The CFO AI Leverage Report

Executive Summary

The center of gravity in enterprise AI buying is shifting to finance. The spending is real and now runs through core budgets, the deciding question has become whether AI lets a team do more without adding headcount, and the person who has to answer for that spend is increasingly the CFO.

The market data is unambiguous. Enterprise generative AI spend hit $37 billion in 2025, more than three times the year before (Menlo Ventures). The share funded from experimental innovation budgets fell from a quarter to 7 percent in a single year, as AI moved into permanent lines (Andreessen Horowitz). And the question that now recruits a room of finance leaders is not whether to adopt AI, but whether it absorbs more work without growing the team.

This report reads that shift from inside the room. The Enterprise AI Buying & Budget Index is an Open Future Forum research program that reads how the executives who hold AI budgets are buying and funding AI, lane by lane. Edition 1 is the CFO read, and its flagship metric, the CFO AI Leverage Index, measures the share of finance leaders who say AI now lets them grow output without growing the team.

The early signal from our own rooms is already pointing the same way as the macro. In the first responses, three in five finance leaders we asked said finance, not the CEO or IT, signs off on AI purchases. These are early reads on small bases, reported as what they are, and they firm up edition over edition as the responses grow.

The Answers We Have Now

This section is the data in hand. It is drawn from Open Future Forum event registration records across more than 20 events in 2026, more than 200 executive registrations in total. The figures are reported at the lane level rather than event by event, because the lane is the unit that matters, and as rounded floors of distinct registrants. They describe demand and audience composition, not spend. Spend figures arrive with the instrument described later, once it is fielded.

Finding 1: Finance and security are the deepest operating-executive lanes

In 2026 Open Future Forum convened more than 200 executive registrations across more than 20 events. Read at the lane level, where distinct registrants are reported as rounded floors, the shape is clear: more than 100 founders, more than 60 finance leaders and operators, more than 50 security leaders, and more than 20 marketing and growth leaders.

Founders draw the largest single crowds, but that is a venture audience rather than an enterprise buyer. Among the operating executives this Report tracks, the people who hold budgets, finance and security are the two deepest lanes, each sustained across a series of events rather than one spike. That tracks the macro signal that finance is the clearest near-term AI lane, with the honest caveat, detailed later, that coding and customer support lead raw spend nationally.

Finding 2: The headcount-leverage thesis is the sharpest single draw in finance

The strongest-drawing finance gathering of the year was the one framed around scaling a finance team without adding headcount. It out-recruited general CFO programming on that framing alone. The budget story that fills a room is not "adopt AI." It is "absorb more work without growing the cost base." This is the exact thesis the Index is built to measure.

Finding 3: The room is operators, sellers, and adjacents in roughly equal measure

Finance-lane registrants split three ways. Operators who own the number (CFO, VP Finance, controller, fractional and acting CFOs). Adjacents who sell into it (advisors, partners, vendors who describe themselves by what they teach a CFO). And blurred titles, where people run finance without the formal CFO label. The same pattern holds in security and growth. This is why the instrument is asked of finance leaders only and reported role-tagged, so the operator read stays clean.

One finance event makes the point concretely. Of the twelve people who registered, four were finance operators, counting an acting CFO and an "essentially" CFO. The other eight were three investors, three founders, an analyst, and an advisor. We show the mix rather than calling it a room of twelve CFOs, because the composition is the honest part.

Finding 4: In our events, AI use case sorts the room as much as title

Across the calendar, the Open Future Forum events that recruit best are organized around a specific AI question rather than a generic networking promise, and registrants self-sort by that question. An enterprise research leader and a seed-stage operator register for the same table because they share an AI problem. This is a pattern in our own registration data, not yet a proven shift in executive networking at large: as the evidence section notes later, the major AI communities still group by role and title. We carry it as an early signal worth watching, and as one reason the Index sorts its reads by lane.

