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

Send us Fan Mail 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

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

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

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

Most companies aren’t struggling to buy AI, they’re struggling to use it well. In this episode Chris sits down with Jim Spignardo, Director of Cloud Strategy and AI Enablement at ProArch, to break down what’s really happening inside organizations adopting AI today. Jim shares why many companies are stuck after purchasing licenses, how to move from experimentation to structured adoption, and what separates companies seeing real ROI from those chasing hype. He outlines a practical playbook that starts with executive alignment, prioritizes high-value use cases, and builds toward secure, governed AI systems that scale. They also explore how organizations can recoup AI investments within months, why data governance is the hidden foundation of success, and how to balance rapid innovation with risk management as agents and automation evolve. If you’re leading AI adoption or trying to turn early momentum into measurable business value, this episode offers a clear, experience-backed path forward. Chapters: 00:00 Introduction 00:14 Where Companies Are Today in Their AI Journey 00:49 The Future: AI, Robotics, and What’s Next 01:30 Why AI Strategy Matters for Business Leaders 02:38 Common Challenges: Risk, Use Cases, and Leadership Gaps 05:06 Building an AI Adoption Playbook 06:23 From Buying Licenses to Lacking Direction 10:00 What Executives Need to Understand About AI 13:01 The Shift from Productivity Tools to AI Agents 17:47 How Long It Takes to See Real Results 19:25 Measuring ROI and Tracking AI Value 22:12 Real Example: AI Improving RFP Win Rates 30:12 Change Management and Driving Adoption 31:16 Training, Governance, and Building AI Culture 40:12 Managing Risk While Enabling Innovation 45:04 What’s Next: AI + Robotics Convergence 🔎 Find Out More About Jim Spignardo LinkedIn: https://www.linkedin.com/in/spignardo ProArch: https://www.proarch.com 🛠 AI Tools and Resources Mentioned: Microsoft Copilot https://www.microsoft.com/en-us/microsoft-365/copilot Microsoft Defender for Cloud Apps https://learn.microsoft.com/en-us/defender-cloud-apps/what-is-defender-for-cloud-apps Microsoft Purview (Data Loss Prevention & Information Protection) https://learn.microsoft.com/en-us/purview/ Azure OpenAI Service https://azure.microsoft.com/en-us/products/ai-services/openai-service OpenAI / ChatGPT https://chat.openai.com Claude (Anthropic) https://www.anthropic.com/claude Cursor (AI coding assistant) https://www.cursor.sh

What happens when the AI tool helping you scale your business also gains permanent rights to your voice? In this episode Chris talks with Jesse Jameson, digital marketing veteran and founder of HeyNow Interactive, about the opportunities and emerging risks inside the generative AI ecosystem. Jesse shares his experience participating in a voice licensing program with ElevenLabs, where his AI voice quickly became one of the most widely used on the platform. What began as a simple experiment in passive income through voice cloning eventually uncovered deeper questions around creator consent, data ownership, and how AI companies structure their business models. The conversation explores how leaders should think about AI adoption today, including the tension between rapid innovation and responsible governance. From biometric data rights and AI regulation to the strategic reality that businesses cannot afford to ignore generative AI, Jesse and Chris discuss how executives can embrace AI’s advantages while remaining thoughtful about the risks that come with it. This episode offers an important perspective for leaders navigating AI adoption in a rapidly evolving landscape. Chapters: 00:00 AI Voice Licensing and the Start of a Major Discovery 00:45 Introducing Jesse Jameson and the Rise of AI Voice Technology 03:15 From Early Internet Marketing to the Age of AI 04:22 Joining the ElevenLabs Voice Actors Program 06:13 Discovering Discrepancies in Voice Usage and Payments 08:29 The Consent Problem and Hidden Licensing Terms 10:31 Regulatory Questions and Biometric Data Laws 12:15 The Hidden Risks of Using Generative AI Tools 17:21 Bias, Control, and the Influence of AI Models 26:23 Investigating Platform Abuse and Free Voice Usage 36:29 Documenting the Experience and Reporting to Regulators 44:06 Practical Advice for Leaders Using New AI Tools 🔎 Find Out More About Jesse Jameson LinkedIn: Jesse Jameson Substack: @jpjameson Youtube: @jpjameson Website: https://11laudit.com The Voice Cloning Scam That Hit $11 Billion: https://www.youtube.com/watch?v=2wPdQyrWhl0&t=2s Book: The Conversation You Can't Explain: Finding Yourself in the Age of AI 🛠 AI Tools and Platforms Mentioned ElevenLabs: https://elevenlabs.io/ OpenAI: https://openai.com/ Anthropic: https://www.anthropic.com LLaMA: https://www.llama.com

