Podcast
AI, Actually
Hosted by AnswerRocket · EN
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.
12episodes
Episodes
Newest firstAll episodes
Getting Enterprise Agent Implementations Right: Completed Workflows, Not Just Smarter Models
4d ago00:46:46Tap to summarizeEveryone 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.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.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 how to spot the workflows actually worth automating.You'll learn:Why completed workflows, not models, are where enterprise value actually landsHow to use evaluation and a clear definition of "what good looks like" as the starting point for any buildWhat forward deployed engineers really do, and why the business person beside them matters just as muchWhy seat-based SaaS pricing is breaking and what replaces itHow to pick a first workflow to automate using repeatability, ROI, and human in the loopWhat harnesses are and why they are becoming the next layer of valueLearn more about AnswerRocket: https://answerrocket.com/ Get Ethan Mollick’s book, “Co-Intelligence: Living and Working with AI” here: https://a.co/d/0jl6kAMd Follow the Gang:Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/ Mike Finley, Co-Founder, AnswerRocket & StellarIQ - https://www.linkedin.com/in/mikefinley/ Pete Reilly, Co-Founder, AnswerRocket & StellarIQ - https://www.linkedin.com/in/petereilly Ada Gil, Director, AI Business Transformation - Practice Lead - https://www.linkedin.com/in/ada-gil-84837648/ Chapters: 00:00 Introduction to AI Business Transformation03:54 The Model Alone Isn't Enough08:21 AI Won't Be Implementing Itself11:43 Rebuilding Workflows and Embracing Change13:25 The Future of SaaS and AI Integration18:24 Navigating the Expenses of SaaS Platforms23:14 The Evolution of Standards in AI25:34 Understanding Workflows for Automation28:34 Defining Success in Automation32:35 The Value of Forward Deployed Engineers34:47 Identifying Valuable Workflows38:41 Economic Considerations in Automation40:30 Concluding Thoughts & Harnessing AI43:56 Evaluating Agent Performance45:55 Combining Domain Expertise with Technology#EnterpriseAI #AgenticAI #AIStrategy #AIAgents #CompletedWorkflows #ForwardDeployedEngineers #AIImplementation
Transcribe →Is Your Data AI-Ready? The Semantic Layer and the Last Mile Problem
4w ago00:30:38Tap to summarizeMost 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.That's the real story behind the stat: 87% of AI projects never reach production.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, and Ben Titmus, who leads the data and infrastructure practice at AnswerRocket, to break down what it actually takes to make enterprise AI work at the data layer.You'll learn:What "data readiness" actually means for AI, and why it's different from what your BI dashboards neededHow to define and build a semantic layer iteratively, without a massive multi-year initiativeWhy throwing everything at an LLM produces confident-sounding wrong answers, and how guardrails prevent itHow Microsoft Fabric, Snowflake, Databricks, and BigQuery are each approaching semantic layer infrastructureWhy model-swappability, not model loyalty, is the right architecture posture right nowWhat Chief Data Officers should do in the next 90 days to lead AI transformation from the frontThis episode is designed for: Chief Data Officers, VP Analytics, data engineering leaders, and any executive responsible for making AI work inside a real business.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/Nicole Kosky, Senior Director of Services, AnswerRocket - https://www.linkedin.com/in/nicole-kosky-5b9a3b6/Ben Titmus, Senior Director, Practice Leader - AI Data, Platforms and Infrastructure - https://www.linkedin.com/in/benjamin-titmus-a2817626/ Chapters:00:00 Introduction and Milestones01:40 The Data Dilemma in AI02:59 Understanding Data Readiness05:50 Defining the Semantic Layer08:46 The Importance of Context in AI13:08 Navigating AI's Limitations16:44 Building Guardrails for AI21:12 Achieving ROI in AI Projects26:45 Recommendations for Chief Data Officers#EnterpriseAI #AIStrategy #DataReadiness #SemanticLayer #AIActually #ChiefDataOfficer #AIAgents
Transcribe →The Forward Deployed Engineer: What the Role Really Means (And What We Should Actually Call It)
