AI, Actually

AnswerRocket

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.

  1. 5 DAYS AGO

    Autonomous Agents in the Enterprise and How AI is Disrupting SaaS

    Artificial intelligence has crossed a major threshold. AI is no longer confined to answering questions in a chat window. It's sending emails, scheduling meetings, moving data between systems, and operating with its own credentials and access. This shift from suggestion to action is forcing every enterprise leader to rethink their AI strategy. In this episode, Pete Reilly sits down with Shanti Greene, Jim Johnson, and Stew Chisam to examine three developments reshaping how businesses approach AI. From the OpenClaw experiment that briefly turned the internet into a Skynet-like scenario, to Anthropic's Cowork tool bringing agent capabilities to non-technical workers, to the fundamental question of whether enterprises should build their own software instead of buying it—this conversation tackles the practical implications of AI that actually does things. Topics covered: ✅ How OpenClaw (formerly ClawdBot/MoltBot) represents the future of AI agents with their own identity, proactive behavior, and inter-agent communication ✅ Why enterprise leaders need to start thinking about "onboarding non-human employees" with email addresses, Slack accounts, and human supervisors ✅ Claude Cowork's approach to bringing agent capabilities to business users through local file access and workflow automation ✅ The shifting build-vs-buy decision as AI makes custom software development dramatically faster and cheaper ✅ Whether traditional SaaS companies like Salesforce can survive in an AI-first world Follow the Gang: Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/  Chapters: 00:00 Introduction: The Evolution of AI in Enterprises 01:20 OpenClaw: The Next Iteration of AI Assistant 04:18 Implications of AI Agents in the Workplace 10:01 Understanding the Difference: Traditional AI vs. OpenClaw 14:48 Navigating Security and Accountability in AI 21:29 The Future of Non-Human Employees in Enterprises 26:42 Exploring Claude Cowork: A Shift in Focus 39:29 The Build vs. Buy Debate in CRM Development #AIAgents #AutonomousAI #OpenClaw #ClaudeCowork #EnterpriseAIStrategy #AIWorkflows #AgenticOperations #BuildVsBuy #SaaSDisruption #AIEmployeeOnboarding

    52 min

  2. 27 JAN

    Building Software 10x Faster with AI: A Real-World Walkthrough

    What happens when AI coding agents enter the development process? AnswerRocket's team built a production-ready CRM in just four weeks, showing what's possible with next-generation software development. In this episode, Pete Reilly, Alon Goren, Mike Finley, and Andy Sweet pull back the curtain on what modern AI development actually looks like in practice. Using a custom CRM project as their case study, they demonstrate how coding agents, contextual design, and new architectural patterns are fundamentally rewriting the build versus buy equation for enterprise software. This isn't theory—it's a live demonstration of software that would have taken 10x longer to build just six months ago, with insights on what this means for development teams, IT strategy, and the future of business software. Topics covered: How AI coding agents reduced development time by 10x for production-grade softwareWhy context is now more valuable than data entry in modern CRM designThe shift from "system of record" to "sales assistant" in CRM philosophyHow playbooks and workflows enable AI to follow company-specific processesWhy the build vs. buy decision is becoming "build with composable blocks"What "vibe coding" means and why offshore development strategies need rethinkingHow maintenance changes when AI agents can read source code directly Follow the Gang Mike Finley, CTO, AnswerRocket  - https://www.linkedin.com/in/mikefinley/ Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ Alon Goren, AnswerRocket, CEO - https://www.linkedin.com/in/alon-goren-87889681/ Chapters 00:00 Intro: Demo to AI in Software Development 02:10 Understanding Customer Interactions and CRM Needs 04:39 Reimagining CRMs in the Age of AI 06:00 Demo Walkthrough of the CRM 20:02 What's Possible Now with AI-Assisted Development 21:25 Designing an AI-Compatible Stack 27:32 Flipping the Build vs. Buy Dilemma 32:59 AI's Impact on Offshore Development 35:57 The Future of Business and Software Customization 39:23 Maintaining AI-Assisted Software Solutions #AIDevelopment #CodingAgents #CRMCustomization #VibeCoding #BuildVsBuy #EnterpriseSoftware #AIAgents #SalesAutomation #SoftwareDevelopmentLifecycle #ClaudeCode #AgenticDevelopment #ContextDrivenDesign

