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

Chris Daigle sits down with Hernan Lardiez, COO of RagMetrics, to break down AI evaluations (evals) and why monitoring matters when you put GenAI into production especially in regulated or high-risk environments.

Hernan explains what “good evals” actually look like without getting lost in technical weeds: building test datasets, measuring accuracy and consistency, and then continuously re-testing so you can catch drift before it becomes a business problem.

They compare the “spreadsheet + spot check” approach to automated eval pipelines that can run fast, repeatable tests at scale.

The conversation also covers a practical way to think about pre-production testing vs. in-production monitoring, why token usage and cost should be part of evaluation, and how small RAG tuning decisions (like Top-K chunks) can improve accuracy while cutting token consumption.

If you’re leading AI adoption and you want confidence not guesswork this episode will help you build the control points and guardrails to scale GenAI safely.

🔎 Find Out More About Hernan Lardiez

Hernan Lardiez on LinkedIn
https://www.linkedin.com/in/hlardiez/

RagMetrics
https://ragmetrics.ai/

🛠 AI Tools and Resources Mentioned

RagMetrics - https://ragmetrics.ai
The AI Exchange (Rachel Woods) - https://www.theaiexchange.com/
Chief AI Officer -  https://www.chiefaiofficer.com/

📌 Chapters

00:00 Why regulated industries can’t “hope” with AI
02:04 What model evaluations (evals) actually are
05:08 The two audiences: business owner vs builders
08:52 Pre-production testing vs in-production monitoring
14:23 Why “monitoring is required” to reduce risk
16:14 Manual spreadsheet grading vs automated evals
18:01 Building test datasets + injecting through the pipeline
31:21 Measuring accuracy AND token consumption (cost)
34:01 Continuous evals to catch drift over time
42:11 RAG tuning: Top-K chunks, accuracy vs noise, token savings
49:21 Evals as “low-cost insurance” for production AI
50:27 Closing advice: control points + IT boundaries

In this clip from the Using AI at Work podcast, we explore the challenges of AI implementation, particularly for organizations in regulated markets. The discussion highlights the critical role of effective risk management in navigating potential outcomes.

We identify key stakeholders, like the business owner and the development team, who are crucial for understanding AI requirements and ensuring compliance. This session emphasizes the importance of strategic ai leadership and how ai business can integrate these considerations for successful operations management.

Chris Daigle sits down with Bill Gallagher, leadership expert and longtime advisor to executives, to explore what it really means to use AI at work during periods of rapid organizational change. Bill shares why technology alone never drives transformation and how trust, clarity, and human leadership remain the deciding factors when AI enters the workplace.

The conversation focuses on how leaders can introduce AI without creating fear, resistance, or confusion, how to avoid treating AI as a shortcut instead of a responsibility, and how strong leadership principles apply even more as automation increases. This episode is a practical guide for executives who want to adopt AI while maintaining credibility, alignment, and trust across their teams.

🔎 Find Out More About Bill Gallagher

https://www.linkedin.com/in/billgall/

🛠 AI Tools and Resources Mentioned

ChatGPT
Internal AI tools within organizations

📌 Chapters

00:00 Introduction to Bill Gallagher
04:12 Leadership challenges during AI driven change
10:08 Why trust matters more than technology
17:26 How leaders should talk about AI internally
23:41 Avoiding fear and resistance during AI adoption
30:05 The human role in AI driven organizations
37:18 Leading with clarity in times of uncertainty
43:02 Final thoughts on leadership and AI
48:10 How to connect with Bill Gallagher

Chris Daigle sits down with Kate Bravery, Global Head of Talent Advisory at Mercer, to explore how AI at work is reshaping people strategy, leadership, and workforce decision making. Kate shares how organizations are using AI to support talent planning, skills intelligence, and workforce design while navigating trust, governance, and ethical responsibility.
The conversation focuses on how business leaders can adopt AI in the workplace without losing the human element. Kate explains why AI should augment judgment rather than replace it, how leaders can build confidence using AI powered insights, and what it takes to responsibly deploy AI across HR, talent, and leadership teams. This episode offers a grounded perspective on workplace AI adoption for executives who want progress without unintended consequences.
🔎 Find Out More About Kate Bravery and Mercer
Kate Bravery on LinkedIn
https://www.linkedin.com/in/katebravery

