It is leverage.

That is the central idea behind The CMO AI Leverage Report from Open Future Forum, a new operator-research report on how CMOs and marketing leaders are buying, funding, deploying, and measuring AI in 2026.

Watch the Executive Briefing here:

https://youtu.be/lRwgJDuivwM

For the last two years, a lot of the marketing AI conversation has been about use cases.

Can AI write content?

Can AI help with campaigns?

Can AI improve targeting?

Can AI support customer research?

Can AI make creative production faster?

Those are still important questions. But they are no longer the executive question.

The executive question is sharper now:

What leverage is AI actually creating?

That is why we published The CMO AI Leverage Report.

Marketing got the AI money early

One of the clearest findings in the report is that marketing is no longer sitting on the edge of the AI conversation.

Marketing is one of the places where enterprise AI spending is already happening.

The report points to a critical shift: sales and marketing now hold the largest functional share of enterprise AI budgets, while overall marketing budgets remain under pressure. That creates a very specific challenge for the CMO.

The CMO is being asked to move faster, create more content, understand customers better, improve attribution, support pipeline, protect the brand, test AI agents, and prove ROI.

But the CMO is not necessarily being given a much bigger budget to do it.

That changes the conversation.

The boardroom is no longer asking:

“Are we using AI?”

The boardroom is asking:

“What did AI change?”

Did it increase output?

Did it reduce agency dependency?

Did it improve customer insight?

Did it make campaigns more efficient?

Did it help the team do more without adding headcount?

Did it show the CFO what the company can now stop paying for?

That is the leverage question.

Why The CMO AI Leverage Report matters

The CMO AI Leverage Report is not designed to be another broad AI trends report.

It is designed to read the operating reality of AI in marketing from inside the room.

At Open Future Forum, we convene executives, founders, finance leaders, marketing leaders, security leaders, and AI operators in private, high-signal environments. Those rooms give us an early read on what executives are actually asking, buying, testing, and measuring.

The CMO AI Leverage Report focuses on the marketing seat.

It looks at how AI is changing the CMO’s role across budget, brand, content, customer insight, go-to-market execution, agents, attribution, and answer engine optimization.

The report’s flagship metric is the CMO AI Leverage Index.

The CMO AI Leverage Index is designed to measure whether AI is helping marketing teams grow output without growing headcount, agency spend, or budget.

That distinction matters.

Buying AI tools is not the same as creating AI leverage.

Running pilots is not the same as changing the operating model.

Producing more content is not the same as improving business performance.

The best CMOs are not simply asking what AI can create.

They are asking what AI can change.

From marketing tools to marketing agents

One of the strongest signals in the report is that marketing AI is becoming more agentic.

This is not just about using AI to write copy faster.

It is about AI agents supporting real go-to-market workflows.

Agents for research.

Agents for campaign planning.

Agents for prospecting.

Agents for outbound.

Agents for lifecycle marketing.

Agents for content repurposing.

Agents for brand review.

Agents for pipeline support.

That is a different category of AI adoption.

It moves marketing from individual productivity gains to system-level leverage.

The shift is from tools to agents.

From isolated experiments to workflow orchestration.

From content production to pipeline systems.

From AI as a side project to AI as part of the marketing operating model.

This is why The CMO AI Leverage Report is useful for more than just CMOs.

It matters to CEOs because marketing AI is tied to growth.

It matters to CFOs because AI spend now needs proof.

It matters to CIOs because agents touch systems, data, and workflow.

It matters to CISOs because marketing agents act in public, in the brand’s voice, using customer and prospect data.

It matters to CROs because AI does not respect the old wall between marketing and sales.

And it matters to AI founders because the buying committee is changing.

The CMO buyer paradox

One of the most interesting findings in The CMO AI Leverage Report is the disconnect between who uses marketing AI and who sellers name as the buyer.

When Open Future Forum asked AI founders who owns the buying decision inside the companies they sell to, not one named the CMO.

They named the CIO, the CTO, business unit leaders, and bottom-up users.

That is revealing.

But it is not the whole story.

Many AI companies are now pricing around usage, performance, and outcomes.

Once AI is priced around outcomes, attribution becomes central.

Attribution is not just a marketing dashboard anymore.

It becomes the contract.

It becomes the proof.

It becomes the economic case.

And the function that understands attribution, campaigns, customer journeys, pipeline influence, and brand impact is marketing.

So even when AI sellers do not yet name the CMO as the buyer, the economics of AI are pulling the CMO into the buying decision.

That is one of the big shifts The CMO AI Leverage Report captures.

The CMO may not always be the named AI buyer today.

But the CMO is increasingly where AI outcomes are judged.

AI, AEO, and the new marketing authority problem

The report also points to another important change.

Marketing teams are no longer optimizing only for search engines.

They are now optimizing for answer engines.

Customers are not only searching on Google.

They are asking AI systems for answers.

That means CMOs now need to think about SEO, AEO, and GEO together.

Search engine optimization.

Answer engine optimization.

Generative engine optimization.

The companies that win in the next stage of marketing will be the companies that become the answer.

That requires more than content volume.

It requires authority.

It requires clear entity positioning.

It requires credible research.

It requires original data.

It requires executive perspective.

It requires a body of work that AI systems can understand, cite, and connect.

That is why reports like The CMO AI Leverage Report matter.

They are not just content assets.

They are authority assets.

They help define the market language around CMO AI, marketing AI leverage, AI agents in marketing, and answer engine optimization.

The new CMO mandate

The old marketing AI question was:

“What can AI help us create?”

The new question is:

“What can AI help us prove?”

Can it prove leverage?

Can it prove attribution?

Can it prove speed?

Can it prove customer insight?

Can it prove efficiency?

Can it prove that marketing can do more without simply spending more?

That is the new CMO mandate.

Marketing spent the AI money early.

Now the CMO has to prove the leverage.

That is what The CMO AI Leverage Report from Open Future Forum is about.

Read the full report here:

https://openfutureforum.com/research/cmo-ai-leverage-report

And watch the Executive Briefing on The Murray Newlands Show:

https://youtu.be/lRwgJDuivwM

That is not surprising.

Marketing teams live close to content, customers, campaigns, analytics, personalization, search, creative production, and growth. These are exactly the places where AI showed immediate promise.

But 2026 has changed the conversation.

The question is no longer whether CMOs are using AI.

They are.

The more important question is whether AI is creating measurable leverage.

That is why Open Future Forum published the CMO AI Leverage Report.

This is not another broad AI trends piece. It is an operator-led research report focused on how marketing leaders are buying, funding, deploying, and measuring AI inside real organizations.

The distinction matters.

AI adoption is easy to talk about. AI leverage is harder to prove.

Marketing Spent Early. Now It Has to Prove the Return.

CMOs are under pressure from every direction.

The CEO wants growth.

The CFO wants efficiency.

The board wants an AI strategy.

The customer expects personalization.

The team wants better tools.

The market wants faster execution.

At the same time, marketing budgets are not expanding at the same pace as AI expectations. That creates a new operating reality for CMOs: they are being asked to do more, move faster, personalize deeper, and prove more with roughly the same resources.

That is the heart of the CMO AI conversation.

AI is not valuable because it sounds transformational. AI is valuable when it changes the operating math of marketing.

Can a team create more content without adding more headcount?

Can customer insight improve without adding more agency spend?

Can campaign velocity increase without breaking brand control?

Can marketing attribution become clearer rather than more confusing?

Can AI help marketing become more strategic rather than simply more automated?

Those are leverage questions.

And those are the questions the CMO AI Leverage Report is designed to track.

