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Can AI for leadership help you give your people more attention? In this episode Chris sits down with Jon Harmeling, a Chick-fil-A Operator, Army veteran, and former West Point instructor, to explore how leaders can turn AI productivity gains into better employee coaching and development. Drawing on his restaurant experience and his book, Frontline AI Leadership, Jon shares a practical framework: Machines do. Managers decide. Leaders develop. Chris and Jon discuss using AI to track employee goals, recognize overlooked contributions, prepare for difficult conversations, and follow through on commitments. They also examine how to measure real AI time savings, reduce context switching, and keep human judgment at the center of workplace automation.  Listen to learn how to reinvest the time AI creates in stronger relationships and more intentional leadership. Chapters: (00:00) Introduction (06:06) Management vs. Leadership and the AI Capacity Dividend (15:51) Making Time for Employee Coaching and Development (18:44) Reducing Context Switching When Working with AI (20:37) Measuring Real AI Time Savings and Accountability (23:28) Frontline AI Adoption, AI Search, and Data Privacy (27:49) From AI Agents to Writing Frontline AI Leadership (33:01) Machines Do. Managers Decide. Leaders Develop. (35:50) Tracking Employee Goals with AI (39:22) AI Scheduling and the Limits of Automation (41:19) Preparing for Difficult Conversations with AI Resources: 🔎 Find Out More About Jon Harmeling: Website: https://www.jonharmeling.com  Frontline AI Leadership Book: https://www.jonharmeling.com/book  https://www.amazon.com/dp/B0HNDTHR36  LinkedIn: https://www.linkedin.com/in/jonharmeling/ X: https://x.com/JonHarmeling 🛠 AI Tools and Resources Mentioned: Claude and Claude Cowork: https://claude.com/product/cowork ChatGPT: https://chatgpt.com Support the show

What if your sales team could practice its toughest conversations before speaking to real customers? In this episode, Chris sits down with Paul Burani, Co-Founder and CRO of Atlas Primer, to explore how AI sales roleplay brings realistic practice and personalized feedback into sales training. They discuss how short, regular sessions help teams develop human skills, prepare for objections, and approach high-stakes conversations with greater confidence. Paul explains how to tailor simulations to specific prospects, incorporate your sales methodology, and connect AI sales coaching to measurable business outcomes. He also shares his AI workflow for following up on stalled deals and the data privacy questions leaders should ask before adopting new tools. Listen to discover practical ways to strengthen your team’s skills and make better use of the time AI saves. Chapters (00:00) Introduction (06:19) How Atlas Primer Builds Conversational Skills (09:25) Getting Sales Teams to Embrace AI Coaching (13:05) Building a Regular Sales Practice Habit (14:24) Inside an AI Roleplay Training Session (22:17) Preparing for Specific Prospects with LinkedIn (24:13) Connecting Sales Training to Business Outcomes (29:41) Adapting Sales Methodologies and Extending Coaching (32:53) The Handoff: AI Follow-Ups for Stalled Deals (36:13) Data Privacy Questions Every Leader Should Ask (41:46) Reinvesting AI Time Savings Without Burning Out Teams Resources: 🔎 Find Out More About Paul Burani: LinkedIn:  https://www.linkedin.com/in/pburani linkedin.com Atlas Primer:  https://atlasprimer.com/ Atlas Primer Roleplay:  https://roleplay.atlasprimer.com/ Atlas Primer 🛠 AI Tools and Resources Mentioned: Gong:  https://www.gong.io/ OpenClaw:  https://openclaw.ai/ HubSpot:  https://www.hubspot.com/ Tableau:  https://www.tableau.com/ Microsoft Power BI:  https://www.microsoft.com/en-us/power-platform/products/power-bi The AI-Driven Leader by Geoff Woods:  https://www.aileadership.com/book  Support the show

