From Experimentation to Enterprise: The Fractional Leader Role in AI Adoption

The Demand Signal, Read Carefully

Every year a set of numbers circulates about the rise of interim and fractional leadership, and every year they get flattened in the retelling. It is worth being precise about what the current figures measure, partly because precision is the point of this article and partly because a leader who repeats a number the source does not support has already demonstrated the habit you are hiring them to fix.

The 2026 High-End Independent Talent Report from Heidrick & Struggles reports that requests for interim digital, data, and IT leaders have increased 58% since 2023, an increase the report says came in parallel with the shift of AI from experimentation to enterprise execution.¹ The same report finds that one-quarter of all requests across business functions relate to digital, data, and AI, and that these requests frequently appear in initiatives led by strategy, transformation, and finance groups rather than by technology teams.¹ Demand for interim C-suite leaders is up 151% since 2021. Within the interim role mix by function, digital, data, and IT accounts for 10% of requests, well behind finance at 51%.¹

What the figures do not say

These are not market-size estimates. The report describes itself as an annual examination of Heidrick & Struggles proprietary data from North America and Europe.¹ That means every percentage is a change in the volume of requests reaching one search firm. Request volume at a single firm can move for reasons that have nothing to do with the underlying market: a new practice area, a new geography, a large client that changed how it buys. The direction is credible and consistent with what practitioners see. The magnitude should not be treated as an industry measurement.

Two more points of precision. First, the 25% figure is often repeated as the share of requests that are AI-related. The report groups digital, data, and AI together, so the number covers a data platform replacement and a finance systems implementation alongside anything model-based. Second, within healthcare and life sciences specifically, 25% of project requests relate to digital, data, and AI, which places the sector below technology and services (35%), financial services (29%), and consumer markets (28%), and above industrial (23%).¹ Life sciences is not leading this category. It is in the middle of it.

The supply side is moving too

The demand picture is only half of it. Heidrick & Struggles fielded an online survey in August 2025 that drew responses from 3,810 full-time independent talent across industries and functions, with 64% based in North America and 26% in Europe and Africa.³ Only 11% said they were not using AI at all, and 44% anticipated that clients would expect them to bring AI expertise to engagements.³ That is a self-selected population of independent operators, so read it as a statement about that group rather than about executives generally. It does tell you something useful: the pool of people available to run this kind of engagement has largely absorbed the tooling, and a growing share of them expect to be hired for it.

None of this answers the question a life sciences leader actually has, which is whether a temporary leader can move a company from scattered experiments to an operating model, and under what conditions that fails. The rest of this article is about that.

What Scattered Experiments Look Like From the Inside

The phrase “scattered AI experiments” suggests a company that has been careless. In practice it usually describes a company where several capable people each did something sensible, independently, over about eighteen months. The problem is not any single decision. It is that no one has the full list.

A typical mid-size biotech or specialty pharma company, when it finally does the count, finds somewhere between nine and twenty separate activities that touch AI. They fall into three groups, and the third is the one that changes the shape of the engagement.

The named pilots

These are the ones leadership already knows about. A regulatory writing assistant. A deviation triage or classification tool in quality. A literature screening tool in medical affairs. A forecasting model in commercial. Each has a sponsor, a budget line, and usually a slide. They are the visible portfolio, and they are the smallest part of the real one.

The features you did not procure as AI

This group is larger and more awkward. A vendor adds a summarization feature to an eQMS or a document management system that was validated three years ago. A LIMS release includes an anomaly flagging capability. A safety database vendor turns on case triage assistance. Nobody ran a procurement process, because there was no procurement event. The system was already in place, the supplier pushed an update, and the functionality arrived inside a validated environment under an existing change control process that was not written with model behavior in mind.

The practical question this raises is not whether the feature is good. It is whether anyone assessed it, whether the validation package still describes the system that is running, and whether the periodic review will catch the difference. In most companies the answer at the point of the inventory is no, no, and probably not.

The work nobody wrote down

The third group is individual use of public tools. There is no life sciences specific measurement of this that survives scrutiny, but the general workplace data is consistent. A survey of 1,250 office professionals at organizations with at least $500 million in annual revenue, conducted across the United States, United Kingdom, Australia, and Japan and excluding IT and technology roles, found that two-thirds reported using AI tools at work even though they believed doing so was not permitted under company policy, and 88% reported sharing work-related information with public AI tools.⁴ That is a general workplace sample, not a regulated-industry one, and the specific percentages should not be transplanted onto a GxP population. The direction is not in serious doubt.

What the Fractional Model Is Actually Good At

The case for a fractional or interim leader in this situation is usually made on price and speed. Those are real but they are not the interesting part, and a company that hires on price alone tends to get an advisor rather than an operator. Four other properties do the actual work.

