Most AI initiatives now fail not at the idea stage, and not even at the pilot stage. They fail at the point where someone has to prove the number, and there is no number to prove.
The scale of it
RAND's 2025 analysis found that 80.3% of AI projects fail to deliver their intended business value: a third abandoned before production, another quarter complete but underdeliver, and the rest deliver something but can never justify the cost. MIT's State of AI in Business 2025 report put it more starkly: 95% of generative AI pilots showed no measurable impact on the P&L at all.
Of enterprise AI pilots never reach production, regardless of company size (IDC/MIT).
Of firms abandoned their primary AI initiative in 2025–2026 because they could not prove a path to ROI.
Success rate for projects with quantified success metrics defined upfront, versus those without.
The one thing the survivors do differently
It is not the model, the vendor, or the budget. Projects that define a quantified success metric before they start succeed at more than four times the rate of projects that do not. Survivors decide what "worked" means in numbers, in advance, and baseline against it. Every other project retrofits a justification after the fact, which is exactly when the number turns out not to exist.
What baselining actually looks like
Pick the handful of numbers the business already tracks, or should: hours, quote turnaround, error and rework rate, cash days, on time delivery. Measure them before anything changes. Then measure again after. If a stage will not move one of those numbers, it does not get built, and you find that out before spending on it, not after.
We baseline your metrics first. Then we show the change against them. Every figure we publish for a client is a figure we can defend, because it was measured, not projected.