security.
• Cross-functional collaboration is essential for successful AI integration.
• AI can enhance employee effectiveness rather than replace jobs.
• The future of work will see AI analysts in various business functions.
• Companies must adapt quickly to remain competitive in the AI landscape.

Summary

This episode of the #TechTransformedPodcast explores the role of AI Analysts. Host Keyari Page is joined by guest speaker Andy MacMillan, CEO of Alteryx.

We learn that the term AI Analyst refers to an emerging role of #professionals who help organisations rethink their processes, workflows, and employee capabilities through the use of AI.

This new role bridges the gap between AI systems and tangible #businessoutcomes. In the podcast, we also cover the importance of managing data for AI through an “AI data clearing house”. This helps business analysts prepare data for AI projects.

Through this system, analysts and business owners are able to ensure that compliance and security measures are met.

Tune in for insights on how AI is reshaping roles, boosting efficiency, and transforming customer experiences in the evolving business landscape.

For more tech insights visit: em360tech.com

Shubhangi Dua, podcast host and producer at EM360Tech, , sits down with Heuer to unpack the ambitious yet achievable vision of Zero Ticket IT and how both agentic AI and intelligent automation are poised to change IT operations.

Traditional IT ticketing systems, with their reactive nature and reliance on human intervention, are facing an overdue overhaul. Heuer shares a path towards a more efficient, proactive, and ultimately frictionless IT experience.

What is Zero Ticket IT?

Zero Ticket IT shifts the focus from reacting to individual tickets to directly addressing the source of the issue. As Heuer explains, a major portion (roughly 70 per cent) of IT tickets originate from employee requests and range from password resets to connectivity problems. Another substantial chunk comes from machine alerts, often leading to "alert storms" where a single underlying issue triggers a cascade of notifications.

For instance, imagine an AI-powered conversational interface that can understand an employee's problem. Now this problem can be resolved using a vast knowledge base and service catalog. 

"There's no reason for a human to intervene if you locked your account. If you need to reset your password. There's no reason for a human to have to handle that. It should happen instantaneously," Heuer elaborates. This self-service approach immediately reduces ticket volume by a significant margin.

AI Automation to Resolve Substantial IT Requests

Automation can also solve challenges head-on by integrating AI operations (AIOps) solutions to analyse countless machine alerts, thereby identifying correlations and spotting the root cause. 

"Instead of getting a thousand incidents you have to manage, you get one incident," Heuer states, allowing for precise and rapid resolution.

Heuer highlights that by implementing these two layers — direct interfaces for employees and intelligent automation for machine alerts — organisations can achieve a 60 per cent to 70 per cent reduction in total ticket volume.. 

Some Resolve Systems customers have even seen up to an 80 per cent reduction, with Heuer noting: "We have a telco customer that's gotten to 80 per cent reduction of incidents in their network and infrastructure. We have a retail company that has gotten to 75 per cent reduction. Auto-remediation is the solution for all employee requests."

Heuer envisions a future where the core role of an IT technician evolves from reactive ticket-solving to proactively managing and optimizing AI and automation systems. The focus will be on identifying patterns, improving knowledge articles, and developing new automations. 

The Resolve System CEO said that IT organisations must shift their focus from traditional metrics like agent utilisation and first-touch resolution to employee productivity and digital employee experience. 

Forrester Research suggests that for every 1,000 employees, companies can lose $1.5 million to $3 million in productivity due to IT-related downtime.

"IT's job isn't to work tickets. Its job is to make the business productive, to keep it productive," Heuer says. 

"Start now. Stop waiting, stop with the FUD, stop with the experimenting. Instead, apply AI to a problem. This is real. This isn't a future fantasy world but it’s something you could do today, right now,” the CEO added.

To learn more about Resolve Systems and their work in this space, visit resolve.io and follow them on social media: Resolve Systems LinkedIn and Resolve Systems YouTube.

Takeaways

• Zero Ticket IT aims to solve IT issues at the source.
• Agentic AI can significantly reduce ticket volume.
• The focus of IT should shift from ticket management to employee productivity.
• AI can help eliminate human error in IT operations.
• Education and transparency are crucial for building trust in AI.
• Employee experience directly impacts productivity and engagement.
• IT organisations should measure success through employee productivity metrics.
• Automation can lead to significant cost savings for businesses.
• The future of IT roles will involve managing AI and automation.
• AI's impact on the workforce raises important economic questions.

Chapters

00:00 Introduction to Zero Ticket IT

06:52 Understanding Agentic AI and Automation

13:50 Shifting Focus: From Tickets to Employee Productivity

18:55 Challenges and Trust in AI

26:00 Future Skills for IT Teams

30:59 The Future of Work and AI

About Resolve Systems

Resolve is redefining IT and network operations with an agentic automation and orchestration platform built for a Zero Ticket future. Our AI agents don’t just assist—they autonomously detect, diagnose, and resolve incidents before they impact the business. By transforming reactive, manual workflows into proactive, self-healing systems, Resolve slashes ticket and alert noise by up to 90 per cent, reduces MTTR from hours to minutes, and empowers IT teams to scale without increasing headcount.

Trusted by some of the largest global enterprises, Resolve empowers IT teams to move from reactive firefighting to proactive transformation. Learn more at resolve.io

In this episode of the Tech Transformed Podcast, Shubhangi Dua, podcast host and producer at EM360Tech sits down with Keith Szot, SVP Chief Evangelist at Esper, to talk about how enterprises can extend the lifespan of their current edge devices. They also discuss how such enterprises can easily integrate Edge AI solutions without a complete hardware overhaul.

"Starting out with IT Ops is a tough endeavor. If you are running revenue producing key business operating systems that are out in the field, it's not an easy job,” stated Szot. “With everything that's going on these days, it's not getting any easier. Now, considering tariffs, the impact of AI in terms of how you look at your hardware refresh cycle, it's really tough.”

‘Android is the key’

Specifically alluding to edge devices in restaurant and retail, Szot reflecting on the limitations of traditional operating systems says Android is the key. "If you look at the ISV and the solution provider community, the best and latest solutions for these markets are built on Android.”

“You look at enterprise developers, if you're developing in-house, arguably it's a lot easier to find an Android developer and build a team to focus on creating Android applications than it is in Windows,” he added. “Android is the biggest developer ecosystem in the world in the history of humankind.

Unlike operating systems tied to rapid consumer hardware upgrades, Android offers the flexibility of an open-source project. It allows for greater control over updates and longevity. 

Szot believes that the Android UX is more intuitive. He says that people use phones all the time. “And even if you're an iOS user, the paradigm, if you go to Android, still has a familiarity where the bar to understand how to use the software in the device arguably is lower." This minimises training needs and improves operational efficiency.

In a nutshell, the conversation touches on extending hardware lifecycles for edge devices in the restaurant and retail industry, primarily through the Android flip, to enable the integration of AI at the edge and prepare for future trends like robotics and 5G.

Key Takeaways 

• Extending hardware lifecycles is crucial for cost management.
• Android is becoming the preferred OS for enterprise solutions.
• AI can enhance customer experiences in retail environments.
• Robust hardware design is essential for longevity.
• Transitioning from Windows to Android can save costs.
• Edge AI allows for on-premise processing without latency issues.
• Physical AI will revolutionize the retail and restaurant sectors.
• Quantum computing poses both opportunities and challenges for security.
• Device management is key to maintaining operational efficiency.
• Innovative hardware designs will create new customer experiences.

Chapters

00:00 Introduction to Edge Devices and AI

03:12 Extending Hardware Lifecycles in Retail

06:06 Transitioning from Windows to Android

08:56 Practical Applications of Edge AI

11:47 AI Integration in Restaurant Kiosks

14:55 Managing Existing Hardware with Software Solutions

18:01 The Role of Edge AI in Future Technologies

20:51 Physical AI and Robotics in Retail

24:03 Future Trends: Quantum Computing and Device Management

27:07 Innovations in Hardware for Enhanced Experiences

30:14 Key Takeaways for IT Decision Makers

About Esper

Founded in 2017, Esper is a company known for its modern device management platform, going beyond traditional MDM to power exceptional device experiences with a focus on automation. They aim to redefine how businesses manage their company-managed hardware, particularly Android devices, driving towards a DevOps future.