Apr 2100:40:24Tap to summarizePopularized by Palantir, the term "Forward Deployed Engineer" is everywhere right now.But what does it actually mean, and is it even the right label? In Episode 19, the AI, Actually crew digs into one of the hottest buzzwords in enterprise tech, separating the signal from the noise and getting real about the skills, structures, and organizational shifts that matter.This is a candid conversation about how AI is collapsing traditional role boundaries, and why the ability to understand a business problem may now be more valuable than the ability to write code. The crew also tackles why large enterprises are structurally misaligned with how this work actually gets done, and what education, apprenticeship, and org change need to look like going forward.Topics covered:What "Forward Deployed Engineer" really means, and why "Forward Deployed Consultant" might be more accurateWhy senior engineers are thriving and junior roles are evolving, not disappearingHow AI is inverting the old software architecture model: solve fast, clean up laterOrganizational bottlenecks: why most companies are still structured around the wrong constraintsThe SAP paradox and why business process knowledge is now worth its weight in goldWhat universities and apprenticeship models need to look like for the AI eraSpeakers:Nicole Kosky, Senior Director of Services, AnswerRocket - https://www.linkedin.com/in/nicole-kosky-5b9a3b6/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/Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket - https://www.linkedin.com/in/shantigreene/Chapters: 00:00 Introduction01:43 Understanding the Role of Forward Deployed Engineers04:16 The Evolution of Software Engineering Roles07:22 The Business Analyst vs. Forward Deployed Engineer10:47 Organizational Challenges in Adapting to New Roles12:10 The Importance of Clarity in Problem Solving17:43 Navigating Change Management in Organizations20:44 The Role of AI in Redefining Business Processes28:42 The Future of Software Engineering and Business Skills32:25 The Need for Educational Reform in AI Skills37:58 Conclusion: Embracing the Renaissance Person in Tech#ForwardDeployedEngineer #EnterpriseAI #AIRoles #BusinessAnalyst #TechnicalConsultant #AIOrgChange #AIWorkforce #AgenticDevelopment #EnterpriseSoftware #AIAdoption
Transcribe →How to 10x Your Individual Productivity with AI
Apr 700:49:00Tap to summarizeMost companies are announcing AI strategies and running pilots that go nowhere. Meanwhile, individuals using AI every day are quietly getting 10x leverage on their work, and the gap is widening.The real story isn't about tools or models. It's about people, habits, resistance, and what it actually takes to build a culture where AI creates compounding value.In this episode of AI, Actually, host Pete Reilly is joined by Mike Finley, Nicole Kosky, and new AnswerRocket team member Michelle Hamilton, Director of AI Adoption and Change Management, for a candid conversation on how to move past fear, shadow AI, unused licenses, and the "is this going to replace me?" question that's quietly stalling enterprise adoption everywhere.You'll learn:Why a people-first approach to AI matters more than which model you pickHow to use "meta prompting" to get dramatically better outputs across any modelWhat a personal AI "corpus" is and why it becomes your most valuable competitive assetWhy the most successful AI adopters are building tools, not just using toolsHow to think about the crawl/walk/run framework for individuals and organizationsWhat forward deployed engineers are and why everyone can be one nowThis episode is designed for: Business leaders, team managers, and anyone who knows they should be using AI more but isn't sure where to start.AnswerRocket: https://answerrocket.com/Speakers:Nicole Kosky, Senior Director of Services, AnswerRocket | https://www.linkedin.com/in/nicole-kosky-5b9a3b6/Mike Finley, CTO, AnswerRocket - https://www.linkedin.com/in/mikefinley/ Pete Reilly, COO, AnswerRocket - https://www.linkedin.com/in/petereilly Michelle Hamilton, Director, AI Adoption and Change Management - https://www.linkedin.com/in/michellehamiltonai/ Chapters: 00:00 Navigating the AI Landscape01:25 Embracing a People-First Approach to AI05:45 Tools and Techniques for AI Adoption08:36 Meta Prompting and Leveraging AI Models11:38 The Challenge of Change in AI Integration14:33 Collaboration and Knowledge Sharing in AI17:06 Empowering Non-Technical Users with AI20:18 The Future of AI and Human Collaboration27:58 Using AI for Office Tasks33:18 The Role of AI in Education36:42 Evolving Workflows with AI45:35 Getting Started with AI#AIAdoption #EnterpriseAI #AIActually #ChangeManagement #GenerativeAI #AIStrategy #FutureOfWork
