The CEO is one of the most leveraged people in a company - their decisions set strategy, allocate capital, influence major relationships, and shape who gets hired and fired.
Yet for many CEOs, their supporting functions are rarely built to capture what AI is now capable of. Most of the AI in a principal’s world is used by the people around them, and most of that use is for individual tasks, such as the EA drafting a brief, the Chief of Staff summarizing a pack, or an Analyst pulling important numbers from a spreadsheet.
In fact, more than a quarter of senior executives say they never touch AI themselves, and the majority of the rest spend under an hour a week on it - according to data published by the National Bureau of Economic Research (revised in October 2026).
This is no surprise - in our direct work with some of the most influential CEOs in the United States, we know first-hand how little AI is being used by the CEOs themselves as the work they’re doing is nearly all judgment or relationship based, based on the intelligence they receive from their office. In reality, it’s the teams around these CEOs who are turning to AI.
The emerging opportunity is clear: build a system around the principal that improves how information reaches them, how decisions get made, and how those decisions are carried through the business. Building this calls for someone who can design, integrate, and govern AI-enabled workflows - not just someone who can interact with a chatbot.
In our recently published Blackbook Report (2026), we highlighted that in two years from now, most established Office of the CEO functions will have an AI lead in some capacity. This paper sets out what the seat looks like, what it is for, and how to hire the right person.
Why the role belongs in the Office of the CEO
A common mistake we often see is people confusing an org-wide transformation project with an Office of the CEO program. It’s an understandable instinct, but both mandates are completely different jobs, with different reporting lines, budgets, and expected outcomes.
In this note, we are specifically talking about the OCEO version of the job.
This role has one purpose: to increase the principal’s leverage: how information reaches them, how it is synthesized into something easily digestible, how decisions get made, and how those decisions flow back downstream with an owner attached.
For comparison, company-wide AI transformations are a different undertaking. Where a firm is serious about it, the work belongs to several members of the C-suite at once: the CEO, CTO/CIO, a CPO, and often a Chief AI Officer - usually supported by a number of specialists running individual initiatives. In a recent Bain CEO survey, four in five CEOs are unhappy with the pace of their AI programs. It’s a slow, expensive, and often frustrating process.
Now the OCEO version runs leaner: a handful of people, one principal, a small number of recurring yet crucial workflows, quick decisions, and materials that are more sensitive than almost anything else in the firm. It’s a ‘low-hanging fruit’ with less bureaucracy and huge upside.
There’s a second reason to keep the scope tight and within the OCEO. The principal’s decision history, briefing patterns, and judgment are some of the most important and consequential data points within the company, and almost nobody is encoding and storing them. It usually lives in the heads of a Chief of Staff, an SVP of Operations (OCEO), Executive Assistants, and a few trusted advisers. The problem being, when these people leave, so does the information in their head.
Capturing how a principal actually decides, what they need to see before committing to a decision, and what they consistently ignore or deprioritize is the most valuable work this hire will do, and in practice, it can only happen when someone sits inside of the OCEO with full access and the time to do it.
To summarize, assume AI automation makes the team even a conservative 15-20% more efficient - this is an incredible return on investment, in both time and money, but it also compounds as memory and context build over the years, surviving employee attrition, improving onboarding, and more.
What an AI-enabled Office of the CEO should look like
The place to start is with what the OCEO actually does, which we think about in two layers.
- The readiness layer - it’s always on and covers the ‘who, what, where, and why’ of the principal’s day: briefing, preparation, calendar, logistics, and the operating rhythm. It’s usually owned by Executive Assistants, with a Chief of Staff or CEO orchestrating.
- The decision layer - this runs in three lanes.
- The upstream lane - arrives messy, from every direction, through reporting cycles, from different senior executives and often regarding different initiatives, signals, and problems. Someone has to process it and present it.
- The decision lane - calls are made on the clean information, with or without the principal depending on escalation rules, and someone typically owns the budget and the outcome.
- The downstream lane - decisions become actions, coordination, narrative, and projects, and someone owns the follow-through and the reporting back. This reporting is what closes the loop and becomes next week’s upstream information.
Every part of this system is a good candidate for AI, and every part has a point past which AI should not go - without a human-in-the-loop.
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Part of the office
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Where AI earns its place
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What stays with a person
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| Readiness |
Briefing packs assembled from the systems the office already uses; prep that is ready before the meeting is booked; logistics that resolve themselves |
Judgment about what the principal needs to walk into the room with, and what to leave out |
| Upstream and synthesis |
Pulling reporting, escalations and external signals into one view; first-pass prioritization; flagging what contradicts what was said last quarter |
Deciding what matters, reading the room behind the data, and knowing which sources are not connected |
| Decision |
Scenario analysis, stress-testing an argument, surfacing the precedent for how this principal decided last time |
The decision itself, the escalation call, and ownership of the budget |
| Downstream and accountability |
Turning a decision into tracked actions with owners and dates; chasing follow-through; drafting the update |
Holding people to account, and the conversations that drive a stalled action |
| Narrative and communications |
First drafts, consistency checks across audiences, monitoring how a message landed |
Tone, timing, and anything with investor, regulatory or reputational weight |
| Feedback loop |
Capturing what happened and feeding it back upstream automatically |
Deciding what the office should learn from it |
Most Office of the CEO functions are at the ‘drafting and summarizing’ stage - mostly individuals using authorized or unauthorized tools to draft and summarize, with nothing shared and nothing owned.
The next stage is recurring workflows, with defined owners and cross-over, built once and run every week.
The more advanced stage is agents acting within defined limits, custom dashboards and interfaces, a human-in-the-loop at the right times, multiple data sources plugged in, and a team-wide effort to maintain memory and context.
In most cases, making an ‘AI Innovation Lead’ type of hire is how an office moves from the first stage to the third.
We recommend walking through the above table with your own office in mind. In most offices, the friction or bottleneck sits in the same two places; information reaches the principal too late and raw, and decisions made lose shape on the way to the people who have to act on them. Solving those two problems is where this hire should start.
As a real-world example, consider a global asset manager with billions of dollars in assets under management. A problem many of these companies have had is how long AUM reporting can take to reach leadership; weeks or months can mean decisions at the top lag behind the ‘real-time’ position of the firm. If a company can set up the right system across the firm, plug it into the OCEO ‘ecosystem’ in real-time, that same reporting can happen in days, not weeks. Decisions get made faster, based on better data, and the firm benefits overall due to more informed leadership.
Another example might be a CEO’s weekly operating review. Today, a Chief of Staff spends the better part of a day gathering updates from functional leaders, checking them against promises made last week, pulling the data, and assembling a briefing pack to present to the CEO. After the meeting, someone turns those decisions into actions, finds owners, and chases progress until the next review.
An AI Innovation Lead would build a workflow that collects the updates from approved systems and flags what is missing; compares them with previous commitments, the performance indicators, and the relevant decision history. It could then highlight inconsistencies, overdue actions, and the items that need the CEO’s attention, and produce a briefing pack in the format the CEO prefers, with back links to the underlying evidence.
Such a system would make no decisions and would replace nobody’s judgment. The Chief of Staff is still responsible for running the operating rhythm, the CEO still makes the decisions, and the AI Innovation Lead designs, tests, and maintains the guardrails to enable such a workflow.
What does change is the OCEO stops reconstructing context every week, information arrives with history already embedded and dots connected, and someone is accountable for ensuring this system is always working and always improving.
The above examples are illustrative, and every OCEO will need its own integration, governance, and checkpoints - but they demonstrate the type of thinking required to make this hire work.
The missing ‘builders’ skillset in most offices
Many OCEO functions are asking a Chief of Staff to own the AI layer, which makes sense if the AI work is mostly task-based and recurring. The Chief of Staff role is really designed around judgment, coordination, and becoming an extension of the principal’s authority - the AI it typically involves is usually task-heavy: a tool picked for a specific project, a workflow built for a recurring report, or an ongoing thread for drafting and summarizing.
Designing and governing a system that runs the whole office, holds the principals' context, is safe, and keeps working after its builder has moved on is a different type of work, requiring a different profile.
The beauty of AI is that its capabilities increase exponentially every quarter. What is now possible was never on the roadmap five years ago. This new possibility is creating unprecedented opportunities for OCEO functions to seriously improve the speed at which they operate, increase the accuracy of work, and replace the messy dataset that lives in people’s heads with institutional-grade memory and context, which lives on way beyond the departure of such people.
OCEO functions have started hiring for these skills under various titles: AI Program Lead, AI Innovation Lead, AI Enablement Lead, and many more. The title is less important; what really matters is the problem that needs to be solved and the mandate given to the person.
The difference between an AI user and an AI builder
The market now holds two groups of people that can often look the same on paper.
The first group feels confident with AI; they use it daily to draft, research, and summarize, and talk about the tools fluently. They believe they’re up to date with most of what AI has to offer. The truth is, nearly everybody in corporate America now belongs to this group. What they actually know is how to interact with a chatbot, which is a useful skill in its own right, but a huge miss for what the ‘AI Innovation Lead’ role requires.
The second group is far smaller, highly technical, and hard to find. They understand how AI meets data: what it can reach, what it cannot, and how it responds when a data source is missing. They build systems and processes rather than prompts; they understand the limitations of models, and where a human must be in the loop. Most importantly, they understand what outputs move teams forward in ways that can be measured and relied upon.
These people also understand how to truly harness AI, which is closer to an art than a method. Over-engineering a workflow tends to produce disappointing results for certain tasks, whereas giving the model more room to use its own judgment can produce better results, but with more autonomy comes more variability and risk.
Good builders know when a step should be deterministic, when the model can be trusted to make a decision, and where a person should step in to verify before anything moves on.
These same people understand security at an enterprise level - knowing the difference between a consumer subscription and an enterprise agreement, what the enterprise offerings from Anthropic, OpenAI, and the major cloud providers do and do not guarantee, and when a private deployment or on-premises option is likely the best fit. We explore this further in the section below.
The most sophisticated builders also pay attention to the parts that rarely make a job description - the interface the team will actually use, how easily the OCEO can adapt to what has been built, and when less is more. The best system in the world is worth very little if the people around the principal struggle to work with it.
It’s the second group you should hire for. Hiring from the first group will not just be a case of ‘they’ve bitten off more than they can chew’, but instead, they run the very real risk of accidentally exposing some of the most sensitive information within your company to the outside world.
A point worth noting: this hire needs the authority to match. This person will work across functions and will need complete access to where the information lives and the people guarding it. A CEO should extend their authority in this capacity.
Security, accuracy, and the human-in-the-loop
The OCEO handles the most sensitive material in the firm and makes the decisions with the highest consequences - naturally, security and accuracy are the number one priority.
Around two-thirds of directors admit to summarizing board materials in consumer tools, according to the Diligent Institute. Separately, 93% of C-suite respondents said they had made decisions on AI output drawn from inaccurate data in a La Fosse survey. In May 2026, CB Financial Services became the first public company to file an SEC cybersecurity disclosure, due to an employee submitting customer names, Social Security numbers, and dates of birth into an unapproved AI tool.
None of this is malicious; capable people reach for the fastest tools available - and in many cases, a lack of approved enterprise solutions forces employees to pick up unapproved consumer tools.
In the context of the OCEO, mistakes in outputs become mistakes in decisions. Missing data is often replaced with a confident guess, as opposed to a flag that data is missing, and the incorrect output of AI can often look like a certain and well-informed response.
The defense to this is a ‘human-in-the-loop’ - someone with enough context and experience to know what a correct answer looks like, sitting at the right checkpoint in the workflow.
From a security perspective, every workflow should be classified by the sensitivity of what it touches, from public information through to confidential board material, privileged matters, and the principal's personal life. Each class should have a defined rule and the OCEO should keep a written register of which workflows exist, what data it touches, what class the workflow falls into, and who owns the workflow.
How to hire an AI Innovation Lead
Telling someone to “go do AI” will produce a year of random tools, low adoption, and an OCEO function that works in the same way it always has done. The hire has to be deliberate, and the work starts before the search.
Do the fact-finding first. Walk the OCEO with the people who run it. Where does information come from, in what state, and is it on time? Which decisions fall apart on their way out? What are the Chief of Staff and Executive Assistants rebuilding every week? Where is the principal doing work the OCEO should have already absorbed? Where is the principal having to ‘manage down’? Be equally as clear about where AI should not go. Major judgment calls, relationships, and communication belong to people, and saying so early prevents costly mistakes.
Decide what success looks like. Hours returned to the principal each week, briefing turnarounds in a reliable format, the time to decision reduces, follow-ups happen with less chase, the team feels well informed at all times, the number of recurring workflows that now run without a person assembling them, there is a noticeable uptick in memory and context across the team. Pick a few, measure them before the hire starts, and review them monthly. In reality, the most valuable AI work is often the least visible - manual work should just ‘fall away’.
Watch for admin creep. The OCEO shouldn’t be inheriting extra work as a result of the hire. Every tool the hire introduces should remove a step somewhere else. If the OCEO team finds itself maintaining dashboards and integrations nobody asked for, it is usually a sign the AI work is becoming performative and less useful.
Assign budget and extend authority. The person needs a budget for tooling, the authority to get data access, and a reporting line that allows them to act. They may need to work with external parties and consultants, AI engineers, data experts, and security professionals. This role needs to report up into the OCEO, not into IT - however well-intentioned, IT has a way of turning what could be revolutionary and fast work into something that sits in a ticket queue.
Hire the builder, not the title. The profile is a builder-operator, someone who has shipped working AI systems inside a real business, understands how executives work, has the influence to shape decisions, and can be trusted with everything the OCEO is exposed to. They come from a few places: operations and product-operations people from AI-native companies who want proximity to a CEO, some Chiefs of Staff already doing this work at a high level, and engineers who would rather build for one principal than for a product team.
Test them on the real thing. When you find them, ask them to describe a workflow they have built, end-to-end, in plain language. They should describe where the data comes from, how it reaches the model, what the model does with it, how they ensure the output is accurate, and where the flow breaks.
The long-term advantage of getting this right
Most hires into the Office of the CEO add to what already exists; this one changes the foundations of what the office is. Once the principal’s context, decision patterns, and operating rhythm are captured and encoded in systems that continuously learn, the office gets better each quarter without anybody working harder, and it stops depending on the memory and capability of whoever is in the room.
It’s the early movers who will pull away and why the gap is hard to close later. A competitor who starts building in this capacity 18 months later cannot buy the 18 months back.
Another important consideration: the executive office is the part of the company everybody else is watching. How the principal and the team around them work is the clearest signal any firm sends about what it expects from everybody else. An office that runs on well-built, well-governed AI gives the rest of the company a working example to follow, and it does so without a forced memo.
Conclusion
We think this is soon to be one of the most consequential roles within Office of the CEO functions in the coming years. The Chief of Staff role went from a rarity to a standard seat in less than a decade, and this role will make that same journey, but with an initial scramble for talent. Companies that make the move early will pick up the best and most ambitious talent and begin to make serious headway, whereas those who lag behind will lose out on talent, will suffer competitively, and will likely have to pay more for good talent.
Blackbook Associates has spent a decade building the teams behind exceptional leaders, focusing exclusively on Office of the CXO mandates - Chiefs of Staff, Head of AI Innovation (OCXO), Executive Assistants, and more. If you are thinking about this kind of hire or are curious how others are building their Office of the CXO functions, we would be happy to connect.