The AI Forecast: Data and AI in the Cloud Era

Jaksot

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  Beyond the POC: AWS’s Playbook for Enterprise AI Success 17.09.2026 20min

  Most AI pilots never make it past the demo phase. AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects. In this episode of The AI Forecast, Paul Muller sits down with Praveen Jayakumar, who leads Machine Learning Solution Architecture for Amazon Web Services (AWS) across Asia Pacific and Japan, to explore how enterprises can give AI projects a realistic path to production. Praveen shares what he’s learned working with organizations on machine learning and generative AI deployments, including why teams should build for production while they’re still proving the concept. Observability and evaluation become particularly important as models change, costs scale, and AI systems begin interacting with enterprise data. Paul and Praveen talk about: Why promising AI proofs of concept stall before production How to define success and kill criteria before an AI project begins Why observability should be built into the proof of concept How evaluation frameworks make it easier to test different AI models Why the most powerful frontier model may be the wrong choice How model selection affects the unit economics of enterprise AI Recorded at EVOLVE26 Singapore, this episode offers a practical look at what happens after the AI demo, when teams have to decide what’s ready to scale and what’s better left behind.  If you’re responsible for enterprise AI, this episode will help you build a path to production and keep zombie projects from consuming resources long after their expiration date. Stay in touch with Praveen: Praveen Jayakumar on LinkedIn: https://www.linkedin.com/in/pjpraveen/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.
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  Can AI Make Sense of Pharma’s Messiest Data? 15.09.2026 38min

  Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making. Brian explains how pharmaceutical R&D turns sparse, fragmented data into insights that support drug discovery. With a new drug potentially requiring years of development and billions of dollars in investment, better predictions can have an enormous impact on how quickly promising treatments reach patients. Paul and Brian explore: How knowledge graphs can reveal relationships hidden across fragmented data Where AI can connect qualitative patient experiences with quantitative research Why patient consent complicates how valuable clinical data can be reused How pharma teams can share knowledge without dismantling every data silo Why embedding technologists with scientists can accelerate AI adoption What other industries can learn from pharmaceutical data strategy If you’re responsible for enterprise data or AI strategy, this episode will show you how lessons from pharmaceutical R&D can help turn fragmented information into knowledge that drives better decisions. Stay in touch with Brian: Brian Martin on LinkedIn: https://www.linkedin.com/in/brianm1028/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI. 
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  Running with Scissors: The Enterprise AI Reality 09.09.2026 46min

  Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement. After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous parts of the business. Her favorite function to transform? Procurement. Recorded at EVOLVE26 Singapore, this conversation brings host Paul Muller together with Sol Rashidi, CEO of ExecutiveAI, the world’s first Chief AI Officer, and Chief Strategy Officer of AI Governance & Security at Cyera. A two-time bestselling author and Senior Fellow at Harvard, Sol brings a practitioner’s perspective to what actually happens when enterprise AI meets operational reality.  Sol shares lessons shaped by years of enterprise AI deployments and the postmortems she kept along the way. She challenges the way companies prioritize AI use cases, arguing that business value means little without a realistic path to production.  The conversation turns to: Why so many AI projects remain stuck in proof-of-concept mode What procurement can teach us about practical AI transformation How to choose AI use cases that have a realistic path to production Why top-down and bottom-up AI adoption can both stall What trust between employees and leadership means for AI adoption Why speed is outpacing governance and security The hidden trade-offs behind convenient AI tools How leaders can keep human agency at the center of AI Her vision for what comes next is human-led, AI-supercharged. AI can give individuals capabilities that once required entire teams, but Sol believes people still need to protect the creative judgment and agency that make those capabilities valuable in the first place. As Sol puts it, the goal is to build a world where “AI happens with us and not to us.” Stay in touch with Sol: Sol Rashidi’s website: https://solrashidi.com/ Sol Rashidi on LinkedIn: https://www.linkedin.com/in/sol-rashidi-mba-a672291/ Your AI Survival Guide on Amazon: https://www.amazon.com/dp/B0DJGBY1YY?lv=shuf&channelId=520&plpRedirect=mhFallback +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
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  AI Adoption vs. Adaptation: What Problem Are You Solving? 02.09.2026 57min

