Craig Stanley
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Why everyone pads their estimates

Padding estimates is a sensible response to how organisations treat estimates. Game theory explains why, and what changes the behaviour.

10 October 2026 · 2 min read · Craig Stanley
In short, explained

If you get told off for being late but not for finishing early, you'll always say a job takes longer than it really does. Everyone does it.

People pad estimates because being late is punished and being early isn't rewarded. Each person is behaving sensibly, but the whole organisation ends up planning with inflated numbers. Change the rules and the padding goes away.

Estimate padding is an equilibrium response to asymmetric penalties. Each team pads, so planning absorbs the combined buffer and work expands to fill it. Fix the incentives with pooled contingency, reference-class estimation and records that reward calibration over punctuality.

The game

Each team lead is asked how long their piece of work will take. If they come in late, there's an awkward conversation. If they come in early, nothing happens, or the saved time vanishes into the next request. So the rational move is to add a buffer.

Every team does the same. The plan now contains several buffers stacked on top of each other, and work tends to expand to fill the time available. The project takes longer than if everyone had given honest estimates and shared one buffer.

Why lecturing doesn't work

Telling people to stop padding asks them to accept personal risk for a shared benefit. Nobody wants to go first. The behaviour comes from the incentives, so the incentives are what need to change.

What changes the behaviour

Pool the contingency. Ask for honest estimates and hold one shared buffer at project level, owned by the sponsor. Teams draw on it openly when they need it.

Ask for ranges. "Four to seven weeks, 80% confident" is more honest than "six weeks", and it can be checked for calibration.

Reward accuracy, not punctuality. Track estimates against actuals over time. The people whose ranges are reliable are the ones to trust with bigger plans.

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About me

Craig Stanley

Microsoft AI consultant and technical architect, based in Whitley Bay. Over the last few years I've delivered Microsoft 365 Copilot, Copilot Studio agents, Azure AI Foundry work and governance for UK public sector and financial services organisations.

What interests me is the decision underneath the tool: what it costs, what it risks, and whether a small, transparent model can make it better. I write the methods up here and on Substack so anyone can use them.

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