The AI Forecast: Data and AI in the Cloud Era

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
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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/
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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/
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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/
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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/ 
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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
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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/
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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
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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/
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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/
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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.