The real challenge with AI is not the technology, it is knowing when leaders should trust the machine and when they should not. In this episode Chris sits down with Vasant Dhar, professor at NYU Stern and the NYU Center for Data Science, longtime AI practitioner, and author of Thinking with Machines: The Brave New World of AI. With more than four decades working in artificial intelligence across finance, healthcare, and research, Dhar shares a practical framework for deciding when leaders should trust AI and when human oversight still matters. His “trust map” evaluates two variables: how often the system is wrong and the consequences of its errors. The conversation also tackles why so many AI pilots fail, why fear rather than greed is driving AI adoption in many organizations, and how leaders should prioritize their first AI initiatives. Dhar explains why deep domain knowledge becomes even more valuable in the AI era, why executives must understand their data before deploying AI, and why the future belongs to people who learn to think with machines rather than simply ask them for answers. Leaders who want a clearer way to evaluate AI opportunities and avoid costly missteps will find this discussion well worth their time. Chapters 00:00 Introduction 03:23 The Origin of the “Trust AI” Question 05:14 The Trust Framework: Predictability vs Cost of Error 07:01 Crossing the Automation Frontier 09:07 The Three Barriers Holding Leaders Back from AI 11:51 Why 95% of AI Projects Fail 14:39 How Leaders Should Choose Their First AI Projects 19:17 Fear vs Greed in Today’s AI Adoption 25:20 Why Leaders Should “Think Slowly” About AI Strategy 44:16 The Bifurcation of Humanity in the Age of AI 🔎 Find Out More About Vasant Dhar Website: https://vasantdhar.com Book: Thinking with Machines: The Brave New World of AI Podcast: Brave New World Substack Newsletter: https://vasantdhar.substack.com 🛠 AI Tools and Resources Mentioned ChatGPT https://chat.openai.com Claude https://claude.ai Grok https://x.ai Chief AI Officer (Sponsor) https://chiefaiofficer.com Using AI at Work https://usingaiatwork.com

Most leaders aren’t struggling with AI tools, they’re struggling with how to lead the transformation those tools require. In this episode, Chris interviews Justin Trombold, President of Antesyn Advisors who works with leadership teams navigating the uncertainty of generative AI strategy across industries from healthcare to enterprise services. During the conversation, he explains why most organizations go wrong by treating generative AI as an IT deployment rather than a transformation initiative, centralizing tool decisions while failing to connect use cases to business strategy, incentives, and operating models. Chris and Justin unpack what it actually looks like to deploy AI in the real world: separating enterprise strategy from use-case experimentation, starting small with tightly defined pilots, defining KPIs before declaring success, and anticipating downstream bottlenecks that AI acceleration often creates. They also explore why cross-functional collaboration, incentive alignment, and curiosity matter more than technical horsepower — and why leaders must shift from “installing AI” to building organizational readiness for it. If you want a practical lens for turning generative AI into measurable advantage — without triggering organizational friction — this episode is for you! Chapters: (00:00) Introduction (02:01) Meet Justin Trombold (05:03) What Companies Get Right — and Wrong — About Generative AI (07:38) Why Generative AI Is Not an IT Project (08:55) Centralizing Tools, Decentralizing Use Cases (16:31) Who Should Be in the Room for AI Strategy (17:28) Enterprise Strategy vs. Use Case Execution (20:15) When AI Just Shifts the Bottleneck (29:40) The Five Pillars of AI Readiness (33:18) Designing Small AI Experiments That Scale (41:09) Building Real AI Fluency Inside Your Organization 🔎 Find Out More About Justin Trombold Website: https://www.antesynadvisors.com LinkedIn: https://www.linkedin.com/in/trombold 🛠 AI Tools and Resources Mentioned ChatGPT (OpenAI) https://chat.openai.com Claude (Anthropic) https://claude.ai Gemini (Google) https://gemini.google.com Grok (xAI) https://x.ai

