A measurement model that connects coding-agent usage to business outcomes rather than relying only on spend or activity counts. Teams define the signals that matter to them, such as merge rate, test success, reviewer approval, or rework, and then compare those outcomes against cost and latency.
Expanded Definition
A Custom ROI Funnel is a user-defined measurement path that links coding-agent activity to business value using the signals an organisation actually trusts. Rather than treating volume as value, it traces how agent output moves through a sequence of checkpoints, such as code acceptance, test pass rates, reviewer approval, defect rework, and delivery latency. The term is still evolving in practice because no single standard governs how teams should define the funnel, which means the structure varies across engineering orgs and AI operating models.
For NHI Management Group, the important distinction is that this is not a generic dashboard and not a vendor-fixed KPI set. It is a decision framework for measuring whether an agent is making work faster, safer, and more reliable. The funnel should be designed around the outcomes that matter to the team, then tied back to cost so that efficiency claims can be tested against real delivery impact. That is why it often sits alongside governance practices from the NIST Cybersecurity Framework 2.0, where metrics are expected to support risk-informed decisions rather than vanity reporting. The most common misapplication is counting tokens, prompts, or actions as ROI, which occurs when teams confuse activity volume with measurable business outcome.
Examples and Use Cases
Implementing a Custom ROI Funnel rigorously often introduces measurement overhead, requiring organisations to weigh clearer decision-making against the effort of instrumenting every meaningful step in the workflow.
- A platform team tracks whether agent-generated pull requests reach merge, then compares merge rate against reviewer time and post-merge rework to see if the agent is improving throughput.
- A security engineering group measures whether coding-agent suggestions reduce test failures in sensitive repositories, using NIST Cybersecurity Framework 2.0-style outcome thinking to connect process evidence with operational risk.
- A product organisation follows a funnel from draft code to approved change, then evaluates whether faster completion is offset by higher defect escape rates or slower reviewer turnaround.
- A FinOps-aligned engineering lead compares total agent cost with accepted output, looking at rework, latency, and incident follow-up to judge whether usage is actually economical.
- A governance team defines separate funnels for high-risk and low-risk repositories, because a single ROI model can hide where agent use is genuinely productive versus merely busy.
In mature environments, the funnel is recalibrated when the workflow changes, because what counts as value in one delivery stream may be irrelevant in another.
Why It Matters for Security Teams
Security teams care about Custom ROI Funnels because AI-assisted development can either strengthen or weaken control quality depending on how outcomes are measured. If the funnel rewards speed alone, teams may tolerate weak review, poorer test coverage, or unmanaged code changes that increase downstream exposure. If it rewards only caution, adoption may stall and shadow use may grow. The right model helps security leaders understand whether agentic coding is reducing operational risk or just shifting it elsewhere.
This also matters for identity and access governance when coding agents are granted execution authority in CI/CD pipelines or connected to privileged repositories. In those cases, the funnel should include signals that show whether the agent is operating within approved boundaries, not merely producing output. Teams using NHI-aware controls often find that the measurement model becomes part of the control surface, because usage patterns can reveal over-permissioning, stale workflows, or poorly governed automation. Organisations typically encounter the real cost of a poor funnel only after agent output has created review bottlenecks, regressions, or control gaps, at which point the measurement model becomes operationally unavoidable to fix.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OC-03 | Outcome-based measurement supports organisational objectives and value signals. |
| NIST AI RMF | MAP 1.3 | AI RMF requires context-aware metrics to understand intended use and impact. |
| OWASP Agentic AI Top 10 | Agentic AI guidance emphasises governance of tool-using agents and outcome tracking. | |
| OWASP Non-Human Identity Top 10 | NHI governance depends on understanding how non-human identities behave in workflows. | |
| NIST SP 800-53 Rev 5 | CA-7 | Continuous monitoring requires metrics that show whether controls are effective over time. |
Build ROI funnels that surface control drift, rework, and review failures for monitoring.
Related resources from NHI Mgmt Group
- How should organisations measure identity security ROI beyond license savings?
- What is the difference between IGA ROI and broader identity security ROI?
- How should teams evaluate ROI claims for NHI and privileged access platforms?
- When should organisations prefer standards over custom implementations?
