Standalone operator-research reports reading how executives buy, fund, and get leverage from AI, read from inside the room, lane by lane.
October 2026 is the current Open Future Forum research edition. Fifteen separate research pages examine enterprise AI budgets, buying authority, security, marketing measurement, production agents, governance, founders, investors and dealmaking. Each report serves a distinct executive or investment question, while Executive AI Statistics provides a shared reference for citable findings and respondent bases.
The Executive AI Leverage Report is the flagship synthesis; the CFO, CEO, CISO and CMO reports read the same buying-and-budget question in their own lanes; the AI Transformation Report reads across every lane at once, and the AI Leaders AI Leverage Report is the primary source for production-agent fleets, cost visibility and data access. The Board Director AI Governance Report turns the same operator data into board questions. The YC Founder AI Report reads the supply side from the founders selling AI; the VC & CVC AI Investment Report and the Investor AI Report read the capital side. Three dealmaker reports apply the same figures to one desk each, for private equity, investment banking and general counsel, collected on the dealmaker reports page. September, August and July 2026 editions remain published at their own URLs in the archive sections below, together with three market maps reading the supply side of the CFO, CMO and CISO lanes.
None of it sizes the AI market from above. All of it reads from inside the room: the executives, founders and investors who actually hold AI budgets, build AI, fund AI and use AI daily, answering in their own words. Every figure states its response base. No headline is published below 40 responses. Early reads are labeled as directional. Multi-select questions are reported as any mention, and a comparison between two different questions is labeled descriptive, not a measured gap. A smaller claim, fully backed, beats a larger one that invites the obvious critique.
The cross-functional operating view: who authorizes AI, where the money sits, what proves value and who controls the system after launch, read from 467 unique sign-off answers.
A finance view of AI budget sources, ROI blockers and payback expectations, with the workflow-level cost ledger needed to reconcile benefit with operating cost, from 389 responses.
A decision-rights framework for moving from CEO approval, named by 49 percent of 467 sign-off answers, to named launch, expansion, pause and stop authority for production AI.
Sixty-eight percent of 151 security-instrument respondents name agent access as a top problem, while 34 percent report a dedicated security budget line among the same base; different questions, read together for funding and identity controls.
Marketing adoption and value evidence, with a measurement tree separating workforce capacity, content speed and customer knowledge, from 230 responses.
A four-stage maturity view, exploring, piloting, deployed and embedded, and the evidence gates required to move between stages.
The primary October source for production-agent fleets, cost visibility, bottlenecks, enterprise-data access and planned AI spending, read from the technology seat's own operating room.
An operator-data synthesis for board oversight, with a quarterly dashboard, escalation thresholds and committee ownership; still a Preview Edition until the board lane clears its own 40-response floor.
Founder evidence on pricing models and enterprise buying doorways, from 148 responses, paired with new buyer-side production evidence on what enterprise buyers now demand alongside the product.
Eighty-six percent of 237 portfolio answers report at least one measurable area of AI impact. Investor-reported ownership and impact translated into evidence tests for an AI investment thesis.
A portfolio operating-diligence record for ownership, workflow cost, data access, controls and measurable value, naming the CEO as the most-named rising owner of AI buying at 51 percent.
A transaction data-room and value-creation framework covering AI ownership, benefit evidence, remediation cost and post-close integration, with six diligence questions for deal teams.
A revenue-quality test connecting AI usage, recognized revenue, delivery cost, margin sensitivity and issuer disclosure for transaction diligence.
A legal-control record connecting AI-agent authorization, credentials, data scope, vendor terms, escalation and incident evidence.
The canonical October registry for question wording, response bases, full distributions, exhibits, provenance and data downloads.
The flagship synthesis of the Enterprise AI Buying and Budget Index, read from 2,851 unique executives, founders and investors. The CEO is the most-named signer at 47 percent of 290 operators and 51 percent of 245 investors; business-unit sign-off fell to 4 percent; proving ROI rose from 53 to 65 percent inside August. The Optimism Gap holds at 28 points.
How finance leaders fund, approve and prove AI, from 290 responses. The CFO or finance is named on 43 percent of August sign-off answers, up from 33 through July. Proving ROI blocks 65 percent, 28 percent have no clear AI budget, and four funding archetypes are named for the first time.