  Paul McDonagh-Smith estimates that many business leaders would struggle to define their organization’s problem clearly in fewer than 25 words. With AI, that lack of clarity can quickly turn into fragmented solutions and misplaced expectations. In this episode of The AI Forecast, Paul Muller sits down with Paul McDonagh-Smith, a Visiting Senior Lecturer at MIT Sloan School of Management and a Senior Advisor to NASA's Goddard Space Flight Center, to explore how organizations can approach AI with greater clarity and purpose. Ideas shaping the discussion: Why problem framing can determine the outcome of an AI initiative The difference between adopting AI and adapting with it What the scientific method can teach organizations about AI  Why AI’s imperfections make human judgment even more important How organizational mindsets influence technology outcomes Why organizations should invest in capabilities that AI cannot replicate The growing importance of trust in AI adoption  From healthcare to energy, Paul sees enormous potential for human imagination and machine intelligence to tackle problems once considered out of reach. The future, in his view, will reflect the choices we make today. Want to hear more about the organizational side of AI? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail. Stay in touch with Paul: Paul McDonagh-Smith on LinkedIn: https://www.linkedin.com/in/paulmcdonaghsmith/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can also watch the video version of this episode on The AI Forecast.  
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  The Great AI Re-Architecture Pushes Workloads to the Data Center 26.08.2026 42min

  New research points to a major shift in enterprise IT: more than two-thirds of surveyed data leaders are moving some workloads back into the data center. The catalyst? AI. In today’s episode, Paul Muller is joined by Cloudera CTO Sergio Gago and CPO Leo Brunnick to unpack Cloudera’s survey of more than 1,500 technology and data leaders about how AI is reshaping their infrastructure. As AI moves into production, agents can generate dramatically more queries than human users do, putting new pressure on the systems beneath them. That is forcing enterprises to reconsider where workloads should run and how much flexibility they will need as AI evolves. Sergio and Leo join Paul to discuss what they call the “great AIre-architecture” and why the next era of enterprise AI could look decidedly hybrid. Inside the live conversation: What Cloudera’s The Great AI Re-Architecture survey reveals about AI infrastructure Why enterprises are moving workloads back on premises How agentic AI changes the demands placed on enterprise data The economics driving renewed interest in hybrid cloud How governance changes when agents access enterprise data What the next generation of data engineering could look like The conversation also introduces Cloudera Anywhere Cloud, announced last week at EVOLVE Singapore. Sergio and Leo explain how a common architecture across public and private cloud environments could give enterprises greater freedom to place AI workloads where they make the most sense. AI is putting decades of cloud assumptions back up for debate. For enterprise IT, the next big advantage may be the freedom to choose.  Learn more: Cloudera EVOLVE26: https://www.cloudera.com/events/evolve.html The Great AI Re-Architecture survey report: https://www.cloudera.com/content/dam/www/marketing/resources/analyst-reports/the-great-ai-re-architecture.pdf.landing.html Sergio Gago on LinkedIn: https://www.linkedin.com/in/sergiogh/ Leo Brunnick on LinkedIn: https://www.linkedin.com/in/leobrunnick/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 series and stay up to date on the latest developments in enterprise data and AI.  
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  Vibe Coding to Production: Building AI Apps That Actually Scale 19.08.2026 52min

  Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace. In this episode of The AI Forecast, Paul Muller sits down with Shanea Leven, co-founder of Empromptu AI, to explore what it takes to build production-ready AI applications. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software. Shanea also explores the infrastructure surrounding generative AI applications, including the need to detect drift and evaluate outputs as real-world data flows through a system. As she puts it, the familiar rule still applies: “Garbage in, garbage out.” In their lively discussion, the pair covers: What separates a working AI app from a production-ready one How governance should be built into AI development Why organizations should fingerprint applications before production How non-technical subject matter experts can become effective AI builders How AI coding changes the role of software engineers What companies should capture today to prepare for custom AI models The conversation points toward a future where more people can build software themselves. Engineering teams will play a critical role in making that possible by creating the guardrails that allow ideas to move safely from a prompt into production. Want to hear more about AI governance? Check out Ep 81 | AI Guardrails: How to Govern AI Without Slowing Innovation. Stay in touch with Shanea: Shanea Leven on LinkedIn: https://www.linkedin.com/in/shaneak/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on Cloudera’s YouTube Channel.
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  Enterprise AI Success: What Separates Results from Expensive Experiments 12.08.2026 42min

  Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear? For Mark Ritcey, the answer comes down to disciplined execution. AI programs need a clear business problem and an organization prepared for how the technology changes the way work gets done. In this episode of The AI Forecast, Paul Muller sits down with Mark Ritcey, Vice President of AI and Automation Delivery at Latentbridge and lecturer on AI and machine learning, to examine the decisions that shape enterprise AI success. Mark has spent more than 25 years working across technology and automation, including leading enterprise transformation initiatives in highly regulated industries. He shares what he’s seeing inside AI programs today, where teams commonly go wrong, and why structured experimentation matters as organizations figure out where AI can create real value. What separates AI success from failure, according to Mark and Paul: Why AI projects need a clearly defined business problem The risks of experimenting with AI for its own sake How unrealistic expectations derail enterprise deployments The role of governance as AI moves into production How organizational change affects AI adoption What CEOs and boards should consider before scaling AI His advice for leaders is refreshingly straightforward: AI transformation requires diligent, structured work. There are no shortcuts around understanding the business and building the controls required to put AI into production responsibly. Want to hear another perspective on enterprise AI adoption? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail.   Stay in touch with Mark: Mark on LinkedIn: https://www.linkedin.com/in/markritcey/  +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  How AI Is Helping Humanitarian Teams Make Faster Decisions 05.08.2026 52min

  In a humanitarian crisis, waiting for perfect information isn't an option. Every decision must be made with incomplete data and limited resources in a situation that can change by the hour. For organizations like Mercy Corps, AI is helping teams make sense of that uncertainty. By bringing together information from conflict reports, local media, humanitarian data, and environmental sources, AI can surface the context decision-makers need while leaving human judgment firmly in their hands. In this episode of The AI Forecast, Paul Muller is joined by Josh DeWald, Vice President of Technical Support, Evidence and Program Quality at Mercy Corps, and Rob Dickens, AI Technical Lead at Cloudera. Together, they explore how VERA, an agentic AI platform co-developed by Cloudera and Mercy Corps, is helping humanitarian teams gather information faster and make better-informed decisions.  Inside their discussion: How AI supports humanitarian decision-making during fast-moving crises Why data collection is so difficult in conflict-affected regions How agentic AI combines structured and unstructured information The importance of human oversight in AI-assisted decisions Protecting data privacy and reducing bias in humanitarian AI Lessons enterprise organizations can learn from operating in information-poor environments How AI is improving organizational learning across humanitarian programs The conversation explores what responsible AI looks like when decisions carry humanitarian consequences. Whether you're leading enterprise AI initiatives or working in mission-driven organizations, this episode offers valuable insights into building AI systems that help people make better decisions when the stakes are highest. Stay in touch with Josh and Rob: Josh DeWald on LinkedIn: https://www.linkedin.com/in/josh-dewald-10a6178b/?skipRedirect=true Rob Dickens on LinkedIn: https://www.linkedin.com/in/robert-dickens-73a91340/?skipRedirect=true&originalSubdomain=uk +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  From KPIs to Action: What Comes After The Dashboard? 29.07.2026 49min

