The answer in 80 words

The big AI reports of 2025 and 2026 disagree about almost everything: who runs AI, whether it pays, whether agents survive. This report tests their headline claims against first-party data from 6,055 executive registrations across 15 Open Future Forum events. The short version: ownership claims inflate with distance from the work, deployment is broad but proof is scarce, budgets are moving into core lines while budget clarity falls, and the sellers are pitching different doors than the buyers who sign.

Five numbers that frame the year:

Where this research comes from

Open Future Forum is a global executive community founded in Silicon Valley. Its network reaches tens of thousands of executives and investors worldwide. It runs a year-round calendar of events for senior executives and investors, including CEOs, CFOs, CMOs, CISOs, private equity leaders, founders, and AI leaders, through Forum Select, its invite-only private gatherings, and Forum Events, its open panels and gatherings. Beyond events, Open Future Forum convenes peer groups and executive boards and publishes original research built on first-party survey and qualitative data from its executive network.

The figures in this report come from application-stage instruments fielded across those events in 2026: a selective, role-tagged operator sample drawn from Open Future Forum’s broader executive network. The August 2026 dataset covers 6,055 unique registrations collected April 22 through July 30, 2026, across 15 events, deduplicated to 5,311 unique guests. Every first-party figure carries its response base. Findings feed the monthly editions of the CEO, CFO, CMO, CISO, Executive, YC Founder, and Investor report lines, all published at openfutureforum.com/research.

The year in one chart

Ask five different seats who signs AI purchases, and the answer depends on how far the person answering sits from the work.

Who is answering “The CEO signs AI purchases”
CEO and founder respondents (base 43) 84%
Investors, portfolio view (base 245) 51%
Operator average (base 115) 44%
Finance respondents (base 33) 30%
Technology respondents (base 17) 24%

Seat-cut bases under 40 are directional. Sources: the CEO AI Leverage Report and Investor AI Report, August 2026 editions.

Each chair sees the pen on its own desk: 84% of CEO respondents name the CEO, 61% of finance respondents name the CFO, and technology respondents name the CIO or CTO first. The series calls this the Self-Attribution Effect. And while every seat claims the decision, a growing share of companies have no owner at all: “no single owner yet” doubled to 14% inside July, and not one CEO respondent reports it. That is the Ownership Vacuum, and it is invisible from the top.

The claims, tested

The most-cited AI research of the past year makes strong claims. This section takes ten of them, one at a time, and puts our first-party data next to each: where the executive rooms corroborate the claim, where they contradict it, and where they complicate it.

Claim 1. “The CEO is the AI decision-maker.”

The claim. BCG’s AI Radar 2026, surveying 2,360 executives including 640 CEOs, found 72% of CEOs call themselves their company’s main AI decision-maker.

Our read. Both, depending on who you ask. CEO respondents in our network put the CEO’s signature at 84% (base 43), above BCG’s figure. But operators answering the same question name the CEO at 44% (late July, base 115), finance respondents at 30% (base 33, directional), and technology respondents at 24% (base 17, directional).

Verdict: complicated. BCG’s number is real, but it is a self-report. The closer the respondent sits to the actual purchase and proof, the smaller the CEO’s claimed role gets. The right conclusion is not that CEOs run AI. It is that the CEO’s ownership of AI is largely a view from the CEO’s chair.

Claim 2. “95% of AI pilots produce no measurable return.”

The claim. The widely quoted finding from MIT Project NANDA’s State of AI in Business 2025: roughly 95% of enterprise AI pilots produce no measurable return, with only 5% of organizations translating pilots into real operational or financial impact.

Our read. The rooms are past the pilot stage the claim describes: 71% of the largest finance room already runs an AI tool (131 of 185) and the proof problem MIT points at is exactly what operators name: proving ROI is the top blocker to further AI spend (51%), unchanged in rank since July. The clock, meanwhile, is stretching: the share expecting measurable return inside six months fell from 60% to 54% through July, while “not sure” rose from 11% to 19%.

