AI Radicals

Why the future of software development means stopping reading code and spending 100x more effort verifying it than generating it.In this episode of AI Radicals, host Satyen Sangani talks with Justin McCarthy, Diffusion, and James Cham, Bloomberg Beta, about the concept of the "software factory": using LLMs in fully autonomous loops to produce reliable software at a scale and speed that makes human code review obsolete.Justin and James unpack the core mechanics of a software factory—why the factory metaphor matters (reliable outputs, low variance), why the key engine is the loop, and why the radical goal isn't putting a human in the loop but taking the human out entirely. They explain why verification should consume 100x more tokens than generation, why developers need to stop reading AI-generated code the way they never read compiler output, and why the PR interface should show diffs of NPS scores and P&L impact, not lines of code. They also dig into where this approach works best versus where it doesn't, how small teams are already achieving 100 to 1,000x productivity gains, and why the developers who thrive in this new world may not be who you'd expect."The moment you actually automated an entire loop, you can suddenly get a level of productivity and reliability that, to be honest, we in software development just don't really think about."Listen to this episode to learn:Why fully autonomous loops—not human-in-the-loop systems—are the key to reliable, high-scale AI software generationHow to build verification pipelines that use screenshots, voice synthesis, formal methods, and synthetic data to validate code before a human ever sees itWhy some developers become 1,000x more productive while others "open a bakery"—and how organizations should structure themselves for the power-law future--------“ There are folks that ask to get off the bus when faced with the reality that you really do need to work differently, and your level of intensity, the models do demand a higher level of intensity. You still have to get eight hours of sleep. You have to restore your decision-making budget every night. You have to take care of yourself. You can't be in AI psychosis. You can't be in the mania phase. But once you get through that phase, every minute of your day matters.” – Justin McCarthy “ These tools are jagged in the sense that they're very good at some things and then very bad at things that seem very similar to the thing that they're very good at. And so that feeling and that nuance, there's things that can only be understood if people spend a bunch of time playing with it. And so the desire for easy answers or for frameworks or whatever, I just keep on trying to tell people, "No, no, no. There aren't easy answers yet." The only easy answer is to just keep on playing with it, which I have to admit sometimes goes against, I think, the current zeitgeist for most of corporate America, where they're trying very hard to tame it and to make it seem like it makes sense. But the way they want it to make sense is thinking from Q3 of 2025. And to me, that's the biggest danger. And if I imagine a way that all this would go badly is if we were stuck in software development methodologies from Q3 of 2025, which I have to admit many organizations are stuck at.” – James Cham--------Time Stamps[00:08] What is a software factory?[10:08] Who decides how AI-generated software gets verified?[14:58] How do you start building a software factory?[19:39] What kinds of problems can a software factory handle, and where does it fail?[26:09] How do engineering leaders react to the idea of never reading code?[30:36] Should you cap AI token spending per developer?[36:27] Will today's 10x engineers be the ones who thrive with AI?[38:58] How do you get past AI "haze" and decide where your attention goes?[42:35] What are the different ways to verify AI-built software?[46:10] What role does data play in verifying AI systems?--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Justin McCarthy on LinkedIn: https://www.linkedin.com/in/justinmccarthy/Connect with James Cham on LinkedIn: https://www.linkedin.com/in/jcham/Learn more about Diffusion: https://diffusion.io/Learn more about Bloomberg Beta: https://www.bloombergbeta.