Is artificial intelligence (AI) the new key to competitive advantage in pharma? You’d think so, based on the breathless coverage of advances and hopes in AI-powered drug discovery. The industry has been captivated by clinical trial milestones of AI-discovered drugs and the soaring valuations of companies in this space.

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But AI is software and software can quickly become a commodity. When every company is buying the same game-changing tools, those tools lose their ability to differentiate.

AI can help transform drug discovery, and we shouldn’t discount its potential to help patients. At the same time, life sciences companies must stay focused on the key ingredient of competitive advantage: their intellectual property. That means continuing to invest in research and development (R&D)—and the core technologies and processes that power that R&D across modalities.

AI won’t create an enduring advantage

AI is fast becoming ubiquitous in R&D, even at the largest pharmaceutical companies. Companies that brought the first AI-discovered drugs into clinical trials have gotten the most press coverage, but how long will that first-mover advantage last?

To date, AI is making it quicker to get drug candidates to the clinic, but it isn’t addressing the fundamental need to marry the right candidate and the right target to the right disease. Ultimately, AI is only as good as the data feeding it. For life sciences companies, that includes data from the entire R&D lifecycle: flow data, assay data, protein data, data across a wide variety of instrumentation, and any other data that capture scientists’ insights, experiments, successes, and failures. Pharma companies have always honed their competitive edge through creativity and ingenuity. AI won’t change that.

What’s more, AI won’t create any advantage for companies that are still struggling with the basics of data management. You can’t code around bad business practices. For AI to create differentiated value in the R&D process, the pharmaceutical industry needs to go back to the basics of data and digital transformation.

Pharma needs to keep closing the digital transformation gap

Pharma has been called a digital laggard by some compared to other industries. McKinsey reports that life sciences companies “trail cross-industry leaders in digital maturity by a factor of two to three times.”

That said, the industry made tremendous strides during the pandemic. Healthcare companies have increased their digital capabilities more than any industry except consumer goods since 2019.

Under pressure to deliver new treatments and to be multi-modal in their research, life sciences companies have broken down silos that impede data sharing and digital collaboration. They’ve established digitalization goals that cascade from the C-suite to the research lab. But they’re still struggling to capture, integrate, and analyze data throughout the R&D lifecycle.

For AI to provide a competitive edge, it needs to rest on a strong data foundation that is considerate of data volume, data management, and data quality. Pharma companies did the hard work of establishing a shared vision for digital—now they need to make that vision a reality so they can reap the benefits of AI.

They need to push each other and the vendors that support them toward use of technologies that ultimately support multi-modal research driving toward an AI future. AI can help create a competitive advantage and reduce the 10 years and $2.6 billion cost on average to bring therapies to market. All of this with an eye toward helping patients and users in need of these therapies to get them in an economically affordable way.

It’s about intellectual property, not AI

Intellectual property (IP) is a pharmaceutical company’s lifeblood. It represents the company’s unique knowledge, gained through decades of tireless work by researchers and scientists. The increasingly digital nature of the drug discovery process means that IP is now encoded in data.

Conversely, AI is or will be a commodity. Vendors are building exciting AI products on top of public knowledge. Their models make it easier to glean relevant insights from chemical structures and properties of molecules. These products push the starting point for research forward for every company that buys them.

AI only becomes a unique advantage when it’s deployed atop a foundation of proprietary data that is optimized for use in R&D. Optimization, in this context, refers to both technology and governance.

AI needs clean, standardized, organized data to produce usable outputs. In turn, companies need a strong data governance culture to ensure that the data produced and collected by different teams are consistent. But when computational models are used to design new drugs, the debates will ensue around who owns the IP whether it was derived from AI?

Clean data create a competitive advantage

PwC predicts that soon, “the ability to extract and manage value from data will significantly determine a biopharma company’s shareholder value.” Just as people learn from historical experience, so does AI. Data represent IP, but also represent the discoveries, insights, mistakes, and failures that occurred in the process of creating the data.

To gain a true competitive advantage through AI, pharma companies need to double down on data. When you own the data as part of the model-building exercise, the IP and ownership is less contestable. That means life science businesses need to invest in R&D and the data science that powers it so their scientists and researchers can freely experiment, learn, and iterate.

The pharma industry is undergoing a sea change that holds exciting promise for patients and providers. AI is set to make drug discovery faster and more cost-effective by augmenting scientists’ creativity, ingenuity, and hard work. Now it’s up to pharma to build a foundation on which AI-powered drug discovery can deliver on its potential and help its consumers have access quicker and in the most cost-effective way.

About the Author

Thomas Swalla, CEO of Dotmatics has spent his 25-year career building software businesses both organically and through mergers & acquisitions. Today, Thomas is CEO at Dotmatics leading a team of 800 mission-driven scientists and employees who are focused on helping scientists make the world a healthier, cleaner, safer place to live.