I was at London Tech Week last week.
Two days of sessions, and a lot of corridor conversations with CIOs, CTOs, founders, and enterprise buyers.
The question everyone came in with was simple: how do we get real ROI from AI?
Most enterprises in that room had already spent years on Salesforce, SAP, Microsoft, ServiceNow.
The frustration was consistent: the software exists, the spend happened, but the systems still do not speak to each other well enough to create real intelligence.
→ Nobody wanted another AI tool. They wanted AI that works across what they already have. The sprawl is already exhausting them. → The model conversation has largely settled. The real question now is who owns and controls the data those models run on. Common data gives nobody an edge. → AI is moving from answering questions to executing work. Triggering workflows, updating systems, coordinating teams. Most enterprise stacks were not designed for that level of orchestration. → A small team with the right AI setup can now do what used to need a much larger organisation. That changes how seriously you need to take internal readiness.
The companies getting outcomes from AI did the unglamorous work first. Clean data, connected systems, a single source of truth.
A better model on fragmented data just produces wrong answers faster.
We have seen this closely at Indus Net Technologies (INT.). The readiness gap is real and most companies underestimate how long it takes to close.
If you're a CXO building an AI roadmap right now, has data readiness become a real part of that conversation yet?