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

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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

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Most leaders think AI agents are too technical to build, but the real barrier is not skill, it is clarity.
In this episode Chris talks with Etan Polinger, AI Solutions Architect and Head of AI Solutions, about how non-technical professionals can design, build, and deploy AI agents that drive real business outcomes. Etan breaks down what an agent actually is, how to think about automation versus agentic workflows, and why fundamentals matter more than tools in a rapidly changing AI landscape.
They explore practical examples from inbox automation to project intelligence systems, along with the frameworks Etan uses to help operators move from idea to deployed solution. If you want to move beyond AI curiosity and start building systems that create leverage inside your business, this episode shows you where to begin and how to think about it.

Chapters:
00:00 Introduction
00:12 Why Asking Better Questions Unlocks AI
00:33 What Is Actually Possible With AI Today
00:52 What an AI Agent Really Is
01:46 Bridging AI Hype and Real Execution
03:05 Why Non-Technical People Can Now Build
05:19 Where Business Leaders Should Start
08:52 Real Examples of AI Agents in Action
13:57 The Right Way to Start Building With AI
17:36 How Long It Takes to Learn This Skill
22:13 Why Your AI Builds Keep Breaking
33:29 Common Mistakes When Building Agents
38:02 The SCOUTS Framework Explained
44:20 The Most Powerful Question You Can Ask AI

🔎 Find Out More About Etan Polinger

LinkedIn: 
https://www.linkedin.com/in/etan-polinger 

🛠 AI Tools and Resources Mentioned
AI Agents + Automation Certification
https://www.CAIO.cx/agent
ChatGPT (OpenAI)
https://chat.openai.com
Claude (Anthropic)
https://claude.ai
OpenAI
https://openai.com
Cursor (AI Code Editor)
https://cursor.sh
Lovable (AI App Builder)
https://lovable.dev
OpenClaw (AI Agent Framework)
https://github.com/openclaw/openclaw
N8N (Workflow Automation)
https://n8n.io
Salesforce
https://www.salesforce.com
Notion
https://www.notion.so
Perplexity AI
https://www.perplexity.ai
Context7 (Code + Documentation Tool)
https://context7.com
Chief AI Officer Program
https://chiefaiofficer.com

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Most leaders assume AI in customer service means replacing people, but the data tells a more complicated story.
In this episode Chris talks with Nathan Strum, CEO of Abby Connect, about what actually works when deploying voice AI in real business environments. Drawing on two decades of customer service experience, Nathan explains why AI excels at structured workflows like scheduling, but still struggles with unpredictable edge cases where human judgment matters most. He also shares why many companies that experiment with full automation quietly return to human support, and how Abby is growing both its AI and human workforce at the same time.
The conversation goes deeper into practical implementation, including where AI is safe to deploy today, why outbound AI calling is a high-risk move, and how to design systems that combine speed, scalability, and trust. Nathan also outlines a leadership approach to AI adoption that focuses on reducing friction across systems, reskilling employees, and using AI to enhance rather than replace human capability. This episode gives leaders a grounded, experience-based framework for deciding where AI belongs in their customer experience strategy.

Chapters:
00:00 Introduction
01:00 Where Voice AI Delivers Immediate Value
02:29 Introducing Abby’s AI + Human Strategy
04:21 The Limits of AI in Real Customer Interactions
06:52 Best Use Cases: AI Scheduling vs Human Sales Calls
08:30 Why AI Adoption Is Increasing Human Headcount
11:04 Lessons from Failed “AI-Only” Customer Service Experiments
15:23 Where AI Is Safe vs Risky in Phone Workflows
17:31 Why Transparency About AI Improves Customer Trust
23:06 The Future of Offshore, AI, and Voice Technology
27:29 AI as a System Redesign Tool, Not Just Cost Reduction
29:56 Managing Employee Fear During AI Adoption
32:57 Selling Outcomes Instead of AI Products
35:18 How to Evaluate AI Vendors in Customer Experience

🔎 Find Out More About Nathan Strum
Abby Connect Website
https://www.abbey.com
LinkedIn
https://www.linkedin.com/in/nathanstrum
https://www.linkedin.com/company/abby-connect/
Facebook https://www.facebook.com/abbyconnect/
X: https://x.com/abbyconnect
Website: https://www.abby.com/

🛠 AI Tools and Resources Mentioned
OpenAI
https://openai.com
Anthropic
https://www.anthropic.com
Google Gemini
https://gemini.google.com
ElevenLabs
https://elevenlabs.io