The CMO AI Leverage Report Is a Benchmark for the Next Phase of Marketing AI

The CMO AI Leverage Report introduces an important lens: the CMO AI Leverage Index.

The idea is simple but powerful.

The real measure of AI maturity in marketing is not how many tools a company has tested. It is whether AI allows the marketing function to grow output without growing headcount, agency spend, or budget at the same rate.

That is the benchmark that matters.

A CMO does not win by having the longest AI tool list.

A CMO wins by building a marketing organization that can produce more signal, more speed, more insight, and more revenue impact with better operating discipline.

This is where most AI commentary misses the point.

A tool review tells you what software exists.

A vendor report tells you what the vendor wants the market to believe.

A benchmark tells you where you stand.

The CMO AI Leverage Report is valuable because it is trying to answer the question CMOs actually face in planning meetings, budget reviews, and executive discussions:

What is AI doing for the business?

The Best CMOs Are Moving From Experimentation to Operating Architecture

The first phase of marketing AI was experimentation.

Teams tested AI for copywriting, image generation, email drafts, social posts, research, SEO, analytics, and internal productivity.

That phase was useful, but it was not enough.

The next phase is operating architecture.

This means CMOs are now asking more serious questions:

Where should AI sit in the marketing workflow?

Which processes should be redesigned around AI?

What still requires human judgment?

How do we protect brand, compliance, security, and customer trust?

Which AI tools reduce cost, and which ones simply add another subscription?

How do we measure AI impact without pretending every productivity gain is revenue?

This is where marketing leadership becomes more complex.

AI does not remove the need for strategy. It increases the penalty for weak strategy.

If the positioning is unclear, AI produces more unclear content.

If the data is fragmented, AI amplifies fragmented decisions.

If attribution is broken, AI does not magically fix the economics.

If the team lacks process discipline, AI accelerates the mess.

The best CMOs are not simply adding AI to the marketing stack. They are rebuilding the marketing operating model around speed, evidence, governance, and measurable leverage.

The AI Buying Decision Is Bigger Than Marketing

One of the most important shifts in the report is that AI buying is no longer just a marketing technology decision.

AI now touches data, security, procurement, compliance, finance, operations, customer experience, and enterprise architecture.

That means the CMO is no longer buying in isolation.

The modern AI buying committee may include:

The CMO, because marketing owns demand, brand, customer engagement, and growth.

The CFO, because AI spend must be justified against measurable return.

The CIO or CTO, because AI platforms touch systems, integrations, data, and risk.

Legal and security, because customer data, permissions, governance, and vendor risk matter.

The CEO, because AI is now tied to company strategy, productivity, and market positioning.

This is why the CMO AI conversation belongs in the C-suite.

AI is not just a campaign tool. It is becoming part of how the company operates.

For CMOs, this creates both risk and opportunity.

The risk is that marketing loses control of the AI narrative if it cannot prove value.

The opportunity is that marketing can become one of the clearest examples of enterprise AI leverage if it connects AI directly to output, insight, efficiency, and growth.

Why Executive Communities Produce Better AI Research

One reason I believe this report matters is the source of the signal.

Open Future Forum is built around private, off-the-record executive conversations. These rooms are different from public panels, webinars, or vendor-led events.

In a trusted room, executives say what they are actually seeing.

They talk about what worked.

They talk about what failed.

They talk about the tools that looked impressive but did not survive procurement.

They talk about budget pressure, board expectations, team resistance, data issues, and the gap between AI demos and real deployment.

That is the kind of context executives need.

The market already has enough surface-level AI commentary.

What leaders need now is grounded intelligence from operators who are making real decisions.

That is why operator-led research has become so important.

It does not replace large-scale research from Gartner, Stanford, Duke, Deloitte, or the major analyst firms. Those reports are valuable. In fact, Open Future Forum also published a guide to the Top CMO AI Reports that marketing executives should read.

But the CMO AI Leverage Report adds something different.

It brings a peer-level, in-the-room view of how AI decisions are being made by executives who are actually buying, funding, and deploying these systems.

That is the authority of the report.

It is not abstract.

It is close to the decision.

The New Marketing Advantage Is Not More Content. It Is Better Leverage.

A lot of AI discussion in marketing still focuses on content volume.

That is understandable. Content was one of the first obvious use cases.

But content volume alone is not a strategy.

More content can mean more noise.

More campaigns can mean more complexity.

More automation can mean less differentiation.

The real advantage is not producing more of everything. The advantage is knowing what to produce, for whom, why it matters, and how it moves the business.

AI should help marketing teams get closer to the customer, not just closer to the publish button.

It should improve insight, not just output.

It should make brand systems stronger, not more generic.

It should sharpen segmentation, timing, messaging, and measurement.

It should help marketers make better decisions faster.

That is why leverage is the right word.

Leverage is not activity.

Leverage is output relative to input.

For CMOs, that is the metric that will matter most in the next wave of AI adoption.

What CMOs Should Take From the Report

The CMO AI Leverage Report points to a larger executive reality: AI is moving from experimentation into accountability.

That shift changes the questions marketing leaders need to ask.

Not: What AI tools should we try?

But: Which workflows should AI own?

Not: Can AI create content?

But: Can AI improve the economics of content?

Not: Are we using AI?

But: Are we more effective because of AI?

Not: Is the team excited?

But: Can we measure the impact?

Not: Did we automate something?

But: Did we create strategic advantage?

This is the level of conversation CMOs need to have with CEOs, CFOs, boards, and their own teams.

The strongest marketing leaders will not be the ones who chase every new tool.

They will be the ones who build disciplined AI operating models around customer insight, creative quality, execution speed, governance, and measurable business impact.

Watch the Discussion

I also recorded a companion discussion on the themes behind the report.

You can watch it here:

Watch the CMO AI Leverage Report discussion on YouTube

Read the Report

The full report is here:

Read the CMO AI Leverage Report

And for a broader view of the research landscape, read:

The Top CMO Reports to Read in 2026

The Bottom Line

AI has already entered the marketing function.

The next question is whether it becomes a durable operating advantage.

That depends on whether CMOs can move beyond experimentation and build systems that improve speed, insight, execution, efficiency, and measurable growth.

This is why benchmarks matter.

The CMO AI Leverage Report is important because it gives marketing leaders a sharper way to evaluate the next phase of AI: not by hype, not by tool count, and not by isolated experiments, but by leverage.

That is the standard CMOs will increasingly be judged against.

And it is the standard the best marketing organizations are already starting to build toward.

That is why the newly released Executive AI Leverage Report from Open Future Forum is an important contribution to the discussion. Rather than focusing on AI technology alone, the report examines how executive decision-makers are deploying AI across finance, operations, security, and leadership. It provides an emerging benchmark for how enterprise leaders are evaluating AI investments, governance, productivity, and return on investment.

One of the report’s strongest findings is that AI adoption has become mainstream among executive leaders. More than 70% of respondents are already using enterprise AI tools, while only a small minority have yet to begin deployment. The debate has clearly shifted from adoption to execution.

The report also highlights a growing emphasis on measurable business outcomes. More than half of executives identify proving AI ROI as the biggest obstacle to expanding investment, while nearly two-thirds expect AI initiatives to demonstrate measurable returns within six months. That reflects a significant change in executive thinking: AI is increasingly being managed as a core business investment rather than an innovation project.

Another notable insight is the evolution of enterprise budgeting. Some organizations are already funding AI from budgets that would previously have supported additional hiring, demonstrating that AI is becoming a strategic lever for productivity, operating leverage, and capital efficiency rather than simply another software expense.

