How to measure and defend AI's EBITDA impact to a board or investment committee

The problem with most reporting is measurement, not effort. McKinsey's research captures the consequence: 88% of companies use AI in at least one function, but only 39% can point to any EBIT impact, and usually under five percent. The activity is real; the attributable number is missing.

The method is to baseline each target process before deployment — AP cost per invoice, days-sales-outstanding, contract leakage — measure the delta afterward, and count only the sustained portion. “AP cost per invoice fell from X to Y, releasing $Z, validated over four weeks” is defensible; a productivity anecdote is not. Aggregated across the pockets, this is how a modeled ~$5.3M recovery becomes ~3.6 margin points.

Margin expansion is only half the case. BCG's 2025 study finds AI leaders deliver 3.6 times the three-year shareholder return of laggards, and McKinsey finds digital and AI leaders outperform on total shareholder return by two-to-six times across sectors — evidence that the market re-rates operators who demonstrably run on AI. For a private-equity-held company, that combination — recovered EBITDA plus multiple re-rating — is what converts into enterprise value and improved MOIC at exit. The board does not buy technology; it underwrites a number with evidence behind it.