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

The real challenge with AI is not the technology, it is knowing when leaders should trust the machine and when they should not.
In this episode Chris sits down with Vasant Dhar, professor at NYU Stern and the NYU Center for Data Science, longtime AI practitioner, and author of Thinking with Machines: The Brave New World of AI. With more than four decades working in artificial intelligence across finance, healthcare, and research, Dhar shares a practical framework for deciding when leaders should trust AI and when human oversight still matters. His “trust map” evaluates two variables: how often the system is wrong and the consequences of its errors.
The conversation also tackles why so many AI pilots fail, why fear rather than greed is driving AI adoption in many organizations, and how leaders should prioritize their first AI initiatives. Dhar explains why deep domain knowledge becomes even more valuable in the AI era, why executives must understand their data before deploying AI, and why the future belongs to people who learn to think with machines rather than simply ask them for answers. Leaders who want a clearer way to evaluate AI opportunities and avoid costly missteps will find this discussion well worth their time.
Chapters
00:00 Introduction
03:23 The Origin of the “Trust AI” Question
05:14 The Trust Framework: Predictability vs Cost of Error
07:01 Crossing the Automation Frontier
09:07 The Three Barriers Holding Leaders Back from AI
11:51 Why 95% of AI Projects Fail
14:39 How Leaders Should Choose Their First AI Projects
19:17 Fear vs Greed in Today’s AI Adoption
25:20 Why Leaders Should “Think Slowly” About AI Strategy
44:16 The Bifurcation of Humanity in the Age of AI

🔎 Find Out More About Vasant Dhar
Website:
https://vasantdhar.com 
Book: Thinking with Machines: The Brave New World of AI
Podcast: Brave New World
Substack Newsletter:
https://vasantdhar.substack.com

🛠 AI Tools and Resources Mentioned

ChatGPT
https://chat.openai.com
Claude
https://claude.ai
Grok
https://x.ai
Chief AI Officer (Sponsor)
https://chiefaiofficer.com
Using AI at Work
https://usingaiatwork.com

Most leaders aren’t struggling with AI tools, they’re struggling with how to lead the transformation those tools require.
In this episode, Chris interviews Justin Trombold, President of Antesyn Advisors who works with leadership teams navigating the uncertainty of generative AI strategy across industries from healthcare to enterprise services. During the conversation, he explains why most organizations go wrong by treating generative AI as an IT deployment rather than a transformation initiative, centralizing tool decisions while failing to connect use cases to business strategy, incentives, and operating models.
Chris and Justin unpack what it actually looks like to deploy AI in the real world: separating enterprise strategy from use-case experimentation, starting small with tightly defined pilots, defining KPIs before declaring success, and anticipating downstream bottlenecks that AI acceleration often creates. They also explore why cross-functional collaboration, incentive alignment, and curiosity matter more than technical horsepower — and why leaders must shift from “installing AI” to building organizational readiness for it.
If you want a practical lens for turning generative AI into measurable advantage — without triggering organizational friction — this episode is for you!

Chapters:
(00:00) Introduction
(02:01) Meet Justin Trombold
(05:03) What Companies Get Right — and Wrong — About Generative AI
(07:38) Why Generative AI Is Not an IT Project
(08:55) Centralizing Tools, Decentralizing Use Cases
(16:31) Who Should Be in the Room for AI Strategy
(17:28) Enterprise Strategy vs. Use Case Execution
(20:15) When AI Just Shifts the Bottleneck
(29:40) The Five Pillars of AI Readiness
(33:18) Designing Small AI Experiments That Scale
(41:09) Building Real AI Fluency Inside Your Organization

🔎 Find Out More About Justin Trombold
Website: https://www.antesynadvisors.com
LinkedIn: https://www.linkedin.com/in/trombold 

🛠 AI Tools and Resources Mentioned
ChatGPT (OpenAI)
https://chat.openai.com
Claude (Anthropic)
https://claude.ai
Gemini (Google)
https://gemini.google.com
Grok (xAI)
https://x.ai