Three honesty notes on the counts

• Figures are rounded floors of distinct registrants, kept deliberately conservative. Some executives attend more than one event, so the lane totals are floors rather than exact unique counts, and the per-event detail is held back because the lane is the unit that matters.
• The CFO events draw non-finance titles too (CTOs, CEOs, advisors). So "60+ finance leaders and operators" is defensible; "60 CFOs" overstates it.
• "Surveyed" implies opinion collection. These forms collected screening data. Accurate verbs: convened, brought together, engaged.

Defensible claim language, from the data in hand

• "In 2026, Open Future Forum convened 200+ executive registrations across 20+ events, including 60+ finance leaders, 50+ security leaders, and 100+ founders."
• "Finance automation and enterprise AI security are the two deepest AI demand lanes among the operating executives in the Open Future Forum network in 2026."
• "The single strongest finance-lane draw was the headcount-leverage thesis: doing more without adding finance headcount."

Early Signal from the Room

The first responses to our new questions are in. These are early reads, shown as percentages of the people we asked, on small bases at single events. A note on method: at both the finance and the marketing events, the registration questions changed partway through, so only the first cohort at each event answered the question charted here. That keeps these bases small and self-selected. They are directional, not yet the published figures, and they will firm up as the responses grow over the coming weeks. We are showing them now because the direction is already interesting.

Among the finance leaders we asked, finance is where AI purchases get signed off. Three in five pointed to the CFO or finance, rather than the CEO or IT. The room itself was mixed, more on that in the method note, so we read this as a direction, not a measurement.

Among the marketers we asked, the value ties at the top between creating content faster and knowing the customer better. Two of the seven also chose doing the work of more people, so the headcount-leverage story that defines the finance lane is beginning to show up in marketing as well, even if content and insight still lead.

Read these as the first data points on a line we will track, not as settled numbers. The next editions will carry larger bases and the same questions, so the direction can be watched as it moves.

By the Numbers

The enterprise AI buying shift, in the figures the market is citing in 2025 and 2026. External benchmarks are attributed to their sources. The Open Future Forum figures are early reads from our own rooms, with the base shown.

• $37 billion in enterprise generative AI spend in 2025, about 3.2 times the prior year (Menlo Ventures).
• ~75 percent expected year-over-year growth in enterprise AI budgets (Andreessen Horowitz).
• 25 percent to 7 percent, the one-year fall in the share of AI spend funded from experimental innovation budgets (Andreessen Horowitz).
• 72 percent of CEOs now call themselves their company's main AI decision-maker, double the prior year (BCG AI Radar 2026).
• 6 percent to 2 percent, the drop in expected finance headcount growth from 2025 to 2026 (Gartner).
• $1.65 billion to $13.5 billion, the projected size of the agentic AI security market by 2032, a 42 percent compound annual rate (MarketsandMarkets).

From our own rooms, early reads with the base shown:

• 3 in 5 finance leaders we asked said finance, not the CEO or IT, signs off on AI purchases (Open Future Forum, early read, base of 5).
• 2 of 7 marketers we asked named doing the work of more people as a place AI helps most, behind faster content and sharper customer insight, which tied at the top. The headcount-leverage theme that drives the finance lane is now surfacing in marketing too (Open Future Forum, early read, base of 7, multiple selections allowed).
• Of the AI founders we asked who are charging, about 7 in 10 price on usage or outcomes rather than per seat. None of the sixteen named finance as their buyer (Open Future Forum founder events, 2026).

The Thesis, in One Line

The deciding question in enterprise AI has moved from whether the model is good to whether it lets you do more without adding headcount, and the person who has to answer for that spend is increasingly the CFO. This Report puts a measured number on that, from the room where it is being decided.

What the Finance Seat Sees Across the C-Suite

AI spending does not stay in finance. It runs through it. Every other function's AI budget crosses the CFO's desk for approval, which gives the finance seat a view of the whole C-suite that no single function has of itself. What follows is that view, what each role's AI spend looks like from the seat that has to fund it. These are read from the finance side, not surveys of each function, and they are written that way on purpose.