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The hard part of AI isn't the model — it's everything underneath it and around it.In this season 4 roundup episode of AI Radicals, host Satyen Sangani revisits seven conversations from the season to distill what it actually takes to make AI work inside a real company. The guests span the field: Charlene Li, co-author of Winning with AI; Brian Solis and Dave Wright, ServiceNow's innovation leaders; Erin McIntosh, who runs global data operations at CNA Insurance; Nathalie Berdat, data leader at the BBC; Anant Jhingran, CTO of IBM Software; Eugene Wu, Columbia professor and co-director of the Data Agents and Processes Lab; Francois Ajenstat, Founder and CEO of Golden Analytics; Mark Nelson, Venture Partner at Madrona; Julie De Moyer, who leads data and AI across fifteen beauty brands at LVMH; and Ben Taylor, Former Chief AI Strategist of Dataiku, who predicted most of this three years ago.Across the season, three things held. First, stop calling it a pilot — if it isn't strategic enough to go into production, it isn't strategic enough to do. Second, trust is earned one reconciled metric at a time, starting not with a problem but with a single number that two teams disagreed about. Third, the rules belong in the system, not in the prompt — a stop sign goes at the intersection, not in the head of the user. And underneath all three, something none of these leaders set out to say: the work is coupled, permeable, and never quite finished. Closer to biology than physics."The risk of doing something is better than not doing anything in this space. And I really hate saying that, but that's really where we're at."Listen to this episode to learn:Why executives green-light AI pilots precisely because they haven't decided anything yet — and what changes when you frame AI as a strategic initiative insteadHow IKEA turned a customer service agent into a billion-euro interior design business by retraining 8,200 people instead of cutting themWhere governance actually belongs in an agent-driven world, why the BBC and a nationwide insurer land on opposite sides of the autonomy question--------Time Stamps[00:00] Season 4 roundup: What does it take to make AI work inside a real company?[01:19] Why do enterprise AI pilots never reach production?[03:06] Where do companies waste time and money on AI?[06:06] Should you automate a workflow or reinvent it with AI?[10:12] When AI automation backfires: The Ford cautionary tale[13:17] What are the signs that people don’t trust your data?[18:40] Is data governance really slow and bureaucratic?[21:25] Should agent rules live in the prompt or the system?[28:16] If code isn't the bottleneck anymore, what are people for?[32:48] What does it take to make AI work in a company?--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Charlene Li on LinkedIn: https://www.linkedin.com/in/charleneli/Connect with Erin McIntosh on LinkedIn: https://www.linkedin.com/in/erin-n-mcintosh/Connect with Nathalie Berdat on LinkedIn: https://www.linkedin.com/in/nathalie-berdat-b716b56/Connect with Brian Solis on LinkedIn: https://www.linkedin.com/in/briansolis/Connect with Dave Wright on LinkedIn: https://www.linkedin.com/in/davewright2/Connect with Anant Jhingran on LinkedIn: https://www.linkedin.com/in/anantjhingran/Connect with Eugene Wu on LinkedIn: https://www.linkedin.com/in/eugene-wu-b23417290/Connect with Francois Ajenstat on LinkedIn: https://www.linkedin.com/in/francoisajenstat/Connect with Mark Nelson on LinkedIn: https://www.linkedin.com/in/markthomasnelson/Connect with Julie De Moyer on LinkedIn: https://www.linkedin.com/in/juliedemoyer/Connect with Ben Taylor on LinkedIn: https://www.linkedin.com/in/jepsontaylor Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why the most valuable asset in AI won't be the model — it'll be the layer that captures how your business actually makes decisions.In this episode of AI Radicals, host Satyen Sangani sits down with Ashu Garg, General Partner at Foundation Capital and an early investor in one decacorn and six unicorns, to unpack where enduring value in AI will actually accrue. Four years ago, Ashu was non-consensus bullish on open source AI models. Today that view is consensus — and he's moved on to the next thesis: context graphs.Satyen and Ashu dig into the concept of context graphs — an infrastructure layer that captures the decision traces humans generate as they interact with AI applications, scattered across Slack, email, Salesforce, and a dozen other interfaces. They explore why the gap between open source and closed source models is shrinking to roughly six months, why 75% of tasks no longer need a better model, and what that means for the economics of foundation model providers. "The best AI applications will really need to capture decision traces over time and then figure out how to use those decision traces to improve the decisions they make in the future. That infrastructure layer is the context graph. And our belief is that the most valuable asset over time will be the context graph."Listen to this episode to learn:What a context graph is and why it can only be built in the context of a real application — not as a theoretical enterprise-wide exerciseWhy the six-month gap between open and closed source models means application