The Spending Surge: Why AI Budgets Aren’t Translating Into Results

AI investment is booming — yet value remains elusive. Boards are signing off on bigger budgets. CIOs are greenlighting new platforms. Pilot activity is up. But for many organisations, the business doesn’t look meaningfully different. The data’s richer, the tools are newer, and the outcomes? Flat.

This is the spending surge paradox. Organisations are investing heavily in AI, but they’re not capturing its value. Why? Because they confuse deployment with impact. They track implementation milestones, not operational change. They launch systems, but don’t design for how decisions will improve or costs will shrink.

The problem isn’t budget. It’s disconnect. Between AI and the workflows it’s meant to improve. Between tooling and the people expected to use it. Between strategy and the moments where value is actually created — on the frontlines, in process, under pressure.

The organisations that break through are doing less spray-and-pray, and more pinpoint investment. They’re aligning spend to specific performance shifts: faster onboarding, better demand prediction, tighter resolution loops. They’re treating AI not as a capability project, but as an operational asset.

Spend isn’t the issue. But without clarity on what success looks like — and where it should show up — even the best-funded AI program becomes a cost centre.