Using AI at Work: AI in the Workplace & Generative AI for Business Leaders

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Most manufacturers are chasing the wrong AI problem. In this episode Chris talks with Bryan DeBois, Director of Industrial AI at RoviSys, about why industrial AI for manufacturing requires a different approach than generative AI.
Bryan explains the limits of generative AI on the plant floor, why deterministic systems matter in high risk environments, and how analytical AI, predictive AI, computer vision, and autonomous AI are already being used to improve quality, safety, throughput, and asset performance. Leaders should listen to understand how industrial AI can protect expertise, strengthen operations, and create practical advantage beyond the ChatGPT conversation.
Chapters:
00:00 Introduction
01:16 Why Factory Floor AI Is Different From Knowledge Work AI
03:58 The Four Types of AI Used in Manufacturing Today
05:50 Why Generative AI Fails in High Risk Operational Environments
10:56 Manufacturing Risks Also Apply to Construction and Life Sciences
11:36 The Workforce Crisis Driving Industrial AI Adoption
15:26 Why Manufacturing Careers May Be Safer Than White Collar Jobs
20:42 Why Humanoid Robots Are Not the Future of Manufacturing
24:53 Capturing Tribal Knowledge Before Experts Retire
40:00 Who Should Own AI Inside Manufacturing Organizations
43:24 Meta’s Cicero Project and the Future of Hybrid AI Systems
47:08 Deterministic AI vs Probabilistic AI in Critical Industries
49:27 Where to Follow Brian De Bois and Learn More About Industrial AI

Resources:
🔎 Find Out More About Bryan DeBois
Bryan DeBois on LinkedIn:
https://www.linkedin.com/in/bryan-debois
RoviSys Industrial AI: 
https://www.rovisys.com/ai
RoviSys:
https://www.rovisys.com
🛠 AI Tools and Resources Mentioned:
ChatGPT:
https://chatgpt.com/

Claude:
https://claude.com/

Grok:
https://grok.com/

Meta AI CICERO:
https://ai.meta.com/research/cicero

Google DeepMind AlphaGo:
https://deepmind.google/research/breakthroughs/alphago

Microsoft HoloLens:
https://www.microsoft.com/hololens

Obsidian:
https://obsidian.md

SAP:
https://www.sap.com

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Most AI strategies fail because the organization never changes. In this episode Chris sits down with Melissa Reeve, creator of the Hyperadaptive Model and author of an upcoming book on AI-native organizations, to explore why legacy structures block AI progress and what leaders must redesign to unlock real value.
They discuss how companies can move from siloed, handoff-heavy operating models to adaptive systems built for continuous learning, faster decisions, and human-centered execution. Leaders responsible for transformation, growth, or operating performance will gain a practical lens for turning AI ambition into sustainable organizational change.

Chapters:
00:00 Introduction
00:00 Meet Melissa Reeve and the Hyperadaptive Model
00:00 Why Legacy Operating Models Limit AI Results
00:00 Moving Beyond Automation Thinking
00:00 The Shift to AI-Native Organizations
00:00 Redesigning Roles, Teams, and Workflows
00:00 Building a Human-Centered AI Transformation Strategy
00:00 Creating Continuous Learning Systems
00:00 How Leaders Scale AI Adoption Across the Business
00:00 What the Future Organization Looks Like

🔎 Find Out More About Melissa Reeve
Melissa Reeve LinkedIn 
https://www.linkedin.com/in/melissamreeve
Hyperadaptive Solutions
http://hyperadaptive.solutions
Book Waitlist
https://hyperadaptive.solutions/book
Blueprint Session
https://hyperadaptive.solutions/why-us#contactForm
🛠 AI Tools and Resources Mentioned:
AI Integration Guide
http://hyperadaptive.solutions
AI Learning Flywheel Ebook
http://hyperadaptive.solutions/flywheel-ebook
Applied AI Workshop 
http://hyperadaptive.solutions/labs

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Most companies want innovation, but few can tolerate unpredictable tech costs. In this episode Chris talks with Matt Strippelhoff, Partner, CEO / CRO of Red Hawk Technologies, about how mid-market companies can approach software development with greater financial control and operational confidence. They explore why traditional project models often create risk, and how recurring service models can better align technology execution with business goals.
Matt shares lessons from leading web, mobile, integration, maintenance, and emerging AI initiatives while maintaining strong long-term client retention. Leaders will hear practical ideas for reducing technology uncertainty, modernizing critical systems, and creating a more dependable path to innovation, making this episode well worth your time.