The CEO

Claims the decision, owns the narrative, answers for the vision. In BCG's 2026 survey 72 percent of CEOs called themselves the main AI decision-maker, double the year before. From the finance seat the gap is plain: the CEO sets the AI ambition, but finance is the one asked whether it paid. That gap between the decision-maker and the value-accountability owner is the space this Report sits in.

The CFO

The seat itself. AI arrives here as a budget question before it is a technology question, and the deciding test has become whether it lets a team do more without adding headcount. Finance is moving from the function that approves AI spend to the function held accountable for its return, which is why the flagship metric of this Report is a finance number.

The CMO

Marketing is where finance sees the fastest AI tool sprawl and the hardest attribution. The spend is easy to start and hard to measure, lots of pilots, unclear return. In our own early marketing reads content and customer insight lead, but two of the marketers we asked did name doing the work of more people, so from the finance seat marketing AI reads mostly as a quality-and-speed play, with the first hint of a cost-out one.

The CISO

Security is the AI line finance is most often asked to fund as net-new rather than reallocated, because the risk is new. The agentic AI security market is forecast to grow from about $1.65 billion to $13.5 billion by 2032, a 42 percent compound rate (MarketsandMarkets). From the finance seat this is the spend with the clearest necessity case and the least clean ROI, funded because the downside is unacceptable, not because the return is proven.

The CIO

The build-versus-buy and integration question lands here, and it is where finance and IT negotiate hardest. With AI spend moving out of experimental budgets into core lines, from a quarter to 7 percent funded by innovation budgets in a year (Andreessen Horowitz), the CIO's integration cost is now a permanent line finance has to plan around, not a one-time pilot.

The AI Leader (CAIO)

The newest seat at the table, and from the finance side the hardest to size, because there is no historical baseline. Finance is being asked to stand up a budget line for a function that did not exist two years ago, which is why it draws scrutiny: not doubt about AI, but the ordinary discipline of funding something with no prior year to compare against.

The Digital Transformation Lead

The role that has to convert AI spend into changed operations rather than added tools. From the finance seat this is where the real test of transformation sits, whether the spend shows up as a flatter cost base and higher output per person, or just as more software. That is the headcount-leverage question again, which is why it is the line this Report tracks above all others.

Three seats outside the company, inside the same picture

The venture investor. From the finance seat the VC sets the pace finance has to absorb. The investor consensus for 2026 is not bubble-or-not but concentration, more capital flowing through fewer vendors, which is the exact supply-side mirror of the CFO consolidating spend on what pays. Our own investor room shows the split early: in the first responses, one partner pointed to AI helping customers, cutting costs, and improving products at once, and one said nothing measurable yet. The optimism and the reckoning at the same table, which is what a market looks like mid-shift.

The YC founder. This is the supply side selling into the enterprise, and our own founder room says something the easy story would miss. Of sixteen AI founders we asked who owns the buying decision inside the companies they sell to, not one named finance. They named IT and business-unit leaders, and they priced more often on usage and outcomes than on seats, roughly seven in ten of those charging.

Read from the finance seat, that is not a contradiction of the thesis, it is the leading edge of it: the builders are already pricing for outcomes, which is the model that eventually puts the value question, and therefore finance, in the room. Finance is not yet their buyer. Their pricing says it is coming.

Private equity. A lane this Index does not yet convene, named here because the finance logic extends to it cleanly. For an operating partner, AI is the headcount-leverage thesis applied to a whole portfolio at once, more output per company without more people, read straight through to margin. It is the purest version of the test this Report measures, and a natural future edition rather than a present claim.

Read together, these are one argument seen from one seat: AI buying authority is distributing across the C-suite and the capital around it, but the accountability for what it returns keeps concentrating on finance. That is the shift the Index measures, and it is why a CFO read is the right place to start.

The Same View, by Industry

Industry sits underneath all of this, because a CFO in a bank and a CFO in a hospital are funding very different AI. Open Future Forum does not yet run industry-specific rooms, so this read is drawn from external benchmarks rather than our own data, and it is written as the finance-seat view of where each sector's spend is going. Five industries lead the enterprise picture.