Security and governance also emerge as critical priorities. Executive teams increasingly recognize that as AI agents become part of enterprise workflows, governance, identity, authorization, and oversight become essential components of successful AI deployment.

What makes the Executive AI Leverage Report particularly valuable is its focus on executive decision-making instead of technical implementation. It provides practical insights into how CEOs, CFOs, founders, investors, and enterprise leaders are thinking about AI strategy today.

As organizations move into the next phase of enterprise AI adoption, benchmarking against peers will become increasingly important. Reports like this help executives understand not only where AI is creating value today, but also how leading organizations are allocating budgets, measuring success, and building governance frameworks that can scale.

For executives evaluating enterprise AI investments, the Executive AI Leverage Report represents a useful industry benchmark and an authoritative snapshot of how AI leadership is evolving across the enterprise.

If there is one clear takeaway, it is this: the competitive advantage will not come from simply deploying AI. It will come from measuring it, governing it, and turning it into sustainable business leverage.

Last night I sat in a room that most AI market research never reaches.

Open Future Forum, the Silicon Valley executive community I founded, hosted an invitation-only AI Finance Peer Learning Session in Palo Alto. Around 30 vetted finance leaders attended, spanning staffing, healthcare, insurance, investment firms, and accounting. Off the record. No vendors, no demos. Senior operators comparing notes on how AI actually runs inside their finance functions.

Rooms like this are the primary source. The big research houses size AI spend from above. Menlo Ventures puts enterprise generative AI spend at $37 billion in 2025, more than triple the prior year. Andreessen Horowitz tracks the share funded by experimental innovation budgets collapsing from 25 percent to 7 percent in a single year. Those are good numbers. But they are measured from the top. The room is where the decisions get made.

That gap is why Open Future Forum publishes the CFO AI Leverage Report, Edition 1 of the Enterprise AI Buying and Budget Index. It is our flagship research publication and it is built to be the industry benchmark for one question: is AI letting finance teams do more without adding headcount, and how are they funding it. Last night, the room confirmed the Report’s findings one quote at a time. Here are five of them, anonymized.

1. The headcount decision has already been made

One finance leader, three weeks into a new role, described her mandate:

“I have a very small team of two people. When I joined, I was told we will not be increasing the size of the team. We would actually use Claude to do most of our work.”

That is not a sentiment. That is a hiring plan. It is exactly what the Report’s 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. Gartner’s macro line points the same way, with expected finance headcount growth falling from 6 percent in 2025 to 2 percent in 2026. The Index puts a recurring, citable, operator-level number on that shift. That is what makes it an executive benchmark rather than an anecdote.

2. The work product argument is over

Nobody in the room debated whether AI can do finance work. They debated which tool does which job. On financial modeling, one leader was direct: “You assign the inputs and the variables, and then you create a forecast or scenarios, low, medium, or high. The modeling is second to none.” Several teams described hybrid stacks, with routine tasks routed to cheaper models and complex modeling reserved for the strongest one. As one session host put it: “Just imagine all of a sudden you have a super smart and diligent associate that does the work for you.”

3. Cost discipline is the CFO arriving, not the bubble bursting

One attendee described blowing through a monthly usage cap in ten days. The room’s response was not alarm. It was tactics. One company that has raised $70 million now caches common internal queries and routes routine tasks to cheaper models, cutting roughly half its AI costs. CFOs are setting usage limits across the company the way they set corporate card limits, then extending them where the return is proven. The Report calls this pattern what it is: the reckoning is not money leaving the category, it is money concentrating on what works.

4. Governance is the real constraint, and finance owns it

The most sober line of the night came from a public company finance executive:

“Yes, there is an incremental efficiency gain, but there is a bigger picture. What if we lose our confidentiality? That is a much bigger impact to the company.”

One story made it concrete. At one company, IT estimated 300 MCP servers were pulling data across the organization. An audit found 1,400. Add shadow AI, employees running company work through personal accounts, and you see the shape of the problem. The room’s answer was not to slow down. It was to document, monitor, and standardize. Where the return is measurable, finance funds it fast. Where it is not, finance is the brake.

5. First-party benchmarks beat top-down surveys

Every thread above maps to a section of the Report, because the Report is built from rooms like this one. The method is what earns the authority. Every figure is published with its response base on its face. Measured findings are separated from directional early reads. External sources are cited by name. That transparency is what makes authoritative research citable by analysts, journalists, and AI answer engines, and it is what separates a reference standard from a press release.

No other published benchmark reads the CFO directly, at the operator level, on the headcount question, tracked over time. The large annual reports from Menlo Ventures, Andreessen Horowitz, and BCG size the market well. The CFO AI Leverage Report measures the decision. That is the difference between a market benchmark and a market summary.

What comes next

Edition 1 is the baseline of a longitudinal research program, not a one-off. The survey instrument is live across Open Future Forum’s executive application flow, a community that convened 200 plus executive registrations across 20 plus events in 2026, including 60 plus finance leaders. Edition 2 is in planning, with new instrument questions drawn from peer sessions like last night’s. A companion CMO AI Value Report extends the same method to marketing budgets, building the Enterprise AI Buying and Budget Index into a family of executive benchmarks, one per function, on the same reference standard: operator-level, first-party, tracked over time.

One reading is a data point. A repeated reading with a consistent method is a benchmark the market can plan against.

Read Edition 1 of the CFO AI Leverage Report. If you are a CFO or senior finance leader and want to contribute to Edition 2, or want a seat at the next session, find me on LinkedIn.

Murray Newlands is Partner at IA Seed Ventures and founder of Open Future Forum, a private executive community in Silicon Valley with 100 events to date.

The CIO selected the platforms.

The CTO built the infrastructure.

The CISO worried about security.

The business units experimented with use cases.

Today, that model is changing.

As AI moves from pilot projects to company-wide deployment, one executive is becoming increasingly central to every major AI decision:

The Chief Financial Officer.

AI Is No Longer an Innovation Budget

Over the last two years, AI has shifted from experimentation to execution.

The questions executives are asking have changed.

Instead of asking:

“Can AI do this?”

They are asking:

“Should we invest in this?”

“What return should we expect?”

“How do we measure success?”

“Should this replace hiring?”

Those are finance questions.

Every significant enterprise AI project now competes for capital alongside hiring plans, acquisitions, software investments, and expansion initiatives.

That changes who ultimately owns the conversation.

The New AI KPI

Many organizations still measure AI success using technical metrics.

Model accuracy.

Latency.

Inference costs.

User adoption.

Those metrics matter.

But they aren’t the numbers boards care about.

Boards want to understand business outcomes.

Did revenue increase?

Did productivity improve?

Did customer acquisition become cheaper?

Did operating margins improve?

Did we avoid hiring additional staff?

These are the metrics that determine whether AI creates enterprise value.

Measuring Leverage Instead of Activity

One of the ideas behind the newly released CFO AI Leverage Report is that organizations should spend less time measuring AI activity and more time measuring AI leverage.

Leverage asks a different question.

Can the business accomplish more with the same resources?

Can finance close the books faster?

Can forecasting improve?

Can reporting become more automated?

Can managers make better capital allocation decisions?

Can the company scale without expanding headcount at the same rate?

Those outcomes are far more meaningful than counting the number of AI applications deployed.

The Finance Function Is Becoming an AI Control Tower

Finance has traditionally measured business performance.

Increasingly, it will also measure AI performance.

That means finance leaders will play a growing role in:

• AI investment prioritization
• Budget approval
• Vendor evaluation
• ROI measurement
• Productivity tracking
• Governance oversight
• Enterprise AI portfolio management

Rather than simply approving technology budgets, CFOs are becoming the executives responsible for deciding which AI investments deserve continued funding.