Esper's core offerings provide advanced automation and unmatched operational efficiency, eliminating re-provisioning and enabling exception-based management. Their powerful and scalable platform features kiosk mode, remote control, software deployment, and device grouping, with tailored industry solutions for sectors like healthcare, retail, hospitality, and logistics. They also maintain strong partnerships with key players such as Zebra, Lenovo, and Intel.

"The promise of AI and agentic AI is that we're now building very meaningful automation into the platform such that these teams of 10 are now able to basically actually capture all of the metadata about all of the data cataloged across their entire company," stated Corey Keyser, the head of artificial intelligence (AI) at Ataccama.

In this episode of the Tech Transformed podcast, Shubhangi Dua, a B2B tech journalist and Podcast host at EM360Tech speaks with Keyser from Ataccama, about agentic AI, data quality, and data governance. 

They explore how intelligent automation is shaping enterprise data management, the role of AI in improving data quality, and the importance of trust in AI systems. Additionally, Keyser shares significant insights on Ataccama's unique approach to data governance, practical applications of their AI agent, and how they are keeping pace with the constantly changing AI regulations. 

While the speed and efficiency of AI are undeniable, the question of trust remains. Keyser addressed this directly: "The short answer is you can never fully trust these automations, right? 

“That's why it's really critical to always have data stewards that we will serve. We will always have data engineers that we will serve. We're just looking to improve their productivity. We always assume that there will be humans in the loop who are verifying the tasks orchestrated by AI agents."

Ataccama's One AI Agent exemplifies the practical application of these principles. Keyser added that the AI agent can go and create data quality rules in bulk. “Go through the evaluation and testing of those quality rules in bulk, and then also assign the rules in bulk. Something that would take potentially weeks, can now actually kind of take hours depending on the person."

Takeaways

• Agentic AI is about dynamic planning and semi-autonomous task execution.
• Data governance involves cataloging and managing organisational data.
• Data quality assessment is crucial for ensuring high trust in data.
• AI can significantly speed up the creation of data quality rules.
• Human oversight is essential in AI-driven automation processes.
• Atacama's AI agent improves productivity for data management teams.
• Regulatory compliance is a growing concern for AI applications.
• User experience is key to successful AI integration in organisations.
• The relationship between data and AI is symbiotic and essential.
• Organisations must adapt to evolving AI regulations and standards.

Chapters

00:00 Introduction to Agentic AI and Data Governance

02:41 Understanding Data Quality and Governance

06:30 The Role of AI in Data Management

11:14 Practical Applications of One AI Agent

15:46 Differentiating Atacama in the AI Landscape

19:37 Case Studies of Transformation

20:05 Integrating Data Quality and Governance

22:07 Navigating Regulatory Changes

24:19 Enhancing User Experience with AI

25:29 Key Takeaway for CIOs

“What specific problem are you trying to solve with AI? Do we need a general purpose AI application or would a more focused model be better? How will we manage security, compliance and governance of that model? This process can help to reveal where AI adoption makes sense and where it doesn't," she added. 

In this episode of the Tech Transformed podcast, host Shubhangi Dua, podcast producer at EM360Tech speaks with Hodgson, a seasoned business and technical leader with over 25 years of experience at IBM and Red Hat. They talk about the challenges of scaling AI projects, the importance of open source in compliance with GDPR, and the geopolitical aspects of AI innovation. 

They also discuss the role of small language models (SLMs) in enterprise applications and the collaboration between IBM and Red Hat in advancing AI technology. Joanna emphasises the need for a strategic approach to AI and the importance of data quality for sustainable business practices. While large language models (LLMs) dominate headlines, SLMs offer a cost-effective and efficient alternative for specific tasks.

The podcast answers key questions, like ‘how do businesses balance ethical considerations, moral obligations, and even patriotism with the drive for AI advancement?’ Hodgson shares her perspective on how open source can facilitate this balance, ensuring AI works for everyone, not just those with the deepest pockets.

Hodgson also provides her vision on the future of AI. It comprises interconnected small AI models, agentic AI, and a world where AI frees up teams to create personal connections and exceptional customer experiences.

Takeaways

• Curiosity is a strength in technology.
• AI is becoming embedded in existing applications.
• Regulatory compliance is crucial for AI systems.
• Open source can enhance trust and transparency.
• Small language models are efficient for specific tasks.
• AI should free teams to create personal connections.
• A strategic AI platform is essential for businesses.
• Data quality is key for sustainable business success.
• Collaboration in open source accelerates innovation.
• AI can be used for both good and bad outcomes.

Chapters

00:00 Introduction to the Tech Transform Podcast

01:35 Pivotal Moments in Joanna's Career

05:12 Challenges in Scaling AI Projects

09:15 Open Source and GDPR Compliance

13:11 Regulatory Compliance and Data Security

17:30 Geopolitical Aspects of AI Innovation

22:31 Collaboration Between IBM and Red Hat

23:58 Understanding Small Language Models

29:54 Future Trends in AI and Sustainability

About Red Hat

Red Hat is a leading provider of enterprise open source solutions, using a community-powered approach to deliver high-performing Linux, hybrid cloud, edge, and Kubernetes technologies. The company is known for Enterprise Linux.

They offer a wide range of hybrid cloud platforms and open source technologies—including AI, virtualisation, edge, app development, and automation solutions. The hybrid cloud solutions are built to run their customers’ most important, most complex projects—from the datacenter to clouds to the edge. Their open ecosystem of partners and communities aim to ensure existing investments stay protected, providing unmatched freedom and choices to change, flex, reinvent, or stay the course.

What is Open Source Software (OSS)?

Open source software (OSS) refers to the source code of software that is relatively liberally available for use, modification, and distribution. The source code knowledge is accessible with few restrictions. 

Enterprise open source software is supported by a vendor, usually via a subscription fee. For example, the vendor stabilises and quality-assures the software, certifies it works with an ecosystem of hardware and software, secures it and provides technical support.

OSS is a driver of collaborative development of AI models, fostering transparency, team-backed innovations. This often results in highly adaptable and cost-effective solutions. The open nature allows for peer review, potentially leading to enhanced security and reliability, making it a cornerstone of modern technology.

• #Satellitecommunications are essential for remote field teams.
• They provide safety and tracking for workers in isolated areas.
• Disaster preparedness involves proactive planning and communication.
• User-friendly #technology is crucial for effective utilisation.
• Reliable communication impacts day-to-day operations positively.
• Employers show care for employee safety through #satellitetechnology investment.

Summary

In this episode of #TechTransformed, Jonathan Care discusses the importance of satellite communications for field teams. He is joined by Mark O'Connell, EMEA & Asia Pacific General Manager at Globalstar, and Grace Finn, Senior Account Manager at Peoplesafe. Together, they explore how satellite technology enhances safety, disaster preparedness, and operational efficiency for remote, field workers.

We learn the differences between satellite and cell communication. O’Connell emphasises the importance of satellite communication, stating that due to field workers, there is a requirement to not be reliant on cellphone towers.

O’Connell further clarifies that field teams who are working remotely, and are away from terrestrial communications, need access to constant communication.

Finn also expresses the importance of field worker safety, sharing: “If the unforeseen does happen, they’re protected.” She emphasises that organisations should invest in satellite communications to ensure their teams' wellbeing & security.

Tune in to learn the user-friendly aspects of the technology, its features for challenging environments, and the critical role it plays in ensuring reliable communication.

For the latest tech insights and content visit: EM360Tech.com

In this episode of the Tech Transformed podcast, Kevin Petrie, Vice President of Research at BARC, speaks with Manesh Tailor about the intersection of artificial intelligence (AI) and observability, and how this is positively changing business operations.

Tailor emphasises how intelligent observability has changed beyond simple monitoring to provide real-time insights into customer experience and the entire technology stack. This enables informed decisions across engineering, operations, and business domains, directly linking technical performance to strategic business outcomes.

He also discusses the different stages observability has been through and where it's leading to now. The current wave, Observability 3.0, takes advantage of AI to predict issues and even enable self-healing systems. 