What if your AI tools aren't underperforming because of the technology, but because they don't know your business? In this episode Chris sits down with Rob Collie, CEO of P3 Adaptive, former Microsoft Power BI developer, and author of Fair Game: Customizing AI to Your Business Is Easier Than You Think, to explore why buying ChatGPT or Claude subscriptions isn't enough to achieve meaningful enterprise AI adoption. Rob explains the "knowledge cliff" that prevents generic AI tools from delivering reliable business results, why employees shouldn't be responsible for repeatedly teaching AI how their company works, and how leaders can build customized AI systems grounded in company knowledge. Through practical examples involving Claude Desktop, Claude Cowork, Notion, and AI-assisted workflows, they discuss context engineering, organizational knowledge, trusted sources of truth, and AI governance.  Listen to discover how leaders can move beyond AI experimentation and create systems their teams can actually trust and use. Chapters: (00:00) Why AI Needs Trusted Business Knowledge (02:53) From Microsoft Power BI to the AI Revolution (05:25) Why Buying AI Subscriptions Isn't Enough (09:22) The Claude Desktop Experiment That Changed Everything (11:46) The Knowledge Cliff: Why AI Doesn't Know Your Business (14:16) Bot Sitting and the Hidden Cost of AI Adoption (16:50) Why Business Leaders Must Own AI Context (21:48) How to Customize AI with Claude Cowork (24:12) Building a Personalized AI Editor (39:24) Fair Game and the Future of Business AI Resources:  🔎 Find Out More About Rob Collie LinkedIn: https://www.linkedin.com/in/robcollie/ P3 Adaptive: https://p3adaptive.com/ Raw Data with Rob Collie Podcast: https://p3adaptive.com/rawdatapodcast/ The Goldilocks Altitude Newsletter (Substack): https://goldilocks1.substack.com/  Fair Game: Customizing AI to Your Business Is Easier Than You Think by Rob Collie: https://www.amazon.com/Fair-Game-Customizing-Business-Easier/dp/1615470948  🛠 AI Tools and Resources Mentioned: ChatGPT (OpenAI): https://chatgpt.com/ Claude:  https://claude.ai/ Microsoft Copilot: https://copilot.microsoft.com/ Notion: https://www.notion.so/ OpenAI Codex: https://openai.com/codex/ Model Context Protocol (MCP): https://modelcontextprotocol.io/ Support the show

How do you scale enterprise AI without exposing the sensitive data your business cannot afford to lose? In this episode Chris sits down with Steven Walchek, Founder and CEO of Liminal, to explore enterprise AI security, AI governance, data privacy, and how leaders can safely accelerate generative AI adoption. Steven explains how enterprises can protect sensitive information before it reaches an LLM, why governance needs to enable AI adoption rather than block it, and how AI agents and tools like Claude Code are changing what teams can build and automate. He also shares practical AI use cases, including his Pocket Sales Engineer and AI-powered product prioritization, showing how non-engineers can turn ideas into working applications without writing code. Leaders should listen for a practical look at balancing AI security, governance, productivity, and innovation as generative AI spreads across the enterprise. Chapters: (00:00) Introduction (03:47) The Data Privacy Risks Behind Generative AI (07:13) How Enterprise AI Security and Governance Work (10:29) Protecting Sensitive Data Before It Reaches an LLM (18:22) How AI Coding Tools Are Changing Software Development (23:16) A Crawl, Walk, Run Strategy for Enterprise AI Adoption (28:52) Why AI Governance Should Enable Innovation, Not Block It (31:53) AI Agents, Automation, and the Enterprise Complexity Problem (37:30) Building a Pocket Sales Engineer With Generative AI (42:44) How to Build AI Applications Without Writing Code Resources: 🔎 Find Out More About Steven Walchek Steven Walchek on LinkedIn https://www.linkedin.com/in/swalchek/ Liminal https://www.liminal.ai/ Liminal Resources and Blog https://www.liminal.ai/resources/blog 🛠 AI Tools and Resources Mentioned: ChatGPT https://chatgpt.com/ Claude https://claude.ai/ Claude Code https://claude.com/product/claude-code OpenAI Codex https://openai.com/codex/ Perplexity https://www.perplexity.ai/ Grok https://grok.com/ Meta AI https://www.meta.ai/ Gong https://www.gong.io/ OpenClaw https://github.com/openclaw/openclaw Support the show

