The AI Forecast: Data and AI in the Cloud Era

One developer burned through 300 million AI tokens in a single week. The payoff was far less impressive.
For Todd Trevillion, it's a striking example of what can happen when AI adoption outpaces the controls needed to manage it. And with autonomous AI agents beginning to make decisions on behalf of businesses, the stakes are getting higher.
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast follows Paul Muller as he sits down with Todd Trevillion, technology entrepreneur and co-founder of Citadel Edge, to explore whether enterprises are truly ready for agentic AI and what it takes to deploy the technology responsibly.
Working with organizations in highly regulated industries, Todd has seen how quickly AI ambitions can run into operational reality. He questions whether enterprises are prepared to give autonomous agents greater control when the consequences of their actions can be difficult to predict.
Paul and Todd dive into:
Why agentic AI introduces new risks for enterprise security and governance
What happens when autonomous agents make decisions on behalf of businesses
How uncontrolled token consumption can undermine AI ROI
Why data sovereignty matters in regulated industries
Why AI observability and human oversight remain essential
How to balance AI innovation with enterprise accountability
Todd raises an uncomfortable possibility: what happens when AI agents start making decisions based on other agents’ decisions, with no human watching closely enough to catch a mistake? For enterprises, the potential blast radius could extend far beyond a bad answer.
If you're responsible for enterprise AI strategy or governance, this episode will help you think more critically about the authority you're giving AI agents and the safeguards needed to keep them under control.
Want to hear more about responsible AI adoption? Check out Ep 81 | AI Guardrails: How to Govern AI Without Slowing Innovation.
Stay in touch with Todd:
Todd Trevillion on LinkedIn: https://www.linkedin.com/in/toddtrevillion/
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The AI worked in the sandbox. Then it met the real world.
David Hardoon learned that lesson during an ATM optimization project designed to improve cash-delivery routes. Once deployed, the system collided with traffic and unpredictable schedules, which are the realities that face the people doing the work.
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with David Hardoon, Head of Advanced AI at Accenture, to explore why the human side of AI transformation can determine whether promising technology ever delivers value in production. 
Paul and David discuss:
Why technically successful AI projects can fail when they meet real workflows
What AI leaders can learn by going directly to the people doing the work
Why workforce design should be part of AI transformation from the beginning
How agentic AI could reshape career paths and management structures
Why familiar change-management principles matter again in the AI era
How organizations can balance AI innovation with practical implementation
David also reflects on developing the FEAT principles for responsible AI at the Monetary Authority of Singapore and takes a musical detour into Lestrum, his early research connecting patterns in music with brain activity. Both experiences illustrate his belief that AI works best when grounded in how people actually think and operate.
If you’re leading AI transformation, this episode will push you to look beyond the technology and ask whether your organization and its people are ready for what comes next.
Stay in touch with David:
David Hardoon’s website: www.davidroihardoon.com
David Hardoon on LinkedIn: https://www.linkedin.com/in/davidrh/
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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. 

Your AI agent has credentials. Now, what happens when it goes off script?
An agent can take action and change course before a traditional security review even begins. For Alex Bovee, identity is only the beginning of the security conversation.
In this episode of The AI Forecast, Paul Muller sits down with Alex Bovee, co-founder and CEO of C1 and longtime cybersecurity and identity leader, to explore how autonomous AI agents are changing enterprise security.
Alex argues that zero trust was built for a world where people remained behind the keyboard. AI agents introduce non-deterministic behavior and can relentlessly optimize toward a goal, sometimes finding routes their creators never anticipated.
Paul and Alex’s discussion examines:
Why AI agents require a new approach to identity and access control
How autonomous agents change the zero-trust security model
Why static permissions struggle at machine speed
What real-time enforcement looks like for AI agents
How organizations can identify behavioral drift
How governed “golden paths” can support secure AI adoption
As AI agents take on more work across the enterprise, security will have to keep pace. If you’re responsible for enterprise security, this episode will show you how to keep pace with AI agents operating at machine speed without creating controls that people will work around. 
Stay in touch with Alex:
Alex Bovee on LinkedIn: https://www.linkedin.com/in/alexbovee/
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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/
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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. 

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/ 
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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.

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/
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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/
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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.

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/
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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. 

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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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.

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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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.