Most leaders assume AI in customer service means replacing people, but the data tells a more complicated story.

In this episode Chris talks with Nathan Strum, CEO of Abby Connect, about what actually works when deploying voice AI in real business environments. Drawing on two decades of customer service experience, Nathan explains why AI excels at structured workflows like scheduling, but still struggles with unpredictable edge cases where human judgment matters most. He also shares why many companies that experiment with full automation quietly return to human support, and how Abby is growing both its AI and human workforce at the same time.

The conversation goes deeper into practical implementation, including where AI is safe to deploy today, why outbound AI calling is a high-risk move, and how to design systems that combine speed, scalability, and trust. Nathan also outlines a leadership approach to AI adoption that focuses on reducing friction across systems, reskilling employees, and using AI to enhance rather than replace human capability. This episode gives leaders a grounded, experience-based framework for deciding where AI belongs in their customer experience strategy.

Chapters:

00:00 Introduction
01:00 Where Voice AI Delivers Immediate Value
02:29 Introducing Abby’s AI + Human Strategy
04:21 The Limits of AI in Real Customer Interactions
06:52 Best Use Cases: AI Scheduling vs Human Sales Calls
08:30 Why AI Adoption Is Increasing Human Headcount
11:04 Lessons from Failed “AI-Only” Customer Service Experiments
15:23 Where AI Is Safe vs Risky in Phone Workflows
17:31 Why Transparency About AI Improves Customer Trust
23:06 The Future of Offshore, AI, and Voice Technology
27:29 AI as a System Redesign Tool, Not Just Cost Reduction
29:56 Managing Employee Fear During AI Adoption
32:57 Selling Outcomes Instead of AI Products
35:18 How to Evaluate AI Vendors in Customer Experience

🔎 Find Out More About Nathan Strum

Abby Connect Website
https://www.abbey.com

LinkedIn
https://www.linkedin.com/in/nathanstrum

https://www.linkedin.com/company/abby-connect/

Facebook https://www.facebook.com/abbyconnect/

X: https://x.com/abbyconnect

Website: https://www.abby.com/

🛠 AI Tools and Resources Mentioned

OpenAI
https://openai.com

Anthropic
https://www.anthropic.com

Google Gemini
https://gemini.google.com

ElevenLabs
https://elevenlabs.io

Most companies aren’t struggling to buy AI, they’re struggling to use it well.

In this episode Chris sits down with Jim Spignardo, Director of Cloud Strategy and AI Enablement at ProArch, to break down what’s really happening inside organizations adopting AI today. Jim shares why many companies are stuck after purchasing licenses, how to move from experimentation to structured adoption, and what separates companies seeing real ROI from those chasing hype. He outlines a practical playbook that starts with executive alignment, prioritizes high-value use cases, and builds toward secure, governed AI systems that scale.

They also explore how organizations can recoup AI investments within months, why data governance is the hidden foundation of success, and how to balance rapid innovation with risk management as agents and automation evolve.

If you’re leading AI adoption or trying to turn early momentum into measurable business value, this episode offers a clear, experience-backed path forward.

Chapters:

00:00 Introduction
00:14 Where Companies Are Today in Their AI Journey
00:49 The Future: AI, Robotics, and What’s Next
01:30 Why AI Strategy Matters for Business Leaders
02:38 Common Challenges: Risk, Use Cases, and Leadership Gaps
05:06 Building an AI Adoption Playbook
06:23 From Buying Licenses to Lacking Direction
10:00 What Executives Need to Understand About AI
13:01 The Shift from Productivity Tools to AI Agents
17:47 How Long It Takes to See Real Results
19:25 Measuring ROI and Tracking AI Value
22:12 Real Example: AI Improving RFP Win Rates
30:12 Change Management and Driving Adoption
31:16 Training, Governance, and Building AI Culture
40:12 Managing Risk While Enabling Innovation
45:04 What’s Next: AI + Robotics Convergence

🔎 Find Out More About Jim Spignardo

LinkedIn: https://www.linkedin.com/in/spignardo

ProArch: https://www.proarch.com

🛠 AI Tools and Resources Mentioned:

Microsoft Copilot
https://www.microsoft.com/en-us/microsoft-365/copilot

Microsoft Defender for Cloud Apps
https://learn.microsoft.com/en-us/defender-cloud-apps/what-is-defender-for-cloud-apps

Microsoft Purview (Data Loss Prevention & Information Protection)
https://learn.microsoft.com/en-us/purview/

Azure OpenAI Service
https://azure.microsoft.com/en-us/products/ai-services/openai-service

OpenAI / ChatGPT
https://chat.openai.com

Claude (Anthropic)
https://www.anthropic.com/claude

Cursor (AI coding assistant)
https://www.cursor.sh

What happens when the AI tool helping you scale your business also gains permanent rights to your voice?

In this episode Chris talks with Jesse Jameson, digital marketing veteran and founder of HeyNow Interactive, about the opportunities and emerging risks inside the generative AI ecosystem. Jesse shares his experience participating in a voice licensing program with ElevenLabs, where his AI voice quickly became one of the most widely used on the platform. What began as a simple experiment in passive income through voice cloning eventually uncovered deeper questions around creator consent, data ownership, and how AI companies structure their business models.

The conversation explores how leaders should think about AI adoption today, including the tension between rapid innovation and responsible governance. From biometric data rights and AI regulation to the strategic reality that businesses cannot afford to ignore generative AI, Jesse and Chris discuss how executives can embrace AI’s advantages while remaining thoughtful about the risks that come with it. This episode offers an important perspective for leaders navigating AI adoption in a rapidly evolving landscape.

00:00 AI Voice Licensing and the Start of a Major Discovery
00:45 Introducing Jesse Jameson and the Rise of AI Voice Technology
03:15 From Early Internet Marketing to the Age of AI
04:22 Joining the ElevenLabs Voice Actors Program
06:13 Discovering Discrepancies in Voice Usage and Payments
08:29 The Consent Problem and Hidden Licensing Terms
10:31 Regulatory Questions and Biometric Data Laws
12:15 The Hidden Risks of Using Generative AI Tools
17:21 Bias, Control, and the Influence of AI Models
26:23 Investigating Platform Abuse and Free Voice Usage
36:29 Documenting the Experience and Reporting to Regulators
44:06 Practical Advice for Leaders Using New AI Tools

🔎 Find Out More About Jesse Jameson

LinkedIn: Jesse Jameson

Substack: @jpjameson

Youtube: @jpjameson

Website: https://11laudit.com

The Voice Cloning Scam That Hit $11 Billion: https://www.youtube.com/watch?v=2wPdQyrWhl0&t=2s

Book: The Conversation You Can't Explain: Finding Yourself in the Age of AI

🛠 AI Tools and Platforms Mentioned

ElevenLabs:

https://elevenlabs.io/

OpenAI:

https://openai.com/

Anthropic:

https://www.anthropic.com

LLaMA:

https://www.llama.com

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/jasoneubanks/

🌐 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.