Skip Vanderburg / The role explained
The role, explained
Two roles, one arrangement. A senior AI strategy leader and a senior customer experience design and innovation leader who work with your executive team on a part-time, ongoing basis — bringing the judgment of a Chief AI Officer or a Chief Experience Officer to the decisions that need it, without the cost, search cycle, or permanence of a full-time hire. Engage either seat, or both.
The short version
Definition
A fractional AI strategy advisor is an experienced executive who is engaged part-time and on retainer to help a company decide where to apply artificial intelligence, in what order, under what guardrails, and how to tell whether it worked.
A fractional customer experience design and innovation leader is the same arrangement pointed at the other half of the problem: deciding what experience the business intends to deliver, which journeys get reinvented first, what evidence from customers settles the argument, and how the organization keeps delivering it once the project ends.
They are frequently the same engagement. The AI roadmap decides what the business will be able to do; the experience design decides whether any of it is worth the customer’s time. Splitting them across two vendors is how companies end up with an impressive capability nobody wants to use.
The word "fractional" describes the time commitment — typically a set number of days per month — not the level of the person. The advisor sits at the same table as the CEO, CIO, CTO, or CPO, is accountable for the same outcomes, and stays engaged across quarters so context compounds rather than resetting with each new project.
Why the role exists
The fractional model showed up first in finance — the fractional CFO — and spread to every function where companies need senior judgment more often than they need a full-time salary. AI hit that pattern faster than most, and customer experience design has been sitting in it for years: the same three gaps, one function over.
Executives with real AI depth and enterprise operating experience are scarce, expensive, and rarely available on the timeline a board wants — and senior experience-design leaders who can hold their own in a boardroom are scarcer still. Most searches for a Chief AI Officer or a Chief Experience Officer take longer than the decision that prompted them.
A mid-market company or a division inside a larger enterprise may not have enough AI work — or enough continuous design work — to justify a permanent executive, but has far too much at stake to leave either the strategy or the customer experience to whoever has spare capacity.
Plenty of organizations can run an AI pilot, and plenty can run a design sprint. Far fewer can decide which pilots and which prototypes deserve to become production systems and shipped experiences, what to kill, and how to fund the difference. That is a strategy and design-leadership problem, not an engineering one.
The hard part of enterprise AI is no longer building a model, and the hard part of customer experience was never the visual design. Both come down to deciding what is worth changing — and having someone senior enough in the room to make that call stick.
The work itself
The titles vary — fractional Chief AI Officer, AI advisor, executive AI counsel on one side; fractional Chief Experience Officer, CX leader, head of design on the other. The work is consistent, and in both cases it is mostly about decisions rather than deliverables. Nine of them in each seat.
In the AI seat
Work that is mostly about decisions rather than technology — which ones are worth changing, in what order, and under what guardrails.
Establishes a clear, non-hype point of view on where AI creates advantage in your business — and, just as importantly, where it does not.
Turns that thesis into a sequenced plan tied to business decisions and measurable outcomes, prioritized with explicit frameworks rather than the loudest opinion in the room.
Reviews what is already underway, promotes the few efforts worth scaling, and gives leadership the cover to stop the ones that will not pay off.
Defines the guardrails — data handling, model and vendor review, human-in-the-loop thresholds, audit trails — so speed does not create exposure.
Separates capability from marketing in build-versus-buy decisions, platform selection, and pricing structures that are still being invented.
Converts technical reality into choices, risks, and investment narratives that a board can act on — and prepares the executive who has to present them.
Raises the AI fluency of the leaders who will own this after the engagement ends, so capability stays in the company.
Answers the organizational questions — who owns AI, how work gets funded, what a central team does versus the business units.
Keeps the strategy coherent across quarters as the technology, the vendors, and the regulatory picture keep moving underneath it.
In the customer experience seat
The same retained arrangement, pointed at what customers actually receive — and at whether the organization is built to keep delivering it.
Establishes what the business is actually promising customers, in language specific enough to design against and to say no with — and what it is deliberately not competing on.
Puts research back in front of the argument — what customers do, where they give up, what they say when nobody from the company is in the room.
Redesigns the two or three journeys that carry the revenue and the trust, end to end, across every channel and team that touches them.
