Decision debt is stalling your AI transformation

Governance is ready. The tough decisions are not.

Many stalled AI projects aren’t waiting for the next model release. They’re waiting for the person who owns the project to make a tough decision.

As the second half of 2026 proceeds, the distribution of outcomes across firms with active AI initiatives is wider than optimists might have predicted.

The governance is in place, the budgets have been approved, and the licences rolled out - yet the operating model is the same as it was last year.

I’ve spent many an hour over the course of my corporate career in a workshop or meeting that danced around the real choice a business faces.

Sometimes you can build a stakeholder coalition and get the right outcome. Other times, politics and commercial realities push the tougher calls down the agenda.

One example is a use case that crosses departmental boundaries. Stakeholders need to agree that the way finance and sales run the process must change to realise the benefits. This is a 30-second decision from a leader, but a 10-week drama at many firms.

Deciding who is accountable for AI output, and what risk profile is acceptable for work that reaches customers or regulators, is a big part of the complex decision-making that can accelerate benefits realisation or cause a project to stall.

The core idea is this: decision debt is the accumulation of tough calls you haven’t made. It increases cost, confusion and resistance in much the same way as tech debt - or keeping an underperforming supplier in place.

The consequence of this is that when leaders do not make tough decisions, people reporting into them and the people reporting into those people start to become more cautious and risk-averse.

People worry more about their job security and the political consequences of making a judgement call. They start to wait for permission and preserve the old processes. That is the opposite of what an AI transformation, or any change initiative, needs.

Paid subscribers will be able to read on to see the five questions I would use to expose the decision debt inside an AI change initiative and how I think about this challenge for AI projects of all sizes.

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