Financial services

The CFO's own industry is the furthest along. Financial services leads enterprise AI adoption in most 2026 surveys, around 85 to 89 percent on Deloitte's numbers, and NVIDIA's State of AI 2026 placed it among the sectors showing the strongest adoption and return. AI-native startups have already taken 91 percent of the finance-and-operations software category, the highest startup share of any category (Menlo Ventures). From the finance seat this is why the finance lane is the deepest: the buyer and the best-proven use case are the same person.

Technology and software

The highest adoption of any sector, around 92 percent, and the largest single line of raw AI spend. Coding is the biggest departmental AI use case, roughly $4 billion and more than half of departmental AI spend. From the finance seat this is the honest caveat to the whole report: software and engineering, not finance, still spend the most on AI in absolute terms. Finance is the fastest-rising buyer, not yet the biggest.

Healthcare and life sciences

The fastest accelerator, climbing from roughly 38 percent adoption in 2024 to about 67 percent in 2026 as regulatory clarity arrived. The early return shows up in administrative cost as much as clinical breakthrough, with documentation and workload reduction where the measurable savings land. From the finance seat healthcare is the clearest case of spend gated by compliance, the same pattern the CISO paragraph describes: necessity funded ahead of proven return.

Manufacturing and industrials

Slower to adopt, around half to two thirds of firms, but with the cleanest cost-out case. The spend concentrates on predictive maintenance, quality, and supply-chain optimization, and it grew sharply year over year. From the finance seat this is the easiest AI to underwrite, because the return is a measurable drop in downtime and waste rather than a productivity claim.

Retail and consumer

Among the highest adopters of agentic AI, close to half of firms, with the value in demand forecasting, personalization, and inventory. From the finance seat retail is where AI touches revenue more directly than cost, which makes it the sector where the headcount-leverage question matters least and the growth question matters most.

The pattern across all five is the one this Report measures. Where the return is measurable, finance funds it fast. Where it is not, finance is the brake. The industry does not change the question. It changes how quickly the answer comes.

AI Transformation, Measured

AI transformation is the move from scattered pilots to AI embedded in how a function actually runs, changing its cost structure, its hiring plan, and who controls its budget. Most coverage of it is narrative. This Index turns part of it into a measured line, read at the operator level, function by function.

Three of the Index's own readings are transformation metrics:

• Buying Stage is a maturity reading: how far a function has moved from exploring AI to scaling it across the work.
• Headcount Leverage is the clearest single transformation signal in finance: the point at which output stops tracking headcount.
• Budget Authority captures a governance shift: the AI budget moving toward the CFO as AI becomes a core line rather than an experiment.

This is where the macro says transformation is actually real. Value shows up when AI is embedded in workflows rather than bolted on, spend has moved out of innovation budgets and into core lines, and the next wave of agents is starting to carry budgets and outcomes of their own. The Index reads those shifts from inside the functions where they happen.

What AI People Say About the Spend

The buyer side of this report is the CFO funding AI. The supply side is the founders, operators, and investors building and selling it, and in 2026 their view of the spend has split into a sharp, useful debate that a buying-and-budget report has to carry.

The reckoning

The dominant story among operators in 2026 is a reckoning on return. MIT found that roughly 95 percent of generative AI pilots produced no measurable profit. S&P Global reported that 42 percent of companies abandoned at least one AI initiative in 2025, more than double the prior year's 17 percent. The most cited cautionary tale is Uber, which gave thousands of engineers an AI coding tool, watched adoption pass 80 percent, then found in the spring that the year's entire AI budget was gone in about four months, and capped per-employee spend. Even Microsoft reportedly trimmed some AI coding licenses for cost certainty. The phrase that captures the mood, from a Domino Data Lab data scientist, is the end of pilot purgatory: revenue up, costs down, cycle time reduced, and everything else gets killed.

The throughline is not that AI does not work. It is that buying ran ahead of measurement. The tools largely work, the discipline around them did not, and the bills exposed it. The operators winning are the ones measuring cost per outcome rather than cost per token, and funding only what pays.

