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

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

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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

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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 

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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/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] 

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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.

(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

🔎 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

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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

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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

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