The Executive AI Leverage Report: What 421 Executive Responses Say About AI

Most AI reports measure the market from the outside.

They track spending. They track adoption. They track vendor categories. They ask whether companies are experimenting, investing, or scaling.

That work matters. But it misses the question executives are now asking inside the room:

What leverage are we actually getting from AI?

That is the question behind the new Executive AI Leverage Report from Open Future Forum.

The report is a Preview Edition, published in July 2026, and it is built from a first-party read of the Open Future Forum network: 421 executive responses across seven events, spanning finance, security, growth, and founder rooms.

The purpose is not to claim that one room represents the whole market. It does not. The purpose is more useful: to read what is happening inside a screened, operator-heavy executive network where AI is no longer a theory.

And the early signal is clear.

The question has moved from “Are we using AI?” to “Can we prove AI is creating leverage?”

The network is already deployed

One of the strongest findings in the report is that many executives are already past the exploration stage.

At the largest Open Future Forum finance event of 2026, 71 percent of respondents said their team was already running Claude or another AI tool, while only 8 percent had not started.

That matters because much of the public AI conversation still talks as though enterprise AI is mainly experimental.

Inside these executive rooms, the posture is different.

AI is already in use. The debate is no longer about whether to adopt it. The debate is about return, control, governance, cost, process, and accountability.

That is a more serious phase of the market.

The gate is proof

The report’s most important business finding may be this:

Proving ROI is now the main blocker to further AI spend.

Among Open Future Forum finance-room respondents, 53 percent named proving ROI as the main thing stopping them from spending more on AI.

That answer was far ahead of other blockers, including security, compliance, integration, data readiness, and talent.

This is a meaningful shift.

Executives do not appear to be rejecting AI. They are rejecting unmeasured AI.

The market has moved from excitement to evidence. The next dollar is gated by proof.

And the proof window is short.

According to the report, 62 percent of finance-room respondents expect measurable return on an AI investment in under six months, and 79 percent expect it within a year.

That has major implications for founders, vendors, operators, and internal AI teams.

If your AI story takes eighteen months to prove, you may be selling to a buyer who wants evidence in six.

One in six already funds AI from would-be headcount

The clearest leverage signal in the report is the headcount finding.

Among finance-room respondents, 17 percent said this year’s AI budget comes at least partly from money that would otherwise have gone to headcount.

That is roughly one in six.

This is not just adoption. It is budget substitution.

It suggests that AI is starting to move from a software line item into a workforce-planning decision.

That does not mean every company is replacing people with AI. The report is careful on that point. But it does show that, for a meaningful minority of finance leaders in the network, AI is already being evaluated against hiring.

That is the leverage thesis becoming measurable.

AI is not just helping people work faster. It is beginning to change how executives think about capacity.

The CEO signs. Finance has to prove it worked.

Another important finding is the gap between authority and accountability.

When asked who signs off on a new AI purchase, 47 percent named the CEO, while 26 percent named the CFO or finance and 21 percent named the CIO or CTO.

That sounds like CEO ownership.

But the report’s deeper point is that the person who signs the deal is not always the person who has to prove the return.

The CEO may hold the pen. Finance often has to answer whether the investment paid off.

That gap matters.

It explains why AI buying can feel fast at the top and messy in the middle. It explains why CFOs are becoming more central to AI governance. It explains why AI vendors are increasingly being forced to show usage, value, and outcomes rather than simply selling access.

In other words, the AI buying decision is executive-led, but the AI proof burden is increasingly financial.

Founders are pricing ahead of the buyer

The report also captures a supply-side shift.

Among charging AI founders in the Open Future Forum network, 50 percent price on usage and 18 percent price on outcomes, compared with 25 percent who mention per-seat pricing.

That is important because usage and outcome pricing are more auditable than traditional seat pricing.

A seat tells you who has access.

Usage tells you what is being consumed.

Outcome pricing points toward what the buyer actually received.

That is where the market appears to be going: away from access and toward measurable value.

But there is a mismatch. Founders selling into companies still most often name the CIO or CTO and the business unit as the buyer. Finance is less visible to them.

That creates what the report calls the seat gap.

Inside companies, the CEO and finance are increasingly central to approval and accountability. From the outside, founders are still selling mainly through technology and business-unit doors.

That is how AI sprawl happens.

AI enters through the seats closest to use. It gets measured by the seats closest to budget.

Security sees the risk, but the budget is still catching up

The security findings are smaller and directional, but they are some of the most important signals in the report.

Among security-room respondents, 56 percent named securing AI agents and their access as the biggest AI security problem.

That is a useful distinction.

The leading concern was not only external attacks using AI. It was the access that internal AI systems and agents already have.

In other words, the risk is inside the workflow.

But the budget does not yet match the concern. Only 31 percent of security-room respondents said AI security has its own budget line.

That is the gap to watch.

As companies move from AI tools to AI agents, access control, data governance, audit trails, identity, and permissions become board-level issues.

The report suggests that many security leaders already see the problem. Dedicated budget is still catching up.

The Executive AI Leverage Ladder

The most useful framework in the report is the Executive AI Leverage Ladder.

It describes three stages of executive AI leverage:

Productivity — AI as a thought partner.

This is the first stage. AI helps with drafting, research, summarization, learning, and low-risk analysis. It makes the individual faster, but the work still largely flows through the human.

Capability — AI as a co-worker.

This is the second stage. AI starts completing whole units of work. It builds models, drafts decks, runs repeatable workflows, prepares reconciliations, creates operating materials, and supports entire functions through reusable instructions, skills, or agents.

Context — AI that knows the business well enough to ask the right questions.

This is the third stage. It is the most powerful and the least reached. At this level, AI does not simply answer better. It surfaces the issue before the executive asks. It understands the business context deeply enough to become a strategic sensing layer.

The report’s qualitative read suggests that many advanced operators are moving from productivity into capability. Context is visible, but not fully reached.

That is the next frontier.

The real unit of AI leverage is compression

One of the strongest ideas in the report is that AI leverage should be measured in compression.

Not vague productivity. Not general enthusiasm. Not “AI adoption.”

Compression.

Work that took days now takes hours. Work that took months now takes days. Work that required outside support can now be prototyped internally. Work that sat in a queue can now move immediately.

That is the practical executive definition of leverage.

The report describes finance operators using AI to compress modeling, reconciliations, planning, reporting, contract review, and workflow design.

This is where AI becomes real.

Not in the announcement. Not in the pilot. Not in the tool rollout.

In the before-and-after duration of actual work.

Why this report matters

The Executive AI Leverage Report matters because it reads AI from the operator’s seat.

It does not simply ask whether companies are using AI. It asks where AI is creating leverage, who has to prove it, how fast the proof is expected, what budget it replaces, which seats are accountable, and what practices separate serious operators from casual adopters.

The early answer is direct:

The executive market is deployed.

The proof window is short.

The CEO often signs.

Finance increasingly answers.

Security sees the risk.

Founders are moving toward usage and outcome pricing.

And the next stage of AI leverage belongs to executives who can combine cost discipline, governance, process design, and domain expertise.

That is the real shift.

AI advantage is no longer about who has access to the model.

It is about who can turn the model into measurable leverage.

The bottom line

The Executive AI Leverage Report is not just another AI adoption report.

It is an early baseline for a more important executive question:

What does AI leverage actually look like when operators are getting it?

From 421 responses and the peer-learning rooms behind them, the answer is beginning to take shape.

AI is already in the workflow.

Now executives have to prove it works.

Read the full report here: Executive AI Leverage Report.