Podcast
Using AI at Work: AI in the Workplace & Generative AI for Business Leaders
Hosted by Chris Daigle · EN
On "Using AI at Work", your host Chris Daigle and his expert guests help business leaders, executives, and teams who want to turn artificial intelligence into a real competitive advantage. Each episode shares real-world AI applications and AI transformation stories from companies successfully using AI in the workplace to improve productivity, decision-making, and operations.
You’ll hear from Chief AI Officers, innovators, and forward-thinking executives who are putting generative AI at work, from AI productivity tools and AI-powered workflows to non-technical AI training and workplace AI adoption strategies.
We cover:
- AI for business leaders – how executives use AI to lead change and drive ROI
- Generative AI tools – practical, easy-to-implement solutions for teams
- AI automation in business – streamline operations without massive tech budgets
- Executive AI education – upskilling leaders and managers for the AI era
- Real-world AI case studies – lessons learned from successful AI implementation
- AI in operations management – optimizing processes and reducing costs
- Ethical AI in business – navigating responsible and effective AI use
Whether you’re exploring AI adoption, leading AI-powered transformation, or looking for AI implementation guides, this podcast delivers a clear, non-technical roadmap to succeed in the AI-driven economy.
New episodes weekly.
Start learning how to put AI to work in your business today.
39episodes
Episodes
Newest firstAll episodes
107: Using Generative Engine Optimization (GEO) to Win the AI Search Race with Cole Casperson
2d ago00:55:06Summary readySend us Fan MailThe way customers discover brands is changing faster than most companies realize.In this episode Chris sits down with Cole Casperson, Chief Data Officer and Partner at CrankTank, to explore how AI is transforming search, e-commerce, and customer discovery. Cole explains why traditional keyword-based SEO is giving way to AI retrieval systems that understand meaning, how large language models decide which brands get recommended, and why visibility in AI-generated answers is becoming a critical business advantage.They discuss the rise of Generative Engine Optimization (GEO), the shift from keywords to concepts, how Amazon's AI systems foreshadow what's happening across the web, and why CEOs, product teams, and marketers all need to understand AI-driven discovery. Whether you sell products, services, or enterprise solutions, this episode offers practical insight into how buyers are finding companies today and why leaders who adapt early will be better positioned to compete in the AI era.Chapters(00:00) Introduction(01:22) Meet Cole Casperson and CrankTank(04:16) The New Front Doors of the Internet(05:13) GEO, AEO, and the Evolution Beyond SEO(07:49) From Keywords to Meaning Based Search(12:03) How Google Search Is Adapting to AI(14:08) Will Advertising Influence AI Answers?(20:15) Why Early AI Optimization Matters(24:03) Understanding AI Retrieval and RAG Systems(39:21) Why CEOs Need to Understand AI Discovery(46:42) Does GEO Matter Beyond E-Commerce?(50:28) How Reach Measures AI Visibility🔎 Find Out More About Cole Casperson:Cole Casperson LinkedInhttps://www.linkedin.com/in/cole-casperson-b62209a3 CrankTankhttps://www.cranktank.netREACH by CrankTankhttps://reach.cranktank.net CrankTank LinkedInhttps://www.linkedin.com/company/cranktank🛠 AI Tools and Resources Mentioned:ChatGPThttps://chatgpt.comClaudehttps://claude.aiGoogle Geminihttps://gemini.google.comAmazon Rufushttps://www.amazon.com/rufusAmazon Alexahttps://www.amazon.com/alexaMicrosoft Binghttps://www.bing.comBrave Searchhttps://search.brave.comGoogle Merchant Centerhttps://merchants.google.comGoogle Performance Maxhttps://support.google.com/google-ads/answer/10724817Meta Advantage+https://www.facebook.com/business/advantageRetrieval Augmented Generation (RAG)https://aws.amazon.com/what-is/retrieval-augmented-generation/
Summary106: Using AI Assessment Tools to Reveal Hidden Automation Opportunities with Corey Ganim
