Does your number cast a shadow?
A number that floats measures intent. A number that rests on the ground measures return. The difference is whether you can trace it to something real — a source, a baseline, a definition you can test. Scroll down. This is a descent toward the ground.
The numbers that float
The figures the boardroom quotes. The optimist’s headline and the skeptic’s headline share one property: neither touches the ground. Watch them hover — none of them casts a shadow.
~79%
name AI a top investment priority
Priority is enthusiasm. Enthusiasm is a forecast of spend, not a record of return — so the figure floats.
KPMG Global AI Pulse, Q2 2026
95%
of generative-AI pilots showed no measurable return in six months
The number quoted to attack AI. It is real but routinely misframed and is being actively debunked — a readiness failure, not an AI failure. Even the number the skeptics quote cannot bear weight.
widely cited, MIT-attributed — contested
hours saved / adoption rate / tokens used
The figures your dashboard is proudest of. Each measures activity, not return — the Measurement Trap in a new costume. This instrument refuses to attach a number it cannot ground.
the vanity metrics the framework refuses
The higher a number floats, the less it can bear. Visibility is what lets a number land — and exactly one of the figures above has a definition you can test against your own operation.
7%
of leaders report they can actually prove established AI ROI
KPMG Global AI Pulse, Q2 2026
The one figure that measures proof, not intent. It rests on a definition you can test. This is the number that casts a shadow.
79% call it a priority. 7% can prove it. The gap between those two numbers is this entire framework — and closing it starts with the one thing you can do today: establish a baseline.
Now build yours
Enter your own AI spend, the three dimensions, and your readiness level. The measurement is arithmetic over your entries and nothing else. At Level 0-1 it withholds the number and hands you the Close-Won Brief instead — because a number you have not earned would only float. Watch the readout: when your visibility earns a number, it drops and casts a shadow.
AI spend for the period
The denominator — measured against value created, never a value metric on its own.
Revenue Influenced
Deals AI was traceably part of — tied to the deal, not “someone used ChatGPT once.” Describe what the AI traceably did in your own words, and be honest: this is the question your board will ask, and the tool records your attestation rather than judging it. An entry with the engagement or that description left blank is shown, not counted.
Revenue Protected
Retention or expansion an AI-detected signal caused. If the team would have caught it anyway, it does not count.
Capability Built
What the team can do now that it could not six months ago. Not a dollar figure. Not hours saved.
Your readiness level
Be honest here — it sets the confidence the number is allowed to carry, and the tool checks your claim against the evidence you entered.
The level is not a score. It is the confidence the number below is allowed to carry: not yet trustworthy — you have activity, not outcome visibility. at the first two levels, directional — curated, not comprehensive. at the third, and answerable with a number that connects to real revenue and real capability. at the top two.
The metrics that do not count
Try to add hours saved, adoption, tokens, or any other metric. The tool refuses each with the metric worth watching instead.
Nothing here reaches the value read. The value picture is built only from Revenue Influenced, Revenue Protected, and Capability Built.
Does your number cast a shadow?
Build the measurement on the left from your own entries. If your number can bear weight, it will drop and cast a shadow. If it cannot, this instrument will tell you so instead of inventing one.
Enter your own AI spend, the Three Dimensions, and your readiness level, then build the measurement. The picture that follows is arithmetic over your entries — nothing else.
Keep going
Ground Truth sets the baseline. Two more doors open onto the same instrument.
Framework developed in collaboration with Trisha Merriam and Erin Wiggers on the Value-First Data show.