developers must have open source in the mix, and why two-thirds of enterprise value will continue to be captured at the application layerWhen to fix problems in the model versus the context layer, and which incumbent application companies Ashu thinks are positioned to survive the AI transition — and which aren't--------“ Advancing your automation journey, it's a little bit like the levels of self-driving. As companies go through that journey, they will want to capture many of these decisions in a context graph that's company-wide. Now, the detail you'll have in that will be very different. Think of a company the size of Goldman Sachs or Morgan Stanley, the graph will crash, and it'll actually just be more noise than value. And will those be captured directly? Probably not for the large part. They'll actually be abstracted away from the existing context graph that are application specific. Our goal is to articulate a direction of where do we think value is going to be captured, what's going to be enduring, and what will make AI valuable in a large organization.” – Ashu Garg--------Time Stamps[01:06] Meet Ashu Garg: Foundation Capital partner and early Alation investor[06:01] Should AI application companies build on open source or closed models?[07:56] What is a context graph?[24:48] When should you fix the model and when should you fix the context?[31:26] How far behind is open source AI compared with closed models?[34:23] Where does value accrue in the AI stack?[36:00] Why are OpenAI and Anthropic building their own applications?[43:13] What separates an AI app that learns from one that just executes?[47:05] Episode recap: where AI value goes when models become interchangeable--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Ashu Garg on LinkedIn: https://www.linkedin.com/in/ashugargvc/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why the loudest narratives in AI rarely hold up once you talk to the people actually building the technology.In this episode of AI Radicals, host Satyen Sangani talks with Victor Dey, AI and technology analyst contributing to Fast Company, Forbes, and the AI Journal, about what it takes to cut through the noise of enterprise AI — as a journalist who has interviewed the executives running nearly every major data and agent platform in real time.Victor and Satyen dig into how Victor's practitioner background in data science and NLP shapes the questions he asks CEOs, and why a practitioner's mindset increasingly matters for anyone selling, deploying, or building in AI. They unpack the story behind Victor's counter-narrative on the so-called "SaaS Apocalypse" — how one forthright conversation flipped the market narrative and sparked a wave of CEOs finally speaking up. They explore the race to own the AI "control plane," why competitors are simultaneously building and partnering on orchestration, and why context alone isn't enough: permissions, guardrails, and observability matter just as much. "What I think is more important than context, or equally important as context, is also permissions. What data did it touch? What points did it touch? Was it denied or was it told not to perform certain actions? I think that needs equal amount of light."Listen to this episode to learn:Why the SaaS Apocalypse narrative collapsed once a single CEO with conviction and customer evidence spoke up — and what that reveals about how market stories form and breakWhy the AI control plane is simultaneously the most contested and most collaborative space in enterprise tech, with competitors building rival products while partnering on the same capabilitiesWhy context gets the headlines but permissions, guardrails, and observability deserve equal attention as agents proliferate and incidents mount--------“ With the rise of agents, I think this control plane about whether it be governance, whether it be observability, whether it be analysis, whether it be guardrailing, has become really, really important. Agents are going berserk, and agents are growing. It started with a chatbot use case where it was conversational AI, but now it's agentic AI and agents on particular tasks, agent that can perform particular functions. Now there's tools and there's models and there's ways to create more agents for everyone, even the general public as well. The accessibility has increased. But from an enterprise perspective, when you're talking about sensitive data, it becomes really, really important to govern and orchestrate these agents for the perfect outcome. So everyone now wants to bank on it, and every other company has a control plane or a control tower or this orchestrator competing with each other. It's an interesting thing to see that competitors are collaborating, but they also have their standalone products