Why do companies invest in AI without changing how work gets done? In this episode Chris sits down with Justin Watt, co-founder and CEO of Switchboard, to examine why AI adoption often stalls after teams get access to tools. They explore the move from individual AI use to AI workflow automation that supports work across departments. Justin explains why successful business process automation depends on clear handoffs, usable data, and the right permissions. He shares how simplifying a complicated proposal process created a practical opportunity for AI, then discusses how leaders can find a first use case and build the shared context needed for AI agents. Listen for a grounded approach to enterprise AI implementation that starts with the work your team actually does. Chapters: (00:00) Why AI Adoption Hasn’t Changed the Business (02:23) From Individual AI Use to Team Workflows (05:10) Where AI Workflow Automation Fits (09:57) Finding the First Business Process to Automate (16:12) Mapping How Teams Actually Work (20:13) Choosing an Internal AI Implementation Lead (24:42) Human Oversight for AI Agents (30:01) Building Shared Context From Business Data (36:15) AI Tools for Meetings and Delegation (40:11) Using Claude Code for Business Workflows (43:15) Automating Recurring Updates With an AI Skill Resources: 🔎 Find Out More About Justin Watt Switchboard:  https://www.withswitchboard.com/ Justin Watt on LinkedIn:  https://ca.linkedin.com/in/wattjustin 🛠 AI Tools and Resources Mentioned: Granola:  https://www.granola.ai/ Claude, Claude Code, and Claude Cowork:  https://claude.ai/ ChatGPT and Codex: https://chatgpt.com/ and https://openai.com/codex/ Grok Bot:  https://grok.com/ Notion:  https://www.notion.so Support the show

AI transformation does not start with a massive AI project. In this episode Chris sits down with Denis Romanovskiy, Chief AI Officer at SOFTSWISS, to explore what enterprise AI adoption looks like when someone is actually responsible for turning the technology into business results. Denis explains why the Chief AI Officer role sits at the intersection of technology, operations, change management, security, and employee enablement. They discuss how companies can identify internal AI leaders, why Denis prefers measuring "economic effect" before chasing traditional ROI, and how small employee-led experiments can build momentum across an organization.  Denis also shares lessons from SOFTSWISS's company-wide AI hackathon, including how non-technical employees became active builders when given the right support. For leaders trying to move AI from scattered experimentation into everyday work, this conversation offers a practical look at building adoption, accountability, and human oversight at scale. Chapters: (00:00) Introduction (01:54) What a Chief AI Officer Actually Does (05:35) Why Businesses Need Clear AI Ownership (07:26) Choosing the Right Internal AI Leader (14:38) Measuring AI by Economic Effect, Not Just ROI (19:45) How to Choose AI Use Cases That Build Momentum (22:37) Turning an AI Hackathon Into a Business Tool (27:34) Why Executive Support Drives AI Adoption (29:16) What Non-Technical Employees Can Build With AI Resources: 🔎 Find Out More About Denis Romanovskiy LinkedIn: https://www.linkedin.com/in/dionique  SOFTSWISS: https://www.softswiss.com  🛠 AI Tools and Resources Mentioned: OpenAI: https://openai.com Anthropic: https://www.anthropic.com + NotebookLM: https://notebooklm.google  Support the show

Most AI pilots do not fail because the technology is weak. They fail because the organization was never prepared to turn the experiment into a real operating workflow. In this episode Chris sits down with Ronnie Kwesi Coleman, CEO of Puntt AI and a three-time founder focused on the future of work. They unpack the mistakes that keep enterprise AI stuck in AI pilot purgatory, why expert judgment matters more than simply giving AI more data, how AI agents should learn alongside experienced employees, and why productivity without measurable business outcomes and AI ROI is the wrong target. They also explore the shift from creation to review as a new AI bottleneck, the limitations of the "single company brain" idea, and the risks of building an enterprise AI strategy around one provider. Listen to learn how leaders can move AI pilots from experimentation into production while preserving the judgment, integrations, and strategic flexibility required for scaling AI across the organization. Chapters (00:00) Introduction (03:04) How to Choose the Right AI Pilots (05:47) The Five Deadly Sins of AI Pilot Purgatory (09:46) Starting Small While Planning to Scale (14:04) Moving From AI Productivity to Business ROI (18:02) Capturing Expert Judgment With AI Agents (23:07) How Much Should You Trust AI? (29:04) AI Agents and the New Review Bottleneck (32:33) Why the Single AI Brain Model Falls Short (38:22) Avoiding AI Vendor Lock-In and Hiring Agents Resources: 🔎 Find Out More About Ronnie Kwesi Coleman Puntt AI https://www.puntt.ai/ Ronnie Kwesi Coleman on LinkedIn https://www.linkedin.com/in/ronnie-kwesi-coleman/ 🛠 AI Tools and Resources Mentioned: Puntt AI https://www.puntt.ai/ Anthropic Claude https://www.anthropic.com/claude OpenAI https://openai.com/ Meta https://www.meta.com/ Fall in Love with the Problem, Not the Solution by Uri Levine https://www.simonandschuster.com/books/Fall-in-Love-with-the-Problem-Not-the-Solution/Uri-Levine/9781637746608 Support the show

