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

What changes when AI-enhanced virtual assistants take on more of your business operations? In this episode, Chris sits down with Joe Rare, business builder and founder of Level 9 Virtual, to explore how human-AI collaboration can expand team capacity while keeping people responsible for direction, quality, and customer relationships. Joe explains how his team combines AI analysis with human follow-up to reactivate cold leads, support revenue recovery, and build business workflows.

The conversation covers where AI workflow automation creates value, why some implementations add work without improving results, and how leaders can decide between employee AI training and hiring skilled support.

Joe also shares how he uses AI reporting to make faster business decisions, the 1-3-1 framework for turning problems into recommendations, and his approach to learning with Google’s NotebookLM. Listen to identify practical ways to delegate more effectively, strengthen execution, and preserve the human relationships your business depends on.

Chapters:

(00:00) Introduction
(03:20) Building Businesses Run by Virtual Assistants
(06:41) The 10-80-10 Model for Human-AI Collaboration
(07:52) AI-Enhanced Virtual Assistants for Lead Reactivation
(11:32) Human Quality Control for AI Workflows
(18:15) Why AI Automation Fails to Deliver Business Value
(21:55) Employee AI Training vs. Hiring Skilled Support
(27:07) AI Reporting and the 1-3-1 Decision Framework
(32:31) AI Upskilling and Employee Accountability
(37:55) Learning AI with Personalized Lessons and NotebookLM
(42:34) Keeping Customer Relationships Human as AI Scales

Resources:

🔎 Find Out More About Joe Rare:

Joe Rare | Business Systems and Strategic Delegation:
https://joerare.com/ Joe Rare

Level 9 Virtual | Virtual Staffing and Revenue Recovery:
https://level9virtual.com/ Level 9 Virtual

🛠 AI Tools and Resources Mentioned:

Google NotebookLM / Notebook | Learning and Audio Overviews:
https://notebook.google/AI Research Tool & Thinking Partner

ChatGPT:
https://chatgpt.com/

Claude:
https://claude.com/

Grok:
https://grok.com/

Google Gemini:
https://gemini.google.com/

1-3-1 Decision Framework:
One clearly defined problem, three possible solutions, and one recommended action.

10-80-10 Human-AI Workflow:
Humans define the task, AI handles the heavy lifting, and humans review the result.

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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

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/

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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

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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/

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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/ andhttps://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

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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/

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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/bloghttps://ariso.ai/blog

Ariso LinkedIn:
https://www.linkedin.com/company/ariso-ai

Erkang Zheng on LinkedIn:https://www.linkedin.com/in/erkanghttps://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:

doc@chiefaiofficer.com

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/

Campfirehttps://www.campfire.ai/

Campfire LinkedIn
https://www.linkedin.com/company/meetcampfire

🛠 AI Tools and Resources Mentioned:

Campfirehttps://www.campfire.ai/

Claudehttps://claude.ai/

Anthropichttps://www.anthropic.com/

NetSuitehttps://www.netsuite.com/

SAPhttps://www.sap.com/

Workdayhttps://www.workday.com/

QuickBooks
https://quickbooks.intuit.com/

Xerohttps://www.xero.com/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/fableClaude Code:https://www.anthropic.com/product/claude-codeGPT-5:https://openai.com/gpt-5/

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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 LinkedInhttps://www.linkedin.com/in/schmidberger/

JuntoAI on LinkedInhttps://www.linkedin.com/company/juntoa

JuntoAI
https://juntoai.org/

🛠 AI Tools and Resources Mentioned:

JuntoAIhttps://juntoai.org/ChatGPThttps://chatgpt.com/Claudehttps://claude.com/Geminihttps://gemini.google.com/Google AI Studiohttps://aistudio.google.com/Claude Codehttps://claude.com/product/claude-codeOpenAIhttps://openai.com/

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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/

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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/

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AI adoption stalls when leaders delegate the thinking. In this episode Chris sits down with Geoff Woods, bestselling author of The AI-Driven Leader and founder of AI Leadership, to explore why AI strategy must begin with the CEO and executive team. Geoff explains the difference between using AI for minor tasks and using it as a strategic thought partner for the decisions that shape enterprise value.

They break down the CRIT framework, practical prompts for solving major business problems, a financial review exercise that created stronger operating visibility, and a leadership exercise designed to multiply the value of high-performing employees. Geoff also shares what changed in the updated 2026 edition of his book and how leaders can access its companion training and bonuses. Listen to learn how to move AI from scattered tool usage into a leadership discipline that improves judgment, alignment, and execution.