The political point, in more detail

The second card deserves expanding because it is the least discussed and the most load-bearing. Research on chief data officers found the average tenure of the role to be roughly two and a half years, with what the authors described as a honeymoon period of about eighteen months before major transformational change is expected.⁵⁶ The reasons given for short tenure are not technical. They are unclear expectations, undefined boundaries with the CIO, cultural misalignment when the leader is brought in as an external change agent, and difficulty demonstrating impact.⁶

Read that as a description of the job rather than a description of the people. A newly hired permanent data or AI leader in a life sciences company has roughly eighteen months of political credit, and the first six months of it will be spent on relationships they will need for the next five years. Asking that person to spend their credit on shutting down a VP’s favorite pilot in month three is asking them to make a rational choice against their own interest. Most will not, and they should not be blamed for it.

A fractional leader has a different arithmetic. Their reputation depends on the state of the company when they leave, not on their standing inside it a year later. That makes them structurally better suited to the decisions that are unpopular and correct. It also means their recommendations carry less weight in the room, which is a real trade-off and one of the failure modes covered later.

What the model needs in order to work at all

Analysis of fractional leadership in biotech and life sciences describes the conditions under which it works: a role with a defined endpoint such as a filing, a fundraise, or a launch; real operational ownership rather than an advisory brief; an organization with clear priorities and enough resources to act on decisions; and cultural alignment.⁷ The endpoint condition matters most for the work described here. “Improve our AI maturity” is not an endpoint. “One approved intake path, one named decision maker per decision type, and one use case through the full path by the end of Q2” is.

The Sequence of Work in a Typical Engagement

The sequence below is deliberately conservative. It front-loads the unglamorous work and defers the platform conversation, which is the opposite of how most of these engagements are scoped when a vendor writes the proposal. The durations are ranges observed in practice for companies in the roughly 150 to 1,500 employee band, not benchmarks.

Two things about this sequence are worth flagging. Phases overlap on purpose, because the inventory keeps producing findings after the governance work starts and a design that cannot absorb new findings is the wrong design. And the platform question, which vendors will push toward the front, appears nowhere in the first four phases. Data work has its own sequencing logic and is covered separately in this series. Buying infrastructure before you know what you run and who decides is how companies end up with a governed platform running next to an ungoverned portfolio.

The Inventory, Including the Work Nobody Wrote Down

The inventory is the phase most likely to be done badly, because it looks like an administrative exercise and is treated as one. Done as a survey sent to function heads, it returns the named pilots and nothing else, which produces a governance design built on a false map.

Three passes, not one

Pass one is documentary. Procurement records, active contracts, software renewals, expense reports for anything under the approval threshold, and vendor release notes for every validated system in the estate for the past twenty-four months. The release notes matter more than anything else in this pass, because that is where features you did not procure appear.

Pass two is conversational. Thirty to sixty minutes with each function, asking what people actually do rather than what is approved. This only works with an explicit amnesty, communicated in writing by someone senior enough to make it real: nothing disclosed during the inventory window results in disciplinary action, and the deadline for that protection is stated. Without the amnesty, the inventory is a compliance interview and people answer it as one.

Pass three is technical. Identity provider logs, network egress to known tool domains, browser extension inventories, and expense data for personal subscriptions. This pass is the least comfortable and needs to be handled carefully with works councils and employee representatives in European sites. Its purpose is calibration rather than enforcement: it tells you whether pass two got you 90% of the picture or 40% of it.

What to record for each entry

Eight fields. The temptation to add twenty more should be resisted, because a sixty-field inventory is filled in once and never maintained, and an inventory nobody maintains is worse than none because it creates a record that is confidently wrong.

Choosing the Governance Minimum

The governance minimum is the smallest set of controls that lets each decision be defended after the fact. The word doing the work is minimum. Most companies at this stage do not need an AI policy suite. They need three or four controls that are actually followed.

What “minimum” is anchored to

Two external anchors keep this from being an arbitrary judgment. The NIST AI Risk Management Framework’s GOVERN function describes the requirement plainly: roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization.⁸ That is a statement about clarity, not about volume of documentation. Recent work on scaling AI governance makes the complementary point, arguing that controls should be matched to the type of AI system and the risk involved and embedded directly into workflows, decision rights, and accountability structures rather than run as a separate compliance function.⁹

There is also a floor set by regulation that applies whether or not anything in the portfolio is high risk. Article 4 of the EU AI Act, the AI literacy obligation, has applied since 2 February 2025, and supervision and enforcement by national authorities began on 2 August 2026.¹⁰ It covers providers and deployers and extends to staff and other persons dealing with the operation and use of AI systems on their behalf, which includes contractors and service providers.¹⁰ The Commission has been explicit that no specific level is mandated and there is no single prescribed format, but also that relying only on a system’s instructions for use is typically ineffective.¹⁰ For a company with EU operations, some form of role-appropriate training is part of the minimum, not an optional extra.