The counter-case

The builders and their backers push back, and the capital agrees with them. Global AI spending is still climbing toward an estimated $2.59 trillion in 2026 on Gartner's number, and venture investment hit a record of nearly $300 billion in a single quarter, most of it AI. The investor consensus is not bubble-or-not but bifurcation: enterprises will spend more in 2026 but through fewer vendors, concentrating budget on the products that clearly pay and cutting the rest. Investors polled by GeekWire largely rejected a catastrophic bubble while admitting real excess in early-stage valuations, and Sapphire Ventures called it a golden age of innovation for the founders building through it. SAP's CEO has gone further, arguing it would be foolish to keep charging by subscription when AI automates the work, a sign the supply side expects pricing itself to move toward outcomes.

Why it matters for this report

The two sides are not actually in conflict, and the reconciliation is the point. Spend is rising and consolidating at the same time, which is exactly what a market does when buying authority moves to someone accountable for return. The reckoning is the CFO arriving. The bifurcation is the CFO choosing. That is the buyer shift this report measures, seen from the supply side: when the person who answers for the spend starts asking what it returns, money does not leave the category, it concentrates on what works. The headcount-leverage question is simply the sharpest version of that test.

The Evidence Behind the Theses

The Report rests on five theses. Here is how each holds up against outside data from 2025 and 2026, the support and the counter-evidence, so the claims are calibrated rather than asserted. These external figures are attributed to their sources and used for context. They are not Open Future Forum findings.

Thesis 1: The CFO is becoming the financial gatekeeper of enterprise AI. Partially supported.

Support. A Harvard Business Review and Return on AI Institute survey of 1,006 executives (March 2026) found that only 2 percent of companies place AI value accountability with the CFO, but where they do, 76 percent report the strongest value outcomes, against 53 percent under the CIO or CTO and 32 percent under functional leaders. Bain's 2026 CFO survey found 83 percent of CFOs plan to raise AI budgets by more than 15 percent over two years. Gartner found three quarters of CFOs raising technology budgets for 2026. Among venture-backed finance leaders, median AI tool spend rose from about $2,000 in 2024 to $20,000 in 2025, heading toward $50,000 in 2026 (a venture bank's finance-leader survey, via CFO Brew).

Counter. BCG's AI Radar 2026, drawing on 2,360 executives, found 72 percent of CEOs calling themselves the main AI decision-maker, double the prior year, and Deloitte found about half of companies still put AI infrastructure decisions with IT leadership. So the honest claim is the CFO as financial gatekeeper and value-accountability owner, not the sole buyer. The Index measures the gatekeeper, which is why Budget Authority is tracked as a series rather than asserted.

Thesis 2: AI is decoupling output from headcount, especially in finance. Well supported.

Support. Gartner reports finance headcount-growth expectations falling from 6 percent in 2025 to 2 percent in 2026, a shift it describes as moving from labor expansion to optimization. Klarna roughly halved its workforce since 2022 while more than doubling revenue, lifting revenue per employee past a million dollars, though it later reversed some customer-service cuts after satisfaction dropped, which is a caution rather than a clean win. Bank of America has said AI lets it hold headcount flat and let it drift down through 2026.

Counter. MIT found that roughly 95 percent of generative AI pilots showed no measurable profit-and-loss impact, and McKinsey found only 39 percent of firms see any EBIT impact from AI. The leverage is real, but it is concentrated in a minority of well-executed deployments, which is exactly why the Leverage Index is worth measuring rather than assuming.

Thesis 3: Finance and security are the deepest near-term AI lanes among operating executives. Partially supported, stated carefully.

Support. The agentic AI security market is forecast to grow from about $1.65 billion in 2026 to $13.52 billion by 2032, a 42 percent compound rate (MarketsandMarkets), and Bessemer called securing AI agents the defining cybersecurity challenge of 2026. In finance, AI-native startups have already captured 91 percent of the finance and operations application category (Menlo Ventures, 2025), the highest startup share of any category.