and everyone says they're the best.” – Victor Dey--------Time Stamps[01:13] Victor Dey’s background in data science, NLP, and journalism[02:39] How Victor’s practitioner's training helps him ask the right questions of tech CEOs[07:04] Victor on weighing pitches vs. independent research and the "wow" moments that redirect a story[08:52] How interviewing SAP CEO Christian Klein flipped Victor's thesis on the SaaS apocalypse[16:09] The Control Plane for AI — What it is, why every vendor is racing to claim it, and the odd dynamic of competitors partnering on it[28:57] Underreported stories in AI[37:12] Why context is the buzzword everyone claims, but permissions deserve equal weight[40:44] Predictions for the next 12 months--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Victor Dey on LinkedIn: https://www.linkedin.com/in/victor-dey-897a59189/Read Victor’s work on Fast Company: https://www.fastcompany.com/user/victordRead Victor’s work on Forbes: https://www.forbes.com/sites/victordey/Read Victor’s work on The AI Journal: https://aijourn.com/the-control-layer-by-victor-dey/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Data foundations are the real bottleneck for agentic AI, and most teams are governing the wrong layer.In this episode of AI Radicals, host Satyen Sangani talks with Sanjeev Mohan, industry analyst and author of Designing the AI-Driven Data Foundations, about why AI's biggest promises still rest on unglamorous data fundamentals, and why agents are breaking the old rules of governance.Sanjeev walks through his layered model of metadata, semantics, context, ontology, and knowledge graphs, and the two dig into where data products fit in that stack. They debate whether governance belongs to the agent or the data itself, why deterministic rules must live with the data rather than be re-explained to every model or prompt, and how agentic systems generate entirely new categories of data that nobody has a framework for governing yet. "There's a concept 100% related to the whole idea of context and data products, and that is governance. Now we are in the realm of AI, which is a very different beast to govern."Listen to this episode to learn:Why semantics, not storage or compute, is becoming the real moat in the AI stackWhy agentic governance has to cover input, output, and runtime, not just clean data in and clean data outWhy data teams are entering a golden era, shifting from backup-and-restore work to business strategy and reusable data products--------“ One definition is that data product is a philosophy. It's not a thing. And what data product says is that when you build your artifact, you build it with the product management best practices. For example, there's a data product owner, it has a version number, and it's backward compatible. It's packaged with SLAs and contracts. So if you are packaging your semantic layer and you're building it using the product management best practices, then that's a data product, very much so.” – Sanjeev Mohan--------Time Stamps[01:37] Sanjeev’s book Designing the AI-Driven Data Foundations[03:47] Defining metadata, semantics & context[07:28] Taxonomy, ontology & knowledge graphs[10:01] What is a data product?[20:24] From data governance to agentic governance[27:30] The context layer debate[31:32] The future of data people[38:15] Predictions for the next 90-180 days[42:50] Closing & key takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Sanjeev Mohan on LinkedIn: https://www.linkedin.com/in/sanjmo/Designing the AI-Driven Data Foundations: Architecture, Principles, and Practice: https://www.wiley.com/en-us/shop/general-introductory-computer-science/designing-the-ai-driven-data-foundations-architecture-principles-and-practice-p-9781394396665#aboutauthors-section Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why "America's data steward" is more than a job title, and what it takes to keep fraud intelligence trustworthy in the age of AI agents.In this episode of AI Radicals, guest host Susan Wilson, Sales Leader at Alation, talks with Cara Tice, Chief Data Officer at Early Warning (the company behind Zelle), about what data stewardship really means when trillions of dollars in real-time payments, and the fraud intelligence protecting them, run on your data.Cara and Susan dig into why data products have replaced the "one data platform to rule them all" mentality, why integration and change management matter more than any single tool, and why AI doesn't create data problems, it just exposes the ones that were already there. "Your AI governance is reliant on your data management program. Make sure you've got data governance up and running, because AI is going to rely on your data management controls and the context you create."Listen to this episode to learn:Why "data products" — not centralized platforms — are the fastest path to trustworthy, reusable dataWhy AI governance is really just an extension of data governance, and why the two leaders need to be joined at the hipWhat a new CDO should fix (and what to leave alone) in their first 90 days on the job--------“As a CDO or as a data steward, you need to be obsessed with the customer outcome. Don't lose sight of that. It's easy to lose sight of it when you are focused on plumbing and fixing this issue and that issue. You always have to remind yourself of what's the bigger picture, what's the north star?  