The regulatory direction is set well beyond that. The joint EMA and Heads of Medicines Agencies multi-annual AI workplan runs to 2028 and is organized around four dimensions: guidance, policy and product support; tools and technologies; collaboration and change management; and experimentation.¹¹¹² Companies designing an operating model now are designing into a supervisory environment that is being built at the same time.

Install now, defer deliberately

The deferred column is not a list of bad ideas. Most of those things are appropriate for a company running thirty use cases across four sites. They are wrong for a company running eleven, because they consume the credibility of the new process before it has produced anything. Write the deferral down with the conditions that would trigger reconsideration, so the successor inherits a decision rather than an omission.

Decision Rights: The Artifact That Has to Outlive the Engagement

If the engagement produces exactly one durable artifact, it should be the decision rights grid. Everything else can be rebuilt. This cannot, because it encodes agreements between people that took months to negotiate and that nobody will reopen voluntarily.

Four decisions, four names

The grid has four rows, and each row has one name in it.

• May we start? Who authorizes a team to begin exploratory work, and under what boundaries on data and output use. This is usually delegated fairly low, and it should be, because a high bar here drives work back underground.
• May this go live where it touches a regulated decision? The gate that matters. One person, normally in quality, with a stated basis for the decision and a record of it.
• Who accepts the residual risk? Distinct from the previous row on purpose. Quality can approve a control design without being the party that carries the consequence of the risk that remains. In most companies this belongs to the business owner of the process, and naming them changes behavior noticeably.
• Who can stop it? Including after go-live, on what signal, and without needing to convene anyone. If stopping requires a committee, nothing gets stopped.

Committees advise, people decide

The most common design failure is assigning a decision to a body. Governance groups can flag, recommend, and escalate, but a committee that cannot be held to a decision does not make one, and the real authority remains with whoever owns the delivery date.⁹ This is not an argument against a steering group. It is an argument for the steering group having an advisory charter and the grid having names.

One structural detail worth borrowing from wider governance practice: whoever holds the go-live decision should not report to the person who benefits from the answer being yes. In a life sciences company that separation usually exists already through the quality organization, which is an advantage the sector has over most others and one that is regularly given away by placing AI approval in a technology reporting line.

The relationship between this grid and the compliance or quality oversight function is a separate subject with its own design questions, and is covered elsewhere in this series.

One Use Case End to End as the Template

The fourth phase is where the engagement either produces something transferable or produces a binder. The instruction is narrow: take one use case and run every step of the intended standard path on it, in order, with the records completed, even where a step is plainly oversized for that particular use case.

Choosing the one

Four selection criteria, in priority order:

1. Real demand from real users. Not a demonstration. A team that wants the thing and will be annoyed if it does not arrive.
2. A measurable outcome that is not adoption. Cycle time on a specific step, error rate at a specific check, or volume handled per period. Adoption metrics have their place, but a template built around them teaches the organization to measure the wrong thing.
3. Low enough risk that failure is recoverable. The first pass through a new path will expose defects in the path. Doing that on a submission-critical activity is an unforced error.
4. An owner who will still be there. The template’s value is in the person who carried it, and if they leave in three months the template goes with them.

Notably absent from that list is business value. The first use case is not chosen for return. It is chosen because it is the one that will produce a reusable path, and choosing the highest-value candidate usually violates criterion three.

Every step, including the ones that feel unnecessary

The full path for a use case that influences a regulated decision includes intake and risk classification, a data suitability check, a documented validation approach proportionate to the risk tier, acceptance criteria agreed before testing rather than after, a human review design that states what the reviewer is checking against, a monitoring plan with defined signals, a retraining or model change position, a decommissioning and records retention position, and exit criteria stating in advance what result would cause the work to stop.

For a low-risk internal drafting assistant, several of those steps will take twenty minutes and feel like theater. Do them anyway, and keep the completed records, because the point of the exercise is not the use case. It is that the second team through the path copies a set of completed forms instead of interpreting a procedure. That single difference is most of what separates an operating model from a policy.

Setting exit criteria before starting, and the discipline of stopping work that meets them, is covered in its own right elsewhere in this series. It belongs in the template because a path that has no defined way to end teaches the organization that pilots are permanent.

Where the Model Genuinely Breaks

Four conditions defeat this model. They are worth stating plainly, because the failure modes are predictable and three of the four are visible before the engagement starts.