Turns concepts into prototypes customers have actually used, so the funding conversation happens against evidence rather than conviction.
Defines the handful of experience measures leadership will actually steer by, and connects them to the financial outcomes finance already tracks.
Answers the organizational questions — who owns the journey, where design sits, how research gets funded, what stays in-house — so quality does not depend on one person caring.
Reads the difference between a portfolio and a process when choosing design partners, research vendors, and experience platforms — and scopes the work so you keep the thinking.
Teaches the leaders who will own this to read research, run a critique, and tell a designed decision from a decorated one — so the capability stays in the company.
Keeps the experience coherent as products ship, teams reorganize, and each channel optimizes for its own number — which is how a designed journey quietly becomes five disconnected ones.
Engagement shape
Every engagement is scoped differently, but most follow a recognizable arc. A common structure is two to four days per month on a rolling retainer, with the first weeks weighted more heavily.
Interviews with the executive team, a read of what is already underway, and an honest baseline: where AI efforts stand, what they cost, and what customers experience today. Usually surfaces more in-flight activity — and more accumulated experience debt — than leadership realized.
Identify the decisions, workflows, and customer journeys where the business would actually move, size them against effort and risk, and produce a ranked shortlist with a defensible rationale — something the CFO can question and the roadmap can be built on. In the experience seat this is where research replaces internal conviction.
Turn the shortlist into a phased roadmap with owners, funding, success measures, and governance guardrails — and, on the experience side, a service blueprint and a tested prototype. Deliverable is typically a board-ready plan plus the operating model to run it.
Standing time with the sponsoring executive, checkpoints on active initiatives, vendor and build-versus-buy calls, design and experience reviews before the build is committed, and periodic re-prioritization as results come in. The role is counsel, not delivery management.
A good engagement is designed to end. That means an internal owner who can carry the strategy and the experience, documented decisions and rationale, and no dependency on the advisor to keep the program running.
How it compares
A fractional advisor is not always the right answer, in either seat. Here is an honest read on the six ways companies usually fill these gaps.
| Option | Strongest when | Watch out for | Typical shape |
|---|---|---|---|
| Fractional AI or CX advisor | You need senior judgment continuously, but not 40 hours a week of it — and you want context to accumulate. | Limited hours mean the advisor sets direction and pressure-tests, but does not run delivery day to day. | Monthly retainer, a few days per month, rolling term with a defined exit. |
| Full-time Chief AI or Experience Officer | AI or customer experience is core to the product or the P&L, and there is enough scope to occupy an executive permanently. | Long search, senior compensation, and a permanent seat committed before the strategy is even settled. | Executive hire with equity and a team. |
| Large consulting firm | You need breadth, benchmarks, and a large team to execute a defined program at scale. | Cost, a partner-plus-juniors staffing model, and recommendations that can outlast the relationship that produced them. | Fixed-scope engagement, sizable team, defined deliverables. |
| Systems integrator or dev shop | The strategy is settled and you need capable hands to build and ship it. | Implementation partners are paid to build. They are rarely the right party to decide what should not be built. | Statement of work tied to delivery milestones. |
| Design or innovation agency | You need production design capacity, a campaign, or a concept explored quickly by a dedicated team. | Agencies are paid to produce work. Deciding what experience the business should intend to deliver — and who owns it afterward — usually isn’t in scope. | Project engagement, creative team, defined output. |
| Stretch an internal leader | You have a strong technical or product executive with genuine bandwidth to add this. | Bandwidth is usually the illusion. AI strategy and customer experience are both the thing that slips when the quarter gets hard. | Added scope on an existing role. |
These are not mutually exclusive. A common pattern is a fractional advisor setting direction, governance, and experience intent while an integrator builds and an agency produces — with the advisor accountable to the executive team, not to the delivery contract.
Fit
Evaluating one
The title is unregulated, so the diligence is on the buyer. These separate operators from narrators.
Advisory is stronger from someone who has built and operated the thing — or designed and shipped it — not only recommended it. Ask for specifics on scope, constraints, and what went wrong.
Anyone can generate a list of opportunities, or a wall of concepts. The valuable judgment is knowing what to stop — and having done it in front of a leadership team. In the experience seat, ask what the research said that changed their mind.