New Relic has embraced this two-way street, using AI within its platform. This was in an ambition to help users and "AI monitoring" to track the performance of language models alongside traditional metrics. Such a platform provides a holistic view of system health and the cost implications of AI deployments.

Alluding to the management of AI-powered applications, Tailor says collaboration is key between application and data science teams. Not only does it provide real time data but as a result leads to efficient decision making.

Futuristically, the speedy proliferation of AI agents has both pros and cons for observability. This is where New Relic comes in. It addresses the challenges by constructing a platform-centric "AI orchestrator" with a growing library of AI-native agents. 

In essence, as AI-powered applications become increasingly integral to business operations, intelligent observability is no longer optional. 

Takeaways

• Observability is crucial for understanding unknowns in systems.
• AI enhances observability by providing predictive insights.
• The evolution of observability includes intelligent monitoring.
• Collaboration between technical and business teams is essential.
• Cost efficiency is a key focus in modern observability.
• Real-time data is vital for effective decision-making.
• Self-healing systems represent the future of observability.
• AI and observability must work in tandem for success.
• The complexity of systems is increasing, requiring better tools.
• Observability is applicable across all organizational levels.

Chapters

00:00 Introduction to AI and Observability

03:10 Defining Observability and Its Evolution

05:49 The Role of AI in Observability

08:46 Navigating AI-Driven Applications

11:52 Target Users and Community for Observability

14:57 Collaboration Across Teams

17:55 Challenges and Opportunities in Observability

20:47 The Future of Observability and AI

23:54 Key Takeaways for CIOs and IT Leaders

About New Relic

The New Relic Intelligent Observability Platform empowers businesses to proactively eliminate disruptions in their digital experiences. As the only AI-enhanced platform that unifies and correlates telemetry data, New Relic provides comprehensive clarity across your entire digital landscape. 

By processing the optimal data at the right moment, we shift your problem-solving capabilities beyond proactive measures to a predictive stance, maximizing value and controlling expenditures. This is why global enterprises like Adidas Runtastic, American Red Cross, Domino’s, GoTo Group, Ryanair, Topgolf, and William Hill rely on New Relic to fuel innovation, enhance reliability, and deliver outstanding customer experiences that drive growth.

• #AIagents are #autonomous entities that can perceive and act.
• Human oversight is essential in the initial stages of AI implementation.
• Data quality and trust are critical for effective AI agents.
• Guardrails must be integrated into the design of AI agents.
• Modularity in design allows for flexibility and adaptability.
• #AI should be embedded in data management processes.
• Collaboration between data and application teams is vital.

Summary

In this episode of #TechTransformed, Kevin Petrie, VP of Research at BARC, and Ann Maya, EMEA CTO at Boomi, discuss the transformative potential of AI agents and intelligent automation in business. They explore the definition of agents, their role in automating processes, and the importance of human oversight.

Maya introduces us into the world of AI agents stating that, at its core, it’s an autonomous entity within #AIsystems that can perceive its environment. This creates a deep dive into how they evolved from traditional automation to “observe, think, and act” in novel and autonomous ways.

Maya addresses AI skepticism by acknowledging its growing autonomy while underscoring the current necessity of human oversight. She also highlights data's crucial influence on an agent's perception and decisions, emphasising the need for quality, trustworthy data in effective AI.

Moreover, Maya and Petrie explore AI's practical implications, pointing to Google's agent-to-agent protocol as vital for managing language model interactions and enabling effective communication across diverse agents within complex systems.

For the latest tech insights visit: EM360Tech.com

In this episode of the Tech Transformed podcast, Dana Gardner, a thought leader, speaks with Rob Whiteley, CEO of Coder, about the transformative impact of agentic AI on software development. They discuss how AI is changing the roles of developers, the cultural shifts required in development teams, and the integration of AI agents in cloud development environments.

Agentic AI is seemingly set up for favourable outcomes. Or is it? Agentic AI is believed to shake-up enterprise IT, offering a productivity boost similar to the iPhone's impact. 

This isn't about replacing developers but amplifying their output tenfold. It aims to allow the implementation of rapidly created solutions and iteration that has been unimaginable in the past. This shift requires valuing "soft skills" like communication and collaboration over pure coding proficiency, as developers guide AI "pair programmers."

The synergy of AI agents, human intellect, and Cloud Development Environments (CDEs) is key. CDEs provide secure, governed, and scalable platforms for this collaboration, allowing developers to focus on business logic and innovation while AI handles the coding groundwork. This requires a move from rigid "gates" in development processes to flexible "guardrails" within CDEs. Such a move fosters innovation with built-in control and security.

Flexibility and choice are vital in this constantly advancing AI space. CDEs enable organisations to select the best AI agents for specific tasks, avoiding vendor lock-in by expressing the development environment as code. This leads to practical applications like faster prototyping, enhanced code development, and automated testing, significantly boosting code output. Furthermore, agentic AI democratises development, empowering non-engineers to build solutions.

Preparing for this future requires proactive experimentation through AI labs, engaging early adopters, and viewing AI as an augmentation of human skills. Watch the podcast for more insights on CDEs and the impact of AI agents on enterprise cloud development. 

Takeaways

Agentic AI is a transformative technology for software development.

The role of developers is shifting from hard skills to soft skills.

AI agents can significantly increase productivity in coding tasks.

Organizations need to rethink their development strategies to integrate AI.

Cloud development environments are essential for safely using AI agents.

Choosing the right AI agent is crucial for effective development.

Security and governance are critical when integrating AI into development.

AI can empower non-developers to create applications.

Guardrails are more effective than gates in managing AI development.

Organisations should experiment with AI to find the best fit for their needs.

Chapters

00:00 Introduction to Agentic AI and Developer Roles

03:20 Transformative Impact of AI on Development

06:50 Cultural Shifts in Development Teams

10:30 Integrating AI Agents in Cloud Development Environments

12:49 Choosing the Right AI Agents

15:21 Security and Governance in AI Development

17:29 Coder's Role in the AI Development Landscape

20:14 Expanding Development Beyond Traditional Roles

23:27 Guardrails vs. Gates in Development

26:56 Practical Applications of AI in Code Generation

29:58 Acquiring AI Agents for Development

32:34 Preparing for the Future of Development with AI

About Coder

Coder is a remote-first company with products used by many of the world’s largest enterprises. Coder provides an enterprise platform and open source tools that make it easier than ever to configure, secure, and manage software development environments.

Leave hardware limitations behind and welcome accelerated code execution, providing you and your developers with unmatched efficiency and elevated productivity. This enables an organisation to innovate rapidly and maintain a competitive edge.

• #Customersuccess is about helping customers get value from products.
• The perception of customer success is shifting from a cost centre to a revenue generator.
• #AI can enhance customer success by providing predictive insights and automating processes.
• Customer success teams are uniquely positioned to identify upsell and cross-sell opportunities.
• Collaboration between customer success and sales teams is essential for maximising revenue.
• The integration of AI tools can streamline customer success operations.

Summary

Ever wonder how to transform existing customers into your company’s most powerful growth engine? According to Gainsight’s Chief Revenue Officer, Marilee Bear, it starts with one deceptively simple principle: “helping customers get value from your product.” 

This #TechTransformed episode features Christina Stathopoulos, Founder of Dare to Data, in conversation with Marilee about the dynamic role of customer success – a powerful avenue for building deeper customer bonds, boosting retention rates, and ultimately achieving significant revenue gains.

Tune in to learn how to unlock the potential of customer success as a catalyst for cross-selling and upselling opportunities. Explore how to equip your customer success teams with commercial acumen and harness their potential as a secret weapon for business growth.

Whether you're a #CRO looking to optimise revenue or a business leader navigating the AI revolution, this episode offers invaluable insights into the future of customer success.

For the latest tech insights visit: EM360Tech.com

• #AgenticAI systems can string tasks together for efficiency.
• Real-world applications include supply chain optimisation and knowledge worker augmentation.
• #Dataquality is crucial for effective AI implementation.
• Education and understanding of AI's potential are essential for organisations.
• Governance is key to ethical AI deployment.
• Leadership is critical in adopting AI technologies.
• AI will create new job opportunities, not just displace existing ones.