With data products, you become relentless about the customer outcome because you're delivering to a specific business need, you're creating more capability that is business centric around data. When I think about the features that you would need for data science models, it's the same. Whether your consumer is a data scientist or is a commercial consumer, that data is used to make a decision. So you need to make sure that it's accurate.” – Cara Tice--------Time Stamps[03:04] Stewardship vs. governance — and what "America's data steward" actually means[06:49] What governed data means when the cost of being wrong is fraud[09:41] Speed and precision in fraud modeling, and why data is fragmented everywhere[10:55] Data products: tables, joins, and eating the vegetables without knowing it[13:54] Progress vs. perfection — and where perfection is non-negotiable[16:30] The monolith is gone. Now it's Lego blocks (but not too many)[17:56] The thing that gets you every time: change management[20:50] Vertical slices, quick value, and the CFO's "where's my return?"[22:35] What Cara rebuilds in every CDO role — and what she leaves alone[29:17] The grocery store analogy: connecting the catalog to business outcomes[31:29] Agentic AI, data risk, and the RPA lesson nobody wants to revisit[36:13] AI governance as an extension of data governance[38:34] Rapid fire: the first-six-months mistake and the year-one investment[41:39] Data leaders and AI leaders: joined at the hip--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Cara Tice on LinkedIn: https://www.linkedin.com/in/cara-dailey/Learn more about Early Warning: https://www.earlywarning.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why AI agents fail at scale, and the systems-thinking fix nobody's building yet.In this episode of AI Radicals, host Satyen Sangani talks with Eugene Wu, Columbia professor and co-founder of the Data Agents and Processes (DAPLab), about why building reliable AI agents is fundamentally a systems problem, not a model problem.Eugene explains "semantic coupling," the idea that every layer of an agent's stack, from data retrieval to tool calls to reasoning, is interdependent, so a small failure anywhere can quietly corrupt the final output. He and Satyen draw a parallel to early relational databases absorbing the complexity that applications used to handle themselves, arguing that today's computing infrastructure needs to do the same for agents: managing data flows, enforcing rules deterministically instead of hoping a prompt is followed, and giving agents safe room to explore and fail without real-world consequences. Eugene shares research from his lab on search at massive scale, why even top models struggle to find the right evidence in huge datasets, and a "branchable" computing environment that lets agents try, fail, and roll back cheaply."You need somewhere to ground reliability and quality. If it's not probabilistic, and you can rely on it and it's guaranteed, then the system doesn't need to think about it at all."Listen to this episode to learn:Why "semantic coupling" makes traditional layered software thinking break down for AI agentsWhy pushing rules into the system (not the prompt) turns probabilistic behavior into guaranteed safetyWhy compute-level innovations like branchable environments could redefine what we expect agents to do on our behalf--------“ So many people in the data community and companies are working on data search, but how do you evaluate the end-to-end quality? And how much search is even the critical bottleneck in this kind of end-to-end question? Because what does the agent need to do? It needs to take your question, it needs to figure out how to decompose it into a series of sub-questions. Such as, “Schools near Clinton Hill.” Then it needs to figure out, “Okay, I need to find information about Clinton Hill, and I need to find locations of schools.” And it needs to then search over 40 million documents and datasets that we've collected, and find the right ones. At any given step, if it didn't find the right data, then the whole thing falls apart and you can't answer the question.” – Eugene Wu--------Time Stamps*(01:10): What