The five lines

1. AI investment. Annual AI spend. Disclosed when the company states it, derived when built from figures it states, modeled from indirect evidence (AI headcount, share of R&D or IT, capex) with a wide range. Acquisitions are amortized over five years.
2. Claimed value. Management's quantified AI value claims, in annual dollars, that point at a reported line (a cost line or a revenue line). Claims the company does not attribute to AI are excluded.
3. Verified value. How far the line beat its expected path, capped at the claim and never below zero.
4. Net AI return. Verified value minus AI investment for the same year. Negative when spend outweighs what the statements show.
5. Impact. Net return in basis points of revenue (margin impact), as a share of operating income, and verified revenue uplift as a share of revenue growth.

Four baselines, one ensemble

Each claim is tested against four expected paths. The published verified value is the average of the two central ones; the range is the lowest to highest of all four, combined with the low-to-high investment estimate.

The peer baseline asks whether the company improved more than similar companies over the same years. If a whole sector's cost ratio fell, the company does not get credit for the sector's tailwind. Peer groups: Financials, Health care, Industrials, Technology & services, Consumer & retail, and Telecom. When the two central methods disagree by more than 2×, or one finds value and the other none, the company loses a confidence point.

AI attribution

Each claim gets a probability that management attributes it to AI rather than to a broad program (restructuring, "efficiency", automation in general). At 0.70 or above it counts in full; 0.40 to 0.70 counts and is flagged for analyst review; below 0.40 it is excluded. An attribution-weighted verified value is published alongside the base figure. Today the probability comes from a transparent keyword model; it is being replaced by the Open-Jev decision model, and every API response names which backend produced it.

Productivity signal

Revenue per employee, change over the covered years, compared with the peer median. It is context for the verdict (the Jevons question: does AI make each employee more productive, or does it just add spend?). It is never added to verified value. Gaps over 40% usually reflect spin-offs, divestitures or pandemic swings and are flagged as not comparable.

Verdict and confidence are separate

• Verdict says what the statements show: Paying off, Partly visible, Not visible, or Not testable.
• Confidence says how sure we are of that verdict, scored 0 to 5: disclosed investment (2 points, 1 if derived), a testable claim (1), methods agree (1), clear AI attribution (1). 4 or more is High, 2 to 3 Medium, 0 to 1 Low.

Built on open methods

The approach follows published, open-source causal-inference practice so any customer can reproduce it:

• diff-diff and CausalPy: difference-in-differences and synthetic-control estimators, the basis of the peer baseline.
• CausalImpact (TensorFlow Probability): Bayesian structural time-series counterfactuals, next once quarterly lines are loaded.
• MAPIE: conformal prediction intervals, to replace the min-max range with calibrated 80% and 95% intervals.
• Open-Jev: open decision models for claim attribution, category and lifecycle calls.

What this does not claim

Financial statements cannot prove causation. Verified value means the numbers are consistent with the claim beyond trend and beyond peers. Zero means the claim is not visible in reported results yet, which can be true of real but small or early benefits. This site publishes data and method only. It is not investment advice.