AI can make your team faster while quietly making them worse at thinking. In this episode Chris sits down with Rana Gujral, cognitive AI leader, CEO of Behavioral Signals, and author of The AI Instinct, to explore what happens when AI moves from a productivity tool into the cognitive infrastructure businesses use to make decisions. Rana explains why leaders need to distinguish between employees who are genuinely deciding and those who are simply approving AI output, how reversibility can determine when rigorous decision trails are necessary, and why organizations should deliberately preserve opportunities for people to exercise judgment without AI. He also shares his personal process for working with AI on strategic decisions and challenges leaders to measure what AI is doing to their team's thinking, not only their output. Listen to learn how to capture AI's advantages without quietly outsourcing the judgment your organization will need when the easy answers disappear. Chapters: (00:00) Introduction (03:34) AI as Cognitive Infrastructure (07:47) Building Rules for Human and AI Decisions (10:46) Preventing Automation Bias and Protecting Human Judgment (13:42) Redesigning Organizations Around AI Decision-Making (19:58) How AI Sculpts Human Attention (22:40) Are Your People Deciding or Just Approving? (25:14) When AI-Assisted Decisions Need an Audit Trail (28:26) Rana's Personal Process for Strategic AI Decisions (31:06) Building Senior Talent When AI Does the Junior Work (39:24) What Is AI Doing to the Way Your Team Thinks? Resources: 🔎 Find Out More About Rana Gujral Rana Gujral Website:  https://ranagujral.com/ Rana Gujral on LinkedIn:  https://www.linkedin.com/in/ranagujral Behavioral Signals:  https://behavioralsignals.com/ Book: The AI Instinct: The Future of AI and Human Decision-Making https://ranagujral.com/book 🛠 AI Tools and Resources Mentioned: Claude:  https://www.anthropic.com/claude Chat GPT:  https://openai.com/ Gemini:  https://gemini.google.com/ Chief AI Officer:  https://chiefaiofficer.com/ Wiley:  https://www.wiley.com Support the show

AI may automate more execution, but that makes judgment, coordination, and management more important. In this episode Chris sits down with Erkang “Ken” Zheng, founder and CEO of Ariso, to explore why the next major opportunity for AI at work is not simply producing more content, but helping people manage work more effectively. Ken explains why every knowledge worker increasingly needs management skills, including tracking, prioritization, communication, accountability, and reflection. He also breaks down Ariso’s distributed approach to the “company brain,” the role of an AI chief of staff, and how individual AI partners can improve coordination while preserving access controls and privacy. Listen to understand how leaders can use AI to strengthen the management layer of work as execution becomes increasingly automated. Chapters: (00:00) Introduction (04:37) Why Everyone Is a Manager Now (06:25) The Work Around the Work (08:28) Management Skills That Matter More With AI (11:59) Rethinking the AI Company Brain (14:48) Building the Company Brain From the Individual Up (19:18) When an AI Intelligence Layer Starts Making Sense (23:43) What an AI Chief of Staff Should Actually Do (29:19) How Ariso Differs From DIY AI Agents (38:11) How Quickly Teams Can See Value From AI Resources: 🔎 Find Out More About Erkang “Ken” Zheng Ariso:  https://ariso.ai/ Ariso Blog: https://ariso.ai/blog Ariso LinkedIn: https://www.linkedin.com/company/ariso-ai  Erkang Zheng on LinkedIn: https://www.linkedin.com/in/erkang 🛠 AI Tools and Resources Mentioned: Ariso / Ari: https://ariso.ai/ ChatGPT: https://chatgpt.com/ Claude and Claude Code:  https://www.anthropic.com/claude Fathom:  https://fathom.video/ Fireflies.ai:  https://fireflies.ai/ OpenClaw: https://openclaw.ai  📩 Reach Out to Chris Daigle: [email protected]  Support the show