Transcribe →AI Consulting Isn't Dead, The Hourly Rate Is & And Why POCs Aren't Enough
Mar 2400:39:09Tap to summarizeEvery week, someone declares that AI will make consultants obsolete. What we actually see is AI breathing new life into consulting, with practically every model maker teaming up with consulting firms for delivery support.The real story isn't about whether consulting is dead. It's about whether the consulting model your company is paying for is built for this era or the last one.In this episode of AI, Actually, Jim Johnson hosts Evan Gatch, Nicole Kosky, and Andy Sweet for a candid conversation on what enterprise AI consulting actually looks like right now: what's working, what's failing, and what clients should be demanding from their partners.You'll learn:Why IT-led AI initiatives consistently underperform and what the Fortune 500 failures have in commonHow the consulting business model is shifting from hourly rates to outcome-based and gain-share agreementsWhy prototypes are easier to build than ever and why that makes the POC-to-production gap more dangerous, not lessWhat "enterprise grade" actually means when it comes to agentic AI solutionsHow to structure a use case prioritization framework before you start buildingWhy organizational change management is the constraint that technology alone can't solveThis episode is designed for: CDOs, VP-level analytics and data science leaders, AI transformation leads, and enterprise consulting buyers navigating AI strategy decisions.Learn more about AnswerRocket's enterprise AI solutions: https://answerrocket.com/SpeakersAndy 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/Evan Gatch, VP Consulting Sales, AnswerRocket - https://www.linkedin.com/in/evan-gatch-1531482/ Chapters00:00 Introduction and Meet the Experts02:10 Consulting in the Rapidly Evolving AI Landscape04:14 The Changing Nature of Consulting Services07:18 Aligning AI Solutions with Business Goals14:02 The Role of Software Engineering Skills in AI17:17 Organizational Change and AI Adoption Challenges21:42 Enterprise-Grade AI Solutions and Production Readiness29:28 Structured Approaches to AI Opportunities32:45 Outcome-Based Pricing and Trust in Consulting38:29 Closing Remarks and Future Outlook#AIConsulting #EnterpriseAI #AIStrategy #OutcomeBased #AgenticAI #AITransformation #AIActually
Transcribe →Will Vibe Coding Kill Software Engineering?
Mar 1000:44:57Tap to summarizeEvery week, someone declares that vibe coding will kill software development. Every week, they're wrong — but not for the reasons most people think.The real story isn't about job loss. It's about an explosion in software creation that most business leaders aren't prepared for.In this episode of AI, Actually, the crew breaks down what vibe coding actually means for enterprise software, why the "death of engineering" narrative misses the point, and what executives need to understand and do, before their competitors get there first.You'll learn:Why Jevons Paradox predicts we'll have more software than ever, not lessThe "compound engineering" principle that separates durable AI-built code from slopWhy every executive needs to personally try vibe coding, not just read about itWhat Jack Dorsey's layoffs at Block, parent company of Square, actually signal for enterprise workforce strategy?Agency vs. intelligence: the new career currency in an AI-powered worldThe socioeconomic upside of AI democratization that almost nobody is talking aboutThis episode is designed for: CTOs, engineering leaders, Chief Data Officers, innovation executives, and enterprise decision-makers navigating AI transformation and workforce strategy.Learn more about AnswerRocket's enterprise AI solutions: https://answerrocket.com/accelerators/ SpeakersPete Reilly, COO, AnswerRocket | https://www.linkedin.com/in/petereillyMike Finley, CTO, AnswerRocket | https://www.linkedin.com/in/mikefinley/ Shanti Greene, AnswerRocket | https://www.linkedin.com/in/shantigreene/ Stew Chisam, Operating Partner, StellarIQ | https://www.linkedin.com/in/stewart-chisam-7242543/Chapters00:00 The Evolution of Software Development05:20 The Future of Custom Software10:53 Agent-Friendly Interfaces and Integration16:58 The Importance of Curiosity in Software Development26:07 Re-architecting for Modern Standards31:04 The Impact of AI on Traditional Jobs38:11 Navigating Job Losses and Transitioning in the Workforce#VibeCoding #EnterpriseAI #FutureOfWork #SoftwareDevelopment #AIStrategy #WorkforceDisruption #GenerativeAI