  Your dashboard can tell you sales are down, but it can't tell you why or what to do next. Dashboards have become the default way to monitor a business. Bhaskar Sunkara argues they're only the starting point. The next step is AI that understands business context and helps leaders move from insight to action.  In this episode of The AI Forecast, Paul Muller sits down with the founder and CEO of Bicycle AI to explore how agentic AI is reshaping enterprise analytics. After helping pioneer application monitoring, Bhaskar now focuses on a different question: how AI can help businesses understand why something changed and what action to take next.  Bhaskar and Paul break down: Why traditional dashboards fall short for business decision-making How agentic AI moves from reporting problems to recommending actions The role of business ontology in connecting data, context, and outcomes Why data quality and traceability remain essential for trustworthy AI How AI can automate root cause analysis across technical and business systems Why human judgment remains central to enterprise decision-making What it takes to build proactive, AI-driven business operations Bhaskar sees AI as a force multiplier for decision-makers, assembling the evidence so leaders can focus on judgment and accountability. The result is faster, more informed decisions backed by business context. Want to learn more about enterprise AI decision-making? Check out Ep 80 | Decision Logic: The Difference Between an Answer and a Decision    Stay in touch with Bhaskar: Bhaskar Sunkara on LinkedIn: https://www.linkedin.com/in/bhaskarsunkara/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Behavior Change: How Startups Are Making AI Stick 22.07.2026 32min

  Behavior change is the biggest hurdle in AI adoption. AI only creates value when people make it part of their everyday work. In this episode of The AI Forecast, Paul Muller sits down with Varun Puri, CEO and co-founder of Yoodli, to discuss why successful AI adoption starts with changing how people work. Drawing on his experience at Google, Google X, and as the founder of an AI startup, Varun shares practical lessons on embedding AI into everyday workflows and building habits that stick. From AI-generated leadership briefings to a daily gratitude agent, Varun explains how small behavioral shifts can unlock outsized results, and why the most valuable AI tools are the ones people actually use. Varun and Paul’s conversation explores: Practical ways startups are using AI to improve daily operations How AI gives leaders real-time visibility across the business Why incentives often undermine successful AI initiatives Why emotional intelligence may become more valuable in the AI era Personal AI workflows that help leaders stay focused and effective Throughout the discussion, Varun explains how AI becomes most valuable when it helps people do their best work, rather than simply automating tasks.  Whether you're rolling out AI across an enterprise or scaling a startup, this episode offers practical ideas for integrating AI into everyday work.  Want to hear more about leading successful AI adoption? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail  Stay in touch with Varun: Varun Puri on LinkedIn: https://www.linkedin.com/in/varun-puri001 +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  AI Guardrails: How to Govern AI Without Slowing Innovation 15.07.2026 34min

  AI is already inside your organization. The question is whether you know where. As Lisa Pent says, “You can’t govern what you can’t see.”  In this episode of The AI Forecast, Paul Muller sits down with Lisa Pent, CEO and Founder of PentEdge, to discuss why AI visibility is becoming a board-level issue and what organizations can do to govern AI with confidence. Lisa makes the case that visibility is the foundation of effective AI governance.  Throughout her career in investment banking and fintech, Lisa explains why AI governance must move beyond policies and annual audits. She explains how organizations can gain visibility into AI use and build stronger governance around it.  Their conversation explores: Why AI governance must become continuous How data lineage creates a stronger audit trail The risks created by shadow AI How to assess AI risk across critical business systems What boards should be asking about AI oversight Practical ways to monitor AI across the enterprise If AI is becoming part of your organization, this conversation provides a practical framework for effective oversight. To hear more about governing data in the age of AI, check out Ep 72 | The Data Governance Coach: From Data Error to Insight.   Stay in touch with Lisa: Lisa on LinkedIn: https://www.linkedin.com/in/lisapent/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Decision Logic: The Difference Between an Answer and a Decision 08.07.2026 35min

