Building the Pharma Business Case: Scale AI Out of Pilot Into Production
Article
In the first installment of this series, we looked at why so many AI projects in life sciences stall before they start: scattered strategy, missing governance and fragmented data. But strong foundations only earn you the right to face the next hurdle, turning ambition into a business case that survives contact with production.
77%
of pharma and life sciences leaders say they are still between proof of concept and scalable production.
20%
of life sciences organizations consistently deploy AI at scale and achieve measurable, repeatable value.
The industry is rich in experimentation but lacks in scaling.
You learn from experimentation, but you harness real value at
the delivery & operations stages.
Successful AI initiatives within pharma and biotech hinge on three areas:
01
Identifying value and building the right frameworks
02
Crafting — and implementing — a winning business case
03
Quantifying value and scaling success
Here we examine the second pillar: building and implementing a winning business case. We look at where AI creates real impact across the pharma value chain and the platforms that turn individual wins into scalable capability. Below, we outline five practices that separate the use cases that reach production from the ones stuck in pilot purgatory.
THE NUMBERS
30–50%
AI-driven selection improves the identification of top-enrolling clinical trial sites by 30% to 50% and accelerates enrollment by 10% to 15% or more.
40%
GenAI-assisted medical writing can cut the time to author a clinical study report by roughly 40%.
18-25%
Predictive maintenance using traditional machine learning cuts unplanned machine downtime by 30% to 50% and slashes maintenance costs by 18% to 25%, a repeatable task where classic AI beats GenAI.
THE PROBLEM
Two decisions determine whether an AI project earns its keep. The first is which use case to pursue. Opportunities sit at every step of the value chain, from target identification in discovery to site selection in trials, report generation in regulatory and predictive maintenance in manufacturing. Faced with hundreds of options, many companies scatter budget or chase whatever a competitor announced.
The second decision is which kind of AI to deploy. AI is a family of techniques, not a monolith. Deterministic automation handles high-volume, rules-based work — document routing, data formatting, compliance checks — with speed, consistency and full auditability. Classic machine learning finds patterns in structured data; it's the right tool for predictive maintenance, site selection and demand forecasting.
Generative AI excels at cognitive, language-heavy tasks: synthesizing a decade of research files, drafting a clinical study report, generating regulatory narratives. However, the value of these technologies depends on the problem they're being asked to solve. A hallucination that helps spark a new hypothesis in drug discovery may be useful, while the same behavior in clinical reporting could have serious consequences. The best approach depends on factors such as data quality, business objectives and tolerance for risk, all of which influence whether deterministic automation, machine learning, generative AI or agentic systems are the most appropriate choice.
Reaching for the most familiar or most talked-about technique means paying a premium for a worse fit.
THE IMPERATIVE
Successful Companies:
Map candidate use cases across the full value chain before committing budget and prioritize the few where expected impact, data readiness and organizational fit intersect.
Match the technique to the problem, the data, the desired outcome and the organization's tolerance for error: deterministic automation where consistency and auditability matter, machine learning where structured prediction or classification creates value, and generative AI or agentic systems where reasoning, synthesis, ambiguity handling or hypothesis generation matter most.
Default to the least powerful model that gets the job done. GenAI's non-deterministic output and compute costs are a poor fit for tasks where classic ML is cheaper, faster and reproducible.
Weigh total cost, including compute, monitoring and human oversight, against the value of the task before committing. Strong AI governance ensures spending stays predictable and aligned with business value.
THE NUMBERS
76.5%
More than three in four pharma and life sciences leaders say they are still between proof of concept and scalable production, indicating that the governance, delivery practices and infrastructure needed for scale remain a work in progress for many organizations.
24%
of AI proofs-of-concept are in production at pharmaceutical companies, lagging healthcare provider use cases at 35%.
40%+
of agentic AI initiatives are expected to be canceled by the end of 2027 due to unclear business value and rising costs.