70 percent of CEO-seat respondents name the CEO as signer and expect payback inside six months, against 42 percent at finance. 34 percent fund AI with money that would have gone to headcount, almost double the finance seat.
67 percent of 110 senior security leaders name securing AI agents and their access as their top problem, 73 percent in August. 37 percent hold a dedicated AI security budget line. That distance is the Security Funding Gap, 30 points, and it is widest at the CISO's own chair.
81 percent of marketing and growth leaders are past exploring agents. Asked for the first time where AI makes the biggest difference, they split three ways: doing the work of more people 50 percent, knowing the customer 43, creating content faster 43.
How far AI has moved from pilots into the way functions run, across finance, security, growth, founder and investor rooms. Net-new money funds AI at 41 percent, 21 percent substitute headcount, and the security budget line grew from 33 to 41 percent inside August.
The AI Investor Conviction Index is 86 percent: corporate venture 90, venture 84. Conviction tracks ownership, investors who name any AI owner see nothing measurable in 5 to 9 percent of portfolios, against 41 percent for those who cannot.
How AI founders price and sell, from 148 responses. Usage pricing leads at 43 percent, while 2025 and 2026 batches price on outcomes at 35 and 29 against 14 for older batches. Founders enter through three doorways; the CEO signs.
Investors reading AI across whole portfolios, from 245 responses. 51 percent name the CEO as the seat that increasingly owns AI buying. Investors who can name the owner see nothing measurable in 9 percent of portfolios; those who cannot, 41 percent.
The first read of the technology seat as its own chair. It reports the most unowned AI decisions of any seat at 30 percent, claims the least authority, and is the one operating seat that does not name itself the signer first.
Three questions boards should ask about AI, each with a number behind it. The governance gap is least visible from where the board sits: the CEO seat reports an unowned AI decision at 2 percent, technology at 30.
Six findings put as AI due-diligence questions: who signs, which budget line pays, whether the headcount claim holds, how long payback is given, who can reach the agents, and what share of portfolios show anything measurable.
Revenue quality in an AI company is set by which seat bought it and how it pays: usage pricing at 43 percent, outcome at 24, and a 28-point payback gap between the CEO seat and finance. Written for the equity story and the IPO window.
Liability for an agent follows whoever signed first, and 11 percent of companies cannot name that seat. Agent access, shadow AI, use policies, and the vendor terms companies are now demanding.
Every September 2026 statistic in citable form: sign-off, budgets, payback, the Security Funding Gap, marketing, founder pricing, portfolios and the network, each with its base and source report.
The annual flagship of the Enterprise AI Buying and Budget Index. Ten headline claims from the year’s most-cited AI research, BCG, MIT, PwC, Gartner, a16z, McKinsey, S&P Global, Menlo Ventures, KPMG, and OECD, tested one at a time against first-party data from 6,055 executive registrations, with a verdict on each: corroborated, contradicted, or complicated. Who owns AI, what it returned, and where the budgets went.
A new vantage point in the Enterprise AI Buying and Budget Index: investors reading AI across entire portfolios rather than single companies. From 245 responses at the network’s first instrumented investor gathering: 51 percent name the CEO as the seat that increasingly owns AI buying, better products lead cost cutting at 51 to 35 percent as where AI shows up, and 16 percent of portfolios show nothing measurable yet. Edition 1 is the baseline the series will track.
Every number the August 2026 editions published, in citable form: budgets, sign-off, ROI expectations, agentic adoption, security, pricing, and portfolios, each statistic with its base and a link to its source report. Built for citation, archived with a permanent DOI.
The capital side, read from the investors themselves: how venture and corporate venture investors are backing AI and reading it inside their portfolios. The flagship metric is the AI Investor Conviction Index — 86 percent in Edition 1, the share of investors who name at least one place AI already makes a measurable difference (base 237). Investors name the CEO as the rising owner of the AI buying decision at 51 percent (base 245), yet 22 percent still cannot say who owns it: conviction is running ahead of the operating model that governs it. Set against the external record, AI took 61 percent of global venture capital in 2025 while 95 percent of enterprise pilots still show no measured profit.