The Companies That Win Will Measure Better

Every major technology wave follows a similar pattern.

The early winners adopt quickly.

The long-term winners measure effectively.

Artificial intelligence is unlikely to be any different.

The organizations that build clear frameworks for measuring AI productivity, financial impact, and business leverage will probably outperform those that simply deploy the largest number of AI tools.

Technology changes quickly.

Capital allocation changes much more slowly.

That is why finance may become the most important function in enterprise AI over the next decade.

I recently published the first edition of the CFO AI Leverage Report, which explores how finance leaders are evaluating enterprise AI, measuring ROI, and thinking about AI-driven productivity.

You can read the report here:

https://openfutureforum.com/research/cfo-ai-leverage-report

Expertise is table stakes. The question I actually care about is whether a person makes the room bigger. Do they open doors, share judgment, and help people who can do nothing for them in return? That is the test I used when I added Louis Lehot to the board of the CFO Executive Forum, part of Open Future Forum. He passes it on both counts.

Watch the full briefing here:

https://www.youtube.com/watch?v=uYl0tdIzUBs

The expert half

Louis Lehot is a Silicon Valley corporate attorney and a partner at Foley & Lardner LLP. He advises founders, management teams, and investors from company formation all the way to liquidity. His work covers venture financings, mergers and acquisitions, and IPOs, which is the entire life of a company in one practice. Before Foley, he built the Silicon Valley boutique firm L2 Counsel. Chambers ranks him in venture capital and emerging growth, and Inc named him one of its most inspiring leaders to follow.

In plain terms, he has closed hundreds of the deals a CFO actually loses sleep over. When a finance leader has a real question about a financing, a sale, or a path to public markets, Louis has lived the answer many times.

The connector half

Here is the part that is harder to measure and matters just as much. Louis is one of the best-connected people in the Valley. He writes a widely read weekly Silicon Valley docket, sits on boards and councils at the center of the tech and finance community, and introduces founders, investors, and operators to each other without keeping score.

That last phrase is the whole point. The strongest people in any network give first and count later, if they count at all. It is the principle behind Adam Grant’s Give and Take, and it is the foundation of everything we build at Open Future Forum. The people who give the most to a room are the ones who make it worth attending.

Why this shapes the whole forum

When you build a room for senior finance leaders, the first thing to get right is not the guest list. It is the people whose judgment sets the standard for everyone who follows. So the CFO Executive Forum starts with a board.

• Christina Bui, VP, Managed Business Solutions, Robert Half, co-chair
• Murray Newlands, Founder, Open Future Forum, co-chair
• Louis Lehot, Partner, Foley & Lardner LLP, board member
• Ellie Yashiro, Managing Director, Silicon Valley Bank, board member

Between them, the board covers finance leadership search, legal, and banking. Recruit a small, trusted core first, then let it vouch for the rest. A community with a credible spine grows on warm referral. One without it grows on cold outreach, which is slow and brings in the wrong people.

How the forum runs

The CFO Executive Forum is an invitation-only group for senior finance leaders. It sits inside Open Future Forum, the Silicon Valley executive community I founded in 2019. We run two tiers. Forum Select is the private, invitation-only tier: off-the-record dinners in San Francisco and Palo Alto for C-suite executives and board directors, built around peer rooms so a CFO sits with CFOs. Forum Events is the open tier: larger public gatherings, including panels, for the broader AI and technology community, and often the first step before Forum Select. For the CFO group, dinners are the heart of it. No agenda, no recording, no pitch.

The takeaway from this briefing is the one I keep coming back to. The best board members are not just experts. They are connectors. Louis Lehot is both, and that is exactly the standard a room of finance leaders deserves.

If you lead finance at a venture-backed company and the forum sounds like your table, you can request an invitation through Open Future Forum.

Murray Newlands is the founder of Open Future Forum, a Partner at IA Seed Ventures, and a Partner at Tilden Family Office Group. He is the author of Online Marketing: A User’s Manual (Wiley), a Fellow of the Royal Society of Arts, and his writing has appeared in Forbes, Inc., Time, and Entrepreneur. He writes Murray’s Newsletter, read by more than 50,000 operators and investors.

Founders. Engineers. Creators. Researchers. Operators. Product leaders. Business builders. People who have already created value elsewhere and want to bring that ambition to America.

But for many of them, the immigration journey is confusing before it even begins.

Which visa route makes sense?

Is EB-1A realistic?

Is EB-2 NIW a better fit?

Should an O-1 come first?

Is E-2 the right path, or just the only option someone mentioned?

And perhaps the biggest question of all: how do you know whether the advice you are getting is actually right for your profile?

That is where Sylvia Gorajek is doing something interesting with America Unlocked.

Sylvia’s work sits at the intersection of immigration strategy, personal positioning, evidence-building, and community support. Her focus is on helping high-achieving professionals understand pathways such as EB-1A, EB-2 NIW, O-1 and E-2 without the usual confusion, guesswork, hidden costs, or generic advice.

It is important to be clear that this is not legal advice. Immigration decisions should always be reviewed with qualified immigration counsel. America Unlocked states that it is not a law firm, and that its content is designed to provide practical guidance rather than legal advice. They do partner with attorneys for legal advice and representation, though.

Immigration is not just a paperwork problem

One of the biggest mistakes people make when thinking about U.S. immigration is assuming it is only about forms.

Of course, documentation matters. Eligibility matters. Evidence matters.

But for high-achieving professionals, the bigger challenge is often narrative.

What have you actually built?

What impact can you prove?

Why does your work matter beyond your current employer?

How do your achievements map to the category you are applying under?

For many strong candidates, the issue is not a lack of achievement. The issue is that their achievement has not yet been organized into a clear, credible story.

A great career is not automatically a great immigration case.

A strong petition requires structure.

Why Sylvia’s approach is different

What I like about Sylvia’s positioning is that she is not just saying, “Here is another visa service.”

She is identifying a deeper problem.

Many talented people do not know how to translate their achievements into a coherent U.S. immigration strategy.

They may have built companies, led major projects, appeared in the media, created revenue, advised others, spoken publicly, hired teams, or contributed to meaningful work in their field.

But they may still not see themselves as “extraordinary.”

That word can be intimidating. It can make people think these categories are only for Nobel Prize winners, celebrities, famous athletes, or people with massive public recognition.

In practice, the question is often more specific: can you prove sustained impact, recognition, importance, originality, or contribution in a way that fits the criteria?

Through America Unlocked, Sylvia is building around strategy, expert support, practical guidance, AI tools, and peer community for people exploring EB-1A, EB-2 NIW, O-1 and E-2 pathways.

That community piece is especially important.

Immigration can be lonely. People often search online, ask friends, speak with one attorney, hear conflicting advice, and get discouraged before they really understand their options.

A good community does not replace legal advice. But it can help people ask better questions, organize their evidence, understand the process, and see how others are approaching similar challenges.

The founder lesson: positioning matters

There is also a broader founder lesson here.

Whether you are raising capital, selling into enterprises, applying for a visa, joining a board, or building a public profile, positioning matters.

You have to explain who you are, what you have done, why it matters, and why the next step makes sense.

That is true for a CEO pitching investors.

It is true for a CFO explaining an AI investment to a board.

It is true for a founder trying to enter the U.S. market.

And it is true for an exceptional professional trying to show why their work belongs in America.

This is why Sylvia’s work is relevant beyond immigration. She is helping people turn achievement into a clear, credible story.

Not hype.

Not exaggeration.

A structured narrative supported by evidence.

Why this matters now

The global competition for talent is not slowing down.