Verdict: corroborated on the gap, contradicted on the frame. The 95% figure reads as a deployment failure. Our data says deployment succeeded and measurement did not. The missing thing is not working AI. It is an owner for the proof, and the widening “not sure” share says the rooms know it.

Claim 3. “Most CEOs see no benefit from AI.”

The claim. PwC’s 29th Global CEO Survey (4,454 CEOs): 56% report neither revenue nor cost benefits from AI, and only 12% report both.

Our read. Investors looking across whole portfolios see the opposite tilt: 51% point to better products as where AI already makes a measurable difference, ahead of cutting costs (35%) and helping customers (34%), with only 16% seeing nothing measurable yet (base 237). The AI Investor Conviction Index, the share of investors naming at least one place AI already makes a measurable difference, read 86% in its July directional seed.

Verdict: contradicted, with a caution. The portfolio view and the CEO self-report cannot both be the whole story. Our own data suggests why: payback confidence falls with proximity to the proof (72% at the CEO seat, 45% at finance, 35% among investors on fast payback). The honest read is that AI’s returns in 2026 are real somewhere in most portfolios and provable in few individual companies.

Claim 4. “Over 40% of agentic AI projects will be canceled by 2027.”

The claim. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027.

Our read. No retreat is visible in the rooms. 81% of marketing and growth respondents are past exploring agentic AI (187 of 230): 36% are building agentic products, 26% piloting, and 20% running agents in production. KPMG’s AI Quarterly Pulse Survey has agent deployment at large organizations rising from 11% in early 2025 to 53% by mid-2026. What we do see are the conditions Gartner’s forecast depends on: 58% of senior security leaders name securing AI agents and their access as their top AI security problem (26 of 45), while only 36% have a dedicated AI security budget line.

Verdict: corroborated on conditions, not yet on outcome. Agents are being deployed faster than they are being funded for security. If Gartner’s cancellations arrive, our data says they will arrive through that gap.

Claim 5. “AI money moved from innovation budgets into core budgets.”

The claim. a16z’s survey of 100 enterprise CIOs: “innovation budgets still made up a quarter of LLM spending; this has now dropped to just 7%.” The money went to core IT and business-unit budgets.

Our read. Corroborated, and the move is messy. Net-new money is the most common AI funding source (41% of late-July finance respondents), but reallocation from other software halved inside July (26% to 14%), and the share reporting no clear AI budget rose from 23% to 34%. Meanwhile 20% of finance respondents, and 33% of CEO and founder respondents, say this year’s AI money would otherwise have been headcount spend.

Verdict: corroborated. AI spend is becoming core spend. The surprise in our data is that becoming core has made the budget harder to see, not easier.

Claim 6. “Almost no one has scaled AI.”

The claim. McKinsey’s State of AI survey: only about one-third of organizations have begun to scale AI, 39% attribute any EBIT impact to it (most of them under 5% of EBIT), and only about 6% qualify as AI high performers.

Our read. Deployment and transformation are different things, and our data separates them. Deployment is broad (71% running tools). Transformation markers lag: 20% of finance respondents fund AI from would-be headcount money, and governance trails deployment everywhere we measure it. The AI Transformation Index, which will track the share of executives who say AI is embedded in how their function runs rather than deployed in isolated workflows, seeds from exactly this gap.

Verdict: corroborated. The rooms are deployed, not transformed. Broad tool usage plus scarce headcount displacement plus lagging governance is what “not scaled” looks like from the inside.

Claim 7. “AI project abandonment is spiking.”

The claim. S&P Global Market Intelligence found the share of companies scrapping most of their AI initiatives jumped from 17% to 42% between 2024 and 2025 (reported by CIO Dive).

Our read. We do not see abandonment in the rooms. We see authority being renegotiated in ways that rhyme with it: budget uncertainty rose eleven points inside a single month (23% to 34%), business-unit sign-off collapsed from 18% to 8%, and “no single owner yet” doubled to 14%. The experimental era, where departments bought their own AI, is closing, and what replaces it is still being assigned.