1w ago01:05:28Summary readySend us Fan MailMost business owners know AI matters, but few know where to start.In this episode Chris sits down with Corey Ganim, entrepreneur, AI consultant, and creator of AI assessment frameworks for small businesses. Corey shares how he helps non-technical business owners identify automation opportunities, reduce manual work, and implement practical AI solutions that generate measurable time savings. From AI skills and projects to voice agents and workflow automation, he explains how leaders can get results without becoming technical experts.The conversation explores AI assessments, token efficiency, brand voice systems, AI-powered knowledge management, and the future of AI agents inside organizations. Corey also shares lessons learned building AI services, evaluating tools, and helping businesses create the data foundations required for long-term AI success. Leaders looking for practical AI implementation strategies will find actionable ideas they can apply immediately.Chapters(00:00) Introduction(05:10) Becoming a Vibe Coder Without a Technical Background(07:51) How Corey Evaluates and Creates AI Tool Content(11:00) Why You Don't Need to Be an AI Expert to Create Value(13:15) Choosing the Right AI Tool for the Job(15:11) Skills, SOPs, and Repeatable AI Workflows(17:10) Token Efficiency, Rate Limits, and Better AI Usage(20:05) Building AI Projects and Custom Knowledge Systems(28:09) Creating a Brand Voice Skill That Sounds Like You(30:45) The AI Assessment Business Model Explained(35:35) AI Assessments, Guarantees, and Time-Saving Opportunities(42:09) Using Voice Agents to Conduct AI Discovery Calls(45:32) Why Every Business Needs an Internal Knowledge Base(49:58) The Future of AI Adoption and Autonomous Agents🔎 Find Out More About Corey GanimCorey Ganim LinkedInhttps://www.linkedin.com/in/coreyganimCorey Ganim Websitehttps://coreyganim.comX (Twitter)https://x.com/coreyganimBuild With AI Podcast:https://podcasts.apple.com/us/podcast/build-with-ai/id1689819329 🛠 AI Tools and Resources Mentioned:Claudehttps://claude.aiChatGPThttps://chatgpt.comClaude Codehttps://www.anthropic.com/claude-codeOpenAI Codexhttps://openai.com/codexGammahttps://gamma.appGoogle Geminihttps://gemini.google.comZapierhttps://zapier.comFutureToolshttps://www.futuretools.ioFathomhttps://fathom.videoSaneBoxhttps://www.sanebox.comTaxJarhttps://www.taxjar.comRetell AIhttps://www.retellai.comTwiliohttps://www.twilio.comElevenLabshttps://elevenlabs.ioNotionhttps://www.notion.so
Summary105: Using Voice AI for Customer Service, Sales, and Enterprise Growth with Shawn Zhang
2w ago00:43:41Summary readySend us Fan MailVoice AI is advancing faster than most organizations realize.In this episode Chris talks with Shawn Zhang, CTO and co-founder of Sanas, the enterprise voice AI company focused on improving global communication through AI. Shawn shares how voice AI is evolving beyond call automation into a foundational layer for communication, context capture, and more natural human interaction with AI systems.The conversation explores enterprise use cases, latency, customer service, sales applications, and what leaders should evaluate before investing in voice AI solutions. Executives exploring AI adoption will gain practical insight into where voice technology delivers value today and where the next wave of opportunity is emerging.Chapters:00:00 Introduction02:27 Why Voice AI Matters for Business Leaders04:47 Are We Early or Late to Voice AI?06:06 Why Speech Is More Than Words07:42 How Sanas Separates Voice Signals and Context11:54 Enterprise Use Cases for Voice AI15:23 Security, Local AI, and Enterprise Deployment21:16 How Leaders Should Evaluate Voice AI Vendors32:00 Why Latency Changes User ExperienceResources:🔎 Find Out More About Shawn Zhang:Shawn Zhang LinkedInhttps://www.linkedin.com/in/shawnbzhangSanas https://www.sanas.ai🛠 AI Tools and Resources Mentioned:OpenAI https://openai.comChatGPT https://chat.openai.comBlock https://block.xyzAIR.AI https://air.aiKlarna https://www.klarna.com
Summary104: Using AI Coworkers to Build Smarter Business Operations with Karl Simon
