AI in life sciences: Seizing part of a trillion-dollar opportunity

Our research shows AI could fuel over $1 trillion in economic growth, and life sciences companies have a prime opportunity to capture their share by acting now to turn AI into a growth engine—before the competition does.

AI has found a home in the life sciences industry. In the last decade, AI-native biotech companies have entered 75 AI-discovered molecules into the clinic, with 67 in clinical trials as of 2023. On the commercial side, leading organizations are using generative AI platforms for marketing content as well as fact-checking and legal reviews.

In short, artificial intelligence is intelligently automating tasks that don’t require human expertise, augmenting those that do and allowing skilled professionals to achieve different and better results through new ways of working.

Yet much of the life sciences industry sees AI primarily as a cost-cutting vehicle. That perspective is limiting opportunities for competitive differentiation and improved health outcomes. As AI tools free up time and resources by handling mundane and rote tasks, organizations that want to generate new business growth from these capabilities must be bold. They need to rethink how work can be done. AI-enhanced productivity can enable life sciences companies to reimagine and transform their clinical and commercial models for greater growth. Cost reduction and productivity gains will follow.

Key life sciences jobs will become growth engines

The exposure of jobs to AI across all industries is a key reason we expect gen AI to add $477 billion to $1 trillion-plus to the US GDP by 2032. That calculation is based on key findings from our “New work, new world” research. We analyzed 1,000 professions to understand how different jobs, including many across the life sciences value chain (see below), could be automated or assisted by generative AI by 2032.

Source: Cognizant and Oxford Economics
Figure 1

To better understand how generative AI for life sciences can drive business growth via these positions, here’s a closer look at AI’s potential impact on just two critical industry jobs when the focus is on how the work is done, rather than reducing headcount.

AI-augmented sales representatives

Generative AI connects a physician’s concerns, prescribing behavior and information consumed across social platforms to provide a coherent summary of what matters to that physician. First, a gen AI tool can review physician prescribing patterns and account histories to identify which physicians the representative should prioritize. Next, it can quickly summarize the representative’s past interactions with those physicians, highlighting the providers’ primary concerns and their communication preferences. Those insights enable sales representatives to personalize every interaction, giving their best prospects the specific content that will persuade them a therapy is valuable.

That ability to make individualized, meaningful connections with one person across multiple channels via generative AI will help the industry evolve toward an influencer marketing model. This model should be especially effective as AI-augmented R&D and clinical trials enable life sciences companies to shift toward more targeted therapies.

AI-augmented clinical trial manager

Generative AI for life sciences will enable companies to launch new products more quickly, yet safely, by powering a faster, more iterative and adaptive approach to clinical trials. Clinical trial managers will use AI tools to design trial protocols and manage trial enrollment and monitoring more effectively. Gen AI will identify new proteins and molecules more quickly and make it easier to identify and evaluate secondary applications for existing therapies and/or the efficacy of drug combinations. Enhanced discovery capabilities plus more efficient trial management will enable life sciences companies to create strategies for entering highly targeted markets and diversifying revenue streams.

Add the ability to market cost effectively to small and even individual audiences, and life sciences companies can become much nimbler and less reliant on a few large-market therapies.

How to implement generative AI for business growth

With major technology players building AI functionality into their popular platforms, life sciences associates are probably already using AI to assist with emails, summarize documents and articles and conduct research. Governance will be critical as this informal use of AI grows. Simultaneously, ubiquitous AI will encourage innovation. Balancing compliance with creative AI use that reveals business opportunities will be easier for life sciences companies with AI implementation strategies that address these key issues:

• Talent and change management strategies. Change management programs should be designed to ensure each changing job role contributes to business growth. In tandem, corporate AI champions need to focus on how generative AI augments expertise and enables associates to focus on rewarding, higher value work.
  
  Recruitment, reskilling and upskilling strategies will need to evolve as the impact of emerging AI capabilities on the skills needed in each position becomes clear.
  
  
• Implications for technology investments. As major platforms determine their AI strategies for life sciences, companies may need to re-evaluate their existing platform investments. AI capabilities for streamlining administrative and other lower-value work should be table stakes. Expect AI-adept associates to want and use AI tools with sophisticated features like personalization.
  
  
• Modernized IT and data management landscape. Companies need to collect, store and manage the economic, clinical, demographic, lifestyle and behavior data that AI feeds on to reveal patterns and insights. Older, closed and proprietary platforms often are incompatible with modern data management and storage needs.
  
  
• Transparency. Clear and consistent communication from life sciences companies about how they are using AI and how those uses benefit their associates, patients, providers and payers will be critical to building the trust necessary for widespread adoption.

Putting generative AI to work

Business growth from gen AI will be an evolution, not a project. Change will be constant and accelerating. As one drug development milestone is met, another will appear. The industry’s bottom lines will be stronger with AI-driven growth. The most rewarding outcomes will be a wider array of patients enjoying better health as effective therapies come to market sooner.

Read our latest white paper, “Capturing life sciences’ share of AI’s trillion-dollar growth opportunity,” where we take a deeper dive at key roles that will be impacted over the next 10 years.