    45 min

  3. 13 JAN

    AgentOps: Why Keeping AI Agents Running Is Harder Than Building Them

    As enterprises deploy AI agents into production, a new operational challenge emerges: how do you monitor and maintain systems that don't fail with error codes, but instead drift subtly away from expected performance? In this episode, the AI, Actually crew tackles the emerging discipline of AgentOps—the practice of keeping AI agents performing at peak business value over time. The discussion cuts through the hype around "self-learning" and "automated" AI to reveal the hard truth: agentic systems require continuous human oversight, just like human employees do. From probabilistic model behavior to reasoning model complexity, the team explores why traditional IT monitoring approaches fall short and why businesses need to rethink who owns these digital workers. Topics covered: Why AgentOps is fundamentally different from traditional DevOps and LiveOpsThe three levels of agent complexity and three types of drift that can derail performanceWhy traditional IT support models don't work for goal-driven agentic systemsThe organizational challenge of bringing together business knowledge, AI expertise, and technical skillsWhy there's no "blue screen of death" for agent failures—and what that means for monitoring Follow the Gang: Nicole Kosky, Senior Director of Services, AnswerRocket | https://www.linkedin.com/in/nicole-kosky-5b9a3b6/Joey Gaspierik, Director of Enterprise Sales, AnswerRocket | https://www.linkedin.com/in/joey-gaspierik-4a613642/Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/ Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/ Chapters: 00:00     Introduction to AgentOps 02:38     Defining Agentic Operations 09:30     The Role of Human Oversight 11:01     Understanding Performance Degradation 17:27     The Complexity of Monitoring Agents 26:07     Organizational Challenges in AgentOps 31:00     The Future of Agentic Operations 35:26     What's An Agent? Hashtags: #AIImplementation #EnterpriseAIAdoption #OrganizationalChange #AILiteracy #GeneralistEngineers #LastMileProblem #VibeCoding #AIROI #RevenueGeneration #OpenAIWhitePaper Keywords: Agent Ops, AI agents, enterprise AI, LLM monitoring, model drift, probabilistic systems, agentic AI, AI operations, AI governance, AI deployment, DevOps, LiveOps, reasoning models, AI scalability, digital workers

    39 min

  4. 16/12/2025

    Open AI’s Playbook for Scaling AI, Why Generalists Are Winning, and Revenue-Driven ROI

    The old IT playbook is officially dead. Quarterly release cycles, endless approval committees, and throwing requirements over the wall? None of that works when AI models evolve every few weeks. In this episode, Pete Reilly sits down with Jim Johnson, Alon Goren, and Shanti Greene to unpack OpenAI's new white paper, "From Experiments to Deployments: A Practical Path to Scaling AI," and share what they're seeing on the ground with enterprise clients. This isn't theory—it's a frontline report from teams who are helping Fortune 500 companies navigate the messy reality of AI adoption. The conversation tackles the organizational upheaval required to move fast, the "last mile problem" that kills so many AI projects, and why the future belongs to curious generalists who can bridge business and technology. They also explore how AI is shifting ROI conversations from cost savings to revenue generation, and why vibe-coding a working prototype in hours is now entirely possible (with some important caveats about technical debt). Topics covered: Why IT and business teams must close the collaboration gap to succeed with AIThe "last mile problem": getting from 80% complete to actually usefulHow generalists with medium depth are becoming more valuable than deep specialistsBuilding an AI-literate workforce through curiosity and continuous learningShifting from cost-savings ROI to revenue-generating AI productsThe reality of vibe-coding: prototypes in hours, but engineering rigor still matters Follow the Gang: Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ Alon Goren, Founder, AnswerRocket | https://www.linkedin.com/in/alon-goren-87889681/Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/ Chapters: 00:00     Introduction to AI Deployment Challenges 02:52     The Shift from IT to Business Collaboration 05:41     The Role of Generalists in AI Integration 08:42     Redefining AI Projects and Business Goals 11:35     Accelerating Development Timelines with AI 14:47     Building an AI Literate Workforce 17:45     The Changing Landscape of ROI in AI 20:58     Final Thoughts on Embracing AI #AIImplementation #EnterpriseAIAdoption #OrganizationalChange #AILiteracy #GeneralistEngineers #LastMileProblem #VibeCoding #AIROI #RevenueGeneration #OpenAIWhitePaper Keywords: AI implementation, enterprise AI adoption, organizational change, AI literacy, generalist engineers, last mile problem, vibe coding, AI ROI, revenue generation, OpenAI white paper

