AI in biotech and pharma: building the foundations for long-term value

2026 is a turning point at which AI investments must prove the tangible, measurable value for pharma and biotech companies. With increasing pressure from boards and investors, leaders need to demonstrate financial and operational returns while moving beyond pilots to scalable, value-generating systems embedded in existing R&D workflows.

AI is already delivering measurable ROI in biotech and pharma, and the fastest wins tend to come from unglamorous places: automating regulatory documentation, streamlining clinical trial reporting, or cleaning up the data pipelines that feed downstream research.

These are not the headline-grabbing use cases, but they are the ones that pass internal audits, satisfy compliance teams, and produce numbers a CFO can sign off on. This pattern is consistent with what we actually see when working with healthcare and life sciences clients.

Yet, with comprehensive validation processes, regulatory approval, and lengthy testing cycles, it can take months or even years for the product to reach patients or researchers compared to other industries.

Therefore, the question is no longer whether AI has any potential – because it obviously does. What pharma and biotech leaders should ask instead is: Are we ready to make AI part of our workflows and build the governance and foundations needed to deliver tangible value?

Why does AI ROI depend less on models and more on foundations?

The discussion often centers on models – their capabilities, accuracy, or the latest advancements in generative AI. And when AI initiatives underperform, the blame is usually on technology. Yet, in many cases, the problem sits somewhere else: organisations are not fully prepared to embrace AI with inaccessible data, unclear ownership, or an operating model that wasn’t designed for continued use.

That’s why it all starts with the right foundations. Leaders have a wide range of AI tools to choose from to streamline workflows, accelerate processes, or boost performance. But the key difference lies in being prepared to integrate these tools. In fact, organisations that take the time to establish a clear AI strategy and use cases can eventually get the most value from it. But still, only a small fraction of pharma companies actually invest time in setting the stage for AI – and that’s the readiness gap leaders need to address before scaling investment.

Before implementing AI, pharma and biotech leaders should answer some key questions:

• Is our data reliable and accessible, or is it stored across multiple systems?
• What measurable outcome should the AI use case improve?
• How will we establish whether the result justifies continued investment?
• Who has access to sensitive information, and how is it controlled?
• Are security safeguards and compliance requirements built into the solution from the start?

AI acts as an amplifier. If your systems are structured and well-organised, AI will accelerate value creation. But if they resemble a maze of outdated, disconnected systems with siloed information, AI will amplify this complexity rather than solve it. That’s why preparing foundations is the critical first step when it comes to AI.

What data foundations are required to unlock scalable AI in pharma and biotech?

Data fuels AI, but it can also become a limiting factor. Even the most advanced AI models can struggle to generate reliable outcomes when built on fragmented, inconsistent, or poorly governed data. As AI adoption grows across biotech and pharma, companies should shift their focus from model selection to data readiness.

Lack of data is no longer a problem; the challenge lies in managing it across disconnected systems. Clinical datasets, laboratory results, genomic data, real-world evidence, and operational information often live in separate environments. Therefore, without a clear strategy for integration and interoperability, AI efforts risk becoming isolated point solutions rather than scalable capabilities.

But data quality is just one part of the equation. What’s also crucial is a clear understanding of data ownership, lineage, and provenance - where the data comes from, how it has been processed, and whether it can be trusted for decision-making. Particularly in highly regulated environments, organisations must validate both the insights generated by AI and the integrity of the underlying data.

Because AI readiness starts with data readiness. Companies that support connected, well-governed data ecosystems will be far better positioned to scale AI across their R&D workflows and operations than those focused solely on adopting the latest models.

Can legacy systems and architecture limit AI scalability in R&D?

In biotech and pharma, data is often scattered across various complex, outdated systems – ranging from laboratory information management systems (LIMS) and EHRs to clinical trial platforms and custom-built applications. These often operate in isolation, use different data standards, or lack the APIs needed for efficient AI integration. Consequently, organisations often end up investing more effort in connecting and preparing data than in building or deploying AI solutions.