The best founders, researchers, engineers, creators, operators, and executives have options. They can build companies in many markets. They can work remotely. They can raise capital internationally.

But the U.S. remains one of the most important markets in the world for ambitious people.

Access to customers, capital, universities, venture networks, enterprise buyers, media, partners, and talent still makes America uniquely attractive.

That is why the immigration pathway matters.

If the process feels impossible, opaque, or unpredictable, people hesitate.

If the process is better understood, more high-quality people can make informed decisions earlier.

That is good for founders.

It is good for companies.

It is good for innovation.

A community model for a high-stakes decision

I have spent a lot of time building executive communities through Open Future Forum, and one thing I have learned is that people make better decisions in trusted rooms.

That applies to CEOs.

It applies to CFOs.

It applies to founders.

It also applies to global talent navigating immigration.

The right community does not simply provide information. It provides context.

It helps people understand what others are doing, what mistakes to avoid, and how to think about their own situation more clearly.

That is why America Unlocked is interesting. It is not just content. It is not just a service. It is a structured support environment for people making one of the most important career and life decisions they may ever make.

Why Sylvia is someone to watch

Sylvia Gorajek is building in a category where trust matters.

People exploring EB-1A, EB-2 NIW, O-1, or E-2 are not casually browsing. They are often making decisions that affect their business, family, location, finances, and future.

That requires clarity.

It requires honesty.

It requires a better way to think through options.

Sylvia’s message is simple but powerful: stop guessing.

Understand your profile.

Understand your evidence.

Understand the route that fits.

Then build the case with intention.

For global talent looking at America, that is a message worth paying attention to.

And for founders, operators, and professionals who have built more than they realize, it may be the difference between assuming the door is closed and discovering there may be a path forward.

Final thought

The immigration process can feel complicated, expensive, and intimidating.

But the first step is often not filing paperwork.

The first step is understanding your story.

What have you done?

Why does it matter?

Who has recognized it?

What evidence exists?

What route is most aligned with your goals?

That is the conversation Sylvia Gorajek and America Unlocked are helping more people have.

And for ambitious global talent looking toward the U.S., that clarity can be the beginning of a very different future.

The hardest AI questions for CFOs are not answered by vendor decks or public reports. They are answered by peers who have already made the decision and can say what actually happened.

CFOs need private peer groups in the AI era because the finance role has changed.

The CFO is no longer only responsible for reporting, budgeting, forecasting, and controls. Increasingly, the CFO is being pulled into AI strategy, AI ROI, AI governance, AI agent approvals, vendor risk, board communication, productivity measurement, and the redesign of the finance function itself.

That is a much wider seat.

It is also a more exposed seat.

Every function now wants AI budget. Every vendor has a confident story. Every board wants to know whether the company is moving fast enough. Every CEO wants efficiency. Every team wants tools.

Some of those AI projects will create real value. Some will be premature. Some will be expensive distractions with a strong narrative.

The CFO has to help decide which is which.

That is why private peer groups matter.

A CFO can read reports, attend conferences, speak to consultants, and listen to vendors. All of that can be useful. But the most valuable information often comes from another finance leader who has already made a similar decision and can say:

We tried that.

Here is what it really cost.

Here is what broke.

Here is what the board asked.

Here is what we would do differently.

That kind of answer is hard to find in public.

It usually comes from a trusted peer.

What AI decisions are CFOs making now?

The AI questions CFOs are asking are practical.

What is the real return on AI?

What does it cost all in?

What work does it replace?

Which AI projects should we fund?

Which ones should we stop?

Who owns the risk if an AI agent takes the wrong action?

What data is leaving the company?

How do we brief the board honestly?

How is AI changing financial planning and forecasting?

Are we moving too slowly, or are we about to spend money on something that is not ready?

These are not abstract technology questions. They are finance and leadership questions.

That is why the CFO has become central to AI decision-making.

Watch: What questions are CFOs asking about AI right now?

https://www.youtube.com/watch?v=5ubLmwNnQlc

Why is AI making the CFO role bigger?

AI has turned many technology decisions into capital allocation decisions.

Marketing wants AI tools.

Sales wants AI agents.

Engineering wants compute.

Customer support wants automation.

Finance wants better forecasting, faster close, automated reporting, and more efficient analysis.

The CFO is often the person who has to ask whether the spend is justified.

What is the return?

What is the risk?

What does this replace?

How do we measure it?

What happens if it fails?

That last question is especially important with AI agents. A normal tool helps a person work. An agent can take action. It can access systems, trigger workflows, move data, and make decisions at speed.

Approving an AI agent is therefore closer to authorizing a new employee than buying software.

A CFO needs to know what the agent can touch, what it costs, how it fails, who catches the failure, what data leaves the company, and who is accountable when something goes wrong.

Watch: What should a CFO ask before approving an AI agent?

https://www.youtube.com/watch?v=bR0jcUpUs_E

Why are public answers not enough?

There is no shortage of AI advice.

Vendors have advice.

Consultants have advice.

Analysts have advice.

Conferences have advice.

Newsletters have advice.

But the CFO’s hardest AI questions are often too specific for broad public advice.

No CFO usually publishes the full story of an AI project that failed to pay back.

No company promotes the AI agent approval that created unexpected risk.

No board deck is shared publicly with the note: this was the slide that caused the real debate.

No vendor case study leads with the hidden costs.

The most useful lessons are often private because they involve uncertainty, mistakes, risk, money, and judgment.

That is why CFOs need rooms where they can hear the honest version.

Watch: Where do CFOs get honest AI advice off the record?

https://www.youtube.com/watch?v=aT_4b-Pvcqw

What makes a private CFO peer group valuable?

A private CFO peer group is valuable when it is small, trusted, relevant, and off the record.

The best CFO communities are not necessarily the biggest. They are the rooms where finance leaders can speak honestly with true peers.

A good CFO peer group answers five questions well:

Who is actually in the room?

Is the conversation off the record?

Is the room peer-first or vendor-led?

Does it run on giving first?

Does it help with the questions CFOs are actually facing now?

For CFOs, candor matters more than scale. A large event can be useful, but it is rarely where someone admits that an AI project is not working, a board conversation was difficult, or a vendor’s assumptions were too optimistic.

The most useful conversations usually happen in smaller rooms.

Watch: What are the best CFO communities to join in 2026?

https://www.youtube.com/watch?v=zCbBtZaGpQc

How do CFOs meet other CFOs in a useful way?

CFOs do not build real peer relationships by collecting business cards.

They build them through referral, repeated contact, and useful exchange.

A warm introduction from someone both people trust moves the conversation forward faster than cold outreach. Then the relationship deepens because the same people keep showing up in the right settings.

A dinner.

A private discussion.

A panel.

A smaller AI session.

A follow-up call.

A helpful introduction.

Over time, a contact becomes a peer.

That matters because the finance seat can be lonely. A CFO is surrounded by a team, a CEO, a board, auditors, vendors, and advisors, but there are not many people inside the company with whom the CFO can think out loud as a true peer.

The team cannot always hear the CFO’s doubts.

The board cannot be the place where the CFO works through uncertainty.

The vendor has an angle.

The CEO has their own pressure.

So the honest working-it-out conversation often has to happen with other CFOs.

Watch: How do CFOs meet other CFOs?

https://www.youtube.com/watch?v=AIzr_nzah2k

Why do CFOs need private peer groups in the AI era?

CFOs need private peer groups in the AI era because many AI decisions are new, expensive, fast-moving, and company-specific.

When a problem is well understood, a framework can help.

When a problem is genuinely new, peer experience becomes more valuable.

What did another CFO actually do?

What did it cost?