Verdict: corroborated as instability, not as retreat. In our network, projects are not being dropped. Budget lines and ownership are being renegotiated. From the outside, renegotiation and abandonment can look alike.

Claim 8. “Enterprise AI is commercializing fast.”

The claim. Menlo Ventures’ 2025 State of Generative AI in the Enterprise: $37 billion in enterprise AI spend growing at 3.2x, with 47% of AI deals reaching production, roughly double the traditional SaaS rate.

Our read. The supply side agrees. 92% of surveyed AI founders are charging for their product, usage-based pricing leads at 43% of 148 founder respondents, per-seat licensing sits at 20%, and outcome-based pricing, where the customer pays when it works, rose from 21% to 29% inside July, the fastest-growing model. Vendors are pricing for measured, transformed workflows.

Verdict: corroborated. This is a post-revenue founder cohort moving to pay-when-it-works terms, selling into a buyer base whose top blocker is proof. Both sides of the market have stopped treating AI as an experiment.

Claim 9. “AI governance is behind AI deployment.”

The claim. KPMG’s AI Quarterly Pulse Survey: agent deployment at large organizations rose from 11% in early 2025 to 53% by mid-2026, but only 26% of organizations have access to real-time insight into what their AI systems cost.

Our read. Corroborated, with movement in both directions. Only 36% of security teams have a dedicated AI security budget line and 33% fund AI security case by case, even though a majority name securing AI agents as their top problem. In our July founder cohorts, zero of 148 founders named the CISO or security function as their buyer: AI enters organizations through doors the security function does not control. The countertrend: “no AI security spend at all” fell from 21% to 6% inside July (directional). Money is starting to arrive against a problem that was already named.

Verdict: corroborated and sharpened. The governance gap is structural: the seat responsible for securing AI is not the seat being sold to, and its funding is arriving after deployment rather than with it.

Claim 10. “Capital has fully committed to AI.”

The claim. By OECD’s count, AI captured 61% of global venture capital in 2025 ($258.7 billion of $427.1 billion), more than double AI’s 30% share in 2022; CB Insights counts a record $225.8 billion raised by private AI companies in 2025, nearly double 2024’s total.

Our read. Conviction matches capital: 86% of investors named at least one place AI already makes a measurable difference across their portfolio (base 237, July). And investors have a distinct read on governance that the funding data lacks: 51% name the CEO as the seat that increasingly owns AI buying across portfolio companies, more than double the CIO/CTO at 24%, while 22% say it is too early to call (base 245).

Verdict: corroborated, with a new layer. The capital commitment is real. What the funding totals miss is that investors now treat AI purchases as company-level bets owned at the top, not departmental tooling decisions. That expectation lands on the CEO whether or not the CEO’s own organization agrees, which is exactly what our ownership data shows.

The claim nobody made

One structural finding in our data has no external counterpart to test against, so we state it plainly. Inside companies, the CEO is the most-named signer of AI purchases (44% late July). Outside, founders selling AI name the business-unit leader as their steadiest buyer door (around 40% in both July cohorts), with CFO or finance mentions doubling to 42% in the late cohort (directional) and CIO/CTO mentions falling 16 points. Security was named by zero of 148 founders in the July cohorts. The seat that signs and the seats that get pitched are different people, and the seat that must secure the result is absent from the transaction. We call this the seat gap, and until the major surveys start asking sellers and signers the same question, first-party operator data is the only place it shows up.

What held and what moved

Because the Index runs monthly, this report can do something the annual surveys cannot: show its own findings moving.

What held. The CEO’s lead as most-named signer, in every pull and from three independent vantage points (operators, founders, investors). Proving ROI as the top blocker, unchanged in rank. The deployment baseline: 71% of the largest finance room running an AI tool. Net-new money as the most common funding source.