Summary

Jeff DeVerter, Field Chief Technology Officer at Pythian, describes agentic AI as “little workers that are going to go off and all do a job. You're now a manager of these AIs that are going to go off and do some work and come back and give you that work product.”

In this episode of the #TechTransformed podcast, Christina Stathopoulos, Founder at Dare to Data, and Jeff DeVerter explore this concept, revealing how agentic systems are re-shaping real-world business scenarios. 

Imagine these agentic AIs as powerful “personal assistants” when empowering leaders to manage data, streamline workflows and drive commercial acumen. 

The discussion goes beyond the possibilities addressing how to prepare your data infrastructure, navigate ethical considerations, and understand AI’s impact on employees, delivering crucial takeaways for CIOs.

For the latest tech insights visit: EM360Tech.com

In this episode of the Tech Transformed podcast, Shubhangi Dua speaks with Nikhil Nandagopal, co-founder and CPO of Appsmith, about the metamorphological impact of AI agents in the workplace. He particularly emphasises the need for organisations to hone in on the advancing capabilities of agentic AI while still maintaining a focus on human collaboration and security. 

Takeaways

• AI agents are autonomous entities designed to achieve specific goals.
• The centralisation of data through AI agents simplifies workflows.
• Conversational interfaces are becoming the norm for accessing information.
• Humans remain integral to AI workflows, acting as moderators.
• Job roles will evolve, requiring new skills and adaptability.
• Critical thinking is essential when interacting with AI outputs.
• Cybersecurity is a major concern with centralised AI systems.
• Self-hosting AI solutions can mitigate cybersecurity risks.
• The future of work will reward soft skills over hard skills

Chapters

00:00 Introduction to AI Agents and Their Impact

03:34 The Shift Towards Conversational Interfaces

05:07 Assisted Workflows and Human-AI Collaboration

10:05 Job Market Evolution in the Age of AI

13:23 Critical Thinking in the Age of AI

15:29 Cybersecurity Concerns with AI

20:31 Preparing for Cyber Threats in AI Systems

22:51 The Future of AI Agents in the Workplace

Mrksic particularly spotlights some underutilised capabilities of AI such as how it can manage up to 90% of repetitive duties, allowing human agents to concentrate on other complex tasks. The conversation also explores the transition from basic service to a broader, more holistic customer experience, necessitating the need for rapid adaptation and experimentation.

AI in contact centers isn't just about cutting costs. This conversation shows how it can truly make a difference – giving agents the tools to shine, providing customers with better, more quality experiences, and even letting AI take care of tasks behind the scenes securely, so humans can focus on what truly matters.

Takeaways

• AI is a dominant force shaping technology today.
• Contact centers have a high volume of repetitive tasks suitable for AI.
• AI can automate up to 90% of tasks in contact centers.
• The role of AI is not just cost-cutting but improving service quality.
• Agentic AI can perform tasks on behalf of users asynchronously.
• Customer experience is now a key focus beyond just service.
• Companies must adapt quickly to avoid falling behind competitors.
• Failing fast and experimenting is crucial for success with AI.
• AI can provide insights that traditional methods miss.
• Investing in AI should be about solving problems, not just keeping up with trends.

Chapters

00:00 Introduction to AI in Contact Centers

02:01 Benefits of AI in Contact Centers

07:37 Transforming Customer Experience with AI

15:42 Understanding Agentic AI

21:27 The Shift from Customer Service to Customer Experience

30:25 Advice for Business and CX Leaders

Real-time AI empowers businesses to make decisions in milliseconds, reacting to changing conditions and seizing fleeting opportunities. It's about more than just analysing historical data; it's about understanding the present and predicting the future, all in the blink of an eye.

Imagine a world where customer service agents have access to the most up-to-the-minute information, resolving issues before they escalate. Envision supply chains that dynamically adjust to disruptions, ensuring products are always available. Envision marketing campaigns that personalise experiences in real time, maximising engagement and driving conversions. 

But real-time AI isn't just about speed; it's also about accuracy. The time to embrace real-time AI is now. Businesses that fail to adapt risk falling behind in an increasingly competitive world. By harnessing the power of real-time data and intelligent agents, enterprises can tap into new levels of performance, innovation, and growth. 

In this episode, Shubhangi Dua, an editor and tech journalist at EM360Tech, speaks to Madhukar Kumar, the Chief Marketing Officer at SingleStore, about the transformative potential of real-time AI for enterprises.

Takeaways

• Real-time AI is essential for modern enterprises.
• The evolution from generative AI to real-time AI is significant.
• Data accuracy and freshness are critical for AI success.
• AI agents will collaborate to enhance business processes.
• Enterprises must manage data silos to improve efficiency.
• Smaller companies can leverage AI to create innovative solutions.
• Data governance is crucial for protecting sensitive information.
• Real-time AI can significantly improve user experience.
• AI will enable professionals to focus on higher-value tasks.
• Harnessing data effectively will be a key differentiator for businesses.

Chapters

00:00 Introduction to Real-Time AI and Its Importance

03:03 The Evolution of AI: From Generative to Real-Time

05:54 Real-Time AI in Enterprises: Advantages and Examples

11:01 The Future of AI Agents and Their Collaboration

16:47 Preparing Enterprises for AI: Data Management and Security

20:47 Business Advantages of Real-Time AI and Future Opportunities

Key Takeaways

• Observability requires a cultural shift towards collaboration.
• Data management is crucial to avoid overwhelming teams.
• AI is transforming IT from reactive to proactive approaches.
• Organizations must start small when implementing AI.
• Understanding company culture is key to AI adoption.
• Uptime is essential; downtime is no longer acceptable.
• AI should supplement human expertise, not replace it.
• Effective data sorting can reduce noise in decision-making.
• Innovation is necessary to maintain a competitive edge.
• Organizations need to establish governance around AI usage.

Chapters

00:00 Introduction to Ryan Worobel and His Journey

07:37 Proactive vs Reactive Approaches in IT

13:31 Implementing AI in Organizations

19:31 Conclusion and How to Connect with Ryan

Learn about importance of creating meaningful digital interactions, the challenges posed by biases in AI, and the need for transparency and education in navigating these technologies. Sam introduces a framework for brands to understand, connect, and serve their audiences effectively, highlighting the potential of AI to enhance consumer experiences while addressing concerns about job displacement and data biases.

Key Takeaways

• The digital experience is rapidly changing with AI at the forefront.
• Brands must prioritize meaningful digital experiences to connect with consumers.
• The shift from the information age to the experience age is significant.
• AI can create unique experiences tailored to individual needs.
• Concerns about AI include job displacement and biases in data.
• Transparency and trust are crucial in AI adoption.
• Education about AI should start from a young age.
• Brands can leverage AI to enhance customer connections.
• Spotify's DJ feature exemplifies effective AI use in consumer engagement.
• Understanding, connecting, and serving are key components for brands in the AI era.

Chapters

00:00 The Evolution of Digital Experience

04:01 Transitioning from Information Age to Experience Age

08:03 Addressing AI Concerns and Biases

14:11 Navigating AI: Understand, Connect, and Serve

Key Takeaways

• AI is essential for navigating the complexities of private markets.
• Data scarcity and non-standardization make AI a necessity in finance.
• Human intuition remains crucial in investment decision-making.
• AI can enhance productivity but should not replace human judgment.
• Generative AI poses new data privacy challenges that must be addressed.
• Last mile SaaS can still thrive despite the rise of generalized AI.
• The future of AI is promising, with ongoing advancements in technology.
• Type 2 thinking in AI is a key area for development.
• Investors must consider the return on intelligence, not just ROI.
• AI's role in finance is to augment human capabilities, not replace them.

Chapters

00:00 Introduction to AI in Private Markets

02:56 The Role of AI in Data Analysis

06:00 Balancing Quantitative and Qualitative Insights

08:50 Data Privacy Concerns in AI

11:54 Generative AI and Last Mile SaaS

14:59 Future of AI and Investment Returns

Key Takeaways

• AI and robotics are transforming industries, including cleaning.
• The balance between automation and human oversight is crucial.
• Data interpretation is more important than just data collection.
• Organizations should start small with AI investments.
• Curiosity is key to identifying unique opportunities in careers.
• AI will create new jobs while automating others.
• Human oversight is essential for ethical AI use.
• Staying curious can lead to career growth and innovation.
• AI should complement human work, not replace it.
• Understanding AI's capabilities can enhance productivity.

