Using Reg’s recent university panel as a springboard, they explore the gap between deliverables and meaning—why built work isn’t just models and sheets, but narrative, negotiation, and leadership across messy human contexts. They compare “tame” digital problems to “wicked” physical-world ones, touch on evidence-based design as a helpful (but partial) tool, and argue that architecture’s core job is guiding a client from uncertainty to a coherent vision that fits a place and its people.

Along the way: pricing realities, junior talent, and why communication is the profession’s power skill. If you’re wrestling with AI hype and real-world practice, this conversation offers a grounded frame.

In this episode:

• Why AI hype forces every profession to clarify its value
• Deliverables vs. outcomes: what clients really pay for
• “Tame” vs. “wicked” problems in the physical world
• Meaning, culture, and narrative as architectural value
• Why this is an exciting moment to enter the field

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He outlines how Terracon audited ERP/CRM issues (synonyms, homonyms, duplicates), moved project files from office servers to a SharePoint backbone with auto-tagging and taxonomy, and now governs intranet content to curb “old vs. new” confusion. David shares his “data lake districts” metaphor (lake house, warehouse, trailer park, homeless) to triage sources and describes assigning owners to the data store and its APIs. We cover no-/low-/pro-code AI tiers, internal vs. client-facing apps, and the human side—reducing fear of change, protecting creativity, and preventing burnout when lower-level tasks get automated.

If you need a roadmap to get from scattered files to AI-ready data with process clarity and cultural buy-in, this episode shows what it looks like at enterprise scale.

In this episode:

• Why “data + process” are the real foundations of AI
• File migration, taxonomy, and practical governance
• ERP realities: duplicates, synonyms, and messy definitions
• A roadmap from no-code → low-code → pro-code AI
• The human risk: adoption fatigue and burnout

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We dig into LiDAR as-builts, why a 3D model isn’t a “digital twin,” and the role of document management and transmittals in cutting delivery from months to weeks. Shawn outlines how standards (IEC/IEEE/ANSI) should be API-accessible, how RAG and knowledge graphs can power useful agents, and why adoption hinges on aligning tools with the sources engineers actually trust.

If you’re trying to make design data reliable—and make change stick—this episode offers concrete steps and a clear north star for the next decade.

In this episode:

• Why substations often “work in spite of the tech”
• The hidden cost of inaccurate as-builts
• LiDAR, point clouds, and safer paths to ground truth
• “Digital twin” vs. a 3D model (and why it matters)
• Standards as APIs + the role of RAG and knowledge graphs

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She traces a path from early programming to enterprise leadership, then shares what’s working now: moving fully out of the data center; using SharePoint for documents and Autodesk Construction Cloud for production; and rolling out an internal AI chat (for proposals, org knowledge—even PTO checks) with governance over any new AI tools.

We discuss CannonDesign’s Digital Products group that builds targeted internal tools when commercial options don’t fit, and a firm culture that pairs operational efficiency with equity and outcomes for clients. Dorinda’s practical advice lands on continuous improvement, clear governance, and storytelling as a change lever.

If you’re deciding when to build vs. buy, how to deploy Copilot alongside internal AI, or how to guide teams through constant change, this is a concise, actionable playbook.

In this episode:

• Cloud migration lessons (and what it unlocks)
• Internal AI tools vs. public AI—where the line is
• Digital Products building internal tools that fit real workflows
• How to start with inefficiencies, not shiny tech
• The underrated skill: storytelling as change leadership

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He explains why “single source of truth” often misleads teams, and how a “single source of entry” plus stable project and person IDs lets ERP, BIM, and everyday tools share the same information without duplication. Brent walks through concrete wins—replacing fragile staffing and fee spreadsheets with internal apps; connecting Revit, specs, and the physical product library so designers are two clicks from what they need; and using Power BI with Excel without re-keying data.

We also cover when to build vs. buy, the project coordinator’s role as a data steward, and why clean, connected data is the only honest foundation for AI. If you’re trying to reduce friction while protecting creativity, this episode gives steps you can act on next week.

In this episode:

• “Single source of entry” as a north star
• Binding ERP + Revit + specs + product knowledge
• Replacing shared spreadsheets with internal apps
• Build vs. buy: when custom tools are worth it
• Why clean data is the prerequisite for AI

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She distinguishes “little-I” innovations (workflow improvements) from “big-I” disruptions (changing how work is delivered) and explains their vision of AI moving from assistant to collaborator—eventually becoming a teammate in organizational charts. We cover practical enablement: upgrading the intranet to quietly prepare for AI, letting staff spin up bots on vetted content, and defining strict boundaries around client and NDA-protected data.

Nora also details JB&B's plan to capture senior expertise through knowledge interviews and why change management starts early and never really ends. If you need a model for structured innovation, safer AI pilots, and culture-forward change, this episode gives you a blueprint you can adapt to your firm.

In this episode:

• How an internal innovation team functions like an “internal vendor”
• Workplace efficiency vs. tech transformation
• AI enablement through intranet and knowledge structure
• Guardrails for sensitive project/client data
• Knowledge capture interviews and the shelf-life of expertise

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Each episode is grounded in real practice: messy handoffs, disconnected systems, outdated documentation, and the cultural friction of change. You’ll hear practical playbooks for making project information trustworthy, reducing duplicate effort, deploying AI responsibly, and preserving institutional knowledge—without interrupting creativity.

Season 1 highlights AI readiness in the real world—not hype. It’s a tour of the foundations that make modern AEC work: clean data, reliable processes, and human-centered change management. Across six conversations, you’ll hear how leaders are replacing brittle spreadsheets, modernizing document and knowledge systems, connecting design data to business systems, and setting governance so AI and automation can actually deliver value.

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