3w ago00:49:54Summary readySend us Fan MailMost companies are using AI, but very few are redesigning work around it.In this episode Chris sits down with Karl Simon, co-founder and CTO of Subatomic, an AI workflow orchestration company, to explore why task based AI adoption is limiting business impact. They discuss the shift from isolated AI use cases toward unified workflows powered by clean data, AI coworkers, and cross functional orchestration. The conversation also explores how organizations may flatten hierarchies as AI takes over information movement and decision support responsibilities.Chris and Karl unpack practical steps leaders can take to move from experimentation into operational transformation, including workflow discovery, data readiness, security, and ROI prioritization. Leaders looking to move beyond AI pilots and toward business redesign will find this episode especially valuable.Chapters:00:00 Introduction01:05 Meet Karl Simon and Subatomic04:59 From Hierarchy to Intelligence Layers07:58 Why Unified Data Changes Everything11:10 What Companies Get Wrong with AI Adoption13:06 Integration vs Workflow Orchestration16:40 What AI Workflow Orchestration Looks Like19:42 Building a Unified Data Layer25:00 Where AI Delivers the Fastest ROI30:47 Security and Compliance by Design33:08 What are AI Coworkers35:00 Managing Teams with AI CoworkersResources:🔎 Find Out More About Karl SimonKarl Simon LinkedIn https://www.linkedin.com/in/karlsimonSubatomic Website https://getsubatomic.ai/Subatomic LinkedIn https://www.linkedin.com/company/subatomicaiSubatomic YouTube https://www.youtube.com/channel/UCvluGpd82E00q-s-wXBx4FQ🛠 AI Tools and Resources Mentioned:Subatomic https://getsubatomic.aiChatGPT https://chatgpt.comMicrosoft Copilot https://copilot.microsoft.comBlock https://block.xyzSequoia Capital https://www.sequoiacap.comNate B. Jones YouTube https://www.youtube.com/@NateBJonesVistage https://www.vistage.com
Summary103: Using AI in Manufacturing: Generative vs. Predictive and Autonomous AI with Bryan DeBois
May 1100:52:07Summary readySend us Fan MailMost 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 Introduction01:16 Why Factory Floor AI Is Different From Knowledge Work AI03:58 The Four Types of AI Used in Manufacturing Today05:50 Why Generative AI Fails in High Risk Operational Environments10:56 Manufacturing Risks Also Apply to Construction and Life Sciences11:36 The Workforce Crisis Driving Industrial AI Adoption15:26 Why Manufacturing Careers May Be Safer Than White Collar Jobs20:42 Why Humanoid Robots Are Not the Future of Manufacturing24:53 Capturing Tribal Knowledge Before Experts Retire40:00 Who Should Own AI Inside Manufacturing Organizations43:24 Meta’s Cicero Project and the Future of Hybrid AI Systems47:08 Deterministic AI vs Probabilistic AI in Critical Industries49:27 Where to Follow Brian De Bois and Learn More About Industrial AIResources:🔎 Find Out More About Bryan DeBoisBryan DeBois on LinkedIn:https://www.linkedin.com/in/bryan-deboisRoviSys Industrial AI: https://www.rovisys.com/aiRoviSys: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/ciceroGoogle DeepMind AlphaGo: https://deepmind.google/research/breakthroughs/alphagoMicrosoft HoloLens: https://www.microsoft.com/hololensObsidian: https://obsidian.mdSAP:https://www.sap.com
Summary102: The AI-Native Company: What Comes After the Org Chart with Melissa Reeve
May 400:52:12Summary readySend us Fan MailMost 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 Introduction00:00 Meet Melissa Reeve and the Hyperadaptive Model00:00 Why Legacy Operating Models Limit AI Results00:00 Moving Beyond Automation Thinking00:00 The Shift to AI-Native Organizations00:00 Redesigning Roles, Teams, and Workflows00:00 Building a Human-Centered AI Transformation Strategy00:00 Creating Continuous Learning Systems00:00 How Leaders Scale AI Adoption Across the Business00:00 What the Future Organization Looks Like🔎 Find Out More About Melissa ReeveMelissa Reeve LinkedIn https://www.linkedin.com/in/melissamreeveHyperadaptive Solutionshttp://hyperadaptive.solutionsBook Waitlisthttps://hyperadaptive.solutions/bookBlueprint Sessionhttps://hyperadaptive.solutions/why-us#contactForm🛠 AI Tools and Resources Mentioned:AI Integration Guidehttp://hyperadaptive.solutionsAI Learning Flywheel Ebookhttp://hyperadaptive.solutions/flywheel-ebookApplied AI Workshop http://hyperadaptive.solutions/labs
Summary101: How to Audit Your Dev Partner in the Age of AI with Matt Strippelhof