What moved. Business-unit sign-off collapsed from 18% to 8% inside July while “no single owner yet” doubled from 8% to 14%. No-clear-AI-budget rose from 23% to 34%. Software reallocation halved as a funding source (26% to 14%). The under-six-month payback expectation eased from 60% to 54% with “not sure” rising to 19%. Outcome-based pricing rose from 21% to 29% of founders, and CFO mentions as the buying-decision owner doubled to 42% (directional). The CFO gained sign-off share faster than any seat (26% to 33%).

What surprised. Authority is centralizing faster than it is being assigned: business-unit sign-off did not migrate to a named owner, it migrated to “no single owner yet.” And the vacuum is invisible from the top: zero CEO respondents report an unowned AI decision, while 29% of technology respondents do.

Constructs and definitions

Each construct below is defined once, in a stable citable form, with its current reading.

The year by seat

CEO. Most-named signer of AI purchases (44% late July, base 115) by operators, corroborated by investors at 51%, and self-reported at 84%. The most aggressive payback read of any seat: 72% expect returns inside six months, against 45% at finance. The seat signs the most and sees the least. Full findings: the CEO AI Leverage Report.

CFO. The seat gaining sign-off share fastest (26% to 33% inside July), first owner of the proof (top blocker at 51%), and holder of the year’s least-noticed problem: 34% of finance respondents report no clear AI budget, up from 23%. Sellers see the finance door opening too (CFO buying-owner mentions doubling to 42%, directional). Full findings: the CFO AI Leverage Report.

CMO. The furthest deployed: 81% of marketing and growth respondents are past exploring agentic AI, with 20% running agents in production, and the seat’s open questions have moved from tools to org design (16 mentions to 6 in open answers). Full findings: the CMO AI Leverage Report.

CISO. Owns the year’s top-named problem, securing AI agents and their access (58%, 26 of 45), with dedicated funding in only 36% of cases and shadow AI second at 27%. The money is starting to arrive: “no AI security spend” fell from 21% to 6% (directional). Full findings: the CISO AI Leverage Report.

Founder. Post-revenue (92% charging) and moving to pay-when-it-works terms: usage-based pricing leads at 43%, outcome-based rose to 29%, per-seat sits at 20%. Pitching the business unit steadily and finance increasingly. Full findings: the YC Founder AI Report.

Investor. Committed and watching the top: 51% name the CEO as the seat that owns AI across portfolios, better products lead as AI’s biggest portfolio difference (51%), and only 35% expect fast payback, the most conservative read of any vantage point. Full findings: the Investor AI Report and the VC & CVC AI Investment Report.

What this means, by seat

For the CEO, the seat cuts are the warning: the 84% self-report is not shared by the people producing the proof, the payback gap with finance is 27 points, and the Ownership Vacuum is invisible from the corner office. Closing the distance between signature and evidence, what our CEO edition calls Decision Distance, is the 2027 job. For the CFO, the mandate is arriving whether claimed or not: sign-off share rising, sellers pitching the finance door, and a budget picture that got blurrier as spend went core. For the CMO, being furthest deployed means marketing hits the org-design and governance questions first. For the CISO, the data argues for making the budget case in payback language to the CFO, because that is where sign-off is consolidating, and the case-by-case era is already ending. For founders, the seat gap is a sales-strategy finding: the steadiest door is the business unit, the opening door is finance, and pricing on outcomes means owning part of the buyer’s proof problem. For investors, conviction against a thin measured-proof record is a spread worth watching in both directions.

2027 outlook: what the rooms expect

Predictions collected across peer-learning sessions and instrument questions, reported as claims from the rooms rather than forecasts of our own: accounting is named as the next function after coding to hand whole units of work to AI. Forward-deployed engineers are entering software stack replacement. AI-native private equity is emerging as a distinct investment style. Non-technical teams shipping products moves from anecdote to pattern. And from the CEO dinner applications themselves (base 41, directional), chief executives want AI pointed at capital and competition before operations: AI for researching VCs (66%), competitive research (63%), and marketing (59%) top the request list. Against those claims, the indexes give us something falsifiable to check in the next edition: whether the CEO AI Leverage Index’s first true reading confirms the 84% self-attribution or breaks it, whether budget clarity recovers as AI spend settles into core lines, and whether the Ownership Vacuum gets assigned or keeps growing.