Look for named, explainable methods that survive a CFO's questioning — and, on the experience side, evidence from customers rather than taste dressed up as expertise.
A clear answer signals a repeatable approach. A vague one signals an engagement that will be invented as it goes.
Reseller margins, vendor referral fees, and implementation upsells all shape advice. Independence should be stated plainly.
A fractional advisor who cannot describe the handoff is describing a permanent dependency.
Common questions
There is overlap, but the shape differs. A consulting engagement is usually scoped to a deliverable and ends when the deliverable lands. A fractional advisor is embedded on an ongoing basis, accumulates context about your business, and is accountable for the outcome of decisions rather than the production of a document.
The practical difference shows up in month four, when the question is no longer "what should our AI strategy be" but "this vendor just changed their pricing model — what do we do?"
A fractional CTO or CIO typically owns the technology function — infrastructure, engineering teams, delivery, security, the whole estate. A fractional AI strategy advisor has a narrower and deeper remit: where AI creates advantage, in what sequence, under what governance, and how the organization decides.
The two coexist well. In many engagements the fractional AI advisor is a peer to an existing CTO rather than a substitute for one.
An agency is engaged to produce work — a redesign, a campaign, a concept, a set of screens — and is measured on what it delivers. A fractional customer experience leader is engaged to decide what experience the business intends to deliver in the first place, which journeys get reinvented, what evidence settles the argument, and who owns the result afterward.
They work well together. A common pattern is the fractional leader setting intent, running the research, and holding the through-line, with an agency or an internal team producing at volume underneath it.
They are not two hobbies. The customer experience work came first — two decades of it, founding a digital experience agency, then leading CX innovation and design for global enterprise clients — and the AI work grew out of building a decision-intelligence platform on top of that. The two questions land on the same executive table anyway: what the business will be able to do, and whether anyone will want it.
That said, each seat can be engaged on its own. Plenty of teams need only one.
Commonly the equivalent of two to four days per month, weighted more heavily in the first six to twelve weeks while the assessment and roadmap take shape, then settling into a steadier cadence of standing executive time plus decision support as it comes up.
Some engagements begin with a single executive briefing or a fixed-length strategy sprint and convert to a retainer only if the fit is right.
Most fractional arrangements are priced as a monthly retainer tied to a committed number of days, sometimes preceded by a fixed-fee assessment. The economics are the point of the model: a fraction of the fully loaded cost of a full-time AI or experience executive, without the search cycle, equity, or severance exposure — and materially less than a comparable consulting or agency program.
Ranges vary widely by scope, company size, and market, so treat any published number skeptically and scope the engagement to the decision in front of you.
The core of the role is strategy, governance, design direction, and decision support — not delivery. That said, an advisor who has built enterprise AI systems and shipped customer experiences will pressure-test architecture, sketch and prototype where a prototype settles an argument faster than a slide, sit in a research session, and recognize when a vendor demo is hiding a problem.
Building the production system itself usually belongs to your team or an implementation partner, with the advisor helping select and govern that partner.
Most often mid-market companies and private-equity-backed businesses where the AI question is board-level but the scope does not justify a permanent executive — and divisions or business units inside larger enterprises that need their own strategy ahead of a corporate one.
Later-stage startups also use the model when the founding team needs senior AI counsel alongside, not instead of, their existing technical leadership.
Fractional refers to hours, not investment. In practice the model concentrates attention: time is scarce and visible, so it goes to the decisions that matter rather than to the standing meetings that fill a full-time calendar.
The honest tradeoff is that a fractional advisor cannot be in every room. That is why the role depends on a strong executive sponsor and a clear internal owner.
Agree the measures at the start. Useful ones include decisions made faster or with better evidence, initiatives stopped and dollars redeployed, AI efforts that reach production rather than stalling, governance in place before it is needed, and an internal leader who can carry the strategy without the advisor.
Beware measures that reward activity — number of pilots, number of tools deployed — over outcomes.
Let's talk
If your team is weighing where to invest in AI — or trying to move from pilots to something that actually runs the business — a short conversation is usually the fastest way to find out whether this model fits.
