Measure the Workflo before you scale it

McKinsey's November 2025 State of AI survey is still the clearest public picture of the gap. Eighty-eight percent of respondents said their organizations regularly use AI in at least one function. About one third said they had begun to scale. Thirty-nine percent reported any enterprise EBIT impact from AI, and about 6 percent qualified as high performers, with at least 5 percent of EBIT attributed to AI. Those high performers were nearly three times as likely as others to say they had fundamentally redesigned individual workflows.

Use-case savings show up often. Enterprise operating leverage is rarer. It shows up as faster cycle time, fewer exceptions, lower cost or risk, and a named owner. Model accuracy alone does not put a number in the monthly pack.

Name the measure first

SHIFT to SCALE starts with that scoreboard. Before a Forward Deployed Engineer builds, the engagement names the measure: cycle time, cost, risk, adoption, or quality. PROVA then ties the shipped workflow to evidence, so the review asks whether the measure moved.

If the outcome cannot go in a monthly pack, the program is still a pilot. Prove one Workflo, then decide what to scale.