“What stayed with me was the discussion it prompted. It reminded organisations that access to an important capability can change because of decisions made somewhere else. As AI moves into production, that’s something leadership teams should understand.”

Jayaram encourages organisations to start with one practical question: “If a critical AI capability were available on Friday and then suddenly unavailable on Monday morning, what would we do?”

“It’s a practical way of understanding how dependent you’ve become, whether you have alternatives, and how quickly you could adapt if circumstances changed,” he said.

Historically, organisations have managed technology risk through procurement, vendor governance, cyber security, and business continuity planning. Those disciplines remain essential, and as AI becomes part of critical operations, they need to extend to the way AI capabilities are selected, integrated and managed. What’s changed is the nature of the dependency itself. Organisations aren’t just relying on infrastructure or software anymore. They’re relying on capabilities that sit inside rapidly evolving ecosystems, shaped by commercial decisions, regulation and geopolitical events well outside their control.

That broadens the questions leaders are asking. Rather than focusing only on whether a capability delivers value today, executive teams are increasingly weighing how resilient that decision will be over time, how quickly they could adapt if circumstances changed, and whether critical services remain resilient as the technology underneath them keeps evolving.

AI now belongs in the same planning conversation as infrastructure failures, supplier changes, and regulatory shifts because the commercial arrangements behind AI capabilities, and the conditions under which they’re available, will continue evolving.

“Frontier AI is still evolving quickly, and the organisations developing these models don’t control every variable that can influence availability. Building flexibility into your architecture from the outset makes it much easier to respond as technology, regulation and commercial arrangements continue to evolve,” Jayaram said.

That’s also changing how organisations evaluate AI itself. Capability remains important, but it’s no longer the only consideration. Cost, flexibility, governance and the ability to adapt over time are now part of the decision as well. Rather than assuming every use case needs the most advanced frontier model available, Jayaram believes organisations should start with the business outcome they’re trying to achieve and work backwards.

“We spend a lot of time talking with customers about minimum viable capability,” he said.

“Not every use case needs the most advanced model available. Start with the outcome you’re trying to achieve, then ask what the minimum level of capability needed to achieve it is. That usually leads to better architectural decisions, gives organisations more flexibility as the market evolves, and makes it much easier to adapt when circumstances change.”

AI will keep evolving, bringing new models, new providers and new opportunities to innovate. That’s good news for organisations because it creates more choice and more ways to apply AI. It also means that decisions made today will need to be revisited as the technology continues to evolve, which is exactly why Jayaram believes strategic decision making matters more now than it used to.

“AI is accelerating the speed at which assumptions can be tested,” he said.