Mercer
https://www.mercer.com

🛠 AI Tools and Resources Mentioned
ChatGPT
https://openai.com/chatgpt

Internal AI systems used for workforce analytics and decision support
📌 Chapters
00:00 Introduction to Kate Bravery
03:42 How AI is changing people strategy
09:15 Using AI for workforce planning and skills insights
15:28 Balancing human judgment with AI recommendations
21:10 Building trust and confidence in AI systems
27:44 Ethical considerations in workplace AI
34:02 Leadership responsibility in AI adoption
40:18 What executives should focus on next
45:56 How to connect with Kate Bravery

In this solo episode of Using AI at Work, Chris Daigle breaks from the interview format to share the AI tool stack he recommends to executives, leaders, and knowledge workers who want real value without chasing every new release.
Chris introduces the concept of “thinking in AI” and explains how leaders move from using AI for isolated tasks to developing an instinctive, organization-wide mindset where AI supports daily work, decisions, and workflows. He also addresses common fears around AI replacing jobs, clarifying the difference between task-level automation, project-level assistance, and full job replacement.
The episode walks through a curated set of AI tools that Chris believes are best-in-class, easy to use, and likely to stick around, helping leaders save time, reduce confusion, and build real context inside their organizations. This is a practical starting point for anyone looking to use AI at work with confidence and clarity in 2026.
🔎 Learn More About Chris Daigle and Chief AI Officer
Chris Daigle on LinkedIn
https://www.linkedin.com/in/chrisdaigle

Chief AI Officer
https://chiefaiofficer.com

🛠 AI Tools and Resources Mentioned
Fathom
https://chiefaiofficer.com/fathom

Perplexity
https://www.perplexity.ai

Comet Browser by Perplexity
NotebookLM
https://notebooklm.google

Gamma
 https://chiefaiofficer.com/gamma

Nano Banana image generation inside Google Gemini
ChatGPT
 https://openai.com/chatgpt

📌 Chapters
00:00 Why this episode is different
01:14 The problem with AI tool overload
02:10 What “thinking in AI” really means
03:22 From discrete AI use to an AI reflex
04:28 Sharing AI wins to build culture
05:35 AI governance and cultural readiness
06:18 Will AI replace jobs
07:15 Tasks, projects, and job-level work
08:42 What AI can realistically automate today
10:02 Economic impact of AI on knowledge work
11:48 Why meeting transcription builds business context
12:57 Using Fathom as an AI meeting assistant
13:45 Replacing Google with Perplexity
14:58 Agentic browsing with the Comet browser
15:42 NotebookLM as a learning environment
16:36 Creating executive decks with Gamma
17:18 Image generation with Nano Banana
18:02 Why community matters for AI adoption
19:22 Final advice for leaders getting started

Chris Daigle sits down with Panos Siozos, CEO and co-founder of LearnWorlds, to explore how AI at work is changing the way we learn, teach, and build real expertise.

Panos explains why access to information is no longer the challenge and why critical thinking, judgment, and structured learning matter more than ever in an AI-driven world. The conversation breaks down the difference between knowledge and understanding, the risk of cognitive laziness when relying too heavily on AI, and why learning still requires friction, effort, and human guidance.

They also discuss how AI should support learning rather than replace it, how credibility and authority are shifting in the age of generative AI, and what professionals and organizations must do to keep skills relevant as AI accelerates. This episode is a grounded look at using AI at work without losing the ability to think, learn, and grow.

🔎 Find Out More About Panos Siozos and LearnWorlds

Panos Siozos on LinkedIn
https://www.linkedin.com/in/siozos/
LearnWorlds
https://www.learnworlds.com

🛠 AI Tools and Resources Mentioned

ChatGPT
https://openai.com/chatgpt

📌 Chapters

00:00 Introduction to Panos Siozos
02:12 Panos’ background in learning and education technology
05:18 Why learning is not disappearing in the AI era
08:44 Knowledge vs real understanding
12:06 The risk of cognitive laziness with AI
16:22 Why struggle and friction matter in learning
20:35 How AI changes authority and credibility
25:11 Turning expertise into meaningful learning experiences
29:54 Using AI to support, not replace, human learning
35:18 Building premium learning products in the AI age
41:02 Final advice for professionals learning with AI