Across the life science industry, AI is everywhere in strategy decks and nowhere in the P&L. Senior leadership teams can point to dozens of pilots, but struggle to name more than a handful of AI initiatives that changed a major decision, created a new capability, or shifted the trajectory of a portfolio. At the same time, boards and investors are asking a sharper question than before: Where is the evidence that these AI investments are building durable advantage, not just cost and complexity?

Context and stakes

The industry is not suffering from a lack of experimentation. Surveys of enterprise AI adoption suggest that roughly 95 percent of pilots fail to deliver measurable business impact or to scale beyond initial trials, often because they are not anchored in clear strategic questions or integrated into real workflows. In parallel, research on innovation performance across sectors finds that top performers treat innovation as a focused, strategy‑aligned portfolio, not as a long list of disconnected projects.^[1][2]

Life science companies are experiencing a similar split. On one side are organizations caught in what some commentators call AI pilot purgatory a state where many proofs of concept are active, but few have clear owners, metrics, or paths to scale. On the other are companies that pick a small number of high‑leverage use cases, design them so that they both solve real problems and build reusable capabilities, and then treat those use cases as building blocks in a broader AI program.

The difference is not simply one of budget or technology. It is a question of how leaders design the path from individual initiatives to enterprise‑level capabilities.

From AI programs to high‑leverage use cases

Many life science organizations have framed AI as a program. The vision slides describe AI supporting target identification, trial design, safety surveillance, and customer engagement across the value chain. In principle, this is correct. In practice, starting from the program level often leads to diffuse investment and slow progress.

An alternative is to treat AI programs as the outcome of a sequence of deliberately chosen use cases, each designed to do three things

In this framing, a high‑leverage use case is not simply a task where AI can be applied. It is a specific combination of problem, data, and workflow where success demonstrably reduces uncertainty or unlocks value, and where the assets created can support future initiatives.

Three patterns illustrate how this can work in practice:

Pattern 1: Focused trial simulation as a proving ground

Sanofi’s decision to invest in AI‑driven trial simulation through partnerships such as QuantHealth is one example of a high‑leverage use case. Instead of treating AI as a generic R&D accelerator, Sanofi has backed an application that tests trial protocols, enrollment assumptions, and outcome scenarios before a study starts. Early analyses suggest that this type of simulation can reduce trial timelines and costs by challenging underperforming designs before they are executed.^[3]

The immediate benefit is a more disciplined approach to trial design for selected assets. The longer‑term value is a set of models, data pipelines, and governance mechanisms that can be extended to other programs. Over time, trial simulation can evolve from a project used on a few studies to a standard input for protocol decisions across the development portfolio.

For leaders, the lesson is that a well‑chosen use case can create a new expectation of how decisions are made. Once teams experience the benefits of simulation for one set of trials, it becomes difficult to return to purely intuition‑driven design elsewhere.

Pattern 2: Rare disease as a strategic laboratory

Rare disease is often treated as a strategic exception important, but structurally constrained by small populations, fragmented data, and complex evidence requirements. Emerging AI applications are beginning to change that calculus. In rare disease, AI‑enabled methods for identifying undiagnosed patients, constructing synthetic control arms, and extracting signal from unstructured clinical notes are not optional enhancements they are often the only way to make programs feasible.^[4]

Because patient numbers are low and data are messy, rare disease forces organizations to innovate in methodology. Techniques such as synthetic control arms built from historical and real‑world data allow companies to reduce or avoid placebo enrollment while maintaining evidentiary standards. Natural language processing pipelines that extract phenotypic patterns from clinician notes, case reports, and patient narratives help surface patients who would otherwise remain invisible.^[4]

These solutions address pressing needs for rare populations. More importantly, they create capabilities that apply far beyond rare indications.

Once an organization has built

it can redeploy those capabilities in oncology, immunology, and other areas where cohorts are increasingly defined by biomarkers and subphenotypes rather than broad labels. Rare disease becomes a strategic laboratory where high‑leverage AI use cases are incubated, tested under pressure, and then extended to the broader portfolio.^[4]

Pattern 3: Infrastructure that follows use cases instead of leading them

Another emerging pattern is visible in moves like Roche’s decision to build a large NVIDIA‑powered AI factory that supports discovery, development, manufacturing, and diagnostics. The risk with infrastructure‑heavy approaches is that they precede clarity on use cases, leading to underused capacity.^[5]

Roche’s public communications suggest a different path. The company has tied infrastructure to specific, high‑leverage applications Lab‑in‑the‑Loop models in discovery, digital twins in manufacturing, accelerated genomics and digital pathology, and health‑care‑grade conversational AI. Each application provides a clear justification for the required compute and data pipelines. At the same time, by designing these applications to share common components, Roche is constructing a capability stack that subsequent use cases can build upon.^[5]

The sequence is important. Instead of asking, “What infrastructure do we need for AI?”, the better question is, “Which high‑leverage use cases justify shared infrastructure, and how can we design them so that new teams can reuse what we build?”.

A simple lens for selecting high‑leverage use cases

For senior leaders deciding where to focus, a practical lens can help distinguish high‑leverage use cases from experiments that are unlikely to scale. One way to do this is to ask four questions for each candidate initiative

1.      Strategic relevance

2.      Capability spillover

3.      Evidence and trust

4.      Operational fit

Use cases that rate strongly across all four dimensions are good candidates for early focus. Initiatives that are interesting technically but weak on strategic relevance, spillover, or operational fit may be better framed as exploratory research, with bounded scope and expectations.

Avoiding AI pilot purgatory

The temptation to explore widely is understandable. AI is evolving quickly, vendors are persuasive, and internal teams are curious. However, the combination of broad exploration and limited governance is what produces AI pilot purgatory a landscape of half‑finished experiments that erode internal trust and external credibility.

Research on innovation portfolios suggests that organizations that regularly review and rebalance their portfolios, pruning projects that no longer align with strategy and doubling down on those that do, see stronger performance over time. Applied to AI in life science, this implies three concrete disciplines^[2]

Over time, this approach turns AI investments from isolated experiments into a coherent capability stack.

Implications for senior life science leaders

For executives responsible for strategy, portfolios, and cross‑functional execution, the core questions are no longer about whether AI matters. They are about where to focus, in what sequence, and how to ensure that each step builds toward a more capable organization rather than adding friction.

Three practical questions can anchor leadership discussions:

Answering these questions does not eliminate uncertainty. It does, however, replace AI pilot purgatory with a path.

Conclusion: choosing the path, not just the destination

Most life science companies share a similar vision for AI a future where development is faster, evidence is richer, safety surveillance is sharper, and engagement is more tailored. The difference between organizations will not be the vision itself, but the path they choose to get there.

Enterprises that rely on diffuse experimentation are likely to continue accumulating pilots without building durable capabilities. Those that identify high‑leverage use cases, design them for spillover, and manage them as part of a focused portfolio are more likely to turn AI from a set of tools into an engine for strategic advantage.

The destination may be shared. The discipline of the path will not be.

References