  Ask an AI system a question, and you'll get an answer. Decision logic determines whether you should trust it.  In this episode of The AI Forecast, Paul Muller sits down with Darlene Newman, Innovation Lead at Duczer East, to explore the hidden layer that helps AI move from pattern matching to practical decision-making.  From semantic layers and ontologies to knowledge graphs and governance frameworks, Darlene unpacks the often-overlooked structures that sit between AI outputs and real-world decisions. She also shares practical guidance on integrating decision-making logic into AI initiatives without adding complexity. Paul and Darlene take a closer look at: Why decision logic is the “why” behind AI decisions How guardrails help prevent hallucinations and unreliable outputs Why knowledge design is becoming a critical AI capability How organizations can build scalable and auditable AI systems Practical approaches for integrating decision logic into AI initiatives Beyond models and prompts, this conversation is about giving AI the context it needs to make better decisions. For enterprise leaders, it offers a practical look at the structures and knowledge foundations that can help AI deliver consistent business outcomes. To hear more about turning organizational knowledge into AI capabilities, check out Ep 69 | Industrial Enterprise AI: Growing Value and Organizational Risk Management  Stay in touch with Darlene: Darlene Newman on LinkedIn: https://www.linkedin.com/in/darlenenewman/ Where Innovation Takes Root newsletter: https://www.linkedin.com/newsletters/innovation-through-the-hype-7272737391067459584/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Why Some AI Products Strike a Chord (and Others Don't) 24.06.2026 50min

  You recognize the tune, but something feels off. That's how Marlon Davis describes many of today's AI initiatives: AI karaoke. Organizations are rushing to add AI to products, but too often they're layering technology onto solutions without fully understanding the customer problems they're trying to solve. In this episode of The AI Forecast, Paul Muller sits down with fractional Chief Product Officer at Devlnio, Marlon Davis, to explore how organizations can move beyond superficial AI efforts and build products that deliver meaningful customer value.  Paul and Marlon take a closer look at: How to identify opportunities where AI genuinely creates value Why product teams should focus on customer problems before AI solutions The importance of anthropology and observing customer behavior How AI can improve product operations and decision-making Why understanding customer workflows matters more than adding AI features How product managers can navigate the rise of AI-assisted development If you're deciding where AI belongs in your product portfolio, this episode provides a grounded approach to identifying opportunities that matter to customers.  To hear more about turning AI investments into business value, check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail  Stay in touch with Marlon: Marlon Davis on LinkedIn: https://www.linkedin.com/in/marlondavis/ Froogel Product Manager Newsletter: https://www.linkedin.com/in/marlondavis/recent-activity/newsletter/  +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Mastering Enterprise AI: Why Some Projects Succeed While Others Fail 17.06.2026 43min

  AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments.  Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.  Paul and Michael take a closer look at: Why AI adoption often stalls despite significant technology investments The role of organizational change management in successful AI programs Common AI adoption patterns and anti-patterns across enterprises How champions inside the organization can accelerate AI success How governance and security evolve as AI scales The importance of focusing on business outcomes rather than technology alone The path from AI investment to business value is often more complex than expected. This episode offers advice on turning AI investments into measurable outcomes while building the organizational foundations for successful scaling.  To hear more about the organizational challenges facing AI adoption, check out Ep 69 | Industrial Enterprise AI: Growing Value and Organizational Risk Management  Stay in touch with Michael: Michael Gray on LinkedIn: https://www.linkedin.com/in/michael-gray-4861663/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  The Rise of VibeOps: How AI Is Transforming Network Automation 10.06.2026 45min

  For decades, network teams have been forced to choose between speed and stability. AI may finally be changing that equation. In this episode of The AI Forecast, Paul Muller sits down with John Capobianco, Head of AI and Developer Relations at Itential and author of “Automate Your Network,” to explore how AI is reshaping the future of network operations. Drawing on decades of experience in network engineering, John explains why network automation has struggled to gain traction and how AI, agents, and Model Context Protocol (MCP) could finally break the bottleneck.  Paul and John take a closer look at: Why network automation adoption has lagged behind other areas of IT How AI can augment network engineers instead of replacing them The role of MCP and AI agents in automating complex workflows Why “AI can write and validate scripts as a pair programmer” How digital twins can reduce risk and improve network resilience The emergence of VibeOps and AI-driven operations For technology leaders navigating AI adoption, this episode explores how AI-powered automation can help network teams move faster without sacrificing reliability. To hear more about AI-powered operations and vibecoding, check out Ep 65 | The Vibecoding Liability: How Unchecked AI Can Kill Cloud ROI  Stay in touch with John: John Capobianco on LinkedIn: https://www.linkedin.com/in/john-capobianco-644a1515/ John’s website: https://www.automateyournetwork.ca/home/ “Automate Your Network” on Amazon: https://www.amazon.com/Automate-Your-Network-Introducing-Enterprise/dp/1799237885 +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  The Space-AI-Quantum Nexus Challenging Governance 03.06.2026 43min

