Your teams are already using AI. The question is whether anyone is validating what it produces.
Roughly 41% of workers use generative AI for work (Federal Reserve FEDS Note, April 3, 2026) — while only 39% of adopting firms report any enterprise EBIT impact (McKinsey, Nov 2025, N=1,993, vendor-sponsored) and 16% report high measurable value (HBR Analytic Services/Appian, Apr 2026, N=385, vendor-sponsored). Adopted with nothing to show, or adopted ungoverned — both are validation problems.
Vendor surveys say 88–91% of companies have adopted AI. The U.S. Census Bureau says 19.8%.
88% is McKinsey (Nov 2025, vendor-sponsored); 91% is RSM's mid-market survey (2025, professional-services vendor); 19.8% is the Census Bureau's Business Trends and Outlook Survey, a ~200,000-firm panel, period ending May 3, 2026. The difference is methodology — and knowing the difference is what we sell. Every number on this site carries its source, sample, dates, and sponsor: Sources & Standards.
The generator never grades its own homework.
Independent validation gates
Validators are deterministic checks or a human judge — never the model that generated the work. Engineered for failure decorrelation, not asserted independence.
A human gate on every external action
Route by reversibility: [auto] for the mechanical and machine-checkable, [judge] for the consequential. Leverage, not autonomy — the human is the outbound channel.
A signed, dated record
Every decision lands in a signed record: what was checked, against which standard version, by whom, on what date. The deliverable is the record — not a live agent.
A named human owns the residual
No checklist reaches the risk nobody has conceived yet. The stack ends in a named person who accepts what remains — this one.
A diagnostic that can end in “don’t proceed.”
Bounded, fixed-duration, and paid — and you keep a signed findings record either way. If the honest answer is no, that’s the answer you get.