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Benefit Realisation

The discipline of defining what an investment should return, baselining it before work starts, and checking after delivery whether the return actually arrived.

AI Strategy

Benefits realisation is the part of programme management that follows the money after the project closes. Each expected benefit is named, given a measure and an owner, baselined before work begins, and given a target. After delivery, someone checks whether the measure moved and by how much, and the result goes back to whoever approved the funding.

Public-sector programmes have run this way for years; the UK’s guidance for major projects insists that each benefit measure is baselined during initiation, before implementation can disturb it. AI programmes mostly skip the step, because pilots are cheap enough to start before anyone treats them as investments. The result is a portfolio whose benefits are asserted and never measured.

The practical test is simple: if the baseline was recorded after the pilot started, the benefit cannot be proven. It can only be estimated, and it should be reported as an estimate.

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AI Strategy

A Joint CFO–CAIO Framework for AI Benefit Realisation

Most AI programmes cannot prove what they returned because nobody recorded the starting point, nobody agreed how to separate the AI's effect from everything else, and the running cost sits in a cloud bill no one maps to a use case. A framework the CFO and the Chief AI Officer run together: baselines, attribution, inference cost accounting and stage-gates that stop funding for pilots that miss their targets.

AI Strategy

What DBS's S$1 Billion in AI Value Measures, and How a Board Should Read It

DBS reported about S$1 billion in economic value from data analytics and AI in 2025. The figure blends revenue, cost avoidance and losses averted, is measured with control groups, and sits outside the audited accounts. Here is what it counts, what it leaves out, and what a board should demand before quoting its own number.