Before you spend another dollar on ads, what if you could test your message against a digital version of your exact market? In today’s episode, Justin Brooke, founder of AdSkills and Agent Skills AI, joins Chris Daigle to break down how synthetic audiences and virtual focus groups are transforming modern marketing. After getting his start interning for Russell Brunson and famously turning $60 into six figures with Google Ads, Justin has spent two decades mastering message-to-market match. Now, he’s using AI to simulate highly detailed customer personas, running ads, landing pages, and even full funnels through structured “virtual focus groups” before a single dollar is deployed. In this conversation, Justin explains how to build high-quality AI personas using real demographic, psychographic, and empathy-map data; how multi-persona scoring systems are outperforming gut instinct; and why this approach may soon become the first step in every serious marketing strategy. He also shares his perspective on emerging agent frameworks like OpenClaw, the security implications leaders need to consider, and where AI is realistically delivering value today—without hype. If you want a practical framework for reducing marketing risk and increasing message precision before you go live, this episode will reshape how you think about AI in your growth strategy. 🔎 Find Out More About Justin Brooke X: @IMJustinBrooke Website: https://www.adskills.com 🛠 AI Tools and Resources Mentioned MindStudio - https://mindstudio.ai Make – https://www.make.com Claude – https://claude.ai OpenAI – https://openai.com DigitalOcean – https://www.digitalocean.com Docker – https://www.docker.com CrewAI – https://www.crewai.com LangChain – https://www.langchain.com Fathom – https://fathom.video Chapters: 00:00 Introduction 03:13 “Virtual Focus Groups” and Why They Matter 03:47 Justin’s Origin Story: From Intern to Advertiser 08:45 From Personas to Synthetic Audiences 15:24 How the System Produces Variations and Picks Winners 20:09 How “Mad Men” Marketers React to Market Feedback 22:21 Building Real ICPs: 1,000+ Words, Not One-Liners 27:15 The New York Times “Digital Twin” and 92% Accuracy 30:13 Tool Stack: MindStudio, Claude Projects, and Agent Frameworks 35:16 OpenClaw, AI Agents & Security Considerations 49:55 Staying Focused: Pick Your Lane in AI

Most companies are experimenting with AI. The leaders who win are rebuilding around it. In this episode, Chris Daigle sits down with Jason Eubanks, Co-Founder and CEO of Aurasell AI, to explore why incremental AI experiments aren’t enough—and why go-to-market teams must shift to an AI-native operating model. Jason explains why simply plugging AI into legacy systems won’t change your productivity model, and why companies that fully embrace intelligent automation now will create an advantage competitors won’t be able to close. They discuss how AI-native architecture can double productivity, eliminate CRM busywork, and cut onboarding time for sales teams by 50%. From removing copy-and-paste workflows to automating outreach, enrichment, and follow-up, Jason outlines what happens when AI doesn’t just provide insights—but executes. He also introduces Aurasell’s new GTM operating system that sits on top of existing CRMs like Salesforce and HubSpot, plus an agent builder that enables powerful AI-driven workflows through simple natural language prompts. If you’re looking to unlock real productivity gains—not just incremental improvements—this episode outlines what that shift actually requires. 🔎 Find Out More About Jason Eubanks LinkedIn: https://www.linkedin.com/in/jasoneubanks/ 🌐 Learn More About Aurasell AI https://aurasell.ai 🛠 AI Tools and Resources Mentioned Aurasell GTM Operating System https://www.aurasell.ai Chat Gpt https://chatgpt.com/ Salesforce https://www.salesforce.com/ HubSpot https://www.hubspot.com Chapters: (00:00) Introduction (01:17) What “AI-native” really means (beyond chat wrappers) (03:02) The productivity gap: why incremental AI adoption fails (06:45) Urgency explained: first movers and 2–3x productivity gains (10:08) Fixing the broken B2B sales productivity model (12:27) Case study: carving out teams to go all-in on AI (15:07) The AI-native GTM platform and unified customer journey (21:26) Cutting onboarding time by 50% with intelligent automation (26:10) Eliminating sales busywork and manual CRM toil (28:46) Agentic workflows: natural language → automated execution

