Why do most enterprise AI projects fail to show ROI?
Most stall before they ever reach the scale where returns show up. The pilot demos well, then the project hits the costs nobody budgeted: evaluation, integration with messy real systems, data cleanup, governance sign-off, and the ongoing expense of running and monitoring the thing. Add a vague success metric — 'improve productivity' with no baseline — and you get projects that consume budget without producing a number anyone can point to. The failure is usually operational and organizational, not the model.
Enterprise AIAI Evaluation & Reliability
Pilots are easy; production is where ROI lives
A proof of concept proves the model can do the task once, in clean conditions. Returns only appear at scale, in production, running against real data and real users. The distance between those two is where most projects quietly end — the pilot succeeds, the rollout never happens, and the spreadsheet shows cost with no offsetting return.
The costs nobody budgets
The line item that kills ROI is everything after the model works: wiring it into legacy systems, cleaning the data it depends on, building evaluation so you can trust it, clearing governance and compliance, and paying to run and monitor it indefinitely. These are large, ongoing, and easy to leave out of the original business case — so the project looks cheaper going in than it is.
Vague metrics guarantee invisible returns
Many projects can’t show ROI because they never defined it. “Make support more efficient” isn’t measurable; “cut average handle time by 20% against this quarter’s baseline” is. Without a baseline and a target set before you start, even a project that works can’t prove it did.
What the successful ones do differently
They pick a problem with a clear dollar value, budget for the whole lifecycle rather than the pilot, and decide up front how they’ll measure success. The model is rarely the hard part. The operating discipline around it is.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
