AI, Actually

Tired of the AI hype? So are we. Welcome to AI, Actually: the podcast that cuts through the noise and gets real about how artificial intelligence can work for your business. In each episode, our resident AI and business transformation experts–along with occasional industry guests–hold a candid, jargon-free conversation on what it takes to get actual value from AI. Join us as we tackle topics like: the real difference between the latest LLM models, why generic AI can't make sense of your messy company data, how to get your GenAI use case off the ground, and what the rise of AI agents means for your business. This is your practical playbook for putting AI to work. No PhD required. AI, Actually is produced by AnswerRocket. Since 2013, our enterprise AI solutions have helped Fortune 500 companies achieve measurable results through their AI transformations. This podcast is where we share what we’ve learned.

AI, Actually is a business podcast hosted by Unknown Host, with 23 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts AI, Actually, a business show with 23 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #23

  Loops vs. Graphs: What These AI Agent Design Patterns Mean for Your Business

  Aug 4, 202641 minS1

  A tweet from Peter Steinberger asking whether AI builders should still be "talking loops" or "shifting to graphs" hit three million views and split the AI world into camps. Most business leaders have no idea what either term means, or why the answer changes how much your AI systems cost, how reliable they are, and whether you can hand them to a compliance officer with a straight face. This week on AI, Actually, Pete Reilly sits down with Mike Finley, Shanti Greene, and Stew Chisam to translate the loops-versus-graphs debate out of engineering-speak and into plain business terms. Using examples pulled straight from AnswerRocket's own AI-automated finance close process, the team breaks down when a simple agent is enough, when you need a full graph of coordinated agents, and why the difference determines whether your AI system is auditable, predictable, and affordable at scale. Find Out: What is a loop? Why is it just another type of graph? An analogy from finance or an org chart that makes this click Why does splitting agent responsibilities improve reliability and cut confusion? How do graphs give you auditability, predictability, and cost control that loops can't? Why does more capable AI mean more human jobs, not fewer? What are the two things IT and business executives should be doing right now? Follow the Gang: Pete Reilly, AnswerRocket & StellarIQ, Co-Founder, linkedin.com/in/petereilly Mike Finley, AnswerRocket & StellarIQ, Co-Founder, linkedin.com/in/mikefinley/ Shanti Greene, AnswerRocket, Head of Data Science and AI Innovation, linkedin.com/in/shantigreene/ Stew Chisam, StellarIQ, Operating Partner, https://www.linkedin.com/in/stewart-chisam-7242543/ Chapters: 00:00 Introduction and Overview of AI Workflow Evolution 01:31 Understanding Loops as Cyclic Graphs in AI 03:53 Practical Business Examples of Loops and Graphs 17:47 Orchestration: Coordinating Specialized Agents 20:38 Governance, Auditability, and Predictability 23:01 Speed, Cost, and Process Optimization 26:46 Choosing Between an Agent and a Graph 30:02 How AI Changes Human Work 35:47 Safeguards and Sandboxes for Enterprise AI #AIAgentArchitecture #AgenticWorkflows #LoopsVsGraphs #AIOrchestration #EnterpriseAIImplementation #MultiAgentSystems #AIGovernance #AICostControl #AgenticAIExplained #AISandboxStrategy

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  Episode #22

  AI Intelligence Isn't Free: Token Costs, TCO, and ROI Math for Enterprise AI

  Jun 23, 202629 minS1

  The token bills are arriving, and a lot of teams do not like what they see. Halfway through 2026, the promise of cheap, abundant intelligence is colliding with enterprise reality. Behind the headlines about new models and rising prices is a simpler story about whether your AI spend is actually buying business value. In this episode of AI, Actually, Jim Johnson, Nicole Kosky, Shanti Greene, and Andy Sweet connect three topics that look unrelated on the surface: early results from Fable 5, the real cost of tokens and total cost of ownership, and how to think about AI ROI. The throughline is one piece of math. Grow the numerator, scope the denominator honestly, and build the business case before you deploy. Topics covered: Early field results from Fable 5 and why not all tokens are created equal Token pricing, the end of the subsidy era, and the surprise-bill problem What actually belongs in the denominator of an AI business case The human in the loop, AgentOps, and the trust gap that paces adoption Follow the Gang Shanti Greene , Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ Andy Sweet , VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ Jim Johnson , President, AnswerRocket - https://www.linkedin.com/in/jim-johnson-bb82451/ Nicole Kosky , Senior Director of Services, AnswerRocket - https://www.linkedin.com/in/nicole-kosky-5b9a3b6/ Learn more about AnswerRocket: answerrocket.com Chapters: 00:00 Introduction to AI topics and guest insights 02:20 Experimenting with Fable 5 and its value 04:00 Understanding token consumption and TCL 05:34 Cost and ROI considerations in AI models 07:52 The role of model reasoning and creativity 10:07 Managing costs and change in AI deployment 13:28 Strategic planning and business case development 15:02 Evolving models and maximizing value 16:51 Testing, evaluation, and human-in-the-loop 18:53 Leadership and organizational adoption of AI 21:15 The "Trust Gap" for Adoption 25:27 Closing thoughts and industry outlook #EnterpriseAI #AIROI #AgenticAI #AIStrategy #LLM #AgentOps #AIActually

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  Episode #21

  Getting Enterprise Agent Implementations Right: Completed Workflows, Not Just Smarter Models

  Jun 9, 202647 minS1

  <p>Everyone is waiting for the next model to make their AI problems disappear. The teams getting real value already figured out the model was never the hard part.<br /></p><p>In this episode, the crew reacts to Nate B. Jones and his argument that the trillion dollar opportunity sits in completed workflows, not smarter chat.<br /></p><p>This is AI, Actually, the show that cuts through the hype and gets practical about putting AI to work. We dig into why the model alone will never get you there, what happens to SaaS in an agentic world, and...

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  Episode #21

  Getting Enterprise Agent Implementations Right: Completed Workflows, Not Just Smarter Models

  Jun 2, 202647 minS1

  <p>Everyone is waiting for the next model to make their AI problems disappear. The teams getting real value already figured out the model was never the hard part.</p><p></p><p>In this episode, the crew reacts to Nate B. Jones and his argument that the trillion dollar opportunity sits in completed workflows, not smarter chat.</p><p></p><p>This is AI, Actually, the show that cuts through the hype and gets practical about putting AI to work. We dig into why the model alone will never get you there, what happens to SaaS in an agentic...

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  Episode #20

  Is Your Data AI-Ready? The Semantic Layer and the Last Mile Problem

  May 12, 202631 minS1

  <p>Most enterprise AI projects don't fail because of the model. They fail because of data, specifically, the gap between having data and having data an AI can actually understand and act on. This is the last mile problem, and it's quietly killing AI ROI across the enterprise.</p><p></p><p>That's the real story behind the stat: 87% of AI projects never reach production.</p><p></p><p>This is Episode 20 of AI, Actually, the podcast that cuts through the hype to help business leaders get real value from AI. Jim Johnson is joined by Andy Sweet, Nicole Kosky...

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