Counter. On raw application spend, coding leads at roughly $4 billion, the single largest category in Menlo's taxonomy, and more than half of enterprise AI budgets go to sales and marketing (MIT). So the claim holds among operating and back-office executives, and on a risk-and-return basis, rather than as the two largest spend categories overall. The Report states it that way.

Thesis 4: Transformation is real where AI is embedded, with spend moving to core budgets and agents starting to carry outcomes. Well supported, and the strongest of the five.

Support. a16z found the share of large-model spend funded from experimental innovation budgets fell from about a quarter to 7 percent in a year, with the rest now in core IT and business-unit lines. Menlo put 2025 enterprise generative AI spend at $37 billion, 3.2 times the prior year, with 47 percent of AI deals reaching production against about 25 percent for traditional software. Outcome pricing is appearing: Intercom charges per resolved conversation, and Gartner expects at least 40 percent of enterprise software spend to shift to usage, agent, or outcome pricing by 2030.

Counter. McKinsey found only 7 percent of firms have fully scaled AI across the enterprise, and Gartner expects more than 40 percent of agentic projects to be canceled by the end of 2027. Embedded AI is scaling, but most of the market is still early, so the Buying Stage Index is built to show exactly where a function sits on that curve.

Thesis 5: Executive communities are reorganizing around AI use case rather than title. Weak, treated as observation.

This is the softest of the five, and it is presented as a pattern rather than a measured shift. The supporting signal is qualitative: private dinners increasingly theme around a specific AI problem, such as evaluations, agents, and infrastructure, rather than around a title. Against it, the major AI communities still sort by role. HumanX groups CIOs with CIOs and Chief AI Officers with Chief AI Officers, and Chief AI Officer peer circles are multiplying, which is title-based grouping, not use-case grouping. So the Report carries this as an emerging observation about how rooms are themed, not as a structural claim, and the registration data showing use-case self-sorting is offered as a local signal worth watching, not proof.

Where this leaves the Report: it leads on Theses 2 and 4, which carry the deepest citable support, states 1 and 3 in their careful form, and treats 5 as a qualitative observation. The instrument described later is built to convert the two anchor theses from outside inference into Open Future Forum's own measured numbers.

About the Index

What This Is

The CFO AI Leverage Report is a recurring read on one question, asked of the finance leaders in the Open Future Forum community: is AI letting your finance function do more without adding headcount, and how are you funding it.

It is built on a base most people cannot reach. Open Future Forum runs invitation-screened CFO dinners and gatherings. The same finance leaders return event after event, which means their answers can be collected directly and tracked over time. That is the asset. This Report reads it.

The flagship number is the CFO AI Leverage Index: the share of finance leaders who say AI now lets them grow output without growing the team. It is the one metric in this space that comes from operators rather than from a panel they will never meet, and it is the metric the rest of the market has started pointing at.

What This Is Not

This is not a market-size estimate, and neither is the Index it belongs to. It does not estimate how much enterprises spend on AI. It does not compete with the large annual reports from Menlo Ventures, Andreessen Horowitz, or BCG, which survey hundreds or thousands of companies. Those reports size the market from above and they do it well.

This Report does something different and smaller. It reads a single question from inside one room, the Open Future Forum finance community, a selective sample of operators. Early editions are directional, the response base is stated on every figure, and nothing here is presented as a probability sample of all enterprises. The value is not scale. The value is that these are the actual people now setting AI budgets, answering in their own words across editions, so the line can be tracked.

The Gap It Fills

Every 2026 venture outlook now says the same thing out loud: the CFO is becoming the real buyer of enterprise AI, finance is the clearest near-term lane, and AI is starting to separate output from headcount. The large reports describe the spend. None reads the CFO directly, at the operator level, on the headcount question specifically, and tracked over time.

That is the gap. The macro names the shift. This Report measures it where it is happening first, among finance leaders running lean on purpose and deciding in real time whether AI changes their hiring plan. It sits next to the big reports, not against them.