Chris Daigle sits down with Hernan Lardiez, COO of RagMetrics, to break down AI evaluations (evals) and why monitoring matters when you put GenAI into production especially in regulated or high-risk environments. Hernan explains what “good evals” actually look like without getting lost in technical weeds: building test datasets, measuring accuracy and consistency, and then continuously re-testing so you can catch drift before it becomes a business problem. They compare the “spreadsheet + spot check” approach to automated eval pipelines that can run fast, repeatable tests at scale. The conversation also covers a practical way to think about pre-production testing vs. in-production monitoring, why token usage and cost should be part of evaluation, and how small RAG tuning decisions (like Top-K chunks) can improve accuracy while cutting token consumption. If you’re leading AI adoption and you want confidence not guesswork this episode will help you build the control points and guardrails to scale GenAI safely. 🔎 Find Out More About Hernan Lardiez Hernan Lardiez on LinkedIn https://www.linkedin.com/in/hlardiez/ RagMetrics https://ragmetrics.ai/ 🛠 AI Tools and Resources Mentioned RagMetrics - https://ragmetrics.ai The AI Exchange (Rachel Woods) - https://www.theaiexchange.com/ Chief AI Officer - https://www.chiefaiofficer.com/ 📌 Chapters 00:00 Why regulated industries can’t “hope” with AI 02:04 What model evaluations (evals) actually are 05:08 The two audiences: business owner vs builders 08:52 Pre-production testing vs in-production monitoring 14:23 Why “monitoring is required” to reduce risk 16:14 Manual spreadsheet grading vs automated evals 18:01 Building test datasets + injecting through the pipeline 31:21 Measuring accuracy AND token consumption (cost) 34:01 Continuous evals to catch drift over time 42:11 RAG tuning: Top-K chunks, accuracy vs noise, token savings 49:21 Evals as “low-cost insurance” for production AI 50:27 Closing advice: control points + IT boundaries In this clip from the Using AI at Work podcast, we explore the challenges of AI implementation, particularly for organizations in regulated markets. The discussion highlights the critical role of effective risk management in navigating potential outcomes. We identify key stakeholders, like the business owner and the development team, who are crucial for understanding AI requirements and ensuring compliance. This session emphasizes the importance of strategic ai leadership and how ai business can integrate these considerations for successful operations management.

Chris Daigle sits down with Bill Gallagher, leadership expert and longtime advisor to executives, to explore what it really means to use AI at work during periods of rapid organizational change. Bill shares why technology alone never drives transformation and how trust, clarity, and human leadership remain the deciding factors when AI enters the workplace. The conversation focuses on how leaders can introduce AI without creating fear, resistance, or confusion, how to avoid treating AI as a shortcut instead of a responsibility, and how strong leadership principles apply even more as automation increases. This episode is a practical guide for executives who want to adopt AI while maintaining credibility, alignment, and trust across their teams. 🔎 Find Out More About Bill Gallagher https://www.linkedin.com/in/billgall/ 🛠 AI Tools and Resources Mentioned ChatGPT Internal AI tools within organizations 📌 Chapters 00:00 Introduction to Bill Gallagher 04:12 Leadership challenges during AI driven change 10:08 Why trust matters more than technology 17:26 How leaders should talk about AI internally 23:41 Avoiding fear and resistance during AI adoption 30:05 The human role in AI driven organizations 37:18 Leading with clarity in times of uncertainty 43:02 Final thoughts on leadership and AI 48:10 How to connect with Bill Gallagher

Chris Daigle sits down with Kate Bravery, Global Head of Talent Advisory at Mercer, to explore how AI at work is reshaping people strategy, leadership, and workforce decision making. Kate shares how organizations are using AI to support talent planning, skills intelligence, and workforce design while navigating trust, governance, and ethical responsibility. The conversation focuses on how business leaders can adopt AI in the workplace without losing the human element. Kate explains why AI should augment judgment rather than replace it, how leaders can build confidence using AI powered insights, and what it takes to responsibly deploy AI across HR, talent, and leadership teams. This episode offers a grounded perspective on workplace AI adoption for executives who want progress without unintended consequences. 🔎 Find Out More About Kate Bravery and Mercer Kate Bravery on LinkedIn https://www.linkedin.com/in/katebravery Mercer https://www.mercer.com 🛠 AI Tools and Resources Mentioned ChatGPT https://openai.com/chatgpt Internal AI systems used for workforce analytics and decision support 📌 Chapters 00:00 Introduction to Kate Bravery 03:42 How AI is changing people strategy 09:15 Using AI for workforce planning and skills insights 15:28 Balancing human judgment with AI recommendations 21:10 Building trust and confidence in AI systems 27:44 Ethical considerations in workplace AI 34:02 Leadership responsibility in AI adoption 40:18 What executives should focus on next 45:56 How to connect with Kate Bravery

