The Enterprise AI Mandate Just Became Your Problem
Enterprise AI strategies get built in boardrooms. They land in functions. By the time the mandate reaches you — the VP of portfolio planning, the head of medical affairs, the SVP of regulatory operations — it's usually a set of bullet points in an enterprise deck and an expectation that you'll figure out what it means for your department.
A lot of our work right now is with life sciences functional leaders sitting in exactly this moment. They don’t need a bigger AI strategy. They need to know where to start — and, just as importantly, what to avoid — in the first stretch after the mandate lands.
The two wrong starts
Most functional leaders default to one of two moves when the mandate lands. Both create activity, but neither defines what the function is actually trying to become.
The pilot race
The instinct is understandable: the mandate lands, the pressure is real, and the most natural response is to run a pilot that demonstrates momentum. Automated literature review, AI-assisted signal detection, predictive analytics for site selection. The pilot itself usually works and delivers the output it was scoped to deliver.
But delivering output and changing how work gets done are two different things. In most cases, the insights from the pilot never flow into decision-making, the surrounding workflows stay the same, and when the pilot wraps up, the result is a slide in a deck rather than a change in how the function works.
The reason pilots stall is rarely technical. It’s that they were never designed to connect to the operating model in the first place. They were designed to exist, to demonstrate activity, and on those terms, they succeed. The problem is that activity is not the same thing as adoption, and most organizations eventually recognize the difference only after several rounds of promising experiments that didn’t stick.
The platform hunt
The other common default is to evaluate AI tools before deciding what those tools actually need to do. Vendors get invited in. Demos get scheduled. A comparison matrix starts taking shape with features, pricing, and integration paths.
This feels rigorous, and at the surface level it is. But it answers the wrong question first. A platform evaluation without a clear picture of which workflows change, which decisions get faster, and which capabilities the team needs is a catalog exercise. It produces a vendor recommendation. It does not produce a plan. The deeper issue is that the platform hunt gives functional leaders something tangible to point to when leadership asks for progress, which makes it feel productive even when it is not moving the function forward in a meaningful way. The vendor gets selected, the integration timeline gets mapped, and the fundamental question of how the function will operate differently remains unanswered.
What actually works
The functional leaders we have worked with who have made real progress share three moves in common.
Move 1: Start by naming the decisions, not the tools
Before evaluating a single platform, the more productive question is a different one entirely: in your function, which decisions take the longest? Which require the most manual effort? Which get made with incomplete information, or later than they should be?
In portfolio planning, it might be the time between data availability and go/no-go decisions on individual programs. In medical affairs, it might be the cycle of turning external literature into positions the organization can support. In regulatory operations, it might be the review and comment loop on submissions that ties up senior people for weeks.
Those decisions are where AI creates value. Not the tools themselves, but the decisions the tools are meant to improve. Once a functional leader has named the two or three decisions that matter most, the technology conversation becomes dramatically simpler, because the team knows what it is actually solving for. And the resulting plan has a logic that leadership can follow, because it starts with an operational pain that everyone in the room already recognizes.
Move 2: Define what “integrated” actually means for your function
The word “integrated” gets used so loosely in enterprise AI conversations that it has become almost meaningless. Every strategy promises integration. Very few define what integration actually requires of the function it is landing in.
This is the piece that most AI strategies skip. They describe the outputs they expect: faster insights, better decisions, reduced manual effort — without ever defining the operating model that produces those outputs. The function is told to adopt AI without being shown what the function looks like once it has.
The leaders we’ve worked with who make real progress build a future state operating model before they build anything else. Specifically, this means: which workflows change, which decision rights shift, what skills the team needs that it does not have today, what’s preserved as-is because it’s already working. The common thread across every engagement where we have done this is that adoption only moves once this picture exists.
Without a future state operating model, pilots are random. With it, pilots are deliberate first steps toward a defined future state.
Move 3: Scope something defensible, not something perfect
The third trap is scope inflation. Once a functional leader starts thinking seriously about AI across their department, it’s easy to end up with a 12- or 18-month transformation plan that spans every workflow in the function. That plan reads well in a slide. It rarely survives the first executive review, and it almost never survives contact with the team that has to execute it.
The better move is narrower and shorter. Identify the two or three workflows where AI creates the most immediate, visible relief, where the manual effort is highest, the pattern is repeatable, and the benefit is something everyone in the department already recognizes.
Scope the engagement long enough to do the thinking properly, with real analysis and recommendations that will hold up when someone in the room pushes back. But keep it short enough to survive executive attention spans and budget cycles. Build it to flex as the AI landscape shifts underneath it, because it will. The result should be something the team can work toward week by week, not a document that gets revisited once a quarter.
This is not about thinking small. It is about choosing where to prove the model first. In a highly regulated, risk-aware industry, people need to see AI working inside their own workflows, on their own data, within their own constraints, before they will trust it with the work that carries the highest stakes.
What the mandate is actually asking for
When the enterprise AI strategy lands in your function, it’s not asking for a technology plan. It’s asking for a functional operating model that accounts for AI — specific to your department, realistic about your constraints, and designed to be adopted, not just presented.
That’s a strategic planning problem, not a technology problem, and it’s solvable on a timeline that matches the pressure most functional leaders are under right now.
The leaders who get this right do not wait for the enterprise strategy to solve their functional problem for them. They build the functional plan themselves. Narrow enough to be credible and grounded in how the department actually operates. Anchored to decisions and workflows that matter, not to platforms and vendor roadmaps. That is the plan that survives the executive review. It is also the one that teams actually adopt, because it answers the question people have been asking since the mandate landed: what does this mean for how I do my work?
These mandates are not going away. If anything, they are intensifying as AI investment in life sciences accelerates. The functional leaders who move first will not be the ones with the biggest budgets or the most advanced tools. They will be the ones who did the harder, less glamorous work of defining how their function actually operates differently, and then built a plan specific enough to execute against.
If your function is navigating the gap between an enterprise AI mandate and what it actually means for how your department operates, our AI Integration Roadmap is designed for exactly this moment. In 6–8 weeks, we help VP/SVP functional leaders build a concrete integration plan — grounded in how your department actually works, and designed to be adopted, not just presented. Learn more at nepf.co/ai-lifesciences.