1. The work needs sustained internal relationship building

Some changes only happen through relationships that take years to build. Getting a manufacturing site with twenty-year tenure in the quality unit to accept an automated review step is one. Negotiating with a works council over monitoring that touches employee activity is another. Rebuilding trust after a failed transformation program is a third. A fractional leader working two or three days a week for six months cannot build that standing, and pretending otherwise produces an engagement that gets polite agreement in meetings and no change in behavior.

The tell is early. If the first four weeks produce a lot of “that will be difficult here” without specifics, the constraint is usually relational rather than technical. The right response is to narrow the scope to what can be done centrally and hand the site-level work to someone permanent, not to push harder.

2. The work requires authority the fractional leader does not have

A fractional leader can decide within their mandate and recommend outside it. Several things in this work are almost always outside it: reallocating budget between functions, changing someone’s objectives, adding or removing headcount, and committing the company to anything in a regulatory filing or a partner agreement. Those require a sponsor to spend their own authority.

If the sponsor will not do that, the engagement produces documents. This is the most common way these engagements fail and the least often named, because it presents as a scoping problem rather than a sponsorship problem. The check is simple and should be run in week two: identify the first decision that will require the sponsor to overrule a function head, and ask them directly whether they will make it. An evasive answer at that point is more useful than a clean one at month five.

3. There is no internal owner to hand to

The transfer phase assumes a successor exists. In companies below roughly 150 people, or in companies where every candidate already has two jobs, that person often does not exist and will not be hired. Hiring a fractional leader in that situation defers the decision rather than making it, and at the end of the engagement the operating model reverts because nobody is carrying it.

The honest answer in that case is usually a smaller scope: a decision rights grid, a maintained inventory, and a much narrower set of controls that a part-time internal owner can actually sustain, or an explicit choice to consume capability from a partner rather than build it. What a small biotech should and should not build in house is covered separately in this series, and it is the right conversation to have before the engagement is scoped rather than after.

4. The engagement becomes a permanent dependency

The fourth failure mode is the dangerous one, because everything about it looks like success. The engagement gets renewed. The sponsor is happy. The fractional leader knows the estate better than anyone and can answer any question in the room. Decisions route to them because that is faster. Two years later they are running an operating model that has never been transferred, and the company is paying an external rate for a permanent function while carrying all the risk of a single point of failure who has no notice period obligation and no succession plan.

This is a failure by the standard the engagement was sold on. It also tends to produce the outcome that the permanent role research describes: unclear expectations and difficulty demonstrating transformational impact, arriving later than usual because the arrangement felt comfortable for longer.⁵⁶

The Handover Test

The handover test is run 30 days after the fractional leader’s last day, not on it. The delay is the entire point. Anything that only works while they are available fails, and that is exactly what a last-day review cannot detect.

Someone independent runs it: an internal audit function, a quality lead who was not part of the design, or a board member who wants an unfiltered answer. Seven checks.

Five or more passes means the transfer worked and the remaining gaps are ordinary operating problems. Three or four means the design is sound and the ownership is not, which is a staffing conversation rather than a redesign. Two or fewer means the operating model did not transfer, and the correct response is not to re-engage the same person on the same terms. It is to establish whether an internal owner exists at all, which is the question from the third failure mode, arriving late.

Conclusion

The demand data is genuine and worth reading carefully rather than repeating. Requests for interim digital, data, and IT leaders are up 58% since 2023 and a quarter of requests across business functions now touch digital, data, and AI, measured through one search firm’s own engagement flow across North America and Europe.¹ That is a real signal about where companies are choosing to buy leadership capacity. It is not a market measurement, and it does not tell any individual company whether a fractional leader is the right answer for them.

What determines that is narrower. A fractional or interim leader is well suited to this work because they bring a pattern the company has not seen, can make early decisions a permanent hire would pay for politically for years, have no structural reason to build an organization around themselves, and are on a clock that forces the handover to be a deliverable rather than an intention. They are poorly suited when the change requires relationships built over years, when the work needs authority the sponsor will not spend, when there is no internal owner to receive the model, and whenever the arrangement drifts into permanence. The last of those is the one to watch, because it is the failure that arrives dressed as success and gets renewed on its own momentum.

The measure of a good engagement is not the sophistication of the governance that was installed. It is how much of it is still running, and being changed by other people, 30 days after the person who designed it stopped answering the phone. Design for that from week one and most of the other decisions become easier.

Sakara Digital works with pharma and biotech organizations moving from scattered AI activity to an operating model that survives the people who built it, including through fractional and interim leadership engagements structured around a defined handover. If you are weighing that route and want an independent view on scope, sequence, and whether you have someone to hand it to, we are happy to have that conversation.

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