the Data Agents and Processes Lab is and why it spans multiple research areas*(04:29): Semantic coupling explained*(19:31): How agents find the right data in a massive data lake*(29:18): What trust means when AI can persuade as well as answer*(42:59): The biggest unlocks for agents over the next 12 months *(56:12): What we'll be talking about in 12 to 18 months--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Eugene Wu on LinkedIn: https://www.linkedin.com/in/eugene-wu-b23417290/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why AI transformations fail and the 90-day plan that actually works.In this episode of AI Radicals, host Satyen Sangani sits down with Charlene Li, bestselling author and strategist, to unpack her new book on how organizations can create real value with AI, not just deploy it.Charlene explains why the biggest mistake leaders make is treating AI as a separate strategy instead of a tool that serves their existing business strategy, and lays out the 90-day framework she and co-author Katia Welch built to help executives move from confusion to a clear AI roadmap. They dig into why "Goldilocks governance" beats both reckless and overly restrictive AI policies, why ownership of AI shouldn't default to IT, and why the obsession with pilots is really a symptom of leaders abdicating strategic responsibility. Charlene also shares real examples from a call center that grew headcount instead of cutting it, to a bank that reskilled instead of laying off, showing what an abundance mindset toward AI looks like in practice."Good governance doesn't slow you down. It actually speeds you up, because you know what you are able to do and what you shouldn't be doing."Listen to this episode to learn:Why AI should never be treated as its own strategy and how to anchor every AI decision in existing business goalsWhy "moving from pilot to production" is really a leadership gap, not a technology gapWhy an abundance mindset is what separates organizations that win with AI from those that stall out--------“ They're green-lighting these pilots because they feel they need to be doing something, but they won't green-light into production because they don't have an overarching AI roadmap that supports their strategy. They haven't put the time and attention to it. They've given it to IT. They have not really thought about what are we trying to do with this? The business leaders have abdicated their responsibility for AI because they themselves don't understand it, and they're not prepared, they don't know, they're not equipped to be able to have a conversation, a strategic conversation with it, because they don't even understand that it is a strategic issue. It's a leadership gap that we have right now.” – Charlene Li--------Time Stamps*(01:20): Charlene’s motivations for writing Winning with AI*(07:40): The 90-day blueprint explained*(12:11): Goldilocks governance & the AI Trust Pyramid*(18:22): Three ways to create value: engagement, efficiency, reinvention *(28:04): Why pilots fail to reach production*(40:15): Satyen’s takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Charlene Li on LinkedIn: https://www.linkedin.com/in/charleneli/Winning with AI: The 90-Day Blueprint for Success: https://www.amazon.com/Winning-AI-90-Day-Blueprint-Success-ebook/dp/B0GQM9PD3P Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why the same old data infrastructure playbook won't survive contact with agentic AI, and what actually has to change underneath.In this episode of AI Radicals, host Satyen Sangani talks with IBM Software CTO Anant Jhingran about why enterprise AI's biggest bottleneck isn't the models, it's the decades-old data and integration infrastructure sitting underneath them.Anant and Satyen dig into why data fundamentals (provenance, metadata, systems of record) haven't changed, even as humans and fixed workflows give way to agents reasoning on the fly. They contrast "AI for data" with "data for AI," why AI's tolerance for messiness still doesn't excuse bad data, and why centralization may matter less as agents run quick, discovery-driven queries instead of big fixed reports. Anant also shares a new focus at IBM: rethinking whether one "golden" code path per product still makes sense when AI makes forking and personalizing variants easy."If you think that agents are just going to do the same thing that you're doing except machines instead of people, it's kind of boring, and I don't think that's going to happen. The real change is they're doing something different that we haven’t done before."Listen to this episode to learn:Why "AI for data" and "data for AI" are two distinct problems enterprises need to solve separatelyWhy agentic, discovery-driven workloads may reduce the need to centralize all your data, but raise the stakes on metadataWhy forking products into many tailored variants, instead of one shared code path, could reshape how software gets built--------“ Something that I wouldn't have thought of three months back or six months back, which is how do we actually build products. And the reason is very simple, is that if you just say that AI is going to help us build products faster, then it doesn't actually create a competitive differentiation because everybody else is creating products faster with AI. So you have to both do things differently and perhaps do different things.” – Anant Jhingran--------Time Stamps*(02:43): Is this AI moment different from past tech shifts?