Chapters

00:00 Introduction to AI and Robotics in Industry

03:00 The Balance of Automation and Human Oversight

05:56 Challenges in Designing Multifunctional AI Systems

08:48 Data Interpretation and Ethical Considerations

12:07 Adapting Organizations to AI

16:58 Identifying Unique Opportunities in AI Roles

However, the transition requires careful consideration of how AI can blend with existing customer success practices. The goal is to ultimately develop a combination of AI capabilities and human empathy, leading to more satisfying and effective customer experiences.

This is where natural language processing (NLP) comes in. The power of NLP can be leveraged to understand customer queries and sentiment analysis to determine their emotional state. 

In this episode, Kevin Petrie, VP of Research at BARC, speaks to Kate Neal, Senior Director of Customer Success at Gainsight, about the evolving role of AI in customer success. 

Takeaways

• AI adoption in customer success is accelerating despite some hesitations.
• Gainsight provides a comprehensive customer operating system, “powered by AI”.
• Natural language processing can significantly enhance customer sentiment analysis.
• Human oversight is crucial in AI applications to ensure accuracy.
• Data quality is essential for effective AI implementation.
• AI can help reduce the administrative burden on customer success teams.
• Collaboration between data and AI teams is necessary for success.
• Understanding AI's capabilities is key for customer success leaders.
• AI is not a replacement for human jobs but a tool to enhance them.

Chapters

00:00 Introduction to AI in Customer Success

03:44 Gainsight's Role in Customer Success

07:11 AI Adoption Trends in Customer Service

10:51 Use Cases of AI in Customer Success

15:12 Natural Language Processing and Customer Sentiment

19:48 Human Oversight in AI Applications

22:06 Collaboration Between Data and AI Teams

23:59 Getting Started with AI in Customer Service

Key takeaways

• Generative AI adoption is crucial for maintaining competitive advantage.
• Organizations that delay AI adoption risk losing market share.
• AI can transform the role of data teams in organizations.
• Effective communication is essential during AI implementation.
• Aligning AI strategy with business goals is critical for success.
• Measuring ROI is more important than just tracking user adoption.
• Keeping humans in the decision-making loop is vital for ethical AI use.
• Generative AI can empower all employees, not just a few.
• Organizations should test AI solutions against complex use cases.

Chapters

00:00 Introduction to James Smith and His Background

03:12 The Importance of Generative AI Adoption

06:05 Transformational Potential of Generative AI in Organizations

08:54 Measuring the Impact of Generative AI

11:54 Aligning AI Strategy with Business Goals

15:05 Addressing Bias and Ensuring Ethical AI Use

18:09 Leveraging Generative AI for Future Trends

23:51 Conclusion and How to Connect with James Smith

Key Takeaways

• AI is transforming telecommunications but comes with challenges.
• Focus on solving big problems rather than getting lost in technology.
• AI-powered maintenance can prevent outages and improve reliability.
• Quantum computing has the potential to revolutionize network security.
• Organizations must prepare for the transition to quantum-safe cryptography.
• Reliability and trust are critical in adopting new technologies.
• Competition in telecommunications is fierce, requiring constant innovation.
• AI systems must be designed to avoid biases and ensure fairness.
• Traceability in AI decision-making is essential for accountability.
• The balance between technology and economics will drive future innovations.

Chapters

00:00 Introduction to AI in Telecommunications

02:48 Challenges of AI Implementation

06:11 The Role of Quantum Computing

12:01 Quantum Safe Cryptography

15:10 The Future of Quantum in Telecommunications

17:58 Competition and Reliability in Tech

20:54 Ensuring Fairness in AI Systems

Key Takeaways

• AI is revolutionizing contact centers and customer interactions.
• Natural language processing is key to understanding customer sentiment.
• Human agents are essential for nuanced customer interactions.
• AI models are not 100% accurate and can introduce bias.
• Transparency in AI decision-making is crucial for customer trust.
• Organizations should balance AI efficiency with human emotional intelligence.
• Predictive analytics can enhance customer loyalty and service.
• Continuous training is necessary for both agents and AI systems.
• Implementing AI should be done gradually to avoid disruption.
• Data-driven decision-making is vital for successful AI integration.

Chapters

00:00 Introduction to AI in Contact Centers

06:02 The Role of Human Agents in AI-Driven Environments

11:49 Ensuring Transparency and Accountability in AI

17:53 Using Predictive Analytics for Customer Loyalty

Wilson shares insights from his experience as the founder of Permutable AI , a startup that builds real-time LLM engines, and emphasizes the need for a balanced view in understanding geopolitical trends and market intelligence. The discussion also highlights the critical checks necessary to ensure the accuracy and reliability of AI-generated information.

Key Takeaways

• AI systems can amplify existing biases in data.
• A balanced view of information is crucial for accuracy.
• Human expertise is essential in interpreting AI outputs.
• Organizations must critically assess AI-driven insights.
• Real-time data analysis can enhance decision-making.
• Misinformation can spread if AI is not properly regulated.
• Ethical considerations are vital in AI usage.
• The integrity of sources impacts AI reliability.
• AI can simplify complex geopolitical dynamics.
• Permutable.ai aims to provide actionable insights for businesses.

Chapters

00:00 Introduction to AI and Data Analysis

05:01 Addressing Bias in AI Systems

09:55 The Role of Human Expertise in AI

14:53 Trusting AI-Driven Market Intelligence

20:01 Conclusion and Future Insights

However, organizations face challenges adopting these new technologies and risks like AI hallucinations need to be mitigated to ensure reliable outcomes.

In this episode, Paulina Rios-Maya, Head of Industry Relations at EM360Tech, speaks with Nikhil Nandagopal, Co-founder and CPO of Appsmith, about the transformative impact of AI on low-code platforms and application development. 

Key takeaways

• Low-code platforms are revolutionizing application development.
• AI tools can generate code but require careful review.
• Routine tasks can be automated, but decision-making still needs human input.
• AI adoption comes with challenges like hallucinations and misinformation.
• Organizations must adapt their culture and processes for AI success.
• Developers need skills in data modeling and security for AI applications.
• AI can simplify user interfaces and enhance user experience.
• Interconnected applications will rely on AI to bridge data gaps.
• Most AI projects fail due to underestimating necessary changes.
• Enterprises face more challenges in AI adoption compared to SMBs.

Chapters

00:00 - Introduction to AI and Low-Code Platforms

02:59 - The Role of AI in Automating Tasks

05:51 - Challenges and Risks of AI Adoption

09:08 - Essential Skills for Developers in AI

12:01 - Future of Interconnected Applications

14:50 - Realities vs. Hype of AI in Enterprises

AI offers a solution by automating routine tasks, streamlining processes, and enabling predictive analytics that anticipate issues before they arise. This not only enhances operational efficiency but also frees IT teams to focus on more strategic initiatives, driving innovation and value creation across the organisation.  

However, adopting AI in enterprise IT systems comes with its own challenges, including ensuring data privacy, overcoming resistance to change, and maintaining the right balance between automation and human oversight. Effective AI integration requires a thoughtful approach—one that leverages AI’s capabilities while retaining human control to ensure accountability and ethical decision-making.

In this episode, Kevin Petrie, VP of Research at BARC, speaks to David Campbell, VP of Product at GoTo, about the challenges faced by modern IT departments, the role of practical AI in addressing these challenges, and the importance of balancing automation with human oversight.

Key Takeaways: 

• AI can modernise IT systems without disrupting operations.
• AI helps identify inefficiencies and provides actionable insights.
• Human oversight is crucial in AI-driven IT processes.
• AI can automate tasks and flag critical issues proactively.
• Security is a top priority for IT departments.
• Data quality is essential for effective AI strategies.
• AI can streamline knowledge sharing across IT teams.
• AI can enhance help desk workflows and reduce resolution times.

