72% of organisations plan to increase their investment in generative AI over the next 12 months, and with AI dominating conversations at a strategic level, you'd expect more CIOs reporting successes. So why aren't they? The answer lies in the fundamentals.

Geopolitical instability, ransomware escalation, and tightening AI regulation are transforming data sovereignty into a boardroom-level risk. Conflict in regions such as Iran, rising geopolitical tensions, and increasingly targeted cyberattacks on national infrastructure have all exposed the fragility of global data dependencies.

CIOs are being asked to scale AI without losing control of security, compliance, or cost. The goal is to let it move fast inside clear guardrails. That's where data governance comes in: it's the control plane that decides what an agent can see, what it can do, and what it can change.

Our recent Data Defined Futures event brought together experts, partners, and customers for a day focused on the impact data will have on organisations’ AI ambition. From insightful speaker sessions to innovative demos, the event showcased how organisations can harness emerging technologies to shape their future.

At THINK this year, IBM made one thing very clear: the AI conversation has moved on. Pilots have been developed, use cases have been proven, and concepts completed. The focus now is on measurable AI integration for businesses.

Networks are the hidden foundation of AI success and explains how evolving infrastructure demands, legacy limitations and modern connectivity approaches will define which organisations can scale AI securely and effectively.

AI, especially agentic AI, is driving massive investment, but most initiatives are stalling before production. Not because of the models - because the data foundation isn't ready. The risk is already there. Organisations don't have enough visibility or control over their data.

With shifts in the market outpacing annual planning cycles, and new regulations introducing higher levels of scrutiny than ever before, organisations find themselves having to react and make decisions quickly, leading to data fragmentation.

The challenge of adopting AI is no longer proving that the technology works, but ensuring that the infrastructure supporting it can keep pace with the scale and speed of these new workloads.