*(08:38): Data quality as a forcing function: "data for AI" vs "AI for data"*(14:23): How advanced is the industry in applying LLMs to old data problems?*(21:14): Federation's comeback & metadata vs. centralization*(32:16): IBM's three strategic pillars & building products differently in the AI era*(51:16): Takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Anant Jhingran on LinkedIn: https://www.linkedin.com/in/anantjhingran/Learn more about IBM: https://www.ibm.com/us-enAnant’s Podcast Context Window: https://www.youtube.com/playlist?list=PLm-EPIkBI3YqXTgboKALGzNmGELWp_oTT Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why mode one thinking keeps most companies stuck—and what it takes to build a company that can keep reinventing itself with AI. In this episode of AI Radicals, host Satyen Sangani talks with ServiceNow’s Brian Solis and Dave Wright and authors of Infinite, about why so many enterprises get stuck chasing ROI on isolated AI use cases instead of using AI to become something genuinely new. Brian and Dave unpack their "mode one, mode two" framework: deciding what existing work deserves to scale with AI (mode one) versus using AI to unlock entirely new value the business couldn't create before (mode two). Using stories like Ford's costly rehiring of quality engineers after over-automating, and IKEA's Billie bot freeing thousands of agents to launch a billion-euro design business, they explain how the real ROI conversation starts with strategy, not use cases. They also dig into why most companies are still stuck optimizing yesterday's workflows, why trust and psychological safety are prerequisites for innovation, and why AI governance has to evolve from a checkbox exercise into managing AI as a true enterprise asset. "AI is not the strategy. If it does become the strategy, it very much limits the impact it's going to have on the organization." Listen to this episode to learn: Why leading with use cases limits AI's impact, and how IKEA turned 8,200 agents into a billion-euro business Why most companies stay stuck optimizing yesterday's workflows instead of reinventing them Why governing AI as an asset is key as agentic AI scales -------- “ You'll see a common set of challenges, like, for example, what's the ROI of AI? That seems to be a popular conversation that has all kinds of different schools of thought around it.  AI is not the strategy. If it does become the strategy, it very much limits the impact it's going to have on the organization and how you can measure its success. Where we have the more successful ROI conversations is if we take a step back and look at, well, what are some of the things that we couldn't do without it? Does this workflow deserve to exist? Does this question help you compete more effectively for 2030? We want to bring the strategy back to the beginning of the conversation.” – Brian Solis -------- Time Stamps *(01:16): Why Brian and Dave wrote a book on AI reinvention  *(05:05): Why "What's the ROI of AI?" is the wrong question *(18:20): Mode one vs. mode two: optimizing yesterday vs. building tomorrow *(27:19): AI maturity: where enterprises really stand today *(31:33): Governing AI as an asset, not an employee *(49:50): Satyen’s takeaways -------- Sponsor This podcast is presented by Alation. Learn more: * Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73 * Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/ * Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/ -------- Links Connect with Brian Solis on LinkedIn: https://www.linkedin.com/in/briansolis/ Connect with Dave Wright on LinkedIn: https://www.linkedin.com/in/davewright2/ Infinite: How Visionary Leaders Transform Today's Businesses into AI-Forward Companies: https://www.amazon.com/Infinite-Blueprint-Leading-Age-AI/dp/1394439024 Read ServiceNow’s AI Enterprise Maturity Index 2026: https://www.servicenow.com/content/dam/servicenow-assets/public/en-us/doc-type/resource-center/white-paper/wp-enterprise-ai-maturity-index-2026.pdf Learn more about ServiceNow: https://www.servicenow.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.