Before you spend another dollar on ads, what if you could test your message against a digital version of your exact market?
In today’s episode, Justin Brooke, founder of AdSkills and Agent Skills AI, joins Chris Daigle to break down how synthetic audiences and virtual focus groups are transforming modern marketing. After getting his start interning for Russell Brunson and famously turning $60 into six figures with Google Ads, Justin has spent two decades mastering message-to-market match. 
Now, he’s using AI to simulate highly detailed customer personas, running ads, landing pages, and even full funnels through structured “virtual focus groups” before a single dollar is deployed.
In this conversation, Justin explains how to build high-quality AI personas using real demographic, psychographic, and empathy-map data; how multi-persona scoring systems are outperforming gut instinct; and why this approach may soon become the first step in every serious marketing strategy. He also shares his perspective on emerging agent frameworks like  OpenClaw, the security implications leaders need to consider, and where AI is realistically delivering value today—without hype.
If you want a practical framework for reducing marketing risk and increasing message precision before you go live, this episode will reshape how you think about AI in your growth strategy.

🔎 Find Out More About Justin Brooke
X: @IMJustinBrooke
Website: https://www.adskills.com

🛠 AI Tools and Resources Mentioned

MindStudio - https://mindstudio.ai
Make – https://www.make.com
Claude – https://claude.ai
OpenAI – https://openai.com
DigitalOcean – https://www.digitalocean.com
Docker – https://www.docker.com
CrewAI – https://www.crewai.com
LangChain – https://www.langchain.com
Fathom – https://fathom.video

Chapters:
00:00 Introduction
03:13 “Virtual Focus Groups” and Why They Matter
03:47 Justin’s Origin Story: From Intern to Advertiser
08:45 From Personas to Synthetic Audiences
15:24 How the System Produces Variations and Picks Winners
20:09 How “Mad Men” Marketers React to Market Feedback
22:21 Building Real ICPs: 1,000+ Words, Not One-Liners
27:15 The New York Times “Digital Twin” and 92% Accuracy
30:13 Tool Stack: MindStudio, Claude Projects, and Agent Frameworks
35:16 OpenClaw, AI Agents & Security Considerations
49:55 Staying Focused: Pick Your Lane in AI

Most companies are experimenting with AI. The leaders who win are rebuilding around it.
In this episode, Chris Daigle sits down with Jason Eubanks, Co-Founder and CEO of Aurasell AI, to explore why incremental AI experiments aren’t enough—and why go-to-market teams must shift to an AI-native operating model. Jason explains why simply plugging AI into legacy systems won’t change your productivity model, and why companies that fully embrace intelligent automation now will create an advantage competitors won’t be able to close.
They discuss how AI-native architecture can double productivity, eliminate CRM busywork, and cut onboarding time for sales teams by 50%. From removing copy-and-paste workflows to automating outreach, enrichment, and follow-up, Jason outlines what happens when AI doesn’t just provide insights—but executes. He also introduces Aurasell’s new GTM operating system that sits on top of existing CRMs like Salesforce and HubSpot, plus an agent builder that enables powerful AI-driven workflows through simple natural language prompts.
If you’re looking to unlock real productivity gains—not just incremental improvements—this episode outlines what that shift actually requires.
🔎 Find Out More About Jason Eubanks
LinkedIn: https://www.linkedin.com/in/jason-eubanks-a775ba
🌐 Learn More About Aurasell AI
https://aurasell.ai
🛠 AI Tools and Resources Mentioned
Aurasell GTM Operating System
https://www.aurasell.ai
Chat Gpt https://chatgpt.com/ 
Salesforce
https://www.salesforce.com/ 
HubSpot
https://www.hubspot.com 
Chapters:
(00:00) Introduction
(01:17) What “AI-native” really means (beyond chat wrappers)
(03:02) The productivity gap: why incremental AI adoption fails
(06:45) Urgency explained: first movers and 2–3x productivity gains
(10:08) Fixing the broken B2B sales productivity model
(12:27) Case study: carving out teams to go all-in on AI
(15:07) The AI-native GTM platform and unified customer journey
(21:26) Cutting onboarding time by 50% with intelligent automation
(26:10) Eliminating sales busywork and manual CRM toil
(28:46) Agentic workflows: natural language → automated execution

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.