Topic starter
04/06/2026 6:47 pm
TL;DR: Boards now expect AI programs to prove business value, and WitnessAI argues that means linking spend to performance, workforce impact, and risk reduction, while controlling pilot purgatory and Shadow AI that erode returns. The decisive shift is that governance is no longer overhead, but the mechanism that makes AI investment defensible.
Editorial analysis by NHI Mgmt Group, based on content published by WitnessAI: “How to measure AI ROI: executive guide”.
By the numbers:
- Only 31% of C-suite leaders expect to measure AI ROI within six months.
Key questions
Q: How should organisations measure AI ROI when Shadow AI is present?
A: Start by treating unknown AI use as part of the cost and risk denominator, not as an edge case.
Q: Why do AI projects often fail to show measurable business value?
A: AI projects often fail because measurement is an afterthought.
Q: What are the signs that an AI initiative is not delivering real ROI?
A: An AI initiative is usually underperforming when it takes too long to reach value, produces limited productivity gains, and fails to improve customer satisfaction or innovation speed.
Practitioner guidance
- Define the full AI investment perimeter Include model licences, API consumption, infrastructure, vendor contracts, integration work, change management, training, governance tooling, and hidden Shadow AI costs in the denominator.
- Build one AI inventory for all use patterns Record sanctioned applications, Shadow AI, agentic deployments, and MCP server connections in a single register and assign an executive owner to each entry.
- Choose a small board-ready metric set Track a limited mix of revenue, efficiency, time-to-value, and employee adoption metrics, with each one owned, baselined, and tied to a reporting cadence.
Bottom line: AI ROI fails when organisations treat usage as value and ignore whether spend is tied to governed business outcomes.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
04/06/2026 6:59 pm
AI ROI has become a governance test, not a finance exercise. Boards are no longer asking whether AI exists in the enterprise; they are asking whether AI investment can be defended in terms they recognise. That changes the burden of proof from enthusiasm to evidence, and it forces programme owners to show value, control, and accountability in the same narrative. Practitioners should treat ROI as a governance discipline with financial consequences, not a retrospective spreadsheet.
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: Should organisations prioritise visibility or controls first in AI ROI programmes?
A: Visibility should come first because controls cannot govern what the organisation cannot see. A reliable inventory reveals sanctioned tools, Shadow AI, agents, and system connections, which then allows policy, accountability, and outcome tracking to work. Once the environment is visible, controls can be linked to financial and operational results instead of operating as isolated compliance tasks.
👉 Read our full editorial: Measuring AI ROI now depends on governance, visibility, and risk