What if the biggest bottleneck in finance isn't the software, but the way work gets done? In this episode Chris sits down with John Glasgow, Founder and CEO of Campfire, an AI-native ERP platform built for finance and accounting teams. John explains why simply adding AI to legacy systems falls short, how agentic workflows are changing accounting operations, and why finance leaders should think of AI as a teammate rather than a tool. He shares real examples of AI handling reconciliations, treasury management, reporting, and close processes while keeping humans accountable for outcomes. The conversation explores practical adoption strategies, how finance teams can build trust in AI, and where human judgment remains essential. Leaders will come away with a clear framework for introducing AI into critical business processes while maintaining accuracy, accountability, and stakeholder confidence. Chapters: (00:00) Introduction (02:00) Meet John Glasgow and Campfire (03:00) Why AI-First ERP Is Different (06:04) From System of Record to System of Work (08:26) AI as Operational Capacity, Not Just Productivity (13:47) Inside Campfire's Ember Agents (16:04) Building Trust in AI for Finance Teams (18:49) Treat AI Like a New Hire (22:13) Where Humans Must Stay in the Loop (28:49) Customer Results and Real World Impact (31:19) Does AI Replace Finance Jobs? (34:27) The Skills Future Finance Leaders Need Resources: 🔎 Find Out More About John Glasgow: John Glasgow LinkedIn https://www.linkedin.com/in/johnglasgow/  Campfire  https://www.campfire.ai Campfire LinkedIn https://www.linkedin.com/company/meetcampfire  🛠 AI Tools and Resources Mentioned: Campfire  https://www.campfire.ai Claude  https://claude.ai Anthropic  https://www.anthropic.com NetSuite  https://www.netsuite.com SAP  https://www.sap.com Workday  https://www.workday.com QuickBooks https://quickbooks.intuit.com Xero https://www.xero.com Support the show

Hiring is getting faster, noisier, and harder to evaluate. In this episode Chris talks with James Terry, Head of US Revenue at Indeed Flex, about how AI is reshaping recruiting from application screening to workforce planning. James explains why rising application volume is pushing employers toward AI interviews, how human review still fits into the process, and where AI can help recruiters evaluate candidate skills at a scale traditional hiring workflows cannot handle.  They also explore how AI can move HR beyond administrative work by connecting workforce, operations, and performance data, plus how James uses tools including Gemini and NotebookLM to accelerate proposals and decision support. The conversation ultimately shifts from replacing jobs to redesigning roles, building stronger AI fluency, and giving teams access to the data that makes AI genuinely useful. Listen for a practical look at what AI adoption becomes when leaders move beyond experimentation and apply it to real operating problems. Chapters 00.00 Introduction 01:57 AI Hiring and the Race to Build AI Fluency 06:42 Putting AI to Work Across Revenue and Operations 10:17 Why Application Overload Is Breaking Traditional Screening 11:51 AI Interviews at Scale With Human Review 15:55 Can AI Identify Better Candidates? 21:30 Turning HR Into a Strategic Business Function 24:57 Using Workforce Data to Reduce Turnover 30:34 From AI Experiments to Real Workflows 34:48 How AI Will Change Jobs and Roles 41:41 Connecting Data, Building a Second Brain, and Shaping Vendor Roadmaps Resources: 🔎 Find Out More About James Terry James Terry on LinkedIn:  https://www.linkedin.com/in/james-terry-33023717 Indeed Flex: https://indeedflex.com/ (Indeed Flex US) Indeed Flex contingent labor webinar featuring James Terry: https://indeedflex.com/employers/resources/industry-reports/webinar-contingent-labor-in-manufacturing-competitive-advantage/ 🛠 AI Tools and Resources Mentioned: Indeed Smart Screening: https://www.indeed.com/employers/smart-screening  Google Gemini:  https://gemini.google.com Gemini Gems: https://support.google.com/gemini/answer/15235603  Google NotebookLM:  https://notebooklm.google.com Snowflake:  https://www.snowflake.com/en Tableau:  https://www.tableau.com/ ChatGPT:  https://openai.com/chatgpt/overview/ Chief AI Officer:  https://chiefaiofficer.com Using AI at Work:  https://usingaiatwork.com Support the show