In this solo episode of Using AI at Work, Chris Daigle breaks from the interview format to share the AI tool stack he recommends to executives, leaders, and knowledge workers who want real value without chasing every new release. Chris introduces the concept of “thinking in AI” and explains how leaders move from using AI for isolated tasks to developing an instinctive, organization-wide mindset where AI supports daily work, decisions, and workflows. He also addresses common fears around AI replacing jobs, clarifying the difference between task-level automation, project-level assistance, and full job replacement. The episode walks through a curated set of AI tools that Chris believes are best-in-class, easy to use, and likely to stick around, helping leaders save time, reduce confusion, and build real context inside their organizations. This is a practical starting point for anyone looking to use AI at work with confidence and clarity in 2026. 🔎 Learn More About Chris Daigle and Chief AI Officer Chris Daigle on LinkedIn https://www.linkedin.com/in/chrisdaigle Chief AI Officer https://chiefaiofficer.com 🛠 AI Tools and Resources Mentioned Fathom https://chiefaiofficer.com/fathom Perplexity https://www.perplexity.ai Comet Browser by Perplexity NotebookLM https://notebooklm.google Gamma https://chiefaiofficer.com/gamma Nano Banana image generation inside Google Gemini ChatGPT https://openai.com/chatgpt 📌 Chapters 00:00 Why this episode is different 01:14 The problem with AI tool overload 02:10 What “thinking in AI” really means 03:22 From discrete AI use to an AI reflex 04:28 Sharing AI wins to build culture 05:35 AI governance and cultural readiness 06:18 Will AI replace jobs 07:15 Tasks, projects, and job-level work 08:42 What AI can realistically automate today 10:02 Economic impact of AI on knowledge work 11:48 Why meeting transcription builds business context 12:57 Using Fathom as an AI meeting assistant 13:45 Replacing Google with Perplexity 14:58 Agentic browsing with the Comet browser 15:42 NotebookLM as a learning environment 16:36 Creating executive decks with Gamma 17:18 Image generation with Nano Banana 18:02 Why community matters for AI adoption 19:22 Final advice for leaders getting started

Chris Daigle sits down with Panos Siozos, CEO and co-founder of LearnWorlds, to explore how AI at work is changing the way we learn, teach, and build real expertise. Panos explains why access to information is no longer the challenge and why critical thinking, judgment, and structured learning matter more than ever in an AI-driven world. The conversation breaks down the difference between knowledge and understanding, the risk of cognitive laziness when relying too heavily on AI, and why learning still requires friction, effort, and human guidance. They also discuss how AI should support learning rather than replace it, how credibility and authority are shifting in the age of generative AI, and what professionals and organizations must do to keep skills relevant as AI accelerates. This episode is a grounded look at using AI at work without losing the ability to think, learn, and grow. 🔎 Find Out More About Panos Siozos and LearnWorlds Panos Siozos on LinkedIn https://www.linkedin.com/in/siozos/ LearnWorlds https://www.learnworlds.com 🛠 AI Tools and Resources Mentioned ChatGPT https://openai.com/chatgpt 📌 Chapters 00:00 Introduction to Panos Siozos 02:12 Panos’ background in learning and education technology 05:18 Why learning is not disappearing in the AI era 08:44 Knowledge vs real understanding 12:06 The risk of cognitive laziness with AI 16:22 Why struggle and friction matter in learning 20:35 How AI changes authority and credibility 25:11 Turning expertise into meaningful learning experiences 29:54 Using AI to support, not replace, human learning 35:18 Building premium learning products in the AI age 41:02 Final advice for professionals learning with AI

