Topic starter
24/10/2026 8:50 pm
TL;DR: AI ROI still depends on proxy productivity metrics and governed inference spend, Tetrate reports, because fragmented providers, regions and subscriptions block a single direct measure. AI reporting needs a governed path that ties output to cost so finance, audit and security can work from the same evidence.
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: 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.
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.
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.
Glossary terms: Proxy Metric, Inference Spend, Showback, Chargeback
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 7 days ago by NHI Mgmt Group
25/10/2026 11:11 am
AI ROI governance fails when organisations confuse measurement with meaning. The article correctly separates the question of what value is from the question of how to observe it. That distinction matters because leadership never accepts raw activity as business value, yet teams still need a reportable proxy before they can improve the model.
A few things that frame the scale:
- A Harris Poll survey of more than 300 technology decision-makers found that 86% expect agentic AI to deliver positive ROI, yet fewer than half had AI governance policies in place.
A question worth separating out:
Q: What is the difference between output proxies and true ROI measurement?
A: Output proxies describe observable activity that stands in for value, while true ROI would require a direct, agreed measure of business return. In AI programmes, true ROI is rarely available immediately, so proxy metrics are the practical control until the organisation can correlate usage with outcomes more reliably.
👉 Read our full editorial: AI ROI measurement depends on proxy metrics and governed spend
This post was modified 7 days ago by NHI Mgmt Group