Transcribe →How Businesses Can Actually Get Started with AI
Feb 2400:45:57Tap to summarizeEnterprise AI adoption is accelerating — but most organizations are still stuck debating tools like ChatGPT while a smaller group is deploying AI agents that operate like digital colleagues.The gap between experimentation and operationalization is widening.In this episode of AI, Actually, leaders from AnswerRocket break down what’s really happening on the front lines of enterprise AI adoption — and share a practical framework for moving from pilots to measurable business impact.You’ll learn:• Why the shift from AI “co-pilot” to AI “colleague” is reshaping enterprise workflows• The leadership mindset separating fast movers from stalled organizations• A crawl-walk-run framework for AI adoption at the individual, team, and enterprise level• How to measure AI ROI (including the adoption J-curve most leaders underestimate)• Why there’s no such thing as an “AI project” — only business problems enabled by AIThis episode is designed for:Chief Data Officers, Heads of Analytics, AI leaders, innovation executives, and enterprise decision-makers navigating AI transformation.Learn more about enterprise AI strategy and operationalization:https://answerrocket.com/SpeakersStew 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 Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ Chapters00:00 Introduction01:41 AI Innovations at the Edge13:55 Understanding the AI Adoption Gap22:20 Practical Steps for Getting Started with AI36:09 Measuring AI ROI at Enterprise Level44:27 Seizing the Opportunity for Mid-Sized Companies#EnterpriseAI #AIAdoption #AIROI #AIAgents #AIStrategy #DigitalTransformation #GenerativeAI
Transcribe →Autonomous Agents in the Enterprise and How AI is Disrupting SaaS
Feb 1000:51:59Tap to summarizeArtificial 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 worldFollow 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 Enterprises01:20 OpenClaw: The Next Iteration of AI Assistant04:18 Implications of AI Agents in the Workplace10:01 Understanding the Difference: Traditional AI vs. OpenClaw14:48 Navigating Security and Accountability in AI21:29 The Future of Non-Human Employees in Enterprises26:42 Exploring Claude Cowork: A Shift in Focus39:29 The Build vs. Buy Debate in CRM Development#AIAgents #AutonomousAI #OpenClaw #ClaudeCowork #EnterpriseAIStrategy #AIWorkflows #AgenticOperations #BuildVsBuy #SaaSDisruption #AIEmployeeOnboarding
Transcribe →Building Software 10x Faster with AI: A Real-World Walkthrough
Jan 2700:45:15Tap to summarizeWhat 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 directlyFollow the GangMike 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/ Chapters00:00 Intro: Demo to AI in Software Development02:10 Understanding Customer Interactions and CRM Needs04:39 Reimagining CRMs in the Age of AI06:00 Demo Walkthrough of the CRM20:02 What's Possible Now with AI-Assisted Development21:25 Designing an AI-Compatible Stack27:32 Flipping the Build vs. Buy Dilemma32:59 AI's Impact on Offshore Development35:57 The Future of Business and Software Customization39:23 Maintaining AI-Assisted Software Solutions#AIDevelopment #CodingAgents #CRMCustomization #VibeCoding #BuildVsBuy #EnterpriseSoftware #AIAgents #SalesAutomation #SoftwareDevelopmentLifecycle #ClaudeCode #AgenticDevelopment #ContextDrivenDesign
Transcribe →AgentOps: Why Keeping AI Agents Running Is Harder Than Building Them
Jan 1300:39:15Tap to summarizeAs 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 monitoringFollow 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 AgentOps02:38 Defining Agentic Operations09:30 The Role of Human Oversight11:01 Understanding Performance Degradation17:27 The Complexity of Monitoring Agents26:07 Organizational Challenges in AgentOps31:00 The Future of Agentic Operations35:26 What's An Agent?Hashtags: #AIImplementation #EnterpriseAIAdoption #OrganizationalChange #AILiteracy #GeneralistEngineers #LastMileProblem #VibeCoding #AIROI #RevenueGeneration #OpenAIWhitePaperKeywords: 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
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