    36 min

  5. 02/12/2025

    Google's Gemini 3 Breakthrough and Bold AI Predictions for 2026

    The race for AI dominance just shifted. Google's Gemini 3 launch was a coordinated ecosystem play that could reshape how enterprises think about AI infrastructure. In this episode, Pete Reilly sits down with Andy Sweet, Shanti Greene, and Stew Chisam to dissect what Gemini 3 really means for enterprise adoption, where the technology is genuinely improving, and where marketing hype obscures practical limitations. The conversation moves beyond surface-level benchmarks to tackle the uncomfortable reality facing IT leaders: foundation models are converging on performance, but the real competitive advantage lies in how you architect solutions on top of them. The team explores Google's commanding lead in multimodal capabilities, the strategic implications of vendor ecosystems, and why enterprises betting everything on a single provider might be making a costly mistake. Then they close with their boldest predictions for 2026, from content exhaustion and the death of infographics to agents working autonomously longer than your employees. What You'll Learn: How Gemini 3's pre-training approach signals continued model improvements and what that means for the scaling law debateWhy Google's multimodal dominance (backed by YouTube, Google Photos, and Drive) creates a moat that competitors can't easily replicateThe critical difference between general intelligence benchmarks and enterprise intelligence that understands your business contextWhy the "stateless" problem keeps plaguing AI solutions and how memory scaffolding becomes essential for business applicationsPredictions for 2026: content exhaustion, shadow IT proliferation, and the moment enterprises realize there's no easy button Follow the Gang: Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ Stew Chisam, Operating Partner, StellarIQ - https://www.linkedin.com/in/stewart-chisam-7242543/  Chapters: 00:00     Introduction to Gemini 3 and Episode Overview 01:45     Gemini 3's Long-Term Planning Capabilities 04:17     Are LLMs Becoming Commoditized Primitives? 08:04     Model Specialization and Jagged Edges 11:15     Why Multi-Vendor Strategy Matters for Enterprises 13:29     OS/2 vs Windows: Best Doesn't Always Win 15:10     The Scaling Law Debate and Pre-Training Improvements 16:12     Enterprise Intelligence vs General AGI 22:41     How Enterprises Should Think About Gemini 3 26:44     Bold Predictions for 2026 28:08     Content Exhaustion and the Infographic Problem 33:06     Agents as Autonomous Team Members #Gemini3 #EnterpriseAIArchitecture #MultimodalAI #AIAgents #VendorLockIn #SemanticLayer #ScalingLaws #PreTrainingCompute #EnterpriseIntelligence #AIPredictions2026 Keywords: Gemini 3, enterprise AI architecture, multimodal AI, AI agents, vendor lock-in, semantic layer, scaling laws, pre-training compute, enterprise intelligence, AI predictions 2026

    36 min

  6. 18/11/2025

    What’s Actually Working in Enterprise AI: Business Value, Success Predictors, and Agent Ops

    Are most AI projects really failing, or are we just too early to judge? In this episode, we cut through the headlines to talk about what's actually happening with enterprise AI adoption. Our team tackles the disconnect between AI hype and business reality, exploring why the "easy button" mentality is holding companies back and what it actually takes to succeed. We discuss why treating AI like magic instead of a project dooms initiatives from the start, how mid-market companies have a unique advantage to leapfrog their larger competitors, and why someone needs to supervise your digital employees. This is a practical conversation about bridging the gap between IT departments and business aspirations, with real talk about what works and what doesn't. What You'll Learn: Why "AI failure" statistics miss the point about early adoptionThe three critical skills needed for successful AI implementationHow to identify which use cases deserve an LLM versus deterministic codeWhat "agentic operations" means and why it's not set-it-and-forget-itWhy mid-market companies can leapfrog enterprise competitors right nowHow to bridge the dangerous gap between IT and business expectations Follow the Gang: Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/ Nicole Kosky, Senior Director of Services, AnswerRocket | https://www.linkedin.com/in/nicole-kosky-5b9a3b6/Joey Gaspierik, Director of Enterprise Sales, AnswerRocket | https://www.linkedin.com/in/joey-gaspierik-4a613642/Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket | Chapters: 00:00     Introduction to AI Engagements01:23     The Reality of AI Success and Failure07:37     Deterministic vs Non-Deterministic Systems11:12     Understanding Variation in AI Answers13:35     Predictors of Success in AI Projects16:41     Agent Operations and Ongoing Management19:19     The Role of Senior Stakeholders in ROI21:39     The Real Work To Do in Agent Operations24:34     Putting Solutions Into Production29:55     The Mid-Market AI Advantage32:32     Closing: Recommendations for AI Success #EnterpriseAIAdoption #AIProjectFailure #AgenticOperations #AIROI #GenerativeAIImplementation #LLMUseCases #MidMarketAIAdvantage #BusinessSponsorship #AISkillsGap #DeterministicVsNonDeterministicSystems Keywords: enterprise AI adoption, AI project failure, agentic operations, AI ROI, generative AI implementation, LLM use cases, mid-market AI advantage, business sponsorship, AI skills gap, deterministic vs non-deterministic systems