The supply side, one month on: usage-based pricing leads at 43 percent, outcome-based rose 8 points inside July, and CFO mentions as the buying owner doubled (directional) as sign-off consolidates toward the money. New vertical playbooks: fintech prices by usage and sells to finance; enterprise software is the outcome-pricing lab; healthcare is the earliest market. Independent research; not affiliated with or endorsed by Y Combinator.
A companion to the CISO AI Leverage Report reading the supply side: a workflow-based map of 63 security AI vendors across agent and model security, AI SOC, identity and access, cloud exposure, application security, data security, GRC, and security automation — paired with first-party demand signals from 79 application-stage survey responses. No scores, no rankings: agent access is the clearest demand signal, and funding is real but uneven.
A companion to the CMO AI Leverage Report reading the supply side: a workflow-based map of 64 marketing AI vendors across content, creative, campaigns, SEO/GEO/AEO, analytics and attribution, lifecycle, social, and the agentic/copilot layer — paired with first-party demand signals from 55 application-stage survey responses. No scores, no rankings: marketing buyers are already piloting or running agents, and what they asked for was proof, not more AI.
A companion to the CFO AI Leverage Report reading the supply side: a workflow-based map of 64 finance AI vendors across close, FP&A, AP and expense, treasury, audit, procurement, tax, and the agentic/copilot layer — paired with first-party demand signals from 421 application-stage survey responses. No scores, no rankings: where the supply is crowded, where the buyers are, and where the two don’t line up yet.
The CEO’s position in AI decisions, now confirmed from three sides: operators name the CEO the most-frequent signer at 44 percent, investors put the CEO at 51 percent across portfolios, and every operating seat over-names itself, a pattern the report names the Self-Attribution Effect. Business-unit sign-off halved inside July while unowned decisions doubled to 14 percent: the Ownership Vacuum, invisible from the top of the org chart.
Innovation budgets fell from 25 percent to 7 percent of enterprise AI spend in a year, yet only 7 percent of firms have fully scaled AI. This cross-lane report reads that transformation gap from inside the room: deployment is real, but funding is still mostly net-new, governance is funded case by case, and the sign-off seat sits far from the work. The flagship metric is the AI Transformation Index, tracking the last transition on the report's own four-stage curve: exploring, piloting, deployed, embedded.
What senior security leaders name as their AI problem and how AI security is funded, from 45 responses, nearly triple the Edition 1 base: 58 percent name securing AI agents and their access as the top problem, more than twice shadow AI at 27 percent, while only 36 percent hold a dedicated AI security budget line and a third fund the work case by case. The top two problems are governance problems, not adversary problems.
Where marketing and growth leaders actually are with agentic AI, from 230 responses: 81 percent are past exploration, the largest single group is building agentic products, and org design has overtaken measurement as the question the rooms bring to peer sessions. The function is restructuring, not experimenting.
The flagship synthesis of the Enterprise AI Buying and Budget Index: what moved across the finance, marketing, security, founder, and investor rooms in 30 days, read from 6,055 registrations and 5,311 unique guests. The August thesis: authority is consolidating faster than accountability, measured through the Self-Attribution Effect, the Ownership Vacuum, and the 27-point Optimism Gap.
How finance leaders are funding, approving, and getting leverage from AI, from 206 application-stage responses. The August read: budget uncertainty rose, not fell, with 34 percent reporting no clear AI budget, software reallocation halved as a funding source, and the CFO’s sign-off share climbed to 33 percent. New this edition: the 27-point Optimism Gap between the CEO seat and the finance seat on AI payback.
Direct, sourced answers to the questions executives ask most, each drawn from the reports above.
These constructs are reviewed every edition, with their current status and latest comparable figure.
Looking for the full source library, including third-party industry research? Browse our open GitHub archive or cite it via its permanent Zenodo DOI: https://doi.org/10.5281/zenodo.21576019 It is also permanently archived on Software Heritage.
The Executive AI Leverage Report's Edition 1 ships once its flagship reading clears the program's response floor. Each report carries a stable URL under /research/ and a dated edition number. Prior editions remain published so each line can be tracked over time.
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