Chapters: 

00:00 - Introduction to AI in Enterprise IT

02:06 - Challenges in Modern IT Systems

04:33 - The Role of AI in IT Modernization

06:57 - Balancing AI Automation and Human Oversight

10:13 - AI's Impact on IT Security

12:34 - Ensuring Data Quality for AI

16:23 - Top AI Use Cases in IT Management

Preparing for future technological trends requires more than just adopting the latest tools—it demands a workforce that is curious and agile. A culture of curiosity fuels engagement, cross-disciplinary collaboration, and creative problem-solving, all of which are critical for navigating the complexities of technologies like AI, blockchain, and quantum computing. 

By investing in curiosity through training, open communication, and a safe space for experimentation, organisations can build a future-ready workforce that embraces change and drives sustainable growth.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Andrew Grill, author of Digitally Curious: Your Guide to Navigating the Future of AI and All Things Tech, about the future of work and the integration of AI. 

Key Takeaways: 

• Digital curiosity empowers individuals to understand technology better.
• Leaders play a crucial role in fostering a culture of curiosity.
• Ethics in AI involves data privacy, transparency, and fairness.
• Curiosity leads to digital fluency, which is essential for the future.
• Organisations should encourage experimentation with new technologies.
• Understanding technology is vital for effective leadership.
• The future workforce will require continuous upskilling and adaptability.

Chapters: 

00:00 - Introduction to Digital Curiosity

03:00 - The Importance of Digital Curiosity in Technology

05:50 - Overcoming Barriers to Technology Engagement

09:13 - Ethics and Responsibility in AI

12:05 - Cultivating a Culture of Curiosity in Organizations

14:58 - Future Technological Shifts and AI Integration

As organizations adopt cloud technologies, the ability to manage financial resources with precision becomes a cornerstone of competitive advantage. Augmented FinOps, a groundbreaking approach that merges traditional financial operations with AI-driven insights, transforms how companies navigate these challenges, empowering decision-makers with unparalleled visibility and control.  

The applicationof AI in FinOps promises to amplify its potential by automating routine tasks, uncovering patterns in vast data sets, and providing predictive analytics to enhance strategic planning. 

In this episode, Jon Arnold, Principal of J Arnold Associates, speaks with Kyle Campos, CTPO at CloudBolt, about Augmented FinOps and the role of AI and ML in automating cost management processes. 

Key Takeaways: 

• FinOps is a community-driven approach to managing cloud spend.
• The shift from CapEx to OpEx has transformed financial operations.
• Augmented FinOps combines AI with traditional FinOps practices.
• Organizations must measure the “insight to action” gap in their processes. 
• Machine learning can automate and optimize cloud spending decisions.
• C-suite expectations often misalign with operational realities.
• Tracking metrics is vital for improving FinOps maturity.
• Investing in FinOps is about optimizing resources, not just cutting costs.

Chapters: 

00:00 - Introduction to FinOps and Cloud Management

02:52 - The Shift from CapEx to OpEx in Cloud Spending

06:13 - The Role of AI in Financial Operations

08:56 - Understanding Augmented FinOps

12:13 - Best Practices for FinOps in the Age of AI

15:00 - Leveraging Machine Learning for Cost Optimization

18:13 - Insight to Action: Measuring FinOps Effectiveness

21:04 - Conclusion and Key Takeaways

CloudBolt is The Cloud ROI Company™. It is singularly focused on solving the most pressing problem with cloud today: increasing return on investment (ROI). With the introduction of Augmented FinOps capabilities, CloudBolt is leveraging AI/ML-informed insights and applying intelligent automation and orchestration proactively and retrospectively to make complete cloud lifecycle optimization a reality. CloudBolt enables organizations to realize the full potential of any cloud fabric by closing the “insight to action” gap. By streamlining, clarifying, and optimizing spend and control, CloudBolt helps organizations place value at the center of every cloud decision. For more information, visit www.cloudbolt.io.

The European Union’s AI Act, one of the most ambitious regulatory frameworks for AI, sets clear standards for transparency, accountability, and risk management. Understanding the implications of this legislation is crucial for businesses looking to innovate responsibly while avoiding potential legal and ethical pitfalls.  

Countries around the world are taking varied approaches to AI governance, with some prioritising privacy and ethical considerations while others focus on fostering technological innovation. This diversity presents challenges and opportunities for organisations striving to implement AI responsibly. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Dr Ellison Anne Williams CEO and founder of Enveil, about the need for model-centric security and the potential of PETs to mitigate risks associated with sensitive data in AI applications.

Key Takeaways: 

• Privacy-enhancing technologies are crucial for data protection.
• The EU AI Act sets a precedent for global AI regulation.
• Organisations must start with the problems they aim to solve.
• Data sensitivity must be considered in AI model training.
• Privacy-enhancing technologies can facilitate cross-border data sharing.
• AI is a neutral tool that requires responsible governance.
• The implementation of privacy technologies is still evolving.
• Global standards for AI governance are necessary for ethical use.

Chapters: 

00:00 - Introduction to AI Governance and Privacy

01:07 - Understanding AI Governance

03:51 - Privacy Enhancing Technologies Explained

08:28 - The Role of the EU AI Act

12:42 - Implementing Privacy Enhancing Technologies

17:20 - Harmonizing AI Governance with Privacy Technologies

They discuss the event's focus on AI's practical applications across various sectors, the importance of ethical considerations in AI, and the collaborative efforts among different industries to harness AI technology responsibly. 

The conversation highlights key discussions and innovations that will be showcased at the summit, emphasizing the transformative impact of AI on businesses and society

Register now and enjoy a 15% discount on Delegate Passes with code EM36015OFF via https://newyork.theaisummit.com/em360tech-reg.

Key Takeaways: 

• AI is showcasing its practical impact across diverse sectors.
• The AI Summit New York focuses on ethical and responsible AI.
• Collaboration is key in the AI community.
• AI is transforming industries like healthcare and retail.
• The summit will address AI's challenges and responsibilities.
• Innovative applications of AI will be highlighted at the event.
• AI can foster inclusivity and equal opportunities.
• Real-world implementation stories will be shared by companies.

Developing AI skills isn’t just about maintaining a competitive edge—it’s about creating opportunities. This dual approach ensures that experts can drive technological advancements while a broad understanding of AI empowers diverse sectors to integrate its potential. Investing in education, upskilling, and industry partnerships will ensure the UK workforce is ready to meet the demands of an AI-driven world. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to James Kuht, CEO and Founder of Inversity, about integrating AI into education and the collaborative effort required from government and society to achieve this goal.

Key Takeaways: 

• The UK has a strong AI talent pipeline concentrated in key areas.
• Specialist and generalist AI skills are both important.
• AI can significantly boost productivity in knowledge-based tasks.
• Teachers need training to effectively integrate AI into education.
• AI skills will be a baseline requirement for future jobs.
• Government and society must collaborate on AI education initiatives.
• AI has the potential to reduce economic inequality.
• Most jobs will evolve rather than disappear due to AI.

Chapters: 

00:00 - The Importance of AI Skills for the UK

03:01 - James Kuht’s Journey in AI

05:57 - Building a Competitive AI Workforce

08:45 - Integrating AI into Education

12:07 - The Role of Government and Society in AI Education

15:01 - Addressing Inequality in AI Access

17:58 - Future-Proofing the Workforce with AI Skills

21:10 - The Impact of AI on Global Industries

Apple Intelligence exemplifies the strengths of on-device AI, with innovations like Siri, Face ID, and real-time photo enhancements all designed to prioritise user privacy while delivering seamless, responsive experiences.  Unlike cloud-based AI, which may send sensitive data to external servers for processing, Apple’s approach ensures that personal information stays on the user’s device and is protected by advanced encryption. This difference builds trust and empowers users with faster, more reliable interactions. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Karel Callens, CEO at Luzmo, about best practices for developers integrating AI into their products. 

Key Takeaways:

• Apple Intelligence operates on-device but can access cloud resources.
• Developers must implement robust security measures for user data.
• Clear data policies enhance user trust in AI solutions.
• Opt-in and opt-out options empower users regarding their data.
• Education on AI usage is crucial for consumer confidence.
• Shared security standards can mitigate AI misuse.
• Regulation is necessary to keep pace with AI advancements.
• Big tech companies have a responsibility to ensure ethical AI use.

