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19/08/2026 3:49 pm
TL;DR: AI agent ROI models often fail because they undercount recurring evaluation, QA, and maintenance costs while overstating time saved, according to Fiddler. The practical issue is not whether agents can create value, but whether organisations can measure fully loaded costs, redeployed benefit, and risk avoidance before deployment.
NHIMG editorial — based on content published by Fiddler: How to Build an AI Agent ROI Calculator That Finance Actually Trusts
By the numbers:
- Only 29% of executives can confidently measure AI ROI, according to Deloitte's 2025 State of AI survey.
- Klarna said its AI assistant handled two-thirds of customer service chats in its first month, equivalent to 700 full-time agents.
Questions worth separating out
Q: How should organisations build a finance-ready ROI model for AI agents?
A: Start with a pre-deployment baseline, then measure benefits across cost reduction, revenue growth, risk mitigation, and strategic optionality.
Q: Why do AI agent programmes often overstate their financial return?
A: They count time saved as value without proving that the time was redeployed into output, revenue, or avoided cost.
Q: What breaks when observability is used instead of access control for AI agents?
A: What breaks is the security boundary itself.
Practitioner guidance
- Baseline the pre-deployment workflow Measure ticket resolution time, error rates, customer satisfaction, and cost per interaction before the agent goes live so you can compare like for like.
- Model evaluation as a recurring cost Include per-evaluation scoring, human QA review, RAG maintenance, and escalation handling in the unit economics, especially when traffic is expected to grow.
- Tie saved time to ledger outcomes Convert hours saved into redeployed capacity, reduced escalations, avoided outsourcing, or revenue lift, and exclude time that disappears into slack.
What's in the full article
Fiddler's full blog covers the operational detail this post intentionally leaves for the source:
- The worked ROI calculator assumptions, including input values for labour, support volume, and cost categories
- The evaluation cost comparison between in-environment and external API-based scoring at scale
- The sample payback-period math for a tier-1 customer support agent
- The discussion of how to present risk mitigation and strategic optionality to finance teams
👉 Read Fiddler's guide to building an AI agent ROI calculator finance can trust →
AI agent ROI and hidden evaluation costs: what finance misses?
Explore further
19/08/2026 4:25 pm
AI agent ROI is increasingly a governance problem, not just a finance exercise. The article shows that measurement breaks when organisations ignore evaluation, review, and monitoring as recurring operational controls. In identity-heavy environments, those controls intersect with delegated access, policy enforcement, and auditability, which makes the business case inseparable from the control plane. Practitioners should treat ROI as evidence of governed operation, not just cost recovery.
A few things that frame the scale:
- Only 29% of executives can confidently measure AI ROI, according to AI Agents: The New Attack Surface report.
- A separate finding shows that 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so.
A question worth separating out:
Q: Should security and identity teams care about AI agent ROI modelling?
A: Yes, because agents increasingly operate through delegated access, policy decisions, and auditable workflows that affect system trust. If the business case ignores access controls, logging, and review overhead, the organisation underestimates the cost of governing the agent itself. That makes finance, IAM, and AI governance interdependent.
👉 Read our full editorial: AI agent ROI fails when finance ignores hidden evaluation costs
