The AI Forecast: Data and AI in the Cloud Era

Cloudera
  • 5.0 (3)
  • Technology
  • Updated Weekly

The introduction of the first computer. The boom of the dotcom renaissance. Now, the dawn of AI. The throughline across each of these momentous inflections in our digital lives has been data. But the presence of data doesn’t mean immediate insights and results. It’s the architectures and systems in place that determine the true value—and trust—of data. In this podcast by Cloudera, The AI Forecast: Data and AI in the Cloud Era explores the past, present, and future of enterprise AI with today’s leading companies and industry experts. You don’t want to miss this.

  1. 5d ago

    Why Successful AI Models Can Still Fail the Business

    An AI project can deliver a great prediction and still fail the business. Rajesh Kelvalkar has seen it happen firsthand. In one mining project, the model worked, but the team discovered that the downstream business process wasn’t configured to act consistently on its recommendations. The technology had done its job, but the transformation hadn’t. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Rajesh Kelvalkar, Senior Advisor at Tech Data APJ, to explore what more than 20 years of transformation work has taught him about putting AI into practice.  Rajesh argues that organizations often approach transformation as a technology initiative, even though much of the work happens elsewhere in the business. Clear ownership and measurable outcomes determine whether an AI pilot becomes part of operations or disappears when the project team moves on. Paul and Rajesh explore: Why pilots built on curated data can stumble in production How repeatable business processes create strong AI use cases What logistics and financial services can teach us about scaling AI Why reusable data assets matter beyond a single pilot How to measure AI against business outcomes Why AI models need continued monitoring after deployment Rajesh also makes the case for treating data as a product. Projects have an end date, but products have ownership and continue to evolve. That mindset becomes especially important for AI systems, where changing conditions can quickly affect the performance of a model that worked well at launch. His advice for leaders planning the next phase of AI starts with the outcome. Identify where AI can influence an end-to-end business process, prove value in a focused use case, and build the foundations that allow successful ideas to be reused elsewhere. If you’re responsible for AI transformation, this episode will help you think beyond the pilot and build AI capabilities designed to scale and sustain. Stay in touch with Rajesh: Rajesh Kelvalkar on LinkedIn: https://www.linkedin.com/in/rajesh-kelvalkar-6b33452/ +++ 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.

  2. Sep 29

    When Everyone Has AI, Your Data Sets You Apart

    After 23 years in machine learning and data science, Cao Hong has seen plenty of AI projects come and go. His measure of success is simple: did it create value for the business? Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Cao Hong, Principal Director of Data Apps at NCS, to explore how organizations can turn AI experimentation into measurable business impact. That becomes harder as enthusiasm for AI pushes expectations higher. Cao Hong argues that organizations need to define the outcome they’re working toward early and then understand how AI will fit into how the business actually operates. Having worked across research and commercial AI projects, Cao Hong has seen how differently the two environments operate. Research can explore problems whose value may be realized years from now. In business, AI ultimately has to earn its investment. That means connecting the technology to a clear use case and establishing how its performance will translate into business value. Paul and Cao Hong explore: How to define value before investing in an AI project Why unrealistic expectations can derail AI initiatives What leaders should consider when selecting AI use cases How data readiness affects AI outcomes Why AI governance needs to evolve alongside adoption Where enterprises can find differentiation as AI models become widely available Cao Hong shares how AI helped a water utility predict demand weeks in advance and rethink decisions years ahead. His takeaway for other enterprises: when everyone can access the same models, the advantage lies in what you can do with your own data.  Want to hear more about turning enterprise data into better decisions? Check out Ep 83 | From KPIs to Action: What Comes After The Dashboard?    Cao Hong on LinkedIn: https://www.linkedin.com/in/hong-cao/  +++ 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.

  3. Sep 23

    Making AI Fluent in Your Business

    Every new AI chat is another first day on the job. Rob Collie describes today’s general-purpose AI as a brilliant new hire who wakes up on their first day every time you start a new conversation. It may understand your industry, but your workflows and business context have to be explained all over again.  In this episode of The AI Forecast, Paul Muller speaks with Rob Collie, founder and CEO of P3 Adaptive, former Microsoft product leader for Excel and Power BI, and author of “Fair Game: Bringing AI Into Reach for All Business” to explore why business context can make or break enterprise AI. Rob brings lessons from decades of business intelligence into the AI era, advocating for starting close to the business and working backward from the decisions people actually need to make in his “Faucet First” philosophy.  Paul and Rob get into: Why business context is critical to getting useful results from AI What “bot sitting” reveals about off-the-shelf AI adoption Why data quality should be evaluated against the business outcome How specialized AI tools can outperform a company-wide “super being” Why systems thinking is becoming more important in the AI era How business leaders can find a practical starting point for AI If you’re responsible for AI strategy or business transformation, this episode will help you get closer to the work by starting with a real business need and building from there. Want to hear more about building AI around real business needs? Check out Ep 79 | Why Some AI Products Strike a Chord (and Others Don't)  Learn more: “Fair Game: Bringing AI Into Reach for All Business” https://fairgamebook.ai/ Raw Data with Rob Collie: https://open.spotify.com/show/1VZLfXgz1hcB8MFG2OSOH9 Rob Collie on LinkedIn: https://www.linkedin.com/in/robcollie/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.

  4. Sep 17

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

    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.

  5. Sep 15

    Can AI Make Sense of Pharma’s Messiest Data?

    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.

  6. Sep 9

    Running with Scissors: The Enterprise AI Reality

    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.

  7. Sep 2

    AI Adoption vs. Adaptation: What Problem Are You Solving?

    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.

  8. Aug 26

    The Great AI Re-Architecture Pushes Workloads to the Data Center

    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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The introduction of the first computer. The boom of the dotcom renaissance. Now, the dawn of AI. The throughline across each of these momentous inflections in our digital lives has been data. But the presence of data doesn’t mean immediate insights and results. It’s the architectures and systems in place that determine the true value—and trust—of data. In this podcast by Cloudera, The AI Forecast: Data and AI in the Cloud Era explores the past, present, and future of enterprise AI with today’s leading companies and industry experts. You don’t want to miss this.

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