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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 problemWhat an AI harness is, and why you're always using one whether you realize it or notHow to decide when to build a custom AI harness versus using a foundation model provider's defaultThe real business risk in the AI regulation debate: vendor concentration, not runaway AIA first look at JEV, the fast, cheap AI classifier already replacing multi-step LLM workflowsThis 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

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 activityHow to define an "atomic unit of output" so you know what you're actually paying forWhy acceptance criteria have to be defined before you build, not afterWhy you can't let an agent evaluate its own output, and why unchecked human evaluation is risky tooHow adoption, not just accuracy, determines whether you're getting the value you're paying forWhy 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

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 droppingHow model routing, caching, and context management quietly drive costThe step-by-step process behind a 33x cost reduction on a live enterprise agentWhen open weight and self-hosted models actually make sense, and when they don'tPractical 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

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 clickWhy 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/petereillyMike 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

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 equalToken pricing, the end of the subsidy era, and the surprise-bill problemWhat actually belongs in the denominator of an AI business caseThe human in the loop, AgentOps, and the trust gap that paces adoptionFollow the GangShanti 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

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. 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 value Learn 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 Transformation 03:54     The Model Alone Isn't Enough 08:21     AI Won't Be Implementing Itself 11:43     Rebuilding Workflows and Embracing Change 13:25     The Future of SaaS and AI Integration 18:24     Navigating the Expenses of SaaS Platforms 23:14     The Evolution of Standards in AI 25:34     Understanding Workflows for Automation 28:34     Defining Success in Automation 32:35     The Value of Forward Deployed Engineers 34:47     Identifying Valuable Workflows 38:41     Economic Considerations in Automation 40:30     Concluding Thoughts & Harnessing AI 43:56     Evaluating Agent Performance 45:55     Combining Domain Expertise with Technology #EnterpriseAI #AgenticAI #AIStrategy #AIAgents #CompletedWorkflows #ForwardDeployedEngineers #AIImplementation

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. 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 front This 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 Milestones 01:40     The Data Dilemma in AI 02:59     Understanding Data Readiness 05:50     Defining the Semantic Layer 08:46     The Importance of Context in AI 13:08     Navigating AI's Limitations 16:44     Building Guardrails for AI 21:12     Achieving ROI in AI Projects 26:45     Recommendations for Chief Data Officers #EnterpriseAI #AIStrategy #DataReadiness #SemanticLayer #AIActually #ChiefDataOfficer #AIAgents

Popularized 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 era Speakers: 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     Introduction 01:43     Understanding the Role of Forward Deployed Engineers 04:16     The Evolution of Software Engineering Roles 07:22     The Business Analyst vs. Forward Deployed Engineer 10:47     Organizational Challenges in Adapting to New Roles 12:10     The Importance of Clarity in Problem Solving 17:43     Navigating Change Management in Organizations 20:44     The Role of AI in Redefining Business Processes 28:42     The Future of Software Engineering and Business Skills 32:25     The Need for Educational Reform in AI Skills 37:58     Conclusion: Embracing the Renaissance Person in Tech #ForwardDeployedEngineer #EnterpriseAI #AIRoles #BusinessAnalyst #TechnicalConsultant #AIOrgChange #AIWorkforce #AgenticDevelopment #EnterpriseSoftware #AIAdoption

Most 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 now This 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 Landscape 01:25     Embracing a People-First Approach to AI 05:45     Tools and Techniques for AI Adoption 08:36     Meta Prompting and Leveraging AI Models 11:38     The Challenge of Change in AI Integration 14:33     Collaboration and Knowledge Sharing in AI 17:06     Empowering Non-Technical Users with AI 20:18     The Future of AI and Human Collaboration 27:58     Using AI for Office Tasks 33:18     The Role of AI in Education 36:42     Evolving Workflows with AI 45:35     Getting Started with AI #AIAdoption #EnterpriseAI #AIActually #ChangeManagement #GenerativeAI #AIStrategy #FutureOfWork

Every 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 solve This 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/ 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/Evan Gatch, VP Consulting Sales, AnswerRocket - https://www.linkedin.com/in/evan-gatch-1531482/  Chapters 00:00 Introduction and Meet the Experts 02:10 Consulting in the Rapidly Evolving AI Landscape 04:14 The Changing Nature of Consulting Services 07:18 Aligning AI Solutions with Business Goals 14:02 The Role of Software Engineering Skills in AI 17:17 Organizational Change and AI Adoption Challenges 21:42 Enterprise-Grade AI Solutions and Production Readiness 29:28 Structured Approaches to AI Opportunities 32:45 Outcome-Based Pricing and Trust in Consulting 38:29 Closing Remarks and Future Outlook #AIConsulting #EnterpriseAI #AIStrategy #OutcomeBased #AgenticAI #AITransformation #AIActually

Every 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 about This 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/ Speakers Pete 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/ Chapters 00:00     The Evolution of Software Development 05:20     The Future of Custom Software 10:53     Agent-Friendly Interfaces and Integration 16:58     The Importance of Curiosity in Software Development 26:07     Re-architecting for Modern Standards 31:04     The Impact of AI on Traditional Jobs 38:11     Navigating Job Losses and Transitioning in the Workforce #VibeCoding #EnterpriseAI #FutureOfWork #SoftwareDevelopment #AIStrategy #WorkforceDisruption #GenerativeAI

Enterprise 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 AI This 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/ Chapters 00:00     Introduction 01:41     AI Innovations at the Edge 13:55     Understanding the AI Adoption Gap 22:20     Practical Steps for Getting Started with AI 36:09     Measuring AI ROI at Enterprise Level 44:27     Seizing the Opportunity for Mid-Sized Companies #EnterpriseAI #AIAdoption #AIROI #AIAgents #AIStrategy #DigitalTransformation #GenerativeAI

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

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

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