What broke?

Did it pay back?

How did they brief the board?

What did they wish they had asked earlier?

Those answers can improve the decision more than another generic report.

The peer benchmark is especially important because AI is moving quickly while trust still moves slowly. By the time a CFO urgently needs a peer to call, it is often too late to start building the relationship.

The private peer group has to exist before the hard decision.

Watch: Why do CFOs need a private peer group in the AI era?

https://www.youtube.com/watch?v=oHNmn_ODfi8

How should CFOs measure AI ROI?

AI ROI is partly a spreadsheet problem, but it is also a peer benchmark problem.

A CFO should still ask the classic finance questions.

What does this AI replace?

What is the full cost?

What is the integration cost?

What is the human oversight burden?

What is the payback period?

What baseline are we measuring against?

What work moved somewhere else instead of disappearing?

But the hardest part is often knowing whether the assumptions are real.

Vendor assumptions are optimistic. Internal champions can be optimistic too. Everyone wants the AI project to work.

A peer who already ran a similar project can make the model more honest.

They can say:

The license was not the real cost.

The data cleanup was the real cost.

The first use case was too ambitious.

The oversight burden changed the ROI.

The payback took twice as long as expected.

The tool saved time, but not where we expected.

That is why private CFO peer groups are valuable. They provide real-world reference points.

Watch: How should a CFO measure the return on AI?

https://www.youtube.com/watch?v=q-yW5mJKI4M

How should CFOs decide which AI projects to fund?

Every function now has AI ideas.

Some deserve funding.

Some need to wait.

Some should be stopped early.

A useful CFO funding discipline is to rank AI projects on three things:

Clarity of return.

Readiness of the use case.

Risk if it goes wrong.

The best AI projects are not always the most exciting. They are the ones where the return is clear, the use case is ready, and the downside is understood.

One mistake companies make is funding too many pilots too shallowly. That creates activity without impact.

A better approach is often to fund a smaller number of projects deeply enough to reach production, then measure what actually happens.

Again, peers matter.

A CFO deciding whether to fund an AI project benefits from knowing what happened when another CFO funded something similar.

Did it reach production?

Did it pay back?

What broke?

Which projects were killed?

What would they do differently?

Watch: How do CFOs decide which AI projects to fund?

https://www.youtube.com/watch?v=4ZA0rb2kZ6A

How should CFOs brief the board on AI?

The board does not need a technical lecture on AI.

It needs a clear view of business impact, financial exposure, risk, speed, and accountability.

A CFO should be able to explain:

Where AI already operates in the business.

Where AI touches customers, dollars, regulated decisions, or sensitive data.

What the company is funding.

What the company is stopping.

What the risk is of moving too fast.

What the risk is of moving too slowly.

Who owns each material AI decision.

What vendors, models, and infrastructure the company depends on.

This is another place where peer groups help.

A CFO briefing the board on AI for the first time can learn a lot from another CFO who has already done it.

What did the directors ask?

What level of detail worked?

What created confidence?

What created unnecessary fear?

What should be reported at every board meeting?

Those lessons are not easy to find in public, but they are extremely useful.

Watch: How should a CFO brief the board on AI?

https://www.youtube.com/watch?v=dxwKXSdulRQ

How is AI changing financial planning and forecasting?

AI is also changing finance itself.

Financial planning and forecasting are obvious examples because FP&A is structured, data-heavy, repetitive, and checkable.

AI can help assemble the forecast, pull data together, flag variance, run scenarios, and produce first-pass analysis.

That does not make finance less important.

It makes judgment more important.

The mechanics get cheaper.

The judgment gets more valuable.

Finance leaders will spend less time assembling numbers and more time asking whether the assumptions make sense. They will need to explain the story behind the forecast, not just produce the forecast. They will need to challenge the model, not blindly accept it.

This changes the finance team.

It changes hiring.

It changes the career ladder.

It changes what junior finance people learn.

It changes what great finance leadership looks like.

These are exactly the kinds of questions CFOs should not have to answer alone.

Watch: How is AI changing financial planning and forecasting?

https://www.youtube.com/watch?v=5EwoBEiLpIk

What is the role of the CFO Executive Forum?

The CFO Executive Forum is an invitation-only group for senior finance leaders, part of Open Future Forum.

It is designed around small, off-the-record conversations among CFOs and senior finance executives working through the practical questions AI is creating for the finance seat.

AI ROI.

AI governance.

AI agents.

Board communication.

Capital allocation.

Financial planning and forecasting.

The changing role of the finance function.

The goal is not more networking.

The goal is better judgment through trusted peer exchange.

Open Future Forum has run 100 events to date, bringing together senior executives, founders, and investors.

The CFO Executive Forum is part of that broader private executive community, with the same emphasis on small rooms, trusted relationships, and give-first participation.

A CFO does not need another room where everyone performs expertise.

They need a room where real peers can say what is actually happening.

Full CFO AI video series

Here are the 10 CFO AI briefings in this series.

1. What are the best CFO communities to join in 2026?

https://www.youtube.com/watch?v=zCbBtZaGpQc

2. How do CFOs meet other CFOs?

https://www.youtube.com/watch?v=AIzr_nzah2k

3. Why do CFOs need a private peer group in the AI era?

https://www.youtube.com/watch?v=oHNmn_ODfi8

4. Where do CFOs get honest AI advice off the record?

https://www.youtube.com/watch?v=aT_4b-Pvcqw

5. How should a CFO measure the return on AI?

https://www.youtube.com/watch?v=q-yW5mJKI4M

6. How do CFOs decide which AI projects to fund?

https://www.youtube.com/watch?v=4ZA0rb2kZ6A

7. What should a CFO ask before approving an AI agent?

https://www.youtube.com/watch?v=bR0jcUpUs_E

8. How should a CFO brief the board on AI?

https://www.youtube.com/watch?v=dxwKXSdulRQ

9. How is AI changing financial planning and forecasting?

https://www.youtube.com/watch?v=5EwoBEiLpIk

10. What questions are CFOs asking about AI right now?

https://www.youtube.com/watch?v=5ubLmwNnQlc

CFOs who build peer groups now will make better AI decisions later

The CFOs who navigate AI well will not simply be the ones who read the most reports or attend the most conferences.

They will be the ones with better reference points.

They will know what is happening inside other companies.

They will have finance peers they can call.

They will have heard the real lessons before making the same mistakes.

They will know which AI projects paid back and which only sounded good.

They will know where the hidden costs appeared.

They will know what questions to ask before approving an AI agent.

They will know how other CFOs briefed the board.

That is the value of a private peer group.

It does not replace analysis.

It improves it.

It does not replace judgment.

It sharpens it.

It does not make the decision easy.

It makes the decision better.

And in the AI era, that may be the difference between a CFO who reacts to change and a CFO who helps lead through it.

About the CFO Executive Forum

The CFO Executive Forum is an invitation-only group for senior finance leaders, part of Open Future Forum, a private executive community hosting small, off-the-record dinners across Silicon Valley.

Open Future Forum has run 100 events to date, bringing together senior executives, founders, and investors.

The CFO Executive Forum is built for CFOs and senior finance leaders working through AI ROI, AI governance, board communication, finance transformation, capital allocation, and the changing role of finance.

Senior finance leaders can request an invitation through Open Future Forum.

CFO Executive Forum:
https://openfutureforum.com/cfo-executive-forum/

Request an invitation:
https://openfutureforum.com/apply

Open Future Forum:
https://openfutureforum.com/

AI agents are starting to look less like software features and more like a new class of worker.

That may sound dramatic, but look at what they are being asked to do. Agents are reading documents, calling tools, sending messages, updating records, handling workflows, remembering context, passing tasks to other agents and acting on behalf of employees or customers.

That is not just automation. That is delegated work.

This is a topic I keep hearing in executive conversations through Open Future Forum, the private executive community I run for CEOs, CFOs, CTOs, CISOs, founders and investors:

https://openfutureforum.com/

It is also why I have been paying close attention to the agent infrastructure category, including companies such as Agentic Fabriq, which is focused on identity, permissioning and governance for AI agents:

https://www.agenticfabriq.com/

The enterprise question is therefore changing. It is no longer only “Which model should we use?” or “How much productivity can we unlock?” The better question is: how do we manage a growing population of non-human workers inside the company?

A human employee has an identity, a manager, a job description, a set of permissions, a performance record, an audit trail and an offboarding process. An AI agent needs the enterprise equivalent.

Without that, companies are not really managing agents. They are releasing autonomous actors into the business and hoping the system keeps working.

The First Problem Is the Agent Org Chart

Every company understands the need for an employee directory. You need to know who works for the company, who they report to, what systems they can access and what role they perform.

The same logic now applies to AI agents.

Most companies will soon need an agent directory. Not a vague list of AI tools, but a clear record of every agent operating inside the business.

That directory should answer:

Who created this agent?

Who owns it now?

What business function does it serve?

Which systems can it access?

Which users can it act for?

What credentials does it use?

What actions has it taken?

When should it be retired?

This sounds simple, but many companies cannot answer these questions today. Agents are being created by engineering teams, business teams, SaaS platforms and low-code tools. Some are obvious. Some are hidden inside workflows. Some were built for a temporary project and quietly kept running.

This is how shadow AI becomes shadow labor.

If a company cannot produce a clear inventory of its agents, it cannot govern them. It cannot audit them. It cannot offboard them. It cannot explain what happened when something goes wrong.

The agent org chart is now part of enterprise AI readiness.

Every Agent Needs a Job Description

A good employee has a job description. It defines what the person is supposed to do, where their authority begins and ends, when they escalate and how they are evaluated.

AI agents need the same discipline.

An agent that handles customer support should not quietly become a refund approval agent. An agent that summarizes sales calls should not begin editing CRM records unless that is explicitly part of its role. An agent that researches vendors should not send external emails unless that authority has been granted.

This is why “personality” is the wrong starting point for enterprise agents. The better term is role definition.

What is this agent for?

How should it speak?

What should it refuse?

When should it escalate to a human?

What systems can it touch?

What actions are outside its authority?

The agent’s tone should never imply more authority than the infrastructure actually grants. A helpful agent should still be bounded. A confident agent should still be accountable. A warm agent should still know when to stop.

In the human workforce, charm is not permission. The same should be true for AI agents.

OAuth Is Not the Whole HR System

OAuth is an important foundation for delegated access. It allows applications to access services without requiring users to hand over passwords. That remains valuable.

But OAuth was not designed to be the full management system for autonomous agents.

An agent may act repeatedly, long after the original approval. It may act for many users. It may call multiple tools. It may interpret instructions incorrectly. It may believe it has authority that was never intended.

That means companies need more than broad scopes and one-time consent.

They need per-agent identity.

They need runtime permission checks.

They need managed credentials.

They need logs that distinguish the human user from the agent acting on behalf of that user.

They need a way to revoke one agent without breaking everything else.

OAuth may get the agent through the front door. It does not answer every management question once the agent is inside.

Credentials Are the Keys to the Building

Every employee gets access to systems. Good companies manage that access carefully. They do not leave keys lying around forever. They do not let old employees keep access indefinitely. They do not give everyone the same master key.

AI agents should be treated with the same care.

An agent that needs to reach Slack, Google Drive, GitHub, Salesforce, an internal database or a customer system needs some form of credential. In prototypes, those credentials often end up inside environment files, configs, repositories or internal databases.

That may be acceptable for a quick demo. It is not a serious production model.

Agents should not hold raw, long-lived secrets. Credentials should live in a controlled layer. The agent should receive short-lived, scoped access for the task at hand. Every use should be logged. Tokens should rotate. Revocation should be simple.

Offboarding matters here. When an employee leaves, the company disables their accounts. But what happens to the agents they built? What credentials are still live? What workflows are still running? What tokens still work?

If the answer requires a manual hunt through code and config files, the company has an agent management problem.

Agent Managers Need Audit Trails

A manager cannot manage what they cannot see.

This is true for people, and it is true for agents.

When an agent takes an action, the company should be able to reconstruct what happened. Which agent acted? Which user was it acting for? What tool did it call? What data did it touch? What permission allowed the action? Was a human approval required? What changed as a result?

This matters for security. It matters for compliance. It matters for customer trust. It also matters for ordinary management.

If an agent creates bad CRM records, who catches it?

If an agent sends the wrong customer message, who can trace the decision?

If an agent retrieves too much context, who sees the waste?

If an agent loops through unnecessary model calls, who notices before the bill arrives?

Audit trails are not just about post-incident investigations. They are how companies learn whether their agents are performing responsibly.

Hallucination Becomes a Workplace Risk

When a chatbot hallucinates, it says something wrong.

When an agent hallucinates, it may do something wrong.

That is a major shift.

An agent may invent a tool call. It may misread a request. It may assume it has permission it does not have. It may save a false memory. It may confidently proceed when it should ask a human.

In a human organization, good management does not assume people never make mistakes. It creates systems that catch errors before they become expensive.

The same applies to agents.

Companies need confidence thresholds, tool validation, runtime permissions, approval gates and audit logs. The point is not to pretend agents will never be wrong. The point is to make sure the wrongness is contained.

An agent should not be able to turn a bad assumption into a damaging action simply because it sounded confident.

Multi-Agent Systems Need Rules of Delegation

Companies already know that delegation can create risk. If one person hands work to another without context, accountability can get blurry. If three departments each do one small part of a process, nobody may see the full risk.

Multi-agent systems create the same problem in software.

One agent retrieves a document. Another summarizes it. A third acts on the summary. Each step may look harmless. The chain may not be harmless.

This is why context passing between agents matters. Context is not just information. It can contain sensitive data, hidden instructions, customer records, internal strategy, credentials or misleading content.

The rule should be least context. Give the receiving agent only what it needs to do its job.

Safe delegation between agents requires redaction, source labels, sensitivity labels, provenance and logs. The company should know not only what an agent did, but what information it received before it acted.

In a human organization, sensitive information is not supposed to move everywhere by default. The same rule should apply to agents.

Agent Memory Is a Personnel File

Memory is one of the most powerful parts of long-running agents. It allows an agent to remember preferences, customer history, workflow context and prior decisions.

But memory is also dangerous.

A wrong answer may disappear. A wrong memory can persist.

If an agent saves a hallucination as fact, that false belief can influence future actions. If an agent is poisoned by a document, email or web page, the false memory can remain long after the original source is gone. If memory is not scoped correctly, one user’s context can bleed into another user’s experience.

This makes memory a governance issue.

Durable memory writes should be deliberate, validated and reversible. The company should know what was written, why it was written, what evidence supported it, when it changed and how it can be removed.

In human terms, memory is not a scratchpad. It is closer to a personnel file or customer record. If it is wrong, it can shape future decisions in ways that are hard to see.

Cost and Latency Are Also Management Problems

When companies discuss agent cost, they often focus on model prices. That is only part of the issue.

Agents become expensive when they call tools unnecessarily, retrieve too much context, repeat work, loop through planning steps or use expensive models for low-value tasks.

This is not just a finance problem. It is a management problem.