About this edition, and the fuller one coming

This is an early edition, published on one quarter of Index data, and it says so. It exists because the findings above are already citable and already moving, and because a report tracking deltas should itself ship in editions. A fuller edition follows as the monthly cycles accumulate, adding what this edition cannot have: trendlines with multiple monthly points per question, deeper seat and vertical cuts on larger bases, and first true readings of the CEO AI Leverage Index and the AI Transformation Index as their flagship questions enter the field. Where this edition tests other people’s claims against our data, the next edition will also test this edition’s.

Join the conversation behind the data

Every figure in this report started as a question asked of executives at an Open Future Forum gathering. The peer communities where those conversations continue are role-specific: the CEO Executive Forum, CFO Executive Forum, CMO Executive Forum, CISO community, and AI Leaders Forum each run private peer discussions on exactly the gaps this report measures. If the ownership data or the proof window describes your seat, the community for that seat is at openfutureforum.com.

Put yourself in the next dataset

The Index’s data comes from the rooms. Upcoming gatherings include CFOs on the Bay (August 21, 2026, San Francisco Bay), the CISO Roundtable Dinner series, and the CEO Private Dinner series in Los Altos Hills. Event pages and applications are at openfutureforum.com/forum-events. Executives who attend are the reason the next edition’s bases are bigger than this one’s.

Questions this report answers

The questions below are the ones executives most often put to the major AI reports. Where our data answers directly, the figure and base are given. Where the best answer is external, it is attributed and linked. Where a question is not yet fielded, the report says so and names when the answer arrives.

What is the Executive State of AI 2026? The Executive State of AI 2026 is the annual flagship report of Open Future Forum’s Enterprise AI Buying and Budget Index. It tests the headline claims of the year’s most-cited AI research against first-party data collected monthly from executives across Open Future Forum’s event network, and it publishes in editions: this August edition first, with fuller editions to follow as new monthly data accumulates.

Ownership and leadership

Who owns the AI buying decision in 2026? The CEO, by three independent reads: 44% of operator sign-off answers, 51% of investor responses, and the CEO’s own 84% self-report. The spread between those numbers is the finding: each seat sees the pen on its own desk.

Are CEOs personally leading AI decisions? By their own account, yes: 84% in our data, 72% in BCG’s AI Radar 2026. By the account of the people running the work, far less: 44% among operators, 24% among technology respondents. Both answers are real; the distance between them is the state of CEO AI leadership.

Do most companies have a Chief AI Officer? IBM’s 2026 Global CEO Study reports 76% of firms now have one. Our data reads ownership by seat rather than title and finds that whoever holds the title, every seat over-names itself as the owner. The dedicated AI-owner question is entering our instrument; a future edition publishes its first reading.

How is AI changing the C-suite? The experimental era is closing fastest of any trend in the data: business-unit sign-off halved to 8% inside July. What replaces it is still being assigned; “no single owner yet” doubled to 14%. Authority is centralizing faster than it is being assigned.

Who do AI startups actually sell to? The business unit steadily (around 40% in both July cohorts), finance increasingly (CFO mentions doubled to 42%, directional), technical buyers less (down 16 points), and security not at all (0 of 148 in the July cohorts).

ROI and money

Is enterprise AI actually producing returns? Investors say yes somewhere in most portfolios (86% conviction, July); operators say proof is the top blocker (51%); the external record ranges from MIT’s 95%-no-impact to Wharton’s three-in-four positive. Returns exist; owned, measured proof is rare.

How long until AI investments pay back? The expectation is stretching: 54% of late-July finance-room respondents expect measurable return inside six months, down from 60% early July, with “not sure” rising to 19%. And the expectation depends on the seat: 72% at the CEO seat, 45% at finance, 35% among investors.