Chapters:

00:00 Introduction
03:23 From Executive Operator to AI-Driven Leader
07:12 Why AI Leadership Belongs to the CEO
10:10 Solving Strategic Problems With the CRIT Framework
15:48 Two Sticky Notes for Daily AI Adoption
17:01 The AI CFO Prompt and Financial Visibility
24:37 Scaling AI From Strategy to Company-Wide Execution
27:19 How AI Champions Drive Cultural Adoption
34:27 What Changed in the Updated Edition
37:58 The Executive Multiplier Exercise
43:16 Book Bonuses, Leadership Training, and the Team Accelerator

🔎 Find Out More About Geoff Woods:

Geoff Woods Official Bio:https://www.aileadership.com/geoff-woods/

Geoff Woods on LinkedIn:https://www.linkedin.com/in/geoff-woods-8534774

Book: The AI-Driven Leader, updated 2026 edition: https://www.aileadership.com/book/

Bonus redemption page:
https://www.aileadership.com/book/redeem

AI Leadership:https://www.aileadership.com/

AI Leadership Newsletter:https://www.aileadership.com/newsletter

The AI-Driven Leader Podcast on Spotify:https://open.spotify.com/show/2qSY9ZFTBJisoKp9eACbeb

The AI-Driven Leader Podcast on Apple Podcasts:https://podcasts.apple.com/us/podcast/the-ai-driven-leader/id1723615439

🛠 AI Tools and Resources Mentioned:

The CRIT Prompt Framework

CRIT stands for Context, Role, Interview, and Task.

Context: Describe the problem in vivid detail.

Role: “You are a world-class chief growth officer who is also an aggressive, growth-minded board member. You think strategically about the business that could put us out of business and how we could build it first. Focus on the 20% that drives 80% of the results.”

Interview: “Interview me. Ask me one question at a time, up to three questions, to gain deeper context.”

Task: “Give me the top three high-impact, non-obvious strategies I can deploy to solve this problem.”

CRIT framework resource:
https://www.aileadership.com/newsletter/crit-happens-the-viral-framework-transforming-how-leaders-think

Financial Review CRIT Prompt

Context: “Here are my financials for the last month.” Attach the relevant financial statements.

Role: “You are a world-class CFO who is excellent at telling a CEO what they do not know, but should know, about their business based on their financials.”

Interview: “Interview me. Ask me one question at a time, up to five questions, to gain deeper context.”

Task: “Tell me the top five things I am not seeing that I should be seeing, and what I should do in the next 30 days to improve the financial health of my company.”

Executive Multiplier Exercise

Write a CRIT prompt that asks AI to help you identify your greatest strengths, including strengths you may not currently be using inside the business. Compare those strengths with the company’s goals and cast a vision for how you could bring 10 times to 100 times more value.

Then identify:

• The high-value work to double down on
• Work that should stop
• Work AI can augment or automate
• Work that still requires a human
• The responsibilities, priorities, onboarding plan, and job description for the person who could take over the remaining work

This exercise is based on the leadership example Geoff shares during the episode.

The Two-Sticky-Note Exercise

Sticky note one: “How can AI help me do this?”

Sticky note two: “Context, Role, Interview, Task.”

Place both notes where you work and use them as a daily trigger to apply AI to meaningful strategic problems rather than only administrative tasks.

ChatGPT
https://chatgpt.com/

Claude
https://claude.ai/

Microsoft Copilot
https://copilot.microsoft.com/

Google Gemini
https://gemini.google.com/

Claude Code
https://www.anthropic.com/claude-code

ElevenLabs
https://elevenlabs.io/

AI Leadership Collective
https://www.aileadership.com/collective/

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The labor market is moving faster than most leaders can see. In this episode Chris sits down with Cary Sparrow, founder and CEO of WageScape, to discuss how real time labor market intelligence is reshaping hiring, compensation, workforce planning, and the competition for talent.

Cary explains why traditional labor market data often moves too slowly for executive decisions, how pay transparency is changing the talent market, and why AI literacy is becoming a practical requirement across roles. The conversation gives leaders a clear view of how AI is affecting both workforce strategy and individual career resilience, making this episode worth listening to for anyone responsible for hiring, talent, or organizational performance.

Chapters:

(00:00) Introduction
(03:50) From Submarine Officer to WageScape Founder
(07:56) Why Traditional Labor Market Data Falls Short
(11:38) Building a Global Labor Market Data Engine
(14:53) How WageScape Uses Claude, Gemini, and ChatGPT
(17:37) Better Pay Decisions With Local Talent Intelligence
(21:02) What Pay Transparency Really Means
(25:57) AI Literacy Is Showing Up in Job Descriptions
(30:55) What Hiring Teams Really Mean by AI Literacy
(36:17) Where AI Creates Growth and Workforce Risk
(43:07) Why Executives Need Strategic AI Fluency
(49:38) Career Advice for Executives and Staff Leaders

Resources:

🔎 Find Out More About Cary Sparrow:

