How to Report AI ROI to Leadership: The One-Page Version

One page, four blocks, one named owner. What the AI work cost, fully loaded. What changed, measured in a unit already on the board pack. What you claim caused the change, with the counterfactual written next to it. What is still unproven. Reports that fail at board level almost never fail because the number was small. They fail because nobody owns the page and the cost side is missing.

That is not a formatting preference. It is the difference between the organisations that can report a return and the ones that cannot, and there is now survey evidence for it.

Belief in AI value is near universal, evidence of it is not

KPMG’s Global AI Pulse for Q2 2026, published June 2026 and fielded 28 April to 25 May 2026 across 2,145 senior leaders in 20 countries at companies above US$50 million in revenue, asked two separate questions. On the first, 76% agreed AI is currently delivering meaningful business value, up 12 points from 64% the previous quarter. On the second, a six-phase self-placement of AI maturity, 7% put their organisation at the final phase, “established ROI”, down a point from 8%.

Those are different questions, so this is not a contradiction. Read together they describe a reporting problem: leaders believe the value is there and cannot yet show it in the form a board accepts.

The pressure is arriving regardless. In the same global sample, 24% named pressure to demonstrate value to investors or the board as a great concern for the next six months, up from 19% the previous quarter, the fastest rise of any governance pressure KPMG tracked.

It is sharper at the top end. In KPMG’s separate US tracking sample of 204 leaders at companies above US$1 billion in revenue, fielded in the same window, 76% put board and investor pressure among the top factors shaping AI strategy for the next six months, second only to data security.

Two variables predict whether the number lands

The same report splits respondents by two structural traits, and the gaps are large.

Organisations with clearly defined accountability for AI reported established ROI at 14%, against 4% for those without: about three times the rate. Organisations with full visibility into AI operating costs reported it at 15%, against 3%: five times.

Most organisations have neither. Cost visibility in the global sample broke down as:

• 35% fully visible and actively monitored (KPMG Global AI Pulse Q2 2026, June 2026)
• 42% somewhat visible (same source)
• 13% visible only after billing arrives (same source)
• 8% largely invisible (same source)

In the US large-cap sample, the fully-visible figure was 26%. So the two fixes are a named owner and a real denominator, and both belong on the page itself rather than in an appendix.

Put the cost line first

Most AI ROI decks open with the benefit. Open with spend instead, because that is the number a board can check independently, and the one they will check.

Fully loaded means inference and model spend, tooling and licences, integration build hours at your actual internal rate, and the human review time the process still consumes after automation. The last item is the one that goes missing, and it is usually the one that decides whether the return is real.

Boards are already acting on this. In the KPMG global sample, 49% of organisations had rephased AI agent deployments because expected costs were starting to outweigh value: 24% scaled back and 25% delayed or paused.

What goes in the “what changed” line

Report the benefit your evidence supports, which for most organisations is efficiency rather than revenue.

Deloitte’s State of AI in the Enterprise, published 21 January 2026 off 3,235 business and IT leaders across 24 countries and surveyed in August and September 2025, found 74% hoping to grow revenue through AI and 20% already doing so. The benefits actually achieved ranked differently:

• 66% productivity and efficiency gains (Deloitte, January 2026)
• 53% better insights and decision-making (same source)
• 40% cost reduction (same source)

A revenue claim is where a board catches overreach, because revenue has other owners and a dozen other causes in the same period. An efficiency claim, stated as capacity, holds up under questioning.

Deloitte also found 37% using AI at surface level with little or no process change, against 34% using it to transform a process. If the process did not change, the saving has not happened yet, and the page should say that in those words.

The attribution line, and the counterfactual next to it

State what you claim caused the change, then state what else could have caused it. Headcount is the claim to handle most carefully.

The US Census Bureau’s Center for Economic Studies working paper CES-WP-26-25, published April 2026 off the 2026 AI supplement to the Business Trends and Outlook Survey covering November 2025 to January 2026, found AI-related employment decreases in 2% of firms. It also found 18% of firms using AI in a business function, 32% employment-weighted, and 57% of AI-using firms running AI in three or fewer functions.

Two consequences for the page. If you book a full-time-equivalent saving, name the role you did not fill or the role you consolidated; without that, report it as capacity rather than cash. And scope the claim to the functions you actually touched, because an enterprise-wide number derived from three automated processes will not survive the first follow-up question.

The one page

Block	What it contains
Spend	Fully loaded cost for the period: inference, tooling, build hours at internal rate, human review hours
Change	Measured before and after, in a unit already on the board pack
Attribution	The claimed cause, the counterfactual, and the functions in scope
Unproven	Claims not yet supported, and the date you expect evidence
Owner	One named person

The owner row does more work than it looks. In KPMG’s global sample, 3% of organisations said accountability for AI-informed decisions is unclear or not formally defined, and a further 13% called it shared. Shared is the version that fails quietly: the page has no author, so nobody defends it when a question lands.

What we could not source

Two figures circulate in this area that we could not stand behind, so we are naming them instead of using them.

The pair claiming 87% of finance leaders face pressure to tie AI spend to outcomes and 66% of boards condition funding on proof of return has no named publisher we could locate. We are not quoting it.

McKinsey’s State of AI 2026, published August 2026, is reported to find 37% of respondents attributing at least some EBIT impact to AI, with 6% qualifying as high performers by attributing 5% or more of EBIT. A stated EBIT threshold is exactly the kind of attribution rule this post argues for. We could not open the source directly during this research, so we describe it as reported rather than citing it as verified.

What our own page looks like

The figures on our agent fleet proof page are written the way this post argues for: 41 registered agents, 9 live in production, 5,450 runs and zero failures across the 13 days measured to 4 July 2026. Every one is dated and scoped to the period it covers, not presented as a running total, because a number without a period attached is the first thing a board discounts.

Producing that page every week, including the unproven block, is most of what a fractional AI officer does for $2,900 a month.

The failure this avoids is the one described in why AI pilots fail: work that was genuinely useful and still cannot survive a budget review. The raw inputs are the four numbers captured before the build, which is where this reporting gets easy or impossible.

Send us your current AI reporting, whether that is one page or fifty slides, and we will send back the one-page version with the gaps marked. Written reply within one business day, no meetings required. Start async.