Apr 2700:48:45Summary readySend us Fan MailMost 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 Introduction00:45 Why Mid-Market Companies Struggle with Tech Spend02:10 The Problem with Traditional Project Pricing04:05 A Fixed Fee Model for Software Development06:20 Reducing Operational Risk Through Predictability08:00 Modernizing Legacy Applications10:15 Building Web, Mobile, and Middleware Solutions12:05 Where AI Assistants Fit Into Business Operations14:10 Driving Retention Through Better Delivery Models16:00 Leadership Lessons for Scaling Technology Investments🔎 Find Out More About Matt StrippelhoffMatt Strippelhoff LinkedInhttps://www.linkedin.com/in/redhawktech/ Red Hawk Technologieshttps://www.redhawk-tech.com/🛠 AI Tools and Resources Mentioned:Claude https://claude.aiChatGPThttps://chat.openai.comGoogle Firebase Studiohttps://firebase.google.com/Gemini https://gemini.google.com/Cursor https://cursor.com/Salesforcehttps://www.salesforce.com/
Summary100: Human Plus AI Strategy: Redefining Team Structure in the Age of Automation with Evan J Schwartz
Apr 2000:57:49Summary readySend us Fan MailMost 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.Chapters00:00 Introduction02:05 Chris Introduces Evan J Schwartz03:40 Person Plus AI vs Doom and Gloom Narratives08:30 Which Industries AI Will Disrupt First09:23 Mentoring Global Students Solving Real Problems with AI11:12 How AI Could Reduce Food Waste at Scale18:30 What Colleges Are Getting Wrong About AI23:38 Why Companies That Wait Will Fall Behind31:19 The Rise of the Steward Role in Business41:30 Use AI for Growth, Not Headcount Cuts🔎 Find Out More About Evan J SchwartzEvan J Schwartz LinkedIn https://www.linkedin.com/in/evan-schwartz-liveAMCS Grouphttps://www.amcsgroup.com🛠 AI Tools and Resources Mentioned:ChatGPT https://chat.openai.comAnthropic Claudehttps://www.anthropic.com/claudeDockerhttps://www.docker.comSAP https://www.sap.comChief AI Officer https://chiefaiofficer.com
Summary99: Using AI Automation to Build Smarter Workflows Across Your Organization with Marc Boscher
Apr 1300:49:38Summary readySend us Fan MailMost 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 Introduction00:00:36 Why Trust and Context Are Critical for AI Agents00:01:00 Context vs Prompts: What Actually Matters00:03:48 Single Player vs Multiplayer AI in Business00:06:30 Why Context Unlocks Enterprise-Level AI Value00:08:28 What “Context” Really Means in AI Systems00:11:34 Building Context-Rich AI Use Cases (Sales Example)00:13:42 Static vs Dynamic Context Explained00:20:12 Why Context Engineering Replaces Prompt Engineering00:24:04 From Human-in-the-Loop to Autonomous AI Systems00:27:29 The Context Gap and Operational Inefficiency00:36:01 Why Change Management Is the Real Bottleneck00:42:03 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.ioSalesforce – https://www.salesforce.comServiceNow – https://www.servicenow.comGitHub – https://github.comHubSpot – https://www.hubspot.comNetSuite – https://www.netsuite.comWorkday – https://www.workday.comChatGPT – https://chat.openai.comClaude – https://claude.aiGemini – https://gemini.google.comCopilot – https://copilot.microsoft.com
Summary98: How to Build AI Agents That Automate Workflows Without Coding with Etan Polinger
Apr 601:00:41Summary readySend us Fan MailMost 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 Introduction00:12 Why Asking Better Questions Unlocks AI00:33 What Is Actually Possible With AI Today00:52 What an AI Agent Really Is01:46 Bridging AI Hype and Real Execution03:05 Why Non-Technical People Can Now Build05:19 Where Business Leaders Should Start08:52 Real Examples of AI Agents in Action13:57 The Right Way to Start Building With AI17:36 How Long It Takes to Learn This Skill22:13 Why Your AI Builds Keep Breaking33:29 Common Mistakes When Building Agents38:02 The SCOUTS Framework Explained44:20 The Most Powerful Question You Can Ask AI🔎 Find Out More About Etan PolingerLinkedIn: https://www.linkedin.com/in/etan-polinger 🛠 AI Tools and Resources MentionedAI Agents + Automation Certificationhttps://www.CAIO.cx/agentChatGPT (OpenAI)https://chat.openai.comClaude (Anthropic)https://claude.aiOpenAIhttps://openai.comCursor (AI Code Editor)https://cursor.shLovable (AI App Builder)https://lovable.devOpenClaw (AI Agent Framework)https://github.com/openclaw/openclawN8N (Workflow Automation)https://n8n.ioSalesforcehttps://www.salesforce.comNotionhttps://www.notion.soPerplexity AIhttps://www.perplexity.aiContext7 (Code + Documentation Tool)https://context7.comChief AI Officer Programhttps://chiefaiofficer.com
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