For context, the external picture those reports paint:

• Menlo Ventures put 2025 enterprise generative AI spend at roughly $37 billion, up from about $1.7 billion in 2023, with over half going to applications.
• Andreessen Horowitz, surveying around 100 enterprise CIOs, found AI budgets set to grow by roughly 75 percent over the following year, and the share funded from experimental innovation budgets falling from a quarter to 7 percent.
• BCG's AI Radar, drawing on more than 2,000 executives in early 2026, found every industry planning to raise AI spend and nearly three quarters of CEOs claiming to be the primary AI decision-maker.

These figures are attributed to their sources and used only for context.

Definitions

• AI budget. Money a company commits over 12 months to generative or agentic AI: tooling and software, internal build and engineering time, outside services, and supporting data and infrastructure. Excludes general cloud and analytics spend that predates AI work.
• Finance leader. A respondent who owns or directly influences the AI budget from the finance seat: CFO, VP Finance, controller, fractional or acting CFO. Answers from advisors, vendors, and non-finance roles are recorded separately and not counted in the headline figures.
• Headcount leverage. Using AI to raise the output of a function without a matching rise in its headcount. The flagship metric measures it.
• Company stage. Venture-stage (pre-Series B), growth-stage (Series B to pre-IPO), and enterprise (public or large private). Reported only where a cell has enough responses to stand alone.
• Respondent base. Every published figure states the number of finance leaders behind it. No headline figure is published below 40 responses.

What We Will Measure

The answers in hand today are demand and composition. This is the spend layer, collected from finance leaders through a short instrument, fielded first at the July CFO dinner and then at every CFO event. These figures are not inferred from registrations. They come from a question an executive answered.

The Flagship Metric: the CFO AI Leverage Index

Question (finance leaders only): In the function you lead, AI is mainly used to: do more without adding headcount / replace planned hires / free existing staff for higher-value work / not yet material.

The Index is the combined share choosing the first two answers, the people for whom AI has changed the hiring math. It is reported as a single percentage with the full distribution beneath it, and it is the line tracked edition over edition. It is the number this Report leads with, because no other report owns it. Budget-direction figures already belong to a16z and BCG. The headcount-leverage read, from CFOs, tracked over time, belongs to no one yet.

Supporting metrics

• Budget Direction. Over the next 12 months your AI budget will increase significantly, increase slightly, hold flat, or decrease. Reported as net direction. Provided for comparability, not as the lead.
• Buying Stage. Exploring, piloting, deployed in one function, or scaling across functions. Reported as a distribution and a simple maturity read.
• Spend Allocation. Largest AI spend line: tooling, internal build, outside services, or data and infrastructure. Reported by category and by stage.

The Instrument

Core (every edition, finance leaders only)

• Headcount leverage (the flagship question above).
• Budget direction over the next 12 months.
• Buying stage today.
• Largest AI spend line.

Rotating deeper set (two or three per edition)

• Source of the AI budget: net-new money, reallocated from planned headcount, or reallocated from other software.
• Budget as a share of revenue or operating expense, in bands.
• Year-over-year budget change, in bands.
• Build versus buy default.
• Largest blocker to spending more: security and compliance, proving return, talent, integration, or data readiness.
• Expected time to measurable return, in months.
• Vendor count direction: consolidating, steady, or adding.
• Final sign-off: who signs the purchase order, even when finance owns the budget.
• Agentic adoption: agents running in production in finance, yes or no.

How It Runs

Sample and Honesty

The figures describe finance leaders in the Open Future Forum community, a selective sample drawn from across multiple markets rather than a probability sample of all enterprises, and they are not presented as one. Every headline figure states its response base, no figure is published below 40 responses, and early editions are framed as directional reads of what operators in this community are doing. A smaller claim, fully backed, beats a larger one that invites the obvious critique.

Assets Each Edition Produces

• The published Report, for Substack and the site, written to be quoted and cited.
• A one-page summary graphic led by the CFO AI Leverage Index.
• A short methodology note, so the Report is citable as a source.
• A finance-community cut, sent to the CFO list as its own short piece.

