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

91: Using AI in Sales to Automate Go-to-Market Execution with Jason Eubanks

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 EubanksLinkedIn: https://www.linkedin.com/in/jasoneubanks/🌐 Learn More About Aurasell AIhttps://aurasell.ai🛠 AI Tools and Resources MentionedAurasell GTM Operating Systemhttps://www.aurasell.aiChat Gpt https://chatgpt.com/ Salesforcehttps://www.salesforce.com/ HubSpothttps://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

90: Using AI at Work to Create an AI Quality Assurance System with Hernan Lardiez

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 LardiezHernan Lardiez on LinkedInhttps://www.linkedin.com/in/hlardiez/RagMetricshttps://ragmetrics.ai/🛠 AI Tools and Resources MentionedRagMetrics - https://ragmetrics.aiThe AI Exchange (Rachel Woods) - https://www.theaiexchange.com/Chief AI Officer -  https://www.chiefaiofficer.com/📌 Chapters00:00 Why regulated industries can’t “hope” with AI02:04 What model evaluations (evals) actually are05:08 The two audiences: business owner vs builders08:52 Pre-production testing vs in-production monitoring14:23 Why “monitoring is required” to reduce risk16:14 Manual spreadsheet grading vs automated evals18:01 Building test datasets + injecting through the pipeline31:21 Measuring accuracy AND token consumption (cost)34:01 Continuous evals to catch drift over time42:11 RAG tuning: Top-K chunks, accuracy vs noise, token savings49:21 Evals as “low-cost insurance” for production AI50:27 Closing advice: control points + IT boundariesIn 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.

89: Using AI at Work to Lead Through Change Without Losing Trust with Bill Gallagher

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 Gallagherhttps://www.linkedin.com/in/billgall/🛠 AI Tools and Resources MentionedChatGPTInternal AI tools within organizations📌 Chapters00:00 Introduction to Bill Gallagher04:12 Leadership challenges during AI driven change10:08 Why trust matters more than technology17:26 How leaders should talk about AI internally23:41 Avoiding fear and resistance during AI adoption30:05 The human role in AI driven organizations37:18 Leading with clarity in times of uncertainty43:02 Final thoughts on leadership and AI48:10 How to connect with Bill Gallagher

88: Using AI at Work to Rethink People Strategy and Leadership with Kate Bravery

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 MercerKate Bravery on LinkedInhttps://www.linkedin.com/in/katebraveryMercerhttps://www.mercer.com🛠 AI Tools and Resources MentionedChatGPThttps://openai.com/chatgptInternal AI systems used for workforce analytics and decision support📌 Chapters00:00 Introduction to Kate Bravery03:42 How AI is changing people strategy09:15 Using AI for workforce planning and skills insights15:28 Balancing human judgment with AI recommendations21:10 Building trust and confidence in AI systems27:44 Ethical considerations in workplace AI34:02 Leadership responsibility in AI adoption40:18 What executives should focus on next45:56 How to connect with Kate Bravery

87: The AI Tool Stack Every Executive Needs

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 OfficerChris Daigle on LinkedInhttps://www.linkedin.com/in/chrisdaigleChief AI Officerhttps://chiefaiofficer.com🛠 AI Tools and Resources MentionedFathomhttps://chiefaiofficer.com/fathomPerplexityhttps://www.perplexity.aiComet Browser by PerplexityNotebookLMhttps://notebooklm.googleGamma https://chiefaiofficer.com/gammaNano Banana image generation inside Google GeminiChatGPT https://openai.com/chatgpt📌 Chapters00:00 Why this episode is different01:14 The problem with AI tool overload02:10 What “thinking in AI” really means03:22 From discrete AI use to an AI reflex04:28 Sharing AI wins to build culture05:35 AI governance and cultural readiness06:18 Will AI replace jobs07:15 Tasks, projects, and job-level work08:42 What AI can realistically automate today10:02 Economic impact of AI on knowledge work11:48 Why meeting transcription builds business context12:57 Using Fathom as an AI meeting assistant13:45 Replacing Google with Perplexity14:58 Agentic browsing with the Comet browser15:42 NotebookLM as a learning environment16:36 Creating executive decks with Gamma17:18 Image generation with Nano Banana18:02 Why community matters for AI adoption19:22 Final advice for leaders getting started