Cary Sparrow LinkedInhttps://www.linkedin.com/in/carysparrowWageScapehttps://wagescape.com/https://wagescape.comWageScape LinkedInhttps://www.linkedin.com/company/wagescape-1

🛠 AI Tools and Resources Mentioned:

WageScapehttps://wagescape.com/ (WageScape)

Claude
https://claude.com/ (Claude)

Anthropichttps://www.anthropic.com/ChatGPT
https://chatgpt.com/OpenAIhttps://openai.com/Google Geminihttps://gemini.google.com/https://gemini.google.comBureau of Labor Statistics
https://www.bls.gov/

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AI in education is no longer a future debate, it is a leadership decision happening now. In this episode Chris talks with Jason Hill, Deputy Superintendent and CBO at Redlands Unified School District, about what AI adoption looks like inside a large K-12 school district with major operational, safety, instructional, and community considerations.

Jason explains how Redlands approached staff AI access, parent concerns, student safety, AI use policies, and the importance of teaching AI literacy as a core readiness skill. He also shares practical lessons business leaders can apply immediately, including acceptable-use levels, human review, workflow automation, and the mindset shift from prompting to delegation.

Listen to this episode to understand how responsible AI adoption happens when leaders balance speed, governance, trust, and real-world execution.

Chapters

00:00 Episode Trailer
02:20 Meet Jason Hill: Leading AI Adoption in K-12 Education
05:41 Overcoming Fear: The Strategy to Flip the AI Switch
11:42 Navigating Parental Pushback and Teacher Concerns
17:05 The Core Argument: Preparing Students for the Future of Work
22:41 What Parents Should Ask Their School Districts About AI
23:49 Implementing Levels of AI Access for Different Age Groups
26:37 Safety First: Filtering and Monitoring AI Interactions
29:41 How Parents Can Get Involved in AI Policy Decisions
35:16 Real-World AI Application: Analyzing Complex Financial Reports
42:11 The Future of AI: From 'The Thing' to an Everyday Tool
47:06 Establishing Acceptable Use Policies in Business and Education

Resources

🔎 Find Out More About Jason Hill

Jason Hill on LinkedIn: https://www.linkedin.com/in/jsn-hill

Redlands Unified School District Business Services: https://www.redlandsusd.net/departments/business-services (redlandsusd.net)

Redlands Unified School District: https://www.redlandsusd.net

Jason Hill email: jason_hill@redlands.k12.ca.us

🛠 AI Tools and Resources Mentioned:

Claude https://claude.ai
Gamma https://gamma.app
ChatGPT https://chatgpt.com
OpenAI https://openai.com
Anthropic https://www.anthropic.com
Google Gemini for Education https://edu.google.com/products/google-workspace-for-education/gemini/
Circle https://circle.so
Codex https://openai.com/index/introducing-codex/

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AI adoption often succeeds or fails in the ordinary work leaders overlook. In this episode Chris sits down with Artem Koren, Co-founder and Chief Product Officer of Sembly AI, to discuss how organizations can turn everyday conversations into structured intelligence, clearer decisions, and measurable execution. Artem’s work focuses on agentic systems, professional workflows, and making AI useful, safe, and practical for teams.

Chris and Artem explore how AI-powered workflows can replace manual meeting admin, surface the work that matters, and help managers spend more time on strategy, judgment, and people. They also discuss responsible AI adoption, trust, and what executives need to understand before scaling AI across the organization. Leaders should listen for a grounded view of how to make AI adoption useful inside real operating teams.

Chapters:

(00:00) Introduction
(04:38) Starting Sembly AI Before the Market Was Ready
(07:42) The Hard Technical Problems Behind AI Meeting Notes
(10:48) Selling AI Meeting Assistants Before AI Was Mainstream
(16:04) From Meeting Notes to Meeting Intelligence
(23:04) Defining Meeting Intelligence for Business Outcomes
(25:31) Turning Meeting Context Into Proactive Workflows
(34:30) How to Evaluate AI Meeting Assistant Tools
(37:30) The Shift Toward Agentic AI in Business Workflows
(45:31) Creating Client Ready Business Materials With AI
(49:53) Where to Learn More About Artem and Sembly AI

🔎 Find Out More About Artem Koren:

Artem Koren Websitehttps://www.artemkoren.com/

Artem Koren LinkedInhttps://www.linkedin.com/in/akoren

Sembly AI Websitehttps://www.sembly.ai/

🛠 AI Tools and Resources Mentioned:

Sembly AIhttps://www.sembly.ai/ChatGPThttps://chatgpt.com/Otter.aihttps://otter.ai/Gong
https://www.gong.io/Fireflies.ai
https://fireflies.ai/Granolahttps://www.granola.ai/Recall.aihttps://www.recall.ai/Revhttps://www.rev.com/Speechmaticshttps://www.speechmatics.com/

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