    36 min

  7. 04/11/2025

    The Decade of the Agent, Enterprise AI Reality, and Why Waiting Will Cost You

    Andrej Karpathy just dropped a reality check: 2025 isn't the "year of the agent,” it's the decade of the agent. But does that mean enterprises should hit pause on their AI initiatives? Not even close. In this episode, the AI, Actually crew tackles the gap between AI hype and enterprise reality. Pete Reilly, Alon Goren, Mike Finley, and Jim Johnson break down why current LLMs aren't perfect, why that doesn't matter for ROI, and how smart companies are capturing value right now versus waiting ten years. From coding agents that ship features overnight to the organizational challenges of keeping AI solutions on the rails, this is your practical guide to navigating the AI landscape today. Topics covered: Why Karpathy's agent timeline doesn't mean you should wait to startWhere current AI models excel (and struggle) in enterprise environmentsThe hidden engineering work between impressive demos and production systemsNarrow use cases delivering ROI today: customer touchpoints, developer productivity, and sales pipelinesThe trade-offs between vendor lock-in and rapid implementationWhy "it's just prompts" misses the entire pointThe three skill sets required to keep AI solutions running in production Follow the Gang: Pete Reilly, COO, AnswerRocket | https://www.linkedin.com/in/petereilly/Alon Goren, Founder, AnswerRocket | https://www.linkedin.com/in/alon-goren-87889681/Mike Finley, CTO, AnswerRocket | https://www.linkedin.com/in/mikefinley/Jim Johnson, Managing Partner, AnswerRocket | https://www.linkedin.com/in/jim-johnson-bb82451/ Chapters:  00:00     Introduction to AI Actually Podcast02:22     The Decade of the Agent05:45     Understanding AI Agents in the Enterprise09:47     Navigating AI Use Cases13:39     Building Modular AI Solutions17:01     Identifying Low-Hanging Fruit for AI Implementation20:29     The Importance of AI in Competitive Landscapes24:36     Organizational Readiness for AI28:16     Closing the Gap for Real Value in AI32:06     Final Thoughts and Advice #AIAgents #EnterpriseAIImplementation #AndrejKarpathy #LLMLimitations #AIROI #CodingAgents #AIVendorLockIn #AIDeployment #AIOrganizationalChange #EnterpriseAIStrategy Keywords: AI agents, enterprise AI implementation, Andrej Karpathy, LLM limitations, AI ROI, coding agents, AI vendor lock-in, AI deployment, AI organizational change, enterprise AI strategy

    38 min

  8. 21/10/2025

    OpenAI Dev Day Reactions and What It Takes to Get Agents in Production

    OpenAI's Dev Day dropped some major announcements this month, but is AgentKit really revolutionary or just another "me too!" moment? The AI, Actually crew shares their reactions to the latest OpenAI releases, and digs into how to successfully implement AI agents in the real world. In this episode, Jim Johnson steps in as host alongside Mike Finley, with special guests Nicole Kosky (who leads AnswerRocket's AI Business Transformation Practice) and Reilly Carrolll (Senior AI Solutions Consultant). Together, they tackle the practical, nitty-gritty challenges of bringing agents to life for enterprise clients—from gathering requirements that users don't know they have, to managing the surprising differences between what stakeholders say they need versus what they actually ask once they're hands-on with an agent. Topics covered: OpenAI's Dev Day announcements and what they actually mean for enterprisesWhy successful agent implementations should follow the Software Development Lifecycle (SDLC)The critical role of context in agent performanceWhy user questions change dramatically from requirements phase to hands-on testingThe emerging discipline of "agentic operations" and why it's non-negotiableStarting small: the power of quick wins over trying to boil the ocean Follow the Gang: Jim Johnson, AnswerRocket, Managing Partner - https://www.linkedin.com/in/jim-johnson-bb82451/ Mike Finley, AnswerRocket, CTO - https://www.linkedin.com/in/mikefinley/  Reilly Carroll, AnswerRocket, Senior AI Solutions Consultant - https://www.linkedin.com/in/reilly-carroll/  Nicole Kosky, AnswerRocket, Senior Director of Services - https://www.linkedin.com/in/nicole-kosky-5b9a3b6/  Chapters: 00:00      Introduction and Guest Introductions 01:30      OpenAI Dev Day Announcements 05:25      Understanding AI Agents 09:05      Practical Implementation of AI Agents 11:11      Challenges in Client Engagement 14:17      Agent Development and User Experience 16:55      The Challenge of Capturing Agent Requirements 19:52      The Importance of Agentic Operations 22:40      Navigating the Future of AI Agents 29:09      Final Thoughts and Advice Keywords: AI agents, OpenAI Dev Day, agent development, enterprise AI implementation, agentic operations, software development lifecycle, AI business transformation, agent context, LLM applications, practical AI strategy

    33 min
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About

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.

Information

  • Creator
    AnswerRocket
  • Years Active
    2025 - 2026
  • Episodes
    14
  • Rating
    Clean
  • Copyright
    © 2025 AnswerRocket

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