Chris Daigle sits down with Tim Cakir, founder of AI Operator, to talk about why most companies feel overwhelmed by AI and how leaders can move past tool overload to real productivity at work. Tim shares his experience training teams across industries and explains why AI adoption fails when organizations chase tools instead of outcomes. The conversation focuses on building human centered AI habits, reducing fear around AI, and helping teams work with AI as a collaborator rather than seeing it as a threat. They also explore how leaders can tailor AI training by role, why behavior change matters more than policies, and how voice AI and assistants are beginning to reshape how people plan, think, and execute at work. This episode is a practical guide for leaders who want progress with AI without burnout or confusion. 🔎 Find Out More About Tim Cakir Tim Cakir on LinkedIn https://www.linkedin.com/in/timcakir/ AI Operator https://aioperator.com 🛠 AI Tools and Resources Mentioned ChatGPT — https://chat.openai.com Google Gemini — https://deepmind.google/technologies/gemini/ Google AI Studio — https://aistudio.google.com Claude — https://claude.ai NotebookLM — https://notebooklm.google ElevenLabs — https://elevenlabs.io Vapi — https://vapi.ai Make — https://www.make.com Zapier — https://zapier.com n8n — https://n8n.io Notion — https://www.notion.so MCP (Model Context Protocol) — https://modelcontextprotocol.io Custom GPTs — https://chat.openai.com/gpts AI Operator — https://www.aioperator.com Internal AI assistants used inside organizations 📌 Chapters 00:00 Introduction to Tim Cakir 02:18 Why leaders feel overwhelmed by AI 05:06 Tool overload vs outcome driven adoption 08:44 Human plus AI collaboration mindset 13:02 Reducing fear around AI at work 17:36 Training teams based on real workflows 22:41 Behavior change vs policy driven adoption 27:15 Using voice AI to reclaim time and focus 31:48 How leaders should evaluate new AI tools 36:05 Where AI Operator helps organizations start 40:12 How to connect with Tim Cakir

Chris Daigle sits down with Mohamed Yousuf, founder of Smart Workforce AI, to explore how AI in the workplace can dramatically improve workforce scheduling, planning, and operational efficiency across industries. Drawing on more than a decade of experience in airlines, healthcare, hospitality, and large scale operations, Mohamed explains how AI powered forecasting and scheduling can reduce overtime, prevent burnout, and help companies put the right people in the right place at the right time. The conversation covers real world AI applications like demand forecasting, seasonal planning, labor law compliance, and intelligent shift swapping, all supported by AI copilots that work alongside human decision makers. This episode is a practical look at how AI productivity tools can turn workforce management from reactive firefighting into a strategic advantage, while keeping human well being at the center of operations. 🔎 Find Out More About Mohamed Yousuf and Smart Workforce AI https://www.linkedin.com/in/mohamed-a-yousuf/ Smart Workforce AI https://smartworkforce.io 🛠 AI Tools and Resources Mentioned ChatGPT https://openai.com/chatgpt AI powered workforce forecasting models Large language models for scheduling and planning Internal AI copilots for workforce management 📌 Chapters 00:00 Introduction to Mohamed Yousuf 01:10 From Airline Scheduling to Workforce AI 03:55 Why Workforce Planning Is a Universal Business Problem 05:21 Using AI to Complement Human Decision Making 06:54 Healthcare staffing and burnout challenges 09:16 Forecasting demand using demographic and immigration data 11:23 Hospitality, events, and seasonal workforce planning 12:48 Avoiding panic hiring and panic firing 14:32 Measuring labor cost savings and productivity gains 17:53 Making AI powered scheduling accessible to smaller teams 21:17 Onboarding and system learning timelines 22:56 Handling labor laws and contract complexity with AI 25:19 AI assistants for managers and employees 27:15 Giving employees more control over schedules 29:52 Minimum team size to benefit from AI scheduling 33:16 The future of micro shifts and flexible work 35:29 Pricing and ROI for small and mid sized businesses 39:34 How to get started with Smart Workforce AI

Chris Daigle sits down with Patrick Leung to explore how AI is being applied inside modern marketing and revenue teams to drive efficiency, consistency, and scale. Patrick shares real examples of how teams are using AI in the workplace to support go to market execution, internal knowledge sharing, and decision making without overwhelming non technical leaders. The conversation covers practical AI adoption for business leaders, how to avoid overcomplicating workflows, and where AI productivity tools deliver the most value today. Patrick also breaks down how organizations can move from experimentation to repeatable AI powered processes that actually support growth. This episode is a grounded look at workplace AI adoption for teams focused on execution, not hype. 🔎 Find Out More About Patrick Leung https://www.linkedin.com/in/puiwah/ 🛠 AI Tools and Resources Mentioned ChatGPT ➡ https://openai.com/chatgpt Internal AI assistants used by marketing and revenue teams 📌 Chapters 00:00 - Introduction to Patrick Leung 04:12 - Where AI Fits Inside Marketing and Revenue Teams 10:36 - Practical AI Use Cases for Execution 17:48 - Supporting Non Technical Teams with AI 24:15 - Avoiding Tool Overload and Overengineering 31:02 - Using AI to Improve Consistency and Speed 38:20 - Moving from Experiments to Scaled Workflows 45:10 - Final Advice for Leaders Adopting AI 48:55 - How to Connect with Patrick Leung

