Proxy Metrics Are Theater Unless Finance Can Trace the Value
A client-ready view of what survives AI ROI scrutiny: ledger lines, task fit, the gain-to-P&L chain of custody, and the difference between efficiency bets and positioning bets.
It is safe to claim AI ROI only when the metric traces to finance: cost displaced, headcount not backfilled, or unit-cost cycle time on a process with a known baseline. Hours saved, adoption rate, perceived productivity, and vendor ROI multiples stay internal signals until finance can reconcile them to the general ledger.
The measurement ladder
Usage, prompts, licenses, demos, and adoption. Useful for change tracking, not ROI.
Speed, quality, or volume improves on bounded work. Real evidence, but still upstream of finance.
The gain survives review, integration, governance, and process change inside the operating workflow.
Cost, headcount, unit cost, margin, or revenue shows up against a finance-owned baseline.
The sign-off test
Owner
Who in finance owns the baseline, counterfactual, denominator, and final value-recognition rule?
Briefing
Is this initiative an efficiency play or a positioning bet, and which measurement standard applies?
Proof
Can the result separate cashed savings from redeployed hours, and include governance, integration, change, and run costs?
What leaders should take from it
Finance can audit hard cost removed, headcount not backfilled, or unit-cost cycle time. Proxy metrics become theater when they never touch an income statement.
Controlled studies show strong gains on bounded work and negative effects on some expert work. The task frontier matters more than the headline number.
Published evidence supports task gains, but not a peer-reviewed, finance-controlled enterprise ROI multiple. Precise vendor ROI figures should not carry the argument.
Attribution, redeployed hours, hidden governance costs, integration work, and review bottlenecks break the chain between a pilot win and booked value.
Infrastructure vendors, consultancies, and acquired observability tools often get paid before the client proves ROI. Treat their numbers as claims to test, not proof.
Three claims run ahead of the evidence: that the MIT NANDA 95% figure is an audited failure rate, that any single productivity effect generalizes across tasks, or that a vendor ROI multiple proves enterprise value. The defensible claim is narrower and more useful: AI ROI survives only when finance can trace it.
The Deep Dive holds the action map: finance baseline, efficiency-versus-positioning classification, cashed-versus-redeployed reporting, shadow-cost budgeting, full claim ledger, and refresh triggers.
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