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 25 episodes on record and a Required Pod Score of 80.

Unknown Host hosts AI, Actually, a business show with 25 episodes published.

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  Episode #26

  Multiplayer AI, Harnesses, and Why Pacing the Frontier Won't Change Your Roadmap

  Sep 29, 202645 minS1

  Your AI agents work great for you. Getting your team to use the same AI agents is a completely different problem, and it's one most companies trying to scale AI haven't solved yet. This episode breaks down why single-player AI tools like Claude Code and Codex don't automatically scale into team-wide workflows, and why the fix isn't a smarter model. What's actually missing is better infrastructure for humans and AI agents to collaborate, and that's where AI harnesses come in. This episode of AI, Actually looks at what an AI harness actually is, the coordination layer that turns a raw model into a reliable business tool, and when it's worth building a custom one instead of using an out-of-the-box harness from a foundation model provider. The team also covers the vendor lock-in risk built into that choice, gives a candid read on the "pause AI" debate sparked by Dario Amodei's blog post, and reacts to JEV, a new fast AI classifier model already replacing multi-step LLM chains in production. You'll learn: Why scaling AI agents across a team is an infrastructure problem, not a smarter-model problem What an AI harness is, and why you're always using one whether you realize it or not How to decide when to build a custom AI harness versus using a foundation model provider's default The real business risk in the AI regulation debate: vendor concentration, not runaway AI A first look at JEV, the fast, cheap AI classifier already replacing multi-step LLM workflows This episode is designed for: business and technology leaders scaling AI agents across their teams, anyone evaluating build vs. buy for AI infrastructure, and leaders trying to separate real AI regulation risk from the headlines. https://answerrocket.com/ Follow the Gang Stew Chisam , Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ Mike Finley , Co-Founder, AnswerRocket & StellarIQ - https://www.linkedin.com/in/mikefinley/ Pete Reilly , Co-Founder, AnswerRocket & StellarIQ - https://www.linkedin.com/in/petereilly Alon Goren , AnswerRocket, CEO - https://www.linkedin.com/in/alon-goren-87889681/ Chapters: 00:00 Introduction to AI evolution and key topics 02:00 Single-player vs. multi-player AI: limitations & collaboration 04:37 Workspace infrastructure for AI collaboration 06:30 Challenges in sharing AI agents and scaling 15:41 What AI harnesses do 18:20 Why harnesses make AI reliable 25:03 Building an AI strategy 26:46 Should businesses pause AI adoption? 33:25 Fast AI classification 40:25 How businesses should move forward #AIAgents #EnterpriseAI #AgenticAI #AIStrategy #AIHarness #AIRegulation #ArtificialIntelligence

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  Episode #25

  Why AI Agent Pricing Should Be Value-Driven

  Sep 8, 202629 minS1

  Most AI is still billed like a utility. You pay for usage and hope it adds up to something worth the spend. That model breaks down once an agent starts finishing entire jobs instead of just responding to prompts. The real story: pricing has to be tied to what the agent actually delivers, not how much of it ran. On this episode of AI, Actually, Jim Johnson, Andy Sweet, Nicole Kosky, and Ada Gil break down why AI agent pricing needs to shift toward value, what that actually requires you to define first, and why most companies aren't set up to measure it yet. You'll learn: Why usage-based pricing breaks down once agents start delivering finished work instead of just activity How to define an "atomic unit of output" so you know what you're actually paying for Why acceptance criteria have to be defined before you build, not after Why you can't let an agent evaluate its own output, and why unchecked human evaluation is risky too How adoption, not just accuracy, determines whether you're getting the value you're paying for Why model cost should flex by task, not default to the most expensive option every time This episode is designed for enterprise leaders evaluating AI agent vendors, building pricing models for AI initiatives, or making the internal case for how AI spend should be measured and justified. Learn more about AnswerRocket's enterprise AI solutions: https://answerrocket.com/ Speakers: 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/ Ada Gil , Director, AI Business Transformation - Practice Lead - https://www.linkedin.com/in/ada-gil-84837648/ Nicole Kosky , Senior Director of Services, AnswerRocket | https://www.linkedin.com/in/nicole-kosky-5b9a3b6/ YouTube Chapters: 00:00 Introduction to AI's ROI and Business Impact 05:13 From AI Tools to Agentic Workflows 08:29 Treating Agents Like Interns 10:44 Building Trust and Measuring Good Enough 13:31 Adoption, Pricing, and Business Value 20:29 Scaling AI Value Through Better Workflow Design #AIAgentPricing #ValueBasedPricing #EnterpriseAI #AIActually #AgenticAI #AIROI #AIStrategy

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  Episode #24

  Token Management, Spend, and How to Actually Bring AI Costs Down

  Aug 25, 202649 minS1

  Managing AI costs has become increasingly difficult. Uber’s CTO made headlines when he shared the company had burned through its entire 2026 AI budget in just 4 months. That's the problem this episode tackles head on. Token prices are falling for a given level of intelligence, but most companies' actual AI spend keeps climbing, because the bar for "good enough" intelligence keeps moving too. Nobody has a clean way to forecast it yet. In this episode of AI, Actually, Jim Johnson sits down with Shanti Greene, Stew Chisam, and first-time guest Jake Barger to break down what's actually driving enterprise AI costs and what to do about it. The conversation moves from the economics of model pricing into a real case study: a 33x cost reduction on a production agent, achieved in about two weeks. You'll get insight into: Why token prices dropping doesn't mean your AI bill is dropping How model routing, caching, and context management quietly drive cost The step-by-step process behind a 33x cost reduction on a live enterprise agent When open weight and self-hosted models actually make sense, and when they don't Practical takeaways for building a repeatable cost optimization process Follow the Gang: Jim Johnson, Managing Partner, AnswerRocket - https://www.linkedin.com/in/jim-johnson-bb82451/ Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - linkedin.com/in/shantigreene/ Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ Jake Barger, Director of AI & Machine Learning, AnswerRocket | linkedin.com/in/jacob-barger-30b1ab1b9/ Chapters 00:00 Introduction and panel overview 02:44 Token Economics: Why Costs Stay High 07:34 Model Routing, Caching & Task Fit 12:22 Continuous Optimization for AI Agents 16:51 The 33x Cost-Reduction Case Study 23:36 Context Management & Smart Escalation 29:28 Open-Weight and Self-Hosted Models 37:22 Enterprise Reliability and Deployment Tradeoffs 44:26 Closing Takeaways: Managing AI Spend #TokenManagement #AICostOptimization #LLMTokenSpend #ModelRouting #AgenticAICosts #OpenWeightModels #SelfHostedLLM #KVCacheOptimization #EnterpriseAIROI #AIInfrastructure

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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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