A human manager would not want an employee spending hours on unnecessary work with no visibility and no budget. The same logic applies to agents.

Each agent should have operational limits. How many calls can it make? How much context can it retrieve? What budget does it have? When does it stop? When does it escalate?

The companies that manage agent cost well will not simply choose cheaper models. They will build visibility into how agents work and place limits around unnecessary activity.

Agent Infrastructure Is Becoming a Management Layer

All of this points to a larger category: infrastructure for managing AI agents as accountable non-human actors.

That includes agent identity, agent permissions, credential management, audit trails, delegated user context, runtime governance, memory controls and visibility across tools.

This is where the comparison to “Okta for agents” becomes useful, even if it is not perfect. Enterprises already understand why identity matters for humans and applications. AI agents extend that problem into a new category of actor.

They can act for users. They can call tools. They can touch data. They can trigger workflows. They can remember. They can delegate. They can make mistakes.

That means they need an enterprise management system.

The Board-Level Question

The board-level question is not simply “Do we have AI agents?”

The better question is: “Are our AI agents managed like accountable actors inside the enterprise?”

That means asking:

Do we know every agent running in the company?

Does every agent have an owner?

Does every agent have a defined role?

Does every agent have its own identity?

Are permissions enforced at runtime?

Are credentials held outside the agent?

Can we revoke one agent’s access?

Can we audit every action?

Can we control what agents remember?

Can we explain agent behavior to a customer, auditor or regulator?

These are not future questions. They are current operating questions for any company moving agents into production.

Related Video Briefings

This Medium post builds on a series of short video briefings on AI agent governance, identity, permissions, credentials, memory and runtime control:

1. Why can’t companies see what their AI agents are doing?
   https://www.youtube.com/watch?v=k08IAHl_dg0
2. Why isn’t OAuth enough to secure AI agents?
   https://www.youtube.com/watch?v=dwoaQTqybcI
3. How should companies manage credentials and tokens for AI agents?
   https://www.youtube.com/watch?v=3_ROOBoqjbY
4. How do you move AI governance from policy to runtime?
   https://www.youtube.com/watch?v=s-Foa97q1eM
5. What does an uncontrolled AI agent actually cost a company?
   https://www.youtube.com/watch?v=goutetezSJM
6. Why do AI agents hallucinate, and how do you catch it?
   https://www.youtube.com/watch?v=8p-Cce2B10M
7. How do you pass context between AI agents safely?
   https://www.youtube.com/watch?v=74A8mTjNo4Q
8. Should AI agents be centralized or decentralized?
   https://www.youtube.com/watch?v=boG2eEBsGT0
9. How do you keep AI agents fast and affordable at scale?
   https://www.youtube.com/watch?v=0luLLtT54G4
10. Do AI agents need a personality, and what are the risks?
    https://www.youtube.com/watch?v=kSW5e_50LbI
11. How do you stop AI agents from corrupting their own memory?
    https://www.youtube.com/watch?v=dpPYFnykjDU

Final Thought

The companies that scale agentic AI well will not treat agents as clever chatbots. They will treat them as a new class of enterprise actor.

That means agents need identity. They need managers. They need job descriptions. They need permissions. They need credentials managed outside themselves. They need audit trails. They need memory controls. They need offboarding.

The future of enterprise AI is not only about making agents more capable.

It is about making them manageable.

Today’s finance leaders are expected to do far more than oversee accounting and financial reporting. CFOs are increasingly responsible for fundraising, investor relations, AI strategy, operational efficiency, board communications, M&A activity, and helping shape the overall direction of their organizations.

As the role expands, many CFOs are turning to executive communities to build relationships, learn from peers, and gain access to trusted advice from leaders facing similar challenges.

The best CFO communities provide more than networking. They create environments where finance leaders can exchange ideas, discuss challenges, and build relationships that help them make better decisions.

Here are ten of the best CFO communities in America for finance leaders in 2026.

Financial Executives International (FEI)

Financial Executives International is one of the most established organizations for senior finance leaders in the United States. Founded in 1931, FEI offers educational programs, local chapters, networking opportunities, and professional development resources for CFOs and finance executives across industries.

CFO Leadership Council

The CFO Leadership Council has built one of the largest CFO-focused networks in America. Through conferences, executive events, local chapters, and online programming, the organization provides opportunities for finance leaders to learn from peers and stay informed about emerging business and finance trends.

The F Suite

The F Suite has become a respected community for senior finance executives. Its focus on relationships, executive-level discussions, and curated programming has helped it build a strong reputation among CFOs and finance leaders.

CFO Alliance

CFO Alliance provides peer networking, executive education, and leadership development opportunities for finance professionals. Its growing membership and national reach make it a valuable resource for CFOs looking to expand their professional network.

World 50

World 50 brings together senior executives from many of the world’s largest organizations. Its private forums and leadership groups offer CFOs access to strategic discussions and peer relationships with leaders from major enterprises.

Young Presidents’ Organization (YPO)

While primarily known as a CEO organization, YPO includes many CFOs and senior finance leaders within its global membership. Its forum-based structure and international network make it one of the most influential executive communities in the world.

Vistage

Vistage has long been recognized for executive peer groups and leadership development. Many CFOs participate in Vistage groups to gain perspective from experienced executives and receive guidance on complex business challenges.

Open Future Forum CFO Executive Forum

Open Future Forum’s CFO Executive Forum brings together venture-backed CFOs, growth-stage finance leaders, investors, and executive operators through private dinners, executive roundtables, golf gatherings, networking events, and peer-driven discussions.

The community focuses on topics including fundraising, AI adoption, operational excellence, executive leadership, finance transformation, and the future of work. Unlike many traditional finance organizations, Open Future Forum emphasizes highly curated experiences designed to foster meaningful relationships and practical conversations among senior leaders.

Its growing presence in Silicon Valley and expanding network of finance executives, CEOs, investors, and technology leaders have helped establish it as one of the most active emerging CFO communities in the innovation ecosystem.

Learn more about the CFO Executive Forum:

https://openfutureforum.com/cfo-executive-forum

Pavilion

Pavilion serves executives across a range of business functions, including finance, sales, marketing, and operations. Its community, leadership resources, and professional development programs have attracted executives from startups, growth-stage companies, and large enterprises.

Operator Collective

Operator Collective brings together experienced operators, executives, and investors from across the technology sector. While not exclusively focused on finance leaders, many CFOs participate because of the community’s strong network and access to experienced technology operators.

Why CFO Communities Matter

The challenges facing finance leaders continue to increase.

Artificial intelligence, cybersecurity, fundraising markets, operational efficiency, talent management, regulatory complexity, and investor expectations all require CFOs to broaden their expertise beyond traditional finance functions.

The strongest CFO communities provide access to peers who understand these challenges firsthand. They create opportunities to exchange ideas, learn from real-world experiences, and build trusted relationships that support better decision-making.

As the CFO role continues to evolve, participation in executive communities is becoming an increasingly important part of leadership development and long-term success.

Choosing the Right CFO Community

There is no single community that is right for every finance leader.

Some CFOs may prioritize education and professional development. Others may focus on peer relationships, industry specialization, executive networking, or access to investors and operators.

The best CFO community is the one that aligns with your stage of growth, leadership goals, and the challenges you are trying to solve.

What is clear is that the most successful finance leaders rarely operate alone. They invest in relationships, surround themselves with trusted peers, and participate in communities that help them navigate an increasingly complex business environment.

Disclosure: Murray Newlands is the founder of Open Future Forum, which is included in this list. Communities were selected based on publicly available information, executive engagement, event activity, professional reputation, community focus, and industry visibility as of 2026.