Glossary entry · Governance
Layer 5 of AI value management: whether the total AI portfolio justifies its cost, risk, dependency, and opportunity cost, comparing investments by strategic fit, value evidence, operating risk, vendor exposure and human capability impact.
This is where a CFO, CEO or board decides to scale, optimise, contain, replatform, renegotiate or stop. It is only as good as the evidence the four layers below it supply.
Attribution coverage is the headline. Starting measures in the KPI library include AI Portfolio Balance Ratio, AI Portfolio Concentration Risk, AI Initiative Kill Rate, AI Portfolio Review Cadence and AI Vendor Consolidation Ratio.
Portfolio strategy is the fifth and last of the five layers of AI value management. Skipping the layers below it is the dominant failure pattern: portfolio decisions made before output quality is established rest on adoption stories rather than outcome evidence.
In practice the layer runs through a regular portfolio review that ends in recorded decisions. See Scale, hold, redesign, stop for the decision rules.
Put it to work
Browser tool. Twenty questions across the five layers of AI value management, scored against concrete behaviours, ending in a maturity level and the two layers to work on first.
Word template. A ready-to-send agenda for the quarterly AI portfolio review: attendees, pre-reads, eight standing items plus the quarterly checks, and a decision and action log.
Excel template. Put cost, claimed and realised value, evidence and risk side by side, and end the review in scale, hold, redesign or stop.
Excel template. A starter scorecard that takes KPIs from the library and maps them to the five layers of AI value management, with baseline, target and owner for each.