Chapters:

00:00 - Introduction to AI: On-Device vs Cloud-Based

02:54 - Understanding Apple Intelligence and Its Benefits

05:47 - Security Measures in AI Integration

09:03 - Building Trust Through Transparency and Regulation

11:50 - Best Practices for Developers in AI Implementation

15:04 - The Role of Education in AI Trust and Security

17:47 - The Future of AI: Regulation and Responsibility

However, developing personas for AI isn’t without its challenges. Ensuring that AI responses remain appropriate, ethical, and unbiased while preserving a unique persona requires careful consideration. From avoiding stereotypes to addressing edge cases, the process demands robust testing and a clear understanding of how diverse user interactions can unfold. 

When personas fail to account for the complexity of real-world scenarios, the risk of inappropriate or harmful responses increases. By combining creative storytelling with ethical AI design principles, organisations can navigate these challenges and build AI systems that are engaging and responsible in their behaviour.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Cobus Greyling, Chief Evangelist at Kore.ai, about the influence of cultural norms and value systems on AI and strategies for maintaining control over AI behaviour. 

Key Takeaways: 

• Personas in AI shape user interactions and trust.
• Cultural norms influence AI decision-making processes.
• Balancing control and agency is crucial for effective AI.
• Adversarial attacks can undermine AI reliability.
• Transparency is essential for user confidence in AI.
• Organisations should not offload too much responsibility to AI.
• AI should enhance human creativity, not replace it.
• Proof of value is necessary for AI technology implementation.

Chapters:

00:00 - Introduction to AI Personas and Their Impact

02:34 - The Role of Personas in AI Behavior

05:51 - Challenges in Ensuring Appropriate AI Responses

09:07 - Cultural Norms and Value Systems in AI

10:30 - Balancing Control and Agency in AI

14:14 - Strategies for Maintaining Control Over AI Behavior

21:24 - The Importance of Responsibility in AI Usage

By simplifying development, low-code and no-code tools open up software creation to a wider range of contributors, from professional developers looking to accelerate delivery times to business users aiming to solve specific problems independently. This democratisation of development reduces the demand for IT resources and fosters a culture of innovation and agility within organisations.

The impact of low-code and no-code technology extends beyond just speed and accessibility; it’s transforming how businesses adapt to change and scale their digital solutions. These platforms allow companies to quickly respond to evolving customer needs, regulatory requirements, and competitive pressures without the lengthy timelines associated with traditional development cycles. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Michael West, Analyst at Lionfish Tech Advisors about LCNC platforms and their benefits. 

Key Takeaways:

• Low-code and no-code platforms enable business solutions without coding.
• These platforms broaden the developer base to include non-technical users.
• Choosing the right platform involves considering functionality, standards, and vendor viability.
• Low-code platforms can handle enterprise-level applications effectively.
• AI integration is transforming how applications are developed.
• Democratisation of development addresses the shortage of professional developers.
• The market for low-code and no-code platforms is rapidly evolving.
• Future trends will focus on AI capabilities and user experience.

Chapters: 

00:00 Introduction to Low-Code and No-Code Platforms

02:59 The Evolution of Development Roles

05:49 Key Considerations for Adopting LCNC Tools

09:04 Democratizing Development and Innovation

11:59 Future Trends in Low-Code and No-Code Markets

While continuously evolving, GPUs are furnishing the computational muscle to execute ever-higher-level cryptographic algorithms without performance penalties. Developers now fully avail of the power of GPU parallelism to perform several thousand encryption tasks simultaneously, which is difficult for traditional CPUs to keep up with. This efficiency is critical in this growing data and rising cyber threat era, where organisations need rapid encryption and robust security. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Agnes Leroy, Senior Software Engineer at Zama, about the significance of encryption in high-stakes industries, the role of women in tech and the importance of mentorship in overcoming barriers in the industry.

Key Takeaways: 

• GPUs have evolved from graphics rendering to critical roles in data security.
• Fully homomorphic encryption allows computations on encrypted data.
• Quantum-resistant methods are crucial for future-proofing encryption.
• High-stakes industries require robust encryption to protect sensitive data.
• Diverse environments in tech foster innovation and collaboration.
• The future of encryption technology is exciting and unpredictable.

Chapters: 

00:00 - Introduction to Cryptography and GPU Programming

01:08 - The Evolution of GPUs in Data Security

03:33 - Challenges in Traditional vs Modern Encryption

05:50 - Quantum Resistance in Encryption Techniques

07:40 - The Future of GPUs in Data Privacy

08:38 - Importance of Encryption in High-Stakes Industries

10:00 - Potential Applications of Fully Homomorphic Encryption

11:42 - Women in Tech: Overcoming Barriers

15:33 - Conclusion and Resources

While AI offers multiple benefits, it's important to acknowledge the potential risks associated with its implementation. Organisations are advised to carefully consider factors such as data privacy, bias, and the ethical implications of AI-generated content. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Prof. Yash Shreshta, Assistant Professor at the University of Laussane, about prompt engineering and its benefits. 

Key Takeaways

• Prompts are commands given to AI to perform tasks.
• Prompt engineering allows users to communicate effectively with AI.
• Iterative processes improve the quality of AI outputs.
• Understanding LLM limitations is crucial for effective use.
• Collaboration can enhance the creative process with AI.
• The role of prompt engineering is rapidly evolving with AI advancements.
• Data privacy is a significant risk when using LLMs.
• Over-reliance on AI can lead to skill degradation.
• Organisations should integrate human creativity with AI.
• Regular training on prompt engineering is essential for maximising LLM benefits.

Chapters

00:00 Introduction to Prompt Engineering and AI

01:30 Understanding Prompt Engineering

04:15 The Importance of Prompt Engineering Skills

06:37 Best Practices for Effective Prompts

08:31 The Evolving Role of Prompt Engineering

11:20 Risks and Challenges of AI in Organizations

13:15 The Future of Creativity with AI

That’s why organisations are embracing AI's strengths to combat these evolving threats. AI-driven solutions can provide real-time detection of fraudulent activities, analyse vast amounts of data to identify patterns and anomalies, and automate response processes. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Xavier Sheikrojan, Senior Risk Intelligence Manager at Signifyd, about AI fraud. 

Key Takeaways:

• Fraudsters have evolved from individuals to organised criminal enterprises.
• AI enables fraudsters to scale their attacks rapidly and strategically.
• Phishing attacks have become more sophisticated with AI-generated content.
• Synthetic identities can be created easily, complicating fraud prevention.
• Opportunistic fraud is impulsive, while proactive fraud is well-planned.
• Businesses often fail to act post-breach due to resource constraints.
• Inaction after a breach can lead to repeated attacks by fraudsters.
• AI must be used to combat AI-driven fraud effectively.
• Balancing fraud detection with customer experience is crucial for businesses. 

Chapters:

00:00 Introduction to AI and Fraud

01:32 The Evolution of Cybercrime

05:43 AI's Role in Modern Fraud Techniques

09:55 Opportunistic vs. Proactive Fraud

12:44 Business Inaction and Its Consequences

15:59 Combating AI-Driven Fraud with AI

Ensuring that AI-driven systems adhere to data protection laws, such as GDPR and CCPA, is critical to avoid breaches and penalties. Balancing innovation with strict compliance and robust data security measures is essential as organisations explore AI’s potential while protecting sensitive information.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Erin Nicholson, Global Head of Data Protection and AI Compliance at Thoughtworks, about the importance of compliance frameworks, best practices for transparency and accountability, and the need for collaboration among various teams to build trust in AI systems.

Key Takeaways:

• AI systems are powerful but require ethical and compliant design.
• Lack of standardisation in AI regulations poses significant challenges.
• AI models often need help with explainability and transparency.
• Compliance frameworks are essential for implementing AI in critical sectors.
• Documentation and audits are crucial for maintaining AI accountability.
• Baselining pre-AI processes helps build public trust in AI systems.
• Organisations should map regulations to the most stringent standards.
• Cross-functional collaboration is vital for effective AI compliance.

