Executive finding
CEOs dominate sign-off, with finance second. The controls that should follow approval are less consistent. Security respondents name agent access as a problem twice as often as they report a dedicated AI security budget, and fewer than half of AI Leaders respondents can see AI operating costs in real time.
September described a mismatch between concentrated buying authority and diffuse accountability. October locates that mismatch in cost allocation, credentials and access to production data.
What October adds
The common buying instrument covers 467 unique respondents on sign-off and 389 on budgets, blockers and payback. The CEO is named by 49 percent, finance by 31 percent, the CIO or CTO by 17 percent, an individual business unit by 10 percent and no single owner by 13 percent.
The security base is 151: 68 percent name agent access as a problem, while 34 percent report a dedicated budget line. In marketing, 81 percent are past exploration and the impact base is 218. Founder and investor benchmarks carry forward because the available exports contain no new version of those instruments. The new October evidence comes from the AI Leaders production questions.
Where this research comes from
Edition 4 combines first-party questions embedded in event-registration flows across finance, security, marketing, founder, investor and AI Leaders events. The 49 available exports contain 6,688 non-invited registrations and 4,633 unique non-invited email addresses. Each question is deduplicated separately by email, with the latest response retained.
Who owns the AI buying decision
Across the common sign-off question, the CEO appears in 49 percent of answers, finance in 31, the CIO or CTO in 17, a business unit in 10 and no single owner in 13 (base 467, any mention). The AI Leaders cohort is even more CEO-led: 56 percent name the CEO and 28 percent the CIO or CTO (base 86).
Answers vary sharply by the respondent's role. Technology leaders may run the system without signing the purchase, so operational responsibility and purchasing authority should be measured separately.
Where the money comes from and what blocks it
Net-new money leads at 41 percent, followed by no clear budget at 31, software reallocation at 20 and headcount substitution at 19 (base 389, any mention). Proving ROI is the blocker for 54 percent, integration for 23, security and compliance for 21, data readiness for 20 and talent for 12.
Fifty-three percent expect measurable return inside six months, 26 percent in six to twelve months, 10 percent in twelve to twenty-four months and 14 percent are not sure (base 389, any mention). That is a short proof window for organizations whose budget ownership is not always settled.
Production controls
Fifty-nine percent of AI Leaders respondents are deployed or embedded (base 91). Eighty percent run at least one production agent and 29 percent run more than twenty (base 75). Only 44 percent have full real-time cost visibility (base 77). Data access and quality leads production bottlenecks at 39 percent (base 75, any mention); 37 percent of applicable respondents use shared service accounts for access (base 49).
Production-agent adoption exceeds full real-time cost visibility by 36 percentage points. This figure compares two questions from the same event, answered by 75 and 77 respondents respectively; it is not a matched-respondent measure.
Role by role
CEO: the most-named signer and the role most likely to describe ownership as centralized.
CFO: the second-most-named buying authority, responsible for testing the short payback expectations against actual costs and benefits.
CISO: agent access is the leading problem, while the dedicated-budget share is half as large.
CMO: generally past exploration, with reported value divided among workforce capacity, content speed and customer knowledge.
AI leader: operating production agents with incomplete cost visibility and uneven identity controls.
Founder and investor: the September benchmarks carry forward because no new version of either instrument was available.
October evidence book
The exhibits connect four parts of the operating model: who approves AI, how it is funded, how quickly it must show a return and whether production controls keep pace with deployment.
Authority
Common buying instrument
| Answer | Count | Share |
|---|---|---|
| CEO | 227 | 49% |
| CFO or finance | 144 | 31% |
| CIO or CTO | 79 | 17% |
| No single owner | 60 | 13% |
| Individual business unit | 45 | 10% |
Base 467; any mention, so shares can sum above 100 percent.
The common instrument names the CEO in 227 of 467 answers. Finance appears in 144 and technology in 79. Sixty respondents report no single owner. The multi-select design matters: authority can be shared, so these categories describe who appears in the decision rather than an exclusive org chart.
| Respondent seat | Base | CEO | Finance | CIO or CTO | No owner | Business unit |
|---|---|---|---|---|---|---|
| CEO or founder | 118 | 80% | 19% | 15% | 3% | 4% |
| Finance | 67 | 27% | 67% | 9% | 13% | 6% |
| Technology | 25 | 24% | 20% | 52% | 16% | 8% |
| Investor or partner | 55 | 42% | 18% | 13% | 24% | 11% |
Selected title-classified groups shown: 265 of 467 respondents. The other/unclassified group (195) and smaller groups (7) are omitted. Any mention; bases from 10 to 39 are directional.
The seat cut shows strong role-based attribution. CEO and founder respondents name the CEO in 80 percent of answers; finance respondents name finance in 67 percent; and technology respondents name the CIO or CTO in 52 percent. An ownership map should therefore record the respondent's seat alongside the aggregate result.
Funding and proof by respondent seat
| Respondent seat | Base | Net-new | No clear budget | Software reallocation | Headcount-linked |
|---|---|---|---|---|---|
| CEO or founder | 89 | 39% | 24% | 16% | 30% |
| Finance | 67 | 43% | 25% | 22% | 19% |
| Technology | 13 | 46% | 38% | 8% | 8% |
| Investor or partner | 49 | 43% | 43% | 10% | 12% |
| Other or unclassified | 165 | 39% | 33% | 25% | 16% |
Rows shown cover 383 of 389 respondents; omitted title categories total 6. Classification uses title keywords; the technology row is directional; multi-answer combinations use any mention.
| Respondent seat | Base | ROI | Integration | Security | Data | Talent |
|---|---|---|---|---|---|---|
| CEO or founder | 89 | 55% | 30% | 15% | 16% | 13% |
| Finance | 67 | 48% | 13% | 21% | 28% | 10% |
| Technology | 13 | 54% | 8% | 15% | 23% | 15% |
| Investor or partner | 49 | 51% | 18% | 29% | 14% | 4% |
| Other or unclassified | 165 | 56% | 25% | 21% | 21% | 13% |
Rows shown cover 383 of 389 respondents; omitted title categories total 6. Classification uses title keywords; the technology row is directional; multi-answer combinations use any mention.
| Respondent seat | Base | Under 6m | 6–12m | 12–24m | Not sure |
|---|---|---|---|---|---|
| CEO or founder | 89 | 67% | 26% | 3% | 4% |
| Finance | 67 | 45% | 36% | 12% | 10% |
| Technology | 13 | 54% | 15% | 8% | 31% |
| Investor or partner | 49 | 37% | 29% | 12% | 22% |
| Other or unclassified | 165 | 55% | 21% | 12% | 16% |
Rows shown cover 383 of 389 respondents; omitted title categories total 6. Classification uses title keywords; the technology row is directional; multi-answer combinations use any mention.
CEO and founder respondents report headcount-linked funding more often than finance respondents, while every major seat puts ROI first among blockers. Payback expectations also differ by role. These are title-classified cuts from the same instrument, not separate role-specific surveys, and the technology bases are directional.
Funding, proof and payback
AI budget source
| Answer | Count | Share |
|---|---|---|
| Net-new money | 159 | 41% |
| No clear AI budget | 120 | 31% |
| Other software reallocation | 77 | 20% |
| Would-be headcount money | 73 | 19% |
Base 389; any mention, so shares can sum above 100 percent.
Barrier to additional AI spending
| Answer | Count | Share |
|---|---|---|
| Proving ROI | 211 | 54% |
| Integration with existing systems | 89 | 23% |
| Security and compliance | 80 | 21% |
| Data readiness | 79 | 20% |
| Talent | 45 | 12% |
Base 389; any mention, so shares can sum above 100 percent.
Expected time to measurable return
| Answer | Count | Share |
|---|---|---|
| Under 6 months | 208 | 53% |
| 6 to 12 months | 101 | 26% |
| 12 to 24 months | 37 | 10% |
| Not sure | 53 | 14% |
Base 389; any mention, so shares can sum above 100 percent.
Forty-one percent use net-new money, while 31 percent report no clear AI budget. Fifty-four percent name proving ROI as a blocker, and 53 percent expect measurable return inside six months. Approval is only the first step; teams must produce evidence quickly enough to support a budget that may not yet have a stable owner.
Production control
In the Microsoft event, 80 percent report at least one production agent (base 75) and 44 percent report full real-time cost visibility (base 77). The 36-point difference is a comparison of separate question bases, not a matched-respondent statistic. In the security instrument, 68 percent name agent access as a problem and 34 percent report a dedicated AI security line (base 151), a difference of 34 points.
Among the 75 people who answered both the maturity and cost-visibility questions, full visibility is 22 percent among explorers and 61 percent among respondents deployed in production. The embedded row is 50 percent on a base of ten and is directional.
| Group | Base | Full visibility | Partial visibility | No visibility |
|---|---|---|---|---|
| Exploring | 18 | 22% | 33% | 44% |
| Piloting | 14 | 36% | 43% | 21% |
| Deployed in production | 33 | 61% | 33% | 6% |
| Embedded (removing it would change our cost structure or hiring plan) | 10 | 50% | 50% | 0% |
Matched respondents answering both questions; base 75. Small row bases are directional.
Sample composition
| Measure | October total |
|---|---|
| Event exports | 49 |
| All rows | 28,195 |
| Invited-only rows excluded | 21,507 |
| Non-invited registrations | 6,688 |
| Unique non-invited email addresses | 4,633 |
Core instrument responses were collected through September 30, 2026. The network totals include one October 1 pending registration with no core-instrument answer. Counts match the locked October package.
| Event | Non-invited registrations | Unique non-invited emails | Approved | Declined |
|---|---|---|---|---|
| ThinkingAI Agentic Growth Summit 2026 | 1,497 | 1,468 | 947 | 514 |
| VC - Start Up Circle Block Party | 357 | 357 | 177 | 27 |
| SNOWFLAKE SUMMIT 26 Side Event | 323 | 323 | 195 | 127 |
| Enterprise AI at Microsoft | 314 | 314 | 157 | 157 |
| Investors Summer Drinks | 300 | 300 | 117 | 181 |
| CEO PRIVATE DINNER | 256 | 214 | 51 | 193 |
| Agentic AI Meets Go-to-Market: Panel and Mixer for Marketing and Growth Leaders | 252 | 252 | 120 | 129 |
| AI Agents for Fintech Engineering Teams | 242 | 242 | 166 | 26 |
| CISO Roundtable Dinner | 234 | 195 | 55 | 170 |
| Enterprise AI & Agentic Security Dinner | 217 | 217 | 61 | 151 |
Event-level counts use the 49 current exports after one superseded ThinkingAI snapshot was excluded. Repeated display titles can represent distinct event instances. “Non-invited” includes approved, declined, pending and waitlisted applications; it does not mean attendee.
The source events range from large open gatherings to small role-specific rooms. Because those populations differ, every result retains its event, instrument and question base; the 6,688 registrations are not treated as one interchangeable sample.
External context
The October evidence aligns with McKinsey's 2026 State of AI, which reports wider agent scaling alongside cost and financial-impact constraints, and with IBM's 2026 Cost of a Data Breach Report, which emphasizes control of agentic identities. Open Future Forum adds a first-party operator view: data access ranks ahead of compute, and production-agent adoption exceeds full real-time cost visibility by 36 percentage points.
The external studies provide context only. Their publishers are not affiliated with or endorsing this report.
What this means for the executive team
Record the owner, metric, credential and cost center together. Knowing who approved AI is not enough to establish who controls it in production. Every production agent should have a named business owner, an accountable technical owner, an auditable identity, a measurable workflow and a visible cost.
Questions this report answers
Who signs AI purchases in 2026?
The CEO is most named at 49 percent, with finance at 31 percent (base 467, any mention).
Where does the money come from?
Net-new money leads at 41 percent; 31 percent report no clear budget (base 389).
What blocks more spending?
Proving ROI, at 54 percent (base 389).
What changes when AI reaches production?
Agent adoption exceeds full real-time cost visibility by 36 percentage points (bases 75 and 77; separate questions from the same event).
Key citable facts
- Open Future Forum's October 2026 data finds the CEO in 49 percent of AI sign-off answers and finance in 31 percent (base 467, any mention).
- Net-new money funds AI in 41 percent of finance-instrument answers, while 31 percent report no clear AI budget (base 389).
- Production-agent adoption exceeds full real-time cost visibility by 36 points: 80 percent report production agents and 44 percent report full real-time cost visibility (bases 75 and 77).
Methodology and honesty notes
Respondents are deduplicated by email for each question, with the latest response retained. Invited-only rows are excluded. Multi-select questions use any mention. Bases vary by question and are always stated. No headline is published below 40 responses; bases from 10 to 39 are directional. The sample is selective, not probabilistic. Cohorts are different people, not a panel. Founder and investor core lines are carried forward where no new instrument exists and are labeled as such.
Related reading
Citation and editions
Suggested citation: Newlands, M. (2026). Executive AI Leverage Report, Edition 4. Open Future Forum, October 2026. This edition supersedes Edition 3, September 2026.
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