Three years into the gen AI wave, the failure mode has changed shape. It’s no longer “we don’t understand the technology.” It’s “we keep funding the easy 20%.”

PwC put a number on it in their 2026 AI Predictions: technology delivers about 20% of an AI initiative’s value. The other 80% comes from redesigning work — so agents handle routine tasks and people focus on judgment. BCG reaches the same shore from a different boat: 70% of AI’s transformative value depends on changes to people, organisation, and process. Algorithms and infrastructure account for 30%.

And yet boards keep approving the 30%. Let’s delve into this.

Focus On: The ratio that should anchor every 2026 AI budget

McKinsey’s most useful contribution to this conversation isn’t another adoption statistic. It’s a planning constraint. Buried in their analysis is what’s now being called the $1:$3 rule: for every dollar spent developing an AI model, organisations should budget three dollars for change management. Workflow redesign, role redefinition, governance, training, and the unglamorous work of teaching people to trust outputs they didn’t produce themselves.

Most organisations are running closer to $1:$0.30. They have it inverted.

The evidence that this matters is now abundant. McKinsey’s State of AI 2025 tested twenty-five organisational variables against EBIT impact from gen AI. The single strongest predictor wasn’t model sophistication, data estate size, or technology budget. It was whether the organisation had fundamentally redesigned its workflows. Only 21% had. The 6% who qualify as AI high performers — the ones attributing more than 5% of EBIT to AI — were nearly three times as likely to have rewired the work itself.

Here’s what makes this awkward. Deloitte’s 2026 State of AI in the Enterprise found that education — not workflow or role redesign — was the number-one way companies adjusted their talent strategies for AI. Only 34% are truly reimagining the business. The other two-thirds are running training programmes on tools their operating models can’t absorb.

The vendor incentive trap

I’ve been watching this pattern accumulate for eighteen months across industries, and the diagnosis is clear. The vendor incentive structure rewards line items: licences, GPUs, copilots, platforms. The board approval process too often rewards line items. The CFO benchmarks against line items. Few approve a forty-million dollars line item called “rewire the operating model” — but that’s what every credible source now says is the binding constraint.

The contrarian read is even sharper. Jing Ho at Ardonio looked at the same McKinsey data and argued that spending more on AI actually makes you twice as likely to fail, once you correct for survivorship bias. Heavy investors include both the high performers and an enormous tail of expensive failures. The variable that separates them isn’t budget. It’s whether the budget sat downstream of a workflow redesign or upstream of one.

What this means for next year’s plan

Forrester now reports that up to 25% of planned 2025 AI spend is being deferred to 2027 in organisations that couldn’t demonstrate Q1 or Q2 returns. The bill has come due. Boards are losing patience. The deferrals will accelerate.

If you’re sitting in front of next year’s AI budget, the first question isn’t which model, which vendor, or which platform. It’s whether the spend you’re about to approve sits downstream of a workflow you’ve already committed to redesigning — or whether you’re hoping the technology will somehow do the redesigning for you. It won’t. It never has.

The 6% aren’t winning because they bought better models. They’re winning because they were willing to break things their peers wouldn’t touch. By the end of 2026, AI spend that doesn’t sit downstream of an operating-model decision will look exactly like the digital transformation spend of 2018: real money, real dashboards, no EBIT.

The only question is whether your board notices before or after the deferral starts.

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Disclaimer: The views and opinions expressed in Chronicles of Change and on my social media accounts are my own and do not necessarily reflect the official policy or position of S&P Global.