Investing in AI and Life Sciences: Topline Considerations for Investors in 2025

Investing in AI and Life Sciences: Topline Considerations for Investors in 2025

Innovation meets opportunity

Artificial intelligence (AI) is rapidly transforming the life sciences industry, offering new pathways to accelerate drug discovery, personalize care and streamline clinical operations. Investors are increasingly drawn to the sector’s potential to unlock value across a five-trillion-dollar healthcare economy. The convergence of massive datasets, maturing AI tools and growing enterprise adoption has created fertile ground for innovation.

From clinical documentation to molecular design, AI is being deployed to solve problems that were previously too complex or resource-intensive to tackle. Yet, distinguishing real breakthroughs from hype remains essential. Companies that demonstrate a clear understanding of where AI plays a role in their ecosystem, product-market fit, proprietary data advantages and measurable return on investment are best positioned to scale. The most promising ventures are those that not only leverage AI to improve outcomes but also integrate seamlessly into existing workflows, creating tangible value for clinicians, patients and payers alike.

At the same time, the market is seeing a surge in valuations for AI-enabled life sciences companies, often driven by aggressive growth projections and investor enthusiasm. While this reflects the sector’s potential, it also underscores the importance of disciplined diligence. Investors must look beyond the buzzwords and assess whether a company has the infrastructure, partnerships and strategic clarity to deliver on its promises. The ability to land and expand, starting with a focused use case and growing into adjacent opportunities, is a hallmark of scalable success in this space. Companies that can demonstrate early traction and a clear roadmap for growth are more likely to sustain momentum and deliver long-term value.

Regulatory and IP strategy: Build early, align often

A critical consideration for investors is whether a company’s AI solution falls under US Food and Drug Administration (FDA) regulation. This depends on its intended use. If the technology is designed to mitigate, prevent, treat, cure or diagnose a disease or condition, or affect the structure or any function of the body, it may be classified as a medical device. Early-stage companies are best positioned when they proactively define their regulatory strategy and anticipate future touchpoints with the FDA. The agency is evolving its approach, including frameworks like the Predetermined Change Control Plan (PCCP), but regulatory clarity remains a moving target. Companies must be prepared to demonstrate how their models are trained, validated and updated, especially as new data becomes available. The FDA is increasingly focused on transparency, reproducibility and trustworthiness – factors that are essential for market approval and public confidence.

Equally important is intellectual property protection. Investors should assess whether a company’s innovations are safeguarded through patents, trade secrets or licensing agreements, and ideally combinations of the three. This includes evaluating ownership of training data, model outputs and any third-party software integrations. A robust IP strategy should align with the company’s commercialization goals and be scalable as the product matures. For example, if a company is generating novel molecules using proprietary algorithms, patent protection may focus on the molecules themselves rather than the underlying code. Conversely, if the value lies in a unique data abstraction method or workflow optimization tool, trade secret protection and strong contractual safeguards may be more appropriate. IP and regulatory strategies must be synchronized to avoid misalignment. What a company claims in its patents should not contradict what it presents to regulators. These foundational elements are essential not only for risk mitigation but also for long-term scalability and investor confidence.

Scaling with purpose

As AI continues to permeate life sciences, the path to scale is increasingly defined by strategic partnerships and thoughtful product design. Successful ventures often collaborate with health systems, payers and biopharma companies to access proprietary data, validate their models and integrate into clinical workflows. These partnerships are not just about distribution; they’re about co-development, trust-building and demonstrating real-world impact. Investors should look for companies that understand their end users deeply and design products that solve specific problems rather than offer generic platforms. Investors should also look for counsel who understand how to assess the offerings.

Looking ahead, AI in the life sciences industry has the potential to fundamentally reshape healthcare, shifting from reactive treatment to predictive, personalized care. We are entering an era where digital therapeutics and fully digitized healthcare operations are becoming viable. The most successful companies will be those that combine technical innovation with thoughtful regulatory and IP planning, strong partnerships and a clear understanding of their product’s value. As the industry matures, we expect to see an increasing shift toward proactive health management, where AI tools help individuals stay healthy rather than simply treating illness. This transformation will require not only technological breakthroughs but also cultural shifts, regulatory evolution and sustained investment in infrastructure and talent.

For more information, or to discuss how these insights apply to your investment strategy, please contact your Cooley life sciences transactions team or Cooley life sciences marketing.