How much are companies spending on AI? Menlo Ventures puts enterprise generative AI spend at $37 billion, growing 3.2x. Our data answers where the money comes from: net-new money leads at 41%, software reallocation halved to 14%, 20% of finance respondents say the money would otherwise have been headcount spend, and 34% report no clear AI budget at all.

Why do AI projects fail? Two mechanisms show up in our data. First, no one owns the proof: proving ROI is the top blocker (51%) and the seat that signs is not the seat that measures. Second, ownership itself is going unassigned: “no single owner yet” doubled to 14% inside July. S&P Global’s 42% abandonment figure is what those two mechanisms look like at population scale.

Are companies abandoning AI? Not in our network. Budget clarity is falling (23% to 34% with no clear budget inside July) and authority is being reassigned, which is renegotiation, not retreat.

Adoption and agents

What percentage of companies use AI? In our operator network, 71% of the largest finance room already runs an AI tool (131 of 185). Across the whole economy the figure is far lower; the US Census figures cited in our CEO edition put national business adoption near one in five. Both are true: executive networks run years ahead of the economy, which is exactly why they are worth reading.

How far along are companies with AI agents? Further than the cancellation headlines suggest: 81% of marketing and growth respondents are past exploring agentic AI (36% building agentic products, 26% piloting, 20% in production), and KPMG tracks agent deployment at large organizations rising from 11% in early 2025 to 53% by mid-2026.

Will agentic AI projects be canceled at scale? The rooms show acceleration (81% past exploration) and underfunded governance (36% with a dedicated AI security line). Cancellations, if they come, come through that gap.

Which business functions are furthest along with AI? In our lanes: marketing and growth lead on agents (81% past exploration), finance is broadly deployed on tools (71%) and owns the proof problem, and security is behind on funding (36% with a dedicated line) while carrying the risk. Deployment runs ahead of governance in exactly that order.

Workforce

Will AI replace jobs or reduce headcount? Our data measures the leading indicator: money. 20% of finance respondents, and 33% of CEO and founder respondents, say this year’s AI money would otherwise have been headcount spend. AI is absorbing headcount budget before it visibly absorbs headcount.

How is AI changing how executives actually work? The rooms describe three rungs (the Executive AI Leverage Ladder): AI as thought partner, AI as co-worker completing whole units of work, and AI with enough context to ask its own questions. Most operators sit at the second rung, and the practices are concrete: multi-day financial models compressed to hours, forecasting cycles from months to days.

What skills matter most in the AI era? The practitioners in our finance rooms are blunt: domain expertise is the multiplier, because AI amplifies the person who knows what to ask and what a wrong answer looks like. They are equally blunt that this edge is temporary if context-rung AI arrives on the timeline the rooms expect.

Governance and risk

What is the biggest AI security risk right now? Securing AI agents and their access: 58% of senior security respondents name it their top problem (26 of 45), more than twice shadow AI at 27%. The top AI security problems are governance problems, not adversary problems: AI-powered attacks rank fourth at 13%.

Is AI governance keeping up with deployment? Not yet, and the gap is structural: in the July founder cohorts, zero of 148 founders sold to the security seat, so AI enters through doors security does not control. The countertrend is real money arriving: “no AI security spend” fell from 21% to 6% inside July (directional).

How common is shadow AI? Second on the security desk: 27% of security respondents name unapproved AI tools among their biggest problems, and audits described in our rooms find several times more live AI connections than IT estimates. Full findings: the CISO edition.

Market and investment

How much venture capital is going into AI? By OECD’s count, 61% of all global venture capital in 2025 ($258.7 billion); CB Insights counts a record $225.8 billion raised by private AI companies. Our investor read matches the money: 86% conviction that AI already makes a measurable difference somewhere in the portfolio (July).

How do AI startups price? Usage-based leads at 43% of 148 founder respondents, outcome-based is the fastest-growing model (21% to 29% inside July), and per-seat sits at 20%.

Which AI vendors lead each category? This report never ranks or recommends vendors. The Index’s Market Maps (CFO, CMO, CISO) map the vendor landscape by workflow category instead.