  AI governance is already struggling to keep pace. Add quantum computing and space infrastructure, and the challenge becomes exponentially harder.  In this episode of The AI Forecast, Paul Muller sits down with technology governance specialist and researcher Preetha Bedi to explore the growing convergence between AI, space, and quantum technologies—and why this nexus is creating entirely new categories of systemic risk. From satellite infrastructure and quantum acceleration to agile lawmaking and fractal risk modeling, Preetha makes the case for a fundamentally different approach to technology oversight in the AI era. Paul and Preetha unpack: Why AI, space, and quantum technologies must be governed as interconnected systems How traditional governance models are failing to keep pace with technological acceleration Why agile lawmaking may become essential for emerging technologies The hidden systemic risks tied to satellite infrastructure and orbital congestion How quantum computing could dramatically amplify AI development Why “fractal thinking” helps model repeating patterns in technological risk If you’re thinking about the future of AI governance, this episode offers a thought-provoking perspective on what happens when innovation outpaces institutional control. To hear more about critical infrastructure in space, check out Ep 73 | Out of This World AI: Inside Spaceflight with Jeanette Epps  Stay in touch with Preetha: Preetha Bedi on LinkedIn: https://www.linkedin.com/in/preethabedi/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Why Enterprise AI Still Breaks at Scale with Ravit Jain 27.05.2026 33min

  As organizations rush to scale AI, many are learning that better models can’t compensate for weak data foundations. AI hype is everywhere, but operational readiness still isn’t. In this episode of The AI Forecast, Paul Muller sits down with Ravit Jain, founder of The Ravit Show and one of the leading voices in the global data and AI community, to explore the trends shaping the future of enterprise AI. Drawing on conversations with hundreds of AI and technology leaders, Ravit shares why the industry is shifting away from experimentation and toward measurable business outcomes. From agentic systems and contextual AI to governance and responsible adoption, he explains why organizations must get the fundamentals right before AI can scale effectively. Together, Paul and Ravit delve into: Why data quality remains the foundation of successful AI How AI is moving from assistant to operator The rise of agentic AI and contextual systems How governance and responsible AI shape long-term adoption Why operational readiness matters as much as the technology itself If you’re building or scaling enterprise AI, this episode offers a practical perspective on separating hype from impact, and what leaders should prioritize as AI moves into its next phase. To hear more about the future of enterprise AI, check out Ep 69 | Industrial Enterprise AI: Growing Value and Organizational Risk Management.   Stay in touch with Ravit: Ravit Jain on LinkedIn: https://www.linkedin.com/in/ravitjain/  Ravit Jain’s website: https://www.theravitshow.com/  The Ravit Show on YouTube: https://www.youtube.com/channel/UC4yopSSlBfw2WAykLPTYH-w  +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Fan Intelligence: Preparing for the World Cup Surge 21.05.2026 36min

