AI ROI measurement depends on proxy metrics and governed spend

At a glance

What this is: This is an analysis of how to report AI ROI when value and spend are distributed across teams, providers and regions, with the key finding that proxy metrics and governed telemetry are the only practical starting point.

Why it matters: It matters because identity, workload and agent attribution determine whether AI costs can be tied to real outcomes, and that governance problem mirrors the same lifecycle control issues seen in NHI and agentic AI programmes.

Context

AI ROI is a governance problem before it is an analytics problem. Organisations often cannot define value in a way leadership accepts, while AI spend is scattered across APIs, seat licenses, cloud projects and direct calls that do not share one reporting model. The result is that output and cost remain disconnected, so teams argue about definitions instead of making decisions.

The identity angle is real even in a finance-oriented ROI discussion. The article shows that costs become hard to govern when usage is tied to people, workloads and agents through different paths, which is exactly where AI identity and NHI controls start to matter. Without a stable way to attribute requests, budgets and outcomes, the programme cannot be measured or governed consistently.

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. Pilot metrics usually describe activity, not value. A credible ROAI model counts completed transactions, attributes them to the right workflow, and lets finance, audit, and security agree on the same evidence.

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. Individual subscriptions, cloud projects, direct API keys and SaaS agents all record cost differently, so spend fragments before finance can compare it. Governance fails when the organisation cannot normalise those paths into one attributed reporting model.

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. If the report cannot explain what drove the number or how it was collected, it is not ready for executive use.

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.

Technical breakdown

Why proxy metrics are the only workable starting point

AI value rarely appears as a clean business number. Engineering teams can measure merged pull requests, customer support resolution time or bug-fix latency, but those are proxies for value, not value itself. The technical problem is not that proxies are imperfect, but that leadership needs a directional signal while the organisation learns which indicators correlate with outcomes. A useful proxy is one the team understands, can defend, and can revise as the programme matures.

Practical implication: Start with a proxy that can be collected now, then document its limits and replacement path.

Why AI spend becomes invisible across providers and identities

AI consumption is fragmented because usage arrives through many identity and billing paths: direct API keys, hyperscaler projects, subscriptions, SaaS agents and home-grown gateways. Each path reports different units, different billing periods and different owners. That means spend cannot be reconciled reliably unless requests are normalised at the point where identity, model access and telemetry meet. In practice, the hardest problem is not cost collection alone, but attributing that cost to a person, team, workload or agent with enough consistency to support showback or chargeback.

Practical implication: Centralise request telemetry so every AI interaction can be attributed to a governed identity.

How a governed AI gateway turns telemetry into management data

A gateway sitting between clients and model providers can record tokens, requests, cost and identity in one schema. That creates a measurement plane that is separate from the model plane, which matters because leaders need comparable data across regions and providers even when the underlying services differ. Once the gateway also supports budgets and per-tenant controls, the same telemetry can support both operational governance and financial reporting. This is the clearest bridge between AI usage control and identity governance in the article.

Practical implication: Use governed routing and per-identity telemetry to make AI usage measurable before leadership asks for ROI.

Breaches seen in the wild

• LiteLLM MCP auth bypass 2026: An exploited LiteLLM MCP auth bypass and default sk-1234 master keys let attackers steal AI gateway master and provider API keys.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

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.

Identity attribution is the hidden control point in AI cost governance. Once usage is split across individual subscriptions, workload projects and shared keys, the programme loses the ability to say who spent what on which outcome. In NHI terms, a request without a durable, governed identity becomes an unallocatable event, not a manageable control signal.

AI governance debt accumulates when telemetry is not designed in from the start. If spend and output are captured in different systems, the organisation spends more time reconciling than governing. The practical conclusion is that measurement architecture is part of AI control design, not a reporting afterthought.

Proxy metrics are not a compromise to be embarrassed by; they are the only honest interim control. Mature programmes iterate toward better measures instead of waiting for a perfect one that never arrives. That posture is consistent with how identity programmes evolve as well: collect, correlate and tighten governance over time.

Named concept: attribution gap. This article exposes the attribution gap between AI usage and business value, where identity, cost and outcome data do not meet in a shared governance model. The practitioner consequence is clear: if the request cannot be attributed, the result cannot be credibly reported or controlled.

From our research library:

• 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.
• Read next: Agentic AI Identity Maturity Model

What this signals

Attribution is now the governance problem behind AI spend. When usage is split across subscriptions, workloads and direct API calls, finance cannot see which identity drove which cost. That creates an operational blind spot for any programme that wants to treat AI as a managed service rather than an uncontrolled expense.

Proxy metrics should be treated as a control surface, not a consolation prize. The right first report is one that leadership can challenge and improve, because waiting for a perfect ROI definition only delays governance. Teams should expect measurement to mature in stages, with identity attribution improving as the telemetry path stabilises.

For practitioners

• 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.
• Measure movement, not just totals Track whether teams move from high spend with low output to more efficient quadrants over time, because directional improvement is often more meaningful than a static ratio.
• Publish the first report as a draft Label the first ROI report as provisional, invite objections, and use those objections to replace weak metrics with better ones in the next reporting cycle.

Key takeaways

• AI ROI reporting breaks when organisations expect a single perfect metric instead of pairing output proxies with spend telemetry.
• Fragmented identity across keys, subscriptions and workload projects makes AI cost attribution a governance problem, not just a finance problem.
• The practical control is to route requests through a governed path so cost, identity and outcome can be measured together.

Key terms

• Proxy Metric: A measurement used in place of the true objective because the true objective is harder to quantify. Proxy metrics are dangerous in security and AI governance because they can be gamed by systems that are optimising for the score rather than the intended outcome.
• Inference Spend: The money consumed each time a model generates an output in response to a prompt or agent action. It is a useful accounting measure, but by itself it does not show whether the usage was productive, risky, or outside policy.
• Showback: Showback is internal reporting that makes AI usage visible without billing the consuming team. It is used when ownership or attribution is still settling, because it lets organisations validate request tagging, build trust in the data, and prepare for formal chargeback later.
• Chargeback: Chargeback is the allocation of technology costs back to the business unit, product, or service that incurred them. For AI workloads, it becomes a governance control when pricing and attribution are reliable enough that cost responsibility can influence design, usage, and prioritisation.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security and identity lifecycle control. It helps practitioners connect identity governance to the broader security programmes that now include AI systems and agents.