Chapters: 

00:00 - Introduction to AI, Data Protection, and Compliance

02:08 - Challenges in AI Implementation and Compliance

05:56 - The Role of Compliance Frameworks in Critical Sectors

10:31 - Best Practices for Transparency and Accountability in AI

14:48 - Navigating Regional Regulations for AI Compliance

17:43 - Collaboration for Trustworthiness in AI Systems

Cloud technologies offer many benefits, including scalability, cost savings, and flexibility. However, they also introduce new risks, such as potential data breaches, unauthorised access, and compliance issues. 

With sensitive data stored and processed off-premises, maintaining control and visibility over that data becomes more complex. As cyber threats continue to evolve, robust data protection strategies are essential to safeguarding information in the cloud.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Sergei Serdyuk, VP of Product Management at NAKIVO, about the factors driving cloud adoption, the importance of having a robust disaster recovery plan, best practices for data protection, and the challenges of ensuring compliance with regulations.

Key Takeaways: 

• Cloud adoption is accelerated by low barriers to entry.
• Scalability in cloud environments is easier than on-premises.
• Data in the cloud is vulnerable and needs protection.
• The shared responsibility model places data protection on the user.
• A comprehensive disaster recovery plan is crucial for businesses.
• Regular testing of disaster recovery plans is essential.
• Data protection strategies must include regular reviews and updates.
• Compliance with data protection regulations is complex and varies by region.
• Balancing security and operational efficiency is a key challenge.

Chapters: 

00:00 - Introduction to Cloud Technologies and Data Protection

01:26 - Factors Accelerating Cloud Adoption

03:48 - The Importance of Data Protection in the Cloud

06:39 - Developing a Comprehensive Disaster Recovery Plan

10:05 - Best Practices for Data Protection

13:31 - Ensuring Compliance in Cloud Environments

15:56 - The Role of Continuous Monitoring in Data Protection

18:19 - Balancing Security and Operational Efficiency

As businesses navigate digital transformation, MSPs are crucial in optimising IT infrastructure, enhancing security, and providing tailored solutions that fuel innovation. With AI-powered tools, MSPs meet today's demands and help businesses stay competitive. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Jason Kemsley, Co-founder and CRO of Uptime, about the proactive strategies that MSPs can adopt using AI, the challenges they face in implementation, and the ethical considerations surrounding AI solutions.

Key Takeaways:

• MSPs are transitioning from traditional IT support to strategic partners.
• AI is enhancing operational efficiency but not replacing human roles.
• Proactive support is often neglected due to resource constraints.
• AI can help MSPs predict and prevent IT issues.
• Challenges in adopting AI include unrealistic expectations and lack of accountability.
• There is potential for MSPs to develop their own AI tools.

Chapters:

00:00 - Introduction to Managed Service Providers (MSPs)

02:03 - The Evolving Role of MSPs in Business Growth

04:00 - AI's Impact on Service Delivery Models

07:22 - Proactive Support Strategies with AI

10:16 - Challenges in Adopting AI for MSPs

12:40 - Ethics and Accountability in AI Solutions

To build trusted AI, it’s crucial to first trust the data. A robust data platform is essential for creating reliable and sustainable AI systems. Tools like Teradata’s ClearScape Analytics help address concerns about AI, including issues like generative AI hallucinations, by providing a solid foundation of trusted data and an open, connected architecture.

In this episode, Doug Laney, Analytics Strategy Innovation Fellow with West Monroe Partners, speaks to Vedat Akgun, VP of Data Science & AI and Steve Anderson, Senior Director of Data Science & AI at Teradata, about trusted AI. 

Key Takeaways:

• Value creation, performance, innovation, and cost-effectiveness are crucial for achieving trusted AI.
• Trusting data is essential before building AI capabilities to avoid biases, inaccuracies, and ethical violations.
• A robust data platform is a foundation for creating trusted and sustainable AI systems.
• Generative AI raises concerns about hallucinations and fictitious data, highlighting the need for transparency and accountability.
• Teradata offers features and capabilities, such as ClearScape Analytics and an open and connected architecture, to address trust issues in AI.

Chapters:

00:00 - Introduction and Defining Trusted AI

01:33 - Value Creation and the Importance of Driving Business Value

03:27 - Transparency as a Principle of Trusted AI

09:00 - Trusting Data Before Building AI Capabilities

14:51 - The Role of a Robust Data Platform in Trusted AI

21:09 - Concerns about Trust in Generative AI

23:03 - Addressing Trust Issues with Teradata's Features and Capabilities

25:01 - Conclusion

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Juan Jose Lopez Murphy, Head of Data Science and Artificial Intelligence at Globant, to discuss the ethical implications of AI and the necessity of building trust with users. 

Key Takeaways:

• Companies often prioritise speed over transparency, leading to ethical concerns.
• The balance between transparency and protecting competitive data is complex.
• AI misuse by malicious actors is a growing concern.
• Organisations must educate users on digital literacy to combat misinformation.
• Confidently wrong information is often more trusted than qualified uncertainty.

Chapters

00:00 - Introduction to AI Transparency

03:03 - Balancing Transparency and Data Protection

05:57 - Navigating AI Misuse and Security

09:05 - Building Trust Through Transparency

12:03 - Strategies for Effective AI Governance

Ready to boost your data literacy and embrace the future of AI?

Key Takeaways: 

• Companies are optimistic about ROI from AI investments.
• Data literacy is crucial for effective AI implementation.
• Technical debt poses challenges for AI infrastructure.
• Data quality and governance are essential for AI success.
• Trust in AI systems is a growing concern.
• Organizations must start with clear business priorities.
• The Data Festival will provide practical insights for AI adoption.

DATA festival is where theory meets practice to create real, actionable knowledge. This event brings together #DATApeople eager to drive the realistic applications of AI in their fields.

Leaving the hype behind, we look at the actual progress made in applying (Gen)AI to real-world problems and delve into the foundations to understand what it takes to make AI work for you. We’ll discuss when, where and how AI is best applied, and explore how we can use data & AI to shape ourfuture.

Sign up now to secure your spot

AI models struggle to learn and perform tasks effectively without high-quality labelled data. Proper data labelling enhances model accuracy, reduces errors, and accelerates the time it takes to train AI systems. Whether you're working with natural language processing, image recognition, or predictive analytics, the success of your AI project hinges on the quality of your labelled data.

In this episode, Henry Chen, Co-founder and COO of Sapien, speaks to Paulina Rios Maya about the importance of data labelling in training AI models. 

Key Takeaways:

• Data labelling converts raw data into structured data that machine learning models can recognise.
• Reducing bias and ensuring data quality are critical challenges in data labelling.
• Expert human feedback plays a crucial role in improving the accuracy of AI training data and refining AI models.

Chapters:

00:00 - Introduction and Background

01:07 - Data Labeling: Converting Raw Data into Useful Data

03:02 - Challenges in Data Labeling: Bias and Data Quality

07:46 - The Role of Expert Human Feedback

09:41 - Ethical Considerations and Compliance

11:09 - The Evolving Nature of AI Models and Continuous Improvement

14:50 - Strategies for Updating and Improving Training Data

17:12 - Conclusion

Curious about how to protect your business from the latest threats like fake IDs, account takeovers, and AI-generated deep fakes? Tune in to our latest episode, where we dive into the essentials of full-cycle verification and real-time transaction monitoring. Find out how AI and machine learning can revolutionise your fraud detection efforts and why staying updated with regulatory changes is crucial for maintaining top-notch security. 

In this episode of Tech Transformed, Alvaro Garcia, Transaction Monitoring Technical Manager at Sumsub, speaks to Paulina Rios Maya, Head of Industry, about the manifestations of identity fraud during the user journey stages and the need for comprehensive fraud prevention measures.

Key Takeaways:

• Identity fraud manifests at different user journey stages, including onboarding and transaction monitoring.
• Businesses must implement full-cycle verification and transaction monitoring solutions to detect and prevent fraud in real time.
• AI and machine learning are crucial in analyzing suspicious user behaviour and spotting complex fraud patterns.
• SumSub offers platform solutions that include KYC, business verification, transaction monitoring, and payment fraud protection.

Chapters: