What is a realistic AI ROI?

There is no reliable published benchmark, because the major studies measure different populations by different methods. Google Cloud's 2025 survey of 3,466 executives at enterprises already using generative AI found 74% reporting ROI within a year. MIT's 2025 review found 95% of organisations getting no measurable P&L return. S&P Global's 2026 research found 37% of initiatives live and delivering value with 46% on track within twelve months. Calculate your own case from your own numbers rather than benchmarking against any of these.

Why do AI ROI studies disagree so much?

Four reasons: who commissioned the research, whether returns were self-reported or measured in the ledger, whether the sample included failed initiatives or only deployed ones, and what measurement window was used. Vendor-commissioned executive surveys of organisations that already deployed successfully will always produce higher figures than independent P&L reviews of everyone who tried.

How do you measure AI ROI?

Capture a baseline first: volume, fully loaded unit cost, error or rework rate, and cycle time, measured before the build starts. Then build the benefit side from four categories in order of defensibility — eliminated external spend, reduced rework, reallocated time, and revenue effects. Build the cost side to include discovery, integration, data preparation, inference at production volume, change management, and ongoing maintenance, not just the build. Agree the measurement window and the failure threshold before work starts.

What is the most defensible AI ROI metric?

Eliminated external spend. A cancelled BPO contract or reduced agency retainer appears in the ledger as a line that used to exist and now does not, which requires no interpretation. MIT found this is also where most documented enterprise returns actually came from, with back-office BPO elimination worth $2–10 million annually in the cases they examined.

Should reallocated time count towards AI ROI?

Present it separately from cash savings rather than adding it in. Freed hours are real, but they only become money if headcount changes or the capacity is visibly redeployed to something that earns. In the worked example above, counting reallocated time as savings shortens the apparent payback from 8.1 months to 2.8 — a difference a finance team will identify, at a cost to the credibility of the next business case.

Why do AI budgets get exceeded?

Because consumption pricing does not behave like a licence. The FinOps Foundation's 2026 survey of 1,192 practitioners found 73% of organisations exceeded their original AI cost projections. Uber exhausted its entire 2026 AI budget by April after agentic coding tools spread from 32% to 84% of its engineers in three months. Bain found token costs halved between December 2024 and December 2025 while consumption grew 450% — falling unit prices invite more usage rather than reducing spend. Model inference at production volume, and budget for caps and monitoring.

How do we attribute results when several things changed at once?

Use a control group where one exists — a region or team left unchanged for the measurement period — or state a share of the effect with the reasoning behind it. A defensible partial attribution is accepted far more readily by finance teams than an implausible claim to the entire delta.

How long does AI ROI take?

It depends on the window you set, which is why you should set it explicitly. Published studies use six months, twelve months, and "within the first year," and each produces a different success rate from broadly the same market. For a scoped back-office automation with a clear baseline, twelve months is a common and defensible horizon.

What should we do if the AI ROI does not justify the project?

Test three changes before abandoning it: move the use case to a back-office process where returns are better documented, change the sourcing from internal build to configured product, or reduce the scope to a proof of concept that answers the expensive uncertainty cheaply. If none of them changes the answer, stopping early with a documented reason is the correct outcome.

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