  With the World Cup on the horizon, global attention is about to shift back to football. For clubs, that spotlight represents a rare opportunity to activate millions of fans. The question is whether they’re ready. As AI reshapes how organizations understand and engage their customers, sports clubs are beginning to rethink what a “fan” really is. In markets like Brazil, where membership programs are a core part of club revenue, teams hold vast amounts of fan data, but it is often fragmented. Ticketing systems, membership programs, e-commerce, and social channels often operate in silos, making it nearly impossible to build a unified view of the fan. In this episode of The AI Forecast, Paul Muller sits down with Caio Nogueira, co-founder of Lupa Data, to explore how AI and data are transforming fan engagement—and why most clubs are still leaving value on the table. Caio and Paul explore the implications of: Why fan data is becoming a measurable business asset The shift from static databases to dynamic, behavior-driven segmentation Using AI to predict churn, personalize experiences, and drive loyalty How leading organizations are moving toward behavior-based segmentation, using data to understand how fans engage (and when they disengage) For any organization managing subscribers or members, the tools to understand your audience already exist. The challenge is bringing the data together in a way that makes it usable. Because when the next global moment arrives, the advantage goes to the teams that already know their fans. To explore another data-driven transformation in sports, check out Ep 62 | How Moneyball's Billy Beane Changed Baseball Forever with Data Analytics    Stay in touch with Caio: Caio Nogueira on LinkedIn: https://www.linkedin.com/in/caio-nogueira-gon%C3%A7alves-52529837/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  Out of This World AI: Inside Spaceflight with Jeanette Epps 13.05.2026 52min

  Human spaceflight is one of the few domains in which data and human judgment must work together flawlessly under extreme pressure. That makes it a powerful lens for understanding what it takes to build resilient, intelligent systems here on Earth. In this Women Leaders in Technology spotlight episode of The AI Forecast, Paul Muller sits down with former NASA astronaut Dr. Jeanette Epps to explore what complex, high-stakes environments can teach us about AI. Drawing on her 235-day mission aboard the International Space Station, Jeanette shares firsthand insights into how tightly integrated systems must function when precision is critical, and uncertainty is unavoidable. From working with autonomous robotic systems like Astrobee to managing unexpected mission anomalies, her experience proves that automation works best when paired with human judgment, not separated from it. On today’s mission, Jeanette and Paul chart a course through: What human spaceflight reveals about managing complex systems Why precision and trust are non-negotiable in mission-critical environments The role of human-in-the-loop decision-making in AI systems Lessons in leadership, resilience, and decision-making under pressure Why baseline testing and known data are essential for trusting AI outputs How to design systems that balance automation with human oversight For leaders building AI-driven systems, this episode offers a rare perspective from one of the most demanding operational environments imaginable. When the stakes are high enough, there’s no room for guesswork—only systems you can trust. To listen to another episode from the WLIT series, check out Ep 66 | Women Leaders in Technology: AI Agents Are Your New Team– Now What?    Stay in touch with Jeanette: Dr. Jeanette Epps on LinkedIn: https://www.linkedin.com/in/jeanette-epps-phd-812aba50/ Dr. Jeanette Epps on Instagram: https://www.instagram.com/jeanette.epps/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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  The Data Governance Coach: From Data Error to Insight 06.05.2026 52min

  In the world of enterprise AI, the pressure on data has changed. What used to be “good enough” now gets amplified by faster decisions, and therefore, faster mistakes. Governance is fundamental in ensuring data trust and integrity. In this episode of The AI Forecast, Paul Muller sits down with The Data Governance Coach, Nicola Askham, to share her pragmatic perspective and assert that governance only delivers value when it’s simple enough for people to use and embedded into day-to-day work. Here’s what business leaders need to know: Why data governance has shifted from compliance to value creation How AI is raising the stakes for data quality and trust Practical ways to start and scale governance without overengineering it Why data governance is more about people than technology Common anti-patterns, from “IT-owned governance” to overly complex frameworks The role of data literacy and communication in driving adoption How to design fit-for-purpose frameworks that evolve with the organization Nicola’s advice is refreshingly direct: start small, keep it simple, and focus on outcomes. In a world shaped by AI, governance evolves alongside the business, sharpening decision-making and protecting against costly mistakes.  For more on data governance, listen to Ep 68 | Agentic AI Is Forcing a Governance Reset.   Stay in touch with Nicola: Nicola Askham on LinkedIn: https://www.linkedin.com/in/nicolaaskham/ Nicola’s website: https://www.nicolaaskham.com/ Nicola’s book on Amazon: https://www.amazon.com/Effective-Data-Governance-Framework-Organization-ebook/dp/B0FY7KWSM8?ref_=ast_author_dp&th=1&psc=1 +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.