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

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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.
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
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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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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/

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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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