Distribution and Co-branding

The Report is a natural co-brand for a finance or capital institution already in the Open Future Forum network, since the CFO read is the flagship. A sponsor receives logo placement and a data appendix. The independence terms below are not negotiable and run in the same edition, so the sponsorship never touches the numbers. Running Edition 1 fully independent, before any logo appears, is the cleaner way to set the tone.

How to Describe It Accurately

True the day Edition 1 ships:

• "The CFO AI Leverage Report reads how finance leaders in the Open Future Forum community are funding and getting leverage from AI."
• "Finance automation is one of the two deepest AI demand lanes among the operating executives in the Open Future Forum network in 2026."

True once the instrument is fielded, always with the response base attached:

• "Among the finance leaders in this edition (n = X), Y percent say AI now lets them do more without adding headcount."

Avoid: any market-size or total-spend claim; any figure without its response base; and the word "surveyed" for the registration data, which is screening, not opinion.

Suggested Citation and Versioning

Cite as: Open Future Forum, Enterprise AI Buying & Budget Index, The CFO AI Leverage Report, Edition 1, June 2026.

The Index is a recurring series, released as the data supports rather than on a fixed schedule. Each edition carries a lane, an edition number, and a date, lives at a stable URL, and supersedes nothing. Prior editions stay published so the Leverage Index line can be tracked. Short handle for repeat reference: the CFO AI Leverage Index.

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Methodology and Disclosure

Methodology Note

Independence and Disclosure

A buying-and-budget read carries weight only if it is clean, and Open Future Forum has commercial interests around these events, so the firewall is stated plainly and up front.

• Open Future Forum runs the events and sells sponsorships, and Murray Newlands does fractional advisory work with AI companies.
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About Our Events

Open Future Forum convenes finance leaders across a program of dinners and gatherings, held in partnership with leading financial institutions, law firms, and professional services firms, and co-chaired with senior finance-industry leaders. These institutions partner on the events. They do not endorse or contribute to this report, which is editorially independent.

About Open Future Forum

Open Future Forum is a private executive community that convenes finance, security, and growth leaders across a program of invitation-screened dinners and gatherings. It runs the CFO Executive Forum, the CISO Executive Forum, and related peer groups. The Enterprise AI Buying & Budget Index is its operator-level research program.

Sources

Third-party figures cited in this report are drawn from the sources below. The four primary data sources link to the original reports. Other sources link to the publisher, where the specific release can be found.

• Menlo Ventures. 2025: The State of Generative AI in the Enterprise. Enterprise generative AI spend, growth, and departmental and category share. menlovc.com
• Andreessen Horowitz (a16z). How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025. AI budget growth and the shift of spend out of innovation budgets. a16z.com
• Boston Consulting Group. BCG AI Radar 2026: As AI Investments Surge, CEOs Take the Lead. Survey of 2,360 executives, including 640 CEOs. bcg.com
• MarketsandMarkets. Agentic AI Security Market, Global Forecast to 2032. Market size and growth rate. marketsandmarkets.com
• Harvard Business Review and the Return on AI Institute. Survey on where companies place AI value accountability. hbr.org
• Bain & Company. 2026 CFO survey on planned AI budget increases. bain.com
• Gartner. Forecasts on finance headcount, enterprise AI spend, and CFO technology budgets. gartner.com
• McKinsey & Company. Research on enterprise AI adoption and value capture. mckinsey.com
• Deloitte. Enterprise AI adoption by industry. deloitte.com
• MIT. Research on generative AI pilot outcomes and measurable profit. mit.edu
• S&P Global Market Intelligence. Data on abandoned AI initiatives. spglobal.com
• NVIDIA. State of AI 2026. Industry adoption and return. nvidia.com
• TechCrunch. Reporting on venture investment and enterprise AI spending patterns. techcrunch.com
• GeekWire. Investor views on the AI market. geekwire.com
• CFO Brew. Reporting on venture-backed finance leader AI tool spend. cfobrew.com

External benchmarks are used for context only. They are not affiliated with this report and do not endorse it.