THE PROBLEM
Two development patterns dominate the industry, and both often lead to failed programs. At one extreme sits small, targeted proofs-of-concept. While safe, cheap and easy to fund, they are often too modest in scope to move the needle. When you try to scale them without first establishing the foundations — governance, data, delivery practices — you spend more time and effort than the AI solution ever saved. At the other end sits large rollouts aimed at a company’s workforce at large, proving to be not specific enough to help any one team significantly. When costs climb and results lag, leadership support erodes. One high-profile failure can chill AI interest for years, which is why preserving momentum requires the right mix of project types, not betting everything on a single approach.
What separates the projects that survive is what they are anchored to. Most AI initiatives start from the technology. Successful ones start from the process. Before you codify work with AI, you need to understand it — you can't explain what you haven't understood, and you can't codify what you haven't explained. Technology is part of the equation, but the real work is understanding how work actually gets done and how it needs to change. A capable tool finds a sponsor, a pilot gets built and the business case is tacked on at the end, with everyone hoping the numbers work out. They usually don’t.
The team turns out to be quicker without the tool. Adoption takes time — force of habit, workflow friction, exception handling and the tacit knowledge that never made it into formal process documentation all pull teams back to what they know, especially without clear guidance on why the tool exists and how to use it. The risk runs deeper when AI shifts people from producing work to merely verifying it: skills erode, exception handling weakens and the organization grows more fragile than the efficiency case first suggested.
Marginal gains vanish behind an extra login, or inference and integration costs mount after launch. GenAI applications also drift; without monitoring, output quality degrades as usage grows.
Unexamined foundations do the deepest damage. AI punishes poor past investments in data and infrastructure. It exposes whether data is accessible across domains, well-understood, embedded in core processes and consistently defined across teams. The groundwork consumes more time and money than the build itself, but skipping it means discovering all your gaps at once — expensively.
THE IMPERATIVE
Successful Companies:
Run focused proofs-of-concept to learn the process and surface hidden costs. At the same time, align them with a clear vision for where AI should take the business, so every pilot contributes to the broader strategy and helps reveal what needs to be fixed.
Define the business outcome
first, and design the solution backward from it.
Model each use case’s financial impact, build a self-sustaining ROI model where early wins fund the next wave, and keep monitoring for drift after launch.
THE NUMBERS
68%
Pharma and biotech companies account for over two-thirds of AI drug-discovery adoption among end users, ahead of academic and research institutes.
THE PROBLEM
There is no AI strategy without a data or platform strategy. AI opens a new consumption layer on top of your existing infrastructure, and it punishes organizations that haven't aligned their data ahead of time faster than any manual process would. Semantic consistency matters just as much as access. If commercial, R&D, clinical and IT do not mean the same thing when they say “patient,” the model will expose that gap faster than any manual workflow did.
Setting up every use case from scratch just leads to longer timelines and bigger price tags. Build it on a platform, and every use case after it inherits the setup, so each solution ships faster and cheaper than the last. What matters is how the platform gets used, not just what it is: focus on ease of use, clear benefits and consistent measurement of actual adoption and usage.
That compounding return is the strongest business-case argument available, but it only accrues to organizations that keep building on the base. A platform with nothing meaningful built on top of it is just expensive. The common mistake is assembling one from whatever tools individual teams happen to use, standing it up and expecting value to follow. It doesn’t. Capability requirements come first, and the payoff arrives only as solutions accumulate on the foundation.
THE IMPERATIVE
Successful Companies:
Set a platform strategy before
the use-case count grows, defining the capabilities, compute and governance the whole portfolio
will draw on.
Treat the platform as a base layer and build reusable components on top of it. Employing standardized, shared assets eliminates repetitive proof-of-concept cycles.
Judge the platform the way you'd judge a product: adoption, ease of use, interoperability and whether each successive use case costs less to ship than the one before.
THE NUMBERS
80%
of pharma firms now use cloud in some form, yet most run on hybrid environments rather than cloud-native.