This is why AI implementation isn’t just a technology project limited to IT systems but an organisation-wide transformation. Before rolling out new AI models, organisations need to assess whether their existing infrastructure can support secure data flows, ensure interoperability, and enable scalable deployment across teams and functions.

In some environments, targeted modernisation, a governed integration layer, or a shared data service may create more reusable value than another isolated pilot. The right approach depends on the use case, the condition of the existing systems, and the level of change the organisation can absorb. Ultimately, AI is only as scalable as the systems it operates within.

Why governance is becoming a core driver of AI ROI

Governance is one of the key factors that determines whether an AI initiative can scale. It makes AI trustworthy, repeatable, and suitable for use in regulated environments.

At its core, AI governance is about creating visibility and control. Pharma and biotech leaders need to know what data a model uses, how it was trained or configured, who can access it, and how its outputs are monitored over time. Equally important is clear documentation and audit trails to support this transparency. In many use cases, especially in research or clinical decision-making, teams should be able to trace how a specific output was generated. The system should also demonstrate that it performs within defined and acceptable parameters, including known edge cases and failure scenarios.

This level of oversight is becoming increasingly important as regulatory demands continue to evolve. Whether driven by internal quality standards or external requirements, organisations need mechanisms for continuous monitoring, validation, and human oversight – not only to mitigate risk, but also to build confidence in AI systems across scientific, operational, and compliance teams.

Strong governance doesn't limit innovation; it creates the conditions that allow AI to move from isolated pilots to enterprise-wide adoption.

How should biotech and pharma companies manage IP, data ownership, and AI partnerships?

As AI partnerships between healthcare organisations and technology vendors increase, intellectual property and data ownership have become strategic concerns. Defining who owns training data and AI-generated outputs, how patient data can be used, or whether vendors can train models on customer information becomes a key differentiator among AI providers.

This is especially important in pharma and biotech, where sensitive research data, proprietary methodologies, patient information, and IP often represent years of investment and competitive advantage.

But it’s also important to consider when evaluating AI models from big players like OpenAI, Anthropic, or Google. All offer healthcare-specific solutions: ChatGPT Health, Claude for Life Sciences (recently, Anthropic released another one for researchers and scientists – Claude Science), and MedGemma. Each one varies in whom this specific solution is for and how it can be used. Therefore, before choosing the model, leaders need to evaluate not only their needs and product specifications. The question is how these models use consumer data, what the implementation and deployment options are, and who’s actually responsible for the outputs.

At the same time, the regulatory landscape in the United States is evolving and remains highly fragmented. Organisations need to navigate a combination of federal-level regulations, state laws and their AI bills, and evolving AI guidance. This creates a patchwork system that adds even more complexity for organisations operating across multiple jurisdictions.

In contrast, Europe is taking a more structured and proactive approach. Under the EU AI Act, some pharma, biotech, and healthcare applications may be classified as “high-risk” depending on their intended purpose. For these, the act can include strict requirements around transparency, data quality, human oversight, and risk management. With other regulations, such as GDPR or the European Health Data Space (EHDS), software vendors across Europe have to design their products with regulatory compliance in mind from the very start.

For many companies, this means AI adoption can no longer be treated as a standalone technology project. Governance, compliance, vendor accountability, and data ownership increasingly shape whether AI systems can be deployed safely and scaled inside real healthcare settings.

Long-term value comes from system transformation, not tool adoption

While innovations in healthcare-related sectors are slower than in other industries, AI usage is increasing rapidly among professionals. But when adopting these technologies, leaders should focus on the tangible impact on operations and the value they provide to researchers and patients.

Artificial intelligence is already generating meaningful returns in biotech and pharma, especially through operational efficiency or data processing. But this transformation will take some time. Innovation is one thing, staying compliant is another.

What I often repeat is that, as it stands now, AI is just a tool that can amplify various processes. But to generate long-term value, pharma and biotech companies need to invest in the data, governance, and system architecture needed to use them safely at scale.