How close are you to AI outcomes?
AI projects stall for two reasons: the data couldn't support the decision, or nothing changed once the answer arrived. They need different fixes, so they are scored separately.
Every question asks about something observable: an artifact, a cadence, a consequence.
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AI Outcomes Score (Example)
AI is built. But it's not being used.
Between Readiness and Adoption, understand which is holding you back.
The ones that cost you points, quoted with their scores, plus the ones you couldn't answer, which are usually the finding.
What to do first, what to leave alone, how to measure success.
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Answered questions stay visible. Click on a previous question to change an answer.
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Metrics governed where a system can read them. Two systems for a cross-functional question, not five. Minutes to act, not weeks. Recurring decisions documented, current and owned. Access that follows your role. Actions recorded well enough to test a past decision. Almost no reporting assembled by hand. Published numbers almost never corrected.
A named executive whose remit this is, not a committee. Usage reviewed on a set cadence. Targets that affect plans, resourcing or reviews when missed. Switching it off would stop real processes. Money from several operating budgets. Someone hired or reassigned for it. What people build maintained as shared infrastructure. A leader using it themselves in the last week.
Two axes, never one
Readiness and adoption are scored separately, 0 to 40 each. A single blended maturity number would hide the exact gap the plan is built around.
Behavioral answers only
Nothing asked how committed leadership is. It asked whether an executive had looked at usage data in the last 90 days, and whether anything would break if access disappeared.
Points, not percentages
Eight questions per axis, four options each, scoring 0, 1, 3 or 5. Eight questions at 5 points is 40, so each axis is worth exactly 40 and the two add to 80. Nothing is scaled or rounded: the answer table below sums to your score. Quadrant boundaries sit at 20 of 40.
Why "not sure" isn't zero
It leaves the denominator instead. Your readiness score is the points you earned scaled across the questions you could answer, and the ones you couldn't are listed back to you rather than counted against you.
Why adoption has no "not sure"
On those questions, not knowing is the answer. If you cannot name who owns AI outcomes, ownership is not legible, which has the same practical effect as nobody owning it. The lowest option absorbs that.
What this cannot see
Self-reported answers about your own organization. It is a structured argument with yourself, not an audit. The value is in which questions you could not answer cleanly.
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What moved your score the most
The answers that cost you the most points.
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R marks a readiness question, A an adoption question.
What you couldn't answer
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Excluded from scoring, not counted as a zero.
Your 90-day action plan and the scoring methodology
Everything above stays on screen. Below: three phases sequenced for {{ planFor }}, the proof point that tells you each one worked, what not to start with, and the full scoring model, question by question, with your answer and its points.
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Build your 90 day plan
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