Guest: John Bradshaw  (LinkedIn)
Company: Akamai
Show Name: An Eye on AI
Topic: Cloud Native

What happens when your five-year cloud commitment becomes a five-alarm fire? John Bradshaw, Field CTO Cloud, EMEA at Akamai, warns that cloud concentration risk has become a “clear and present danger” to modern business—and enterprises need exit planning budgets to survive it.

Long-term cloud contracts promise one thing: deep discounts in exchange for loyalty. Sign up for five years, get a cracking deal. But Bradshaw points out the hidden cost: inflexibility. “If you do that, you have to accept that there is a risk associated with it,” he explains. Industry analysts now classify cloud concentration risk as a critical threat, forcing organizations to rethink their commitment strategies.

The solution isn’t abandoning long-term deals entirely. It’s smarter planning. Bradshaw recommends building exit planning into budgets from day one. “People are putting in a line item that says, if everything goes wrong and we decide we don’t want to do this anymore, how do we get out of it?” This emergency reserve sits in the financial equivalent of a dark cupboard—ready to fund a pivot when needed. It’s the enterprise version of keeping dishwasher replacement money on hand rather than committing to a single brand forever.

From FinOps to ValueOps: Measuring What Matters

But cost control is only half the story. Bradshaw sees a seismic shift from FinOps to ValueOps—from tracking spend to engineering value. “FinOps has been huge for the last 10 years,” he says, “but that’s going to move to value engineering, where you’re looking at your business processes, how they map to your technology, and measuring them accordingly.”

This means mapping entire value chains: customer acquisition costs $10, payment processing costs $5, delivery adds another $5. With this granular view, organizations can justify AI investments based on real economic impact. “If I can change average customer retention from $10 to $8, that’s a $2 saving. I have 2 million customers—that’s $4 million back. So I can spend $2 million to implement that improvement.”

This approach helps teams invest in technologies with proven ROI rather than chasing what’s “cool.” The challenge? Emerging technologies like AI require experimentation before value becomes clear. Five years ago, AI was exciting but undefined. Today, its applications are proven. The next breakthrough might need that same runway.

AI as Cost Intelligence Engine

AI itself is becoming the answer to cost intelligence challenges. Bradshaw predicts agentic AI will transform FinOps and ValueOps by making systems context-aware and predictive. Instead of static cron jobs scaling infrastructure for known events like month-end payroll, AI can dynamically respond to unpredictable variables.

His example: a supermarket using AI to redirect ice cream inventory based on hyperlocal weather forecasts. “It’s really warm there. I’m going to send thousands of ice creams there and redirect the ones going to all my other stores. I haven’t bought any more ice creams—I’ve just diverted them to the right place.” The result? Maximum efficiency without additional costs.

This is the future of cloud economics: not just controlling spend, but engineering value through intelligent, real-time resource allocation. Organizations that master ValueOps with AI-driven insights won’t just save money—they’ll build competitive advantages through operational excellence.

The question isn’t whether to sign long-term cloud contracts. It’s whether you’ve budgeted for the exit ramp and built the value measurement systems to know when you need it.