30%
Only 30% of pharma leaders plan to build proprietary AI platforms in-house.
40%
40% expect a blend of custom development and commercial solutions.
30%
30% are going external-first, relying on third-party platforms and vendors.
THE PROBLEM
Pharma companies need to decide two things before they set up their AI infrastructure: what to build versus buy, and how to maintain flexibility as the market evolves.
Compute and foundation models are best sourced from specialists — hyperscalers run compute cheaper than any pharma could, and only a few firms have the expertise to build foundation models. For a non-tech company, building AI from scratch is slow and rarely succeeds.
The exception is data and the high-value applications built on it. This is pharma's biggest advantage, and it stays close, under direct control. Most large players have adopted a partnership model, with pharma bringing the data and domain expertise while the partner supplies AI and engineering competence. The highest-profile deals cluster in research and clinical settings, where both capabilities are scarce.
Flexibility matters just as much. Vendors and models are increasingly decoupled from infrastructure — you can consume models from Anthropic, OpenAI, Google and others directly, via cloud providers (AWS, Azure, Google Cloud), through deployment platforms (Databricks, Hugging Face) or embedded in enterprise applications. That means your choice of model, vendor and hosting environment can evolve independently, a real advantage in a market where new capabilities appear monthly.
Where you run these workloads varies by sensitivity and criticality. Many pharma companies successfully run sensitive workloads in the cloud through compartmentalization, encryption and vendor partnerships built to meet regulatory requirements. A hybrid setup tends to work best: mission-critical systems stay on-premises or in tightly controlled cloud environments, while analytics and training scale in the cloud.
None of this works without governance — deployment choices only stay flexible when procurement, security, data access and accountability are designed into the operating model from the start. And that flexibility itself has a cost: you'll need to invest in the connectivity and interfaces that keep it working.
THE IMPERATIVE
Successful Companies:
Segment workloads by sensitivity, keeping critical data under direct control and running burst-scale training and analytics in the cloud. Define those boundaries explicitly through AI governance.
Default to proven external solutions for computing and models, reserve in-house builds for proprietary or mission-critical work, and keep
data and its applications internal, partnering where AI competence
is missing.
Preserve optionality without multiplying vendors unnecessarily — the ability to switch, integrate and govern providers cleanly matters more than how many you have. Invest early in the orchestration, interfaces and policy layer that make that flexibility real.
THE NUMBERS
$60–110B
GenAI could unlock between $60 and $110 billion a year in economic value for the pharma and medical-product industries.
$254B
in additional annual operating profit could accrue to pharma worldwide by 2030 with broad industrialization of AI.
THE PROBLEM
The real risk isn't picking the wrong model vendor — it's coupling model choice, tooling and operating model so tightly that every change becomes expensive. In a market where new capabilities appear constantly, that rigidity costs more each month than it saves.
A better path is to focus on harnesses and tooling that enable interoperability across multiple models and vendors. Different functional areas in pharma — clinical operations, research, manufacturing, regulatory affairs — have their own data types, risk tolerances and vendor preferences. The user base varies just as much. A researcher, a regulatory writer and a commercial leader each consume AI through different interfaces, at different cadences and for different types of decisions. Rather than mandating a single platform, successful organizations build an orchestration layer that connects these areas, letting each choose the right tools while maintaining coherence across the enterprise.
THE IMPERATIVE
Successful Companies:
Connect fragmented business unit systems with an orchestration layer; it creates far less disruption than company-wide re-platforming and achieves the same coherence.
Architect for interoperability so capabilities can be added, upgraded or retired without touching the base.
Watch the market and stay willing to adapt; agility is itself a platform requirement now.
CONTRIBUTORS
BERT
CARDOEN
Senior Consultant,
Advisory
DOMINIK
RIEDT
Senior Consultant,
Advisory
SASCHA
ROSENBERG
Senior Consultant,
Advisory
DR. MAXIM
POLIKARPOV
Senior Manager,
Advisory