What should executives do about AI next? From our implications section, one line per seat: close the distance between signature and proof (CEO), claim the mandate that is arriving anyway (CFO), expect the org-design questions first (CMO), make the budget case to finance in payback language (CISO), sell to the doors that are opening (founders), and watch the spread between conviction and measured proof (investors).

Where can I cite these figures? Every figure with base and source lives at Executive AI Statistics, refreshed monthly.

Citable facts

Methodology

First-party figures come from application-stage instruments embedded in Open Future Forum event applications, a selective, role-tagged operator sample drawn from Open Future Forum’s broader executive network. The August 2026 dataset covers 6,055 unique registrations collected April 22 through July 30, 2026, across 15 events, deduplicated to 5,311 unique guests; registrations are screened (3,786 invited, 902 approved, 1,145 declined across the dataset). Operator sign-off and budget figures use the finance-room instrument (bases 91 early July, 115 late July; cohorts are different respondents, not a tracked panel). Seat cuts classify respondents by job title (CEO/founder 43, finance 33, technology 17); investor figures come from the July 27 investor instrument (bases 245 and 237); security figures use a combined base of 45; founder figures use a base of 148. The program’s floor for headline figures is 40 role-tagged responses; figures below that floor are labeled directional. Multi-select questions use the any-mention convention and can sum past 100%. This is a community sample, not a probability sample of all enterprises, and first-party findings are separated throughout from third-party benchmarks, which are attributed and used for context only. External figures tested in this report are drawn from the publishers named and linked at each claim: BCG, MIT, PwC, Gartner, a16z, McKinsey, S&P Global, Menlo Ventures, KPMG, OECD, and CB Insights. This report never ranks, scores, or recommends specific vendors. The author does fractional advisory work with AI companies; sponsors see logos and the data appendix only and do not shape findings. The underlying dataset is archived at Zenodo (DOI 10.5281/zenodo.21576019) and on GitHub.

External sources

Every external claim tested in this report links to its publisher at the point of use. Consolidated for reference:

About the author

Murray Newlands is the founder of Open Future Forum and the author of the Enterprise AI Buying and Budget Index, the research series behind this report. He founded Open Future Forum in Silicon Valley in 2019 and chairs its AI Leaders Forum. He is a Partner at IA Seed Ventures, which invests in early-stage Silicon Valley companies, publishes Murray’s Newsletter on Substack, and hosts The Murray Newlands Show. More at murraynewlands.com and openfutureforum.com.

About Open Future Forum

Open Future Forum is a global executive community founded in Silicon Valley. Its network reaches tens of thousands of executives and investors worldwide. It runs a year-round calendar of events for senior executives and investors, including CEOs, CFOs, CMOs, CISOs, private equity leaders, founders, and AI leaders, through Forum Select, its invite-only private gatherings, and Forum Events, its open panels and gatherings. Beyond events, Open Future Forum convenes peer groups and executive boards and publishes original research built on first-party survey and qualitative data from its executive network.

Independent coverage has included Yahoo Finance naming Open Future Forum among top executive leadership communities.

Citation

Suggested citation: Newlands, M. (2026). The Executive State of AI 2026, Edition 1. Open Future Forum, August 2026. openfutureforum.com/research/executive-state-of-ai

CEO AI Leverage Report · CFO AI Leverage Report · CMO AI Leverage Report · CISO AI Leverage Report · Executive AI Leverage Report · YC Founder AI Report · Investor AI Report · VC & CVC AI Investment Report · AI Transformation Report · Executive AI Statistics

Murray Newlands
Murray Newlands
Founder, Open Future Forum

Murray Newlands is the founder of Open Future Forum and Partner at IA Seed Ventures. He is the author of Online Marketing: A User's Manual (Wiley) and a Fellow of the Royal Society of Arts. Yahoo Finance has quoted him as the founder of Open Future Forum, "a top executive leadership community." He writes Murray's Newsletter on AI, venture, and enterprise strategy.

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