Chris Daigle sits down with Zak Ali, General Manager at Finder, to unpack how search is evolving as people move from traditional search engines to large language models like ChatGPT, Claude, and Gemini. Zak explains why SEO is not dead, how LLMs decide which brands to surface, and why trust signals like authority, recency, and editorial rigor matter more than ever. He shares how Finder adapted its content strategy to show up consistently inside AI answers, what types of long tail queries perform best in LLM search, and how AI powered browsers are changing the future of discovery. They also explore how Finder is approaching AI upskilling internally, why hands on experimentation beats mandates, and how non technical teams are using tools like Claude Code and MCPs to dramatically increase productivity. This episode is a must listen for leaders who want to understand where search is headed and how AI is reshaping how customers find solutions. 🔎 Find Out More About Zak Ali LinkedIn https://www.linkedin.com/in/zak-ali44/ Substack https://thoughtson.substack.com Finder https://www.finder.com 🛠 AI Tools and Resources Mentioned ChatGPT https://openai.com/chatgpt Claude https://claude.ai Perplexity https://www.perplexity.ai Notebook LM https://notebooklm.google 📌 Chapters 00:00 Introduction to Zak Ali and Finder 02:10 SEO vs LLM search and why fundamentals still matter 04:55 How LLMs choose which sources to cite 07:40 Bing, Google, and triangulating AI search results 10:30 Inspecting ChatGPT queries to understand discovery 13:05 Recency, trust, and authority signals for LLMs 15:45 AI generated content and human quality standards 18:35 Long tail queries and why they win in AI search 21:40 Measuring traffic and revenue from LLM discovery 24:10 AI browsers and the future of click data 27:30 Internal AI upskilling without mandates 30:20 Claude Code, MCPs, and terminal based workflows 34:10 Non technical teams building real tools with AI 37:40 Executive blind spots and the knowledge gap 41:05 Getting started with practical AI habits 44:00 Where to follow Zak Ali and keep learning

Chris Daigle sits down with Geoff Gibbins, strategy and AI transformation expert and author of "Critical Intelligence," to explore how companies can redesign their workflows and help their teams develop stronger thinking skills in the age of AI. Geoff shares what he has learned from working with major enterprises on AI transformation, including why so many AI pilots fail, how to avoid automating broken processes, and how to create human AI systems that actually deliver value. They discuss why top companies are shifting from using AI for simple automation to re-engineering entire workflows, how agents fit into modern processes, and why understanding human and AI collaboration is now a competitive advantage. Geoff also explains how individuals can future-proof their careers by focusing on long half-life skills and learning to use AI as a true collaborator rather than a tool. This episode gives leaders a practical lens on AI transformation, human capability development, and how to prepare teams for a hybrid future of people and intelligent agents working together. 🔗 Resources Mentioned Geoff’s Book: Critical Intelligence: Strengthening Human Thinking in the Age of AI https://www.amazon.com/Critical-Intelligence-Strengthening-human-thinking-ebook/dp/B0FKZZCMTZ# Geoff Gibbins on LinkedIn: https://www.linkedin.com/in/geoffgibbins/ ChiefAIOfficer Resources: https://chiefaiofficer.com 📌 Chapters 00:00 Introduction 01:10 Geoff’s background in strategy, innovation, and AI transformation 04:20 Why most AI pilots fail 06:00 Automating broken workflows vs reinventing them 07:40 Marketing to agents vs marketing to humans 09:20 Reinventing work with agents and human AI workflows 12:30 How to integrate humans into agent-driven systems 14:10 Working with process owners inside companies 17:00 Are people afraid AI will take their job 20:00 Individual overwhelm and the pace of change 22:30 The skill variation inside large companies 24:00 How enterprise understanding has changed in the past year 25:30 How companies differentiate when everyone has the same tools 26:30 Designing human AI collaboration inside orgs 27:40 Shifting from "AI as a tool" to "AI as a collaborator" 29:00 How individuals future-proof their skills 31:00 Why prompt engineering is already outdated 32:20 Talking to AI like a collaborator 33:30 How careers may shift as roles evolve 37:20 Advice for non-technical business professionals 38:40 About the book 39:30 Closing Thoughts