The Great Life Sciences AI Sprint: "Buy vs Build" in the Race for Innovation

The Life Sciences industry stands at a critical inflection point in its adoption of AI. While some companies are still debating infrastructure choices, others are already leveraging AI-driven insights to accelerate drug discovery, optimize clinical trials, and enhance commercial decision-making. The question isn’t whether AI will transform pharma—it already is. The real question is: Should companies build their own AI solutions from scratch or adopt commercially available ones?

The Pharma Paradox: Speed in Science, Slow in Systems

Pharma companies have demonstrated remarkable speed in advancing scientific breakthroughs—most notably in the rapid development of COVID-19 vaccines. However, internal technology development often follows a very different trajectory. From my personal experience, large-scale IT projects, particularly those involving AI, can take years to implement and, in many cases, become obsolete before they are fully deployed.

Learning from Other Industries

The tech industry has long recognized the pitfalls of trying to build everything in-house. Even companies with vast resources, like Apple and Google, frequently work with and acquire AI startups instead of developing solutions internally.

The rationale is simple:speed to implementation often outweighs the theoretical benefits of custom-built solutions.

Similarly, financial services—a highly regulated industry like pharma—has embraced a hybrid approach, distinguishing between core technologies (those that create unique competitive advantages) and context technologies (those necessary but not unique). Most firms buy the latter and focus internal development on the former.

The Talent and Cost Challenge

Developing AI solutions internally requires rare, highly specialized talent. Data scientists and AI engineers are in high demand, often drawn to the fast-paced, high-compensation world of tech. Recruiting and retaining such expertise in a pharma setting is not only costly but also difficult given the competitive market.

In contrast, commercial AI vendors already have dedicated teams of specialists working on solutions tailored for pharma. By adopting their technologies, companies gain access to this expertise without the need for extensive internal hiring and retention efforts.

Regulatory Considerations: A Managed Risk

Another frequently cited reason for building AI in-house is compliance with regulatory requirements. However, leading AI vendors serving the pharmaceutical industry are well-versed in these challenges. Many have already built solutions with regulatory compliance, validation, and auditability in mind, often providing extensive documentation to support regulatory approvals.

Developing AI systems internally not only requires technical expertise but also deep regulatory knowledge—another factor that increases time and cost.

When Does Building Make Sense?

To be sure, there are cases where building an internal AI solution is justified. If an AI application is deeply embedded in a company’s proprietary scientific processes—one that provides a true competitive differentiator—then internal development may be worth the investment.

However, this should be the exception rather than the rule. In most cases, the greatest advantage comes from focusing internal efforts on scientific and strategic innovations while leveraging external AI solutions for technological advancements.

A Smarter Approach: Test, Learn, Scale

Instead of a rigid “build versus buy” decision, pharma companies can adopt a more agile approach:

Start with commercially available AI solutionst o gain immediate benefits and insights.

Assess the impactand identify areas where deeper customization or proprietary development may add value.

Scale intelligently, focusing internal resources on areas that provide true competitive differentiation while leveraging external solutions for broader applications.

The Bottom Line: Innovation at Speed

In an industry where speed to market can determine the success of a drug, the ability to rapidly implement AI solutions is a critical competitive advantage. Companies that strategically integrate commercial AI solutions can move faster, focus on their core strengths, and ultimately bring life-changing therapies to patients more efficiently.