Chapters:
00:00 Introduction
00:45 Why Mid-Market Companies Struggle with Tech Spend
02:10 The Problem with Traditional Project Pricing
04:05 A Fixed Fee Model for Software Development
06:20 Reducing Operational Risk Through Predictability
08:00 Modernizing Legacy Applications
10:15 Building Web, Mobile, and Middleware Solutions
12:05 Where AI Assistants Fit Into Business Operations
14:10 Driving Retention Through Better Delivery Models
16:00 Leadership Lessons for Scaling Technology Investments

🔎 Find Out More About Matt Strippelhoff
Matt Strippelhoff LinkedIn
https://www.linkedin.com/in/redhawktech/ 
Red Hawk Technologies
https://www.redhawk-tech.com/

🛠 AI Tools and Resources Mentioned:
Claude
https://claude.ai

ChatGPT
https://chat.openai.com

Google Firebase Studio
https://firebase.google.com/

Gemini
https://gemini.google.com/

Cursor
https://cursor.com/

Salesforce
https://www.salesforce.com/

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Most leaders are asking the wrong AI question. In this episode Chris sits down with Evan J Schwartz, technology leader, adjunct professor, and Chief Innovation Officer, to discuss why AI should be used for growth, not simply cost cutting.
Evan shares his vision for the future organization: flatter companies, human stewards managing AI agents, and teams focused on strategy, relationships, and judgment while automation handles repetitive execution. They also explore AI in education, workforce development, sustainability, and why leaders who wait may lose to faster-moving competitors. 
If you want a practical framework for using AI to grow smarter without losing your people advantage, this episode is worth your time.

Chapters
00:00 Introduction
02:05 Chris Introduces Evan J Schwartz
03:40 Person Plus AI vs Doom and Gloom Narratives
08:30 Which Industries AI Will Disrupt First
09:23 Mentoring Global Students Solving Real Problems with AI
11:12 How AI Could Reduce Food Waste at Scale
18:30 What Colleges Are Getting Wrong About AI
23:38 Why Companies That Wait Will Fall Behind
31:19 The Rise of the Steward Role in Business
41:30 Use AI for Growth, Not Headcount Cuts

🔎 Find Out More About Evan J Schwartz
Evan J Schwartz LinkedIn
https://www.linkedin.com/in/evan-schwartz-live

AMCS Group
https://www.amcsgroup.com
🛠 AI Tools and Resources Mentioned:
ChatGPT 
https://chat.openai.com

Anthropic Claude
https://www.anthropic.com/claude

Docker
https://www.docker.com

SAP
 https://www.sap.com

Chief AI Officer 
https://chiefaiofficer.com

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Most companies think they are “doing AI” but are still stuck in single-player mode.
In this episode Chris talks with Marc Boscher, Founder and CEO of Unito, a workflow integration platform, about why AI adoption breaks down at the organizational level. Marc explains that the real barrier is not model capability, but fragmented systems, missing context, and lack of trust. He introduces the shift from prompt engineering to context engineering, and why connecting systems and data is the key to unlocking AI that works across teams, not just for individuals.
The conversation explores how leaders can move from isolated productivity gains to true enterprise impact by building context libraries, enabling dynamic data access, and reducing operational friction. Marc also breaks down the importance of trust, deterministic vs non-deterministic systems, and why change management remains the biggest challenge. This episode gives leaders a practical lens for turning AI from a tool employees use into infrastructure the business runs on.

Chapters:
00:00:00 Introduction
00:00:36 Why Trust and Context Are Critical for AI Agents
00:01:00 Context vs Prompts: What Actually Matters
00:03:48 Single Player vs Multiplayer AI in Business
00:06:30 Why Context Unlocks Enterprise-Level AI Value
00:08:28 What “Context” Really Means in AI Systems
00:11:34 Building Context-Rich AI Use Cases (Sales Example)
00:13:42 Static vs Dynamic Context Explained
00:20:12 Why Context Engineering Replaces Prompt Engineering
00:24:04 From Human-in-the-Loop to Autonomous AI Systems
00:27:29 The Context Gap and Operational Inefficiency
00:36:01 Why Change Management Is the Real Bottleneck
00:42:03 Deterministic vs Non-Deterministic AI Systems

🔎 Find Out More About Marc Boscher:
LinkedIn: https://www.linkedin.com/in/marcboscher 
Unito: https://unito.io 

🛠 AI Tools and Resources Mentioned:
Unito – https://unito.io
Salesforce – https://www.salesforce.com
ServiceNow – https://www.servicenow.com
GitHub – https://github.com
HubSpot – https://www.hubspot.com
NetSuite – https://www.netsuite.com
Workday – https://www.workday.com
ChatGPT – https://chat.openai.com
Claude – https://claude.ai
Gemini – https://gemini.google.com
Copilot – https://copilot.microsoft.com