Most AI projects do not fail because the model is weak. In this episode Chris talks with Eddie Irvin, founder of Nashville AI Advisory, about the practical work leaders need to do before AI can deliver value. Eddie explains why process clarity, prioritization, and communication between business owners and technical builders are often more important than choosing the newest tool. Chris and Eddie break down how to identify a strong first AI project, why CEOs should stay involved in defining business rules, and how richer context improves results from ChatGPT and Claude. They also cover dictation, roundtable prompting, Codex, Claude Code, and using multiple models to pressure test important decisions. Listen if you want a grounded framework for moving from AI interest to useful systems your business can actually operate. Chapters: (00:00) Introduction (02:56) Bridging the Gap Between Business and Technology (09:13) Where AI Transformation Actually Starts (11:24) Why Process Design Comes Before AI (18:54) Finding and Prioritizing the Right Business Problems (22:28) What a Good First AI Project Looks Like (27:45) Why Clear Processes Make AI Implementation Easier (32:49) What CEOs Should Never Delegate to AI Developers (35:52) Better AI Results Start With Better Context (48:06) Why Leaders Need Hands-On Experience With AI 🔎 Find Out More About Eddie Irvin Eddie Irvin on LinkedIn: https://www.linkedin.com/in/eddie-irvin Nashville AI Advisory: https://nashvilleaiadvisory.com  🛠 AI Tools and Resources Mentioned: ChatGPT: https://chatgpt.com/ ChatGPT Desktop: https://chatgpt.com/download/ Codex: https://openai.com/codex/ Claude: https://claude.ai/ Claude Fable 5: https://www.anthropic.com/claude/fable Claude Code: https://www.anthropic.com/product/claude-code GPT-5:  https://openai.com/gpt-5 Support the show

AI adoption can create value, but it can also create a new organizational bottleneck. In this episode Chris sits down with Dr. Markus Schmidberger, Founder and CTO of JuntoAI, to challenge the assumption that every company needs a Chief AI Officer. They explore the growing AI business gap, why adoption is fundamentally a cultural enablement issue, and how governance, HR, and distributed ownership should work together. Markus shares an in-residence model for discovering use cases, moving the strongest ideas into production, and measuring ROI through agent outcomes rather than token consumption. He also explains why unrestricted experimentation can distract teams from core strategy and how JuntoAI is rethinking professional networking with digital twins and AI agents. Leaders should listen for a practical framework to scale AI without centralizing responsibility or losing strategic focus. Chapters (00:00) Introduction (03:20) Defining the Chief AI Officer Debate (06:37) The Emerging AI Business Gap (09:35) AI Adoption as Cultural Enablement (12:42) Governance and Open AI Access (15:31) Distributing AI Ownership Across the Business (18:20) The In-Residence AI Enablement Model (21:01) Moving from Use Case Discovery to Production (25:45) Measuring AI ROI Through Agent Outcomes (31:38) When AI Adoption Becomes a Strategic Distraction (39:11) JuntoAI and the Future of Business Networking (46:21) A Final Lesson on AI as a Statistical Model 🔎 Find Out More About Dr. Markus Schmidberger Dr. Markus Schmidberger on LinkedIn  https://www.linkedin.com/in/schmidberger/ JuntoAI on LinkedIn  https://www.linkedin.com/company/juntoa JuntoAI https://juntoai.org/ 🛠 AI Tools and Resources Mentioned: JuntoAI https://juntoai.org/ ChatGPT https://chatgpt.com/ Claude https://claude.com/ Gemini https://gemini.google.com/ Google AI Studio https://aistudio.google.com/ Claude Code https://claude.com/product/claude-code OpenAI  https://openai.com/ Support the show

