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

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.