Topic starter
24/10/2026 8:50 pm
TL;DR: AI ROI cannot be measured directly, so teams must combine proxy productivity metrics with governed inference spend across fragmented providers, regions and subscriptions, according to Tetrate. The analytical shift is from waiting for perfect value measurement to building a defensible reporting path that ties output to cost.
Editorial analysis by NHI Mgmt Group, based on content published by Tetrate: “AI ROI Maxxing: How to Report AI ROI to Your Leadership Team”.
Key questions
Q: How should security teams measure AI ROI without relying on pilot metrics?
A: Measure only production outcomes that can be tied to an authorised identity, a bounded task, and a verifiable completion record.
Q: Why does AI spend become hard to govern across tools and regions?
A: Because usage arrives through different identities, billing units and reporting schedules.
Q: What are the signs that AI ROI reporting is not ready for leadership?
A: The clearest signs are that teams are waiting for a perfect metric, spending months debating definitions, and still cannot tie requests to a person, team or agent.
Practitioner guidance
- Define two proxy metrics now Choose one output proxy and one spend proxy that leadership can understand today, then document the known weaknesses of each so the report can be revised rather than debated endlessly.
- Centralise AI request telemetry Route AI calls through a governed path that records identity, tokens, cost and request counts in one schema before the data fragments across providers and billing systems.
- Separate tenant, team and agent budgets Assign budgets at the level where work is actually consumed, whether that is a project, team or agent, so finance can compare like with like instead of averaging unrelated usage.
Bottom line: AI ROI reporting breaks when organisations expect a single perfect metric instead of pairing output proxies with spend telemetry.
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