Fractional fills the gap while you keep hiring. Removes the urgency to settle. Ships production AI in the meantime, so the next exec inherits a system, not a backlog.
The role has been open 45, 90, sometimes 120 days. Your recruiter is grinding cold pipeline. The shortlists keep coming back the same way: senior candidates who pass the bar are already running AI orgs and are not looking. Candidates who are looking either do not have shipped-production credit, or they want a title and comp bump above what the board is ready to underwrite.
Every week the role stays open, the board asks the same question. Every week, the answer is the same deck. The team needing AI cover does not get it. Quarter slips. Plan slips. The candidate you would actually hire still has not surfaced.
The candidates who clear the bar are already running AI orgs somewhere else. That is the market, not your recruiter.
They are not on the market. They are not opening recruiter InMails. The traditional executive pipeline misses them entirely.
The candidates who pass the technical bar are pricing themselves against AI-native comp bands. The board approved last year's number.
Every quarter without an exec is a quarter you are defending a search, not defending a portfolio. The pressure compounds.
The shape of AI leadership has moved twice since the job description was written. Sometimes the candidate gap is a spec gap.
The gap is not idle time. It is the period where your AI initiative runs without an owner, and that has a shape you can predict.
You announce that the company is going to use AI. Then very little of that announcement reaches the floor. Gallup found only 22% of employees say their leadership has communicated a clear plan for AI, while 44% say the company has already started integrating it (June 2025). The further someone sits from the decision, the less they know: 26% of individual contributors could not say whether their organization had implemented AI at all, against 7% of leaders.
People do not stop. They start on their own, on personal accounts. MIT's NANDA project put roughly 90% of employees using personal AI tools for work against about 40% of companies holding an official subscription. Whether that work gets shared is a social question, not a policy one. Slack's Workforce Index found 48% of desk workers would be uncomfortable telling their manager they used AI, with the top reasons being that it feels like cheating or makes them look less competent, and company policy ranking dead last. A KPMG and University of Melbourne study of 48,000 people across 47 countries found 57% hide their AI use and present the output as their own.
So you get pockets. Some people build something good and never tell anyone. Others build the same thing twice. A few ship something into production that nobody reviewed. Retool surveyed 307 CTOs, CIOs and CISOs in May 2026: 75% are working under an AI directive from leadership, 93% are worried about vibe-coded tools reaching production, 19% confirm an AI-generated tool has already caused a production incident, and only 8% say their internal-tool governance is strong. Duplication barely registers as a concern, ranking second-to-last on the priority list, which is usually what a problem looks like right before it gets expensive.
The scarce skill in all of this is not usage. It is knowing what is worth building. Volume and judgment come apart quickly: METR ran a randomized trial where experienced developers were 19% slower with AI tools while believing they had been 20% faster. UpGuard found executives showing the highest rates of regular unsanctioned AI use of any group. Your heaviest AI user is not automatically the person who should decide what your company builds, and engineers have been saying this in public for a while, though that part is testimony rather than a study.
That is the thing fractional covers. Not the org chart hole, the ownership hole underneath it: someone deciding what is worth building, setting the guardrails before people are already in production, and giving the pockets of good work somewhere safe to surface. I work the search alongside your recruiter, write the next version of the job description against what we learn, and step out when the right person signs. The point is to remove the urgency to settle. A role that is not on fire gets a better hire.
Honest counterweight, since you will hear it from a vendor eventually: Wharton's 2025 adoption study found 74% of enterprises that measure gen AI ROI report positive returns. Outcomes here are bimodal, and the split tracks execution discipline rather than model choice. That is the argument for owning the program, not for waiting.
Industry benchmarks put the fully-loaded cost of a failed senior tech exec hire at roughly 2 to 3 times annual comp. Search, onboarding, opportunity cost on the work that did not ship, severance, and re-search. For a Director or VP of AI at mid-market comp bands, that is a high-six-figure to seven-figure mistake.
Six-month ramp before they ship. Risk of exit around month 14 when the spec mismatch becomes obvious. Industry benchmarks for a failed senior tech exec hire run roughly 2 to 3 times annual fully-loaded comp. The wrong-hire scenario costs multiples of the salary, conservatively.
Six months of fractional costs a fraction of a full-time hire. Buys you time to hire right, or time to discover the function you are hiring for has shifted. Either outcome is cheaper than a settled hire that exits 14 months in.
These are ranges, not promises. The point is the order of magnitude. The fractional bridge is roughly one-tenth the cost of a wrong hire, and you keep the search running the whole time.
Director-altitude work, not advisory. Everything here outlasts me and belongs to the next exec.
A real portfolio, classified Now / Next / Later, defensible against board pressure. The "what is our AI plan" question gets a real answer.
Not a prototype, not a deck. A use case in production with an eval bar, used by the team, defended to the board.
What the JD should say based on what we learned. Sometimes the right next hire is two ICs and a contractor, not one Director.
The next exec inherits a vendor stack, contract terms, eval data, and the reasoning. Not a blank slate and a procurement backlog.
Eval suites, prompt regression tests, safety reviews. The org learns what "good" looks like before the full-time exec walks in.
Workforce training in three layers: leadership signals that using AI is safe to admit, a small lab group runs the experiments, and everyone else gets taught the two or three moves that matter for their actual job. The named fix for the adoption ceiling, and the reason people stop building the same thing twice in private.
What AI is allowed to decide alone, what needs a human on it, which data never goes near a model, and where the sanctioned tools live. Written for the person doing the work, not for the audit binder. This is the piece that turns unsanctioned use into visible use.
A bridge is built differently than a full-time role. Cadence and exit terms are designed for that.
Quoted in writing within 48 hours of the discovery call. Day mix scopes against board cadence, exec-staff meeting load, and the first 90-day shipping target.
Every engagement carries the 14-day mutual exit umbrella. After month 3 either party can exit with 30 days notice. Days not worked are refunded. The bridge ends clean.
Hand-off is built in from day 1. Documented portfolio, documented decisions, documented standards. The full-time exec inherits a system, not a backlog.
JD review, panel interview support, evaluation rubrics, reference calls. Everything I learn in the seat informs who we hire into it.
No. The opposite. I work the search with you, help write the job description against what we learn together, and hand off cleanly when the right exec signs. Most fractional engagements end inside 9 months when the full-time hire lands.
Then we ramp down. The 14-day mutual exit guarantee applies on day 1 of every engagement. After month 3 either party can exit with 30 days notice. Days not worked are refunded. The goal is to make your AI exec successful from week one, not to extend the bridge.
Strategy consultants write the deck and leave the org chart with a hole. Fractional fills the hole. I sit in the exec staff, own the AI portfolio defense to the board, ship one production AI use case in the first 90 days, and hand the keys to the full-time hire. Compare on the /vs/ai-strategy-consultant/ page if useful.
That happens. Half the time the AI role spec posted in 2024 is the wrong spec for 2026. One artifact of a fractional engagement is a redesigned role description based on what we learn together. Sometimes the right next hire is two ICs and a contractor, not one Director.
Two paths in. Score your AI portfolio first if you want to see the work before talking. Or book 30 minutes with Edwin and quote the bridge against your search.
Capacity: I hold ~4 active client commitments across Fractional, Audit, and Scoped Build. Mix depends on what is already booked.