Buying AI licenses is easy; changing how work gets done is the real leadership challenge. In this episode Chris sits down with Darren Ward, a Chief Strategic Innovation Officer leading internal AI adoption at a mid-market operating company. Darren shares how he moved from operational leadership into a dedicated AI role and why successful transformation requires executive ownership, practical use cases, and a clear connection to business priorities. They discuss why pull-based adoption outperforms mandatory training, how power users create department-level momentum, and how a tiered stack of Gemini, ChatGPT, Claude, and an internal RAG interface can balance capability, cost, and data access. Darren also explains how policy, prompting fundamentals, human review, and approved tools reduce hallucination risk and shadow AI. Leaders should listen for a grounded playbook for moving from experimentation to measurable adoption without treating AI as another technology side project. Chapters: 00:00 Introduction 02:05 Darren Ward’s Path From Operator to AI Leader 08:04 Why AI Needs Executive Ownership 11:34 Replacing Push Training With Power User Adoption 19:32 Finding Measurable AI Use Cases 25:24 Turning an Engineering Skeptic Into a Champion 29:06 Connecting AI Pilots to Strategic Objectives 31:12 Measuring Usage and Preventing Shadow AI 34:18 What to Do After Buying AI Licenses 37:50 Designing a Three-Tier AI Tool Stack 41:22 Private LLMs, RAG, and Internal Data 51:59 Start Small, Start Now Resources: 🔎 Find Out More About Darren Ward Darren Ward on LinkedIn:  https://www.linkedin.com/in/jdarrenward 🛠 AI Tools and Resources Mentioned: ChatGPT:  https://chatgpt.com/ Google Gemini:  https://gemini.google.com/ Claude:  https://claude.ai/ Claude Code:  https://www.anthropic.com/product/claude-code Microsoft Copilot:  https://www.microsoft.com/en-us/microsoft-copilot OpenAI API:  https://platform.openai.com/docs/ Anthropic API:  https://docs.anthropic.com/ Gemini API:  https://ai.google.dev/gemini-api/docs/ Support the show

Summary The biggest shift in AI may be moving from asking questions to assigning work. In this episode Chris sits down with Bryan McAnulty, founder and product director of Heights Platform and LatchLoop, to explore why leaders should approach AI as a worker assigned to tasks and projects rather than a chatbot producing one-time answers. Bryan explains how long-running agents can conduct research, analyze information, automate recurring work, and support teams without constant human supervision. Chris and Bryan also unpack the operating model leaders need around context, permissions, approval gates, prompt injection, process ownership, and human judgment. Listen to learn how to begin with specialized agents, protect critical systems, and use expanded AI capacity to create better customer outcomes rather than simply reduce headcount. Chapters 00:00 Episode Trailer 02:11 Meet Brian McAnulty: From Creator Platforms to AI Agents 06:36 New Chat vs. New Task: A Fundamental Shift in AI 12:08 Three Ways to Work with AI Agents 17:23 How AI Agents Actually Work Behind the Scenes 24:16 Security & Guardrails: Keeping AI Agents Safe 29:51 Will AI Agents Replace Jobs? A Better Question to Ask 33:30 Owning Your Processes: Why It Matters in the AI Era 38:06 What Is an Agent Harness? Explained 39:38 How Soon Is Too Soon to Deploy AI Agents? 43:24 Closing the Implementation Gap: Getting Started with Agents 🔎 Find Out More About Bryan McAnulty Bryan McAnulty on LinkedIn: https://www.linkedin.com/in/bryanmcanulty LatchLoop: https://www.latchloop.com/ Heights Platform: https://www.heightsplatform.com/ The Creator’s Adventure Podcast: https://www.heightsplatform.com/the-creators-adventure 🛠 AI Tools and Resources Mentioned: ChatGPT: https://chatgpt.com/ ChatGPT Work: https://help.openai.com/en/articles/20001275/ OpenAI Codex: https://openai.com/codex/ OpenAI GPT-5.6: https://help.openai.com/en/articles/20001354-gpt-56-in-chatgpt Claude: https://claude.ai/ Claude Code: https://www.anthropic.com/product/claude-code Claude Cowork: https://www.anthropic.com/product/claude-cowork Claude Tag: https://www.anthropic.com/news/introducing-claude-tag Google Gemini: https://gemini.google.com/ OpenClaw: https://openclaw.ai/ Hermes Agent: https://hermes-agent.nousresearch.com/ Model Context Protocol: https://modelcontextprotocol.io/ GitHub: https://github.com/ Support the show