Inspired by an unprecedented opportunity, the life sciences sector has gone all in on AI. For example, in 2023, Pfizer introduced an internal generative AI platform expected to deliver $750 million to $1 billion in value. And Moderna partnered with OpenAI in April 2024, scaling its AI efforts to deploy ChatGPT Enterprise, embedding the tool’s capabilities across business functions from legal to research. In drug development, German pharmaceutical company Merck KGaA has partnered with several AI companies for drug discovery and development. And Exscientia, a pioneer in using AI in drug discovery, is taking more steps toward integrating generative AI drug design with robotic lab automation in collaboration with Amazon Web Services (AWS). Given rising competition, higher customer expectations, and growing regulatory challenges, these investments are crucial. But to maximize their value, leaders must carefully consider how to balance the key factors of scope, scale, speed, and human-AI collaboration. The early promise of connecting data The common refrain from data leaders across all industries—but specifically from those within data-rich life sciences organizations—is “I have vast amounts of data all over my organization, but the people who need it can’t find it.” says Dan Sheeran, general manager of health care and life sciences for AWS. And in a complex healthcare ecosystem, data can come from multiple sources including hospitals, pharmacies, insurers, and patients. “Addressing this challenge,” says Sheeran, “means applying metadata to all existing data and then creating tools to find it, mimicking the ease of a search engine. Until generative AI came along, though, creating that metadata was extremely time consuming.” ZS’s global head of the digital and technology practice, Mahmood Majeed notes that his teams regularly work on connected data programs, because “connecting data to enable connected decisions across the enterprise gives you the ability to create differentiated experiences.” Majeed points to Sanofi’s well-publicized example of connecting data with its analytics app, plai, which streamlines research and automates time-consuming data tasks. With this investment, Sanofi reports reducing research processes from weeks to hours and the potential to improve target identification in therapeutic areas like immunology, oncology, or neurology by 20% to 30%. Achieving the payoff of personalization Connected data also allows companies to focus on personalized last-mile experiences. This involves tailoring interactions with healthcare providers and understanding patients’ individual motivations, needs, and behaviors. Early efforts around personalization have relied on “next best action” or “next best engagement” models to do this. These traditional machine learning (ML) models suggest the most appropriate information for field teams to share with healthcare providers, based on predetermined guidelines. When compared with generative AI models, more traditional machine learning models can be inflexible, unable to adapt to individual provider needs, and they often struggle to connect with other data sources that could provide meaningful context. Therefore, the insights can be helpful but limited.   Sheeran notes that companies have a real opportunity to improve their ability to gain access to connected data for better decision-making processes, “Because the technology is generative, it can create context based on signals. How does this healthcare provider like to receive information? What insights can we draw about the questions they’re asking? Can their professional history or past prescribing behavior help us provide a more contextualized answer? This is exactly what generative AI is great for.” Beyond this, pharmaceutical companies spend millions of dollars annually to customize marketing materials. They must ensure the content is translated, tailored to the audience and consistent with regulations for each location they offer products and services. A process that usually takes weeks to develop individual assets has become a perfect use case for generative copy and imagery. With generative AI, the process is reduced to from weeks to minutes and creates competitive advantage with lower costs per asset, Sheeran says. Accelerating drug discovery with AI, one step at a time Perhaps the greatest hope for AI in life sciences is its ability to generate insights and intellectual property using biology-specific foundation models. Sheeran says, “our customers have seen the potential for very, very large models to greatly accelerate certain discrete steps in the drug discovery and development processes.” He continues, “Now we have a much broader range of models available, and an even larger set of models coming that tackle other discrete steps.” By Sheeran’s count, there are approximately six major categories of biology-specific models, each containing five to 25 models under development or already available from universities and commercial organizations. The intellectual property generated by biology-specific models is a significant consideration, supported by services such as Amazon Bedrock, which ensures customers retain control over their data, with transparency and safeguards to prevent unauthorized retention and misuse. Finding differentiation in life sciences with scope, scale, and speed Organizations can differentiate with scope, scale, and speed, while determining how AI can best augment human ingenuity and judgment. “Technology has become so easy to access. It’s omnipresent. What that means is that it’s no longer a differentiator on its own,” says Majeed. He suggests that life sciences leaders consider: Scope: Have we zeroed in on the right problem? By clearly articulating the problem relative to the few critical things that could drive advantage, organizations can identify technology and business collaborators and set standards for measuring success and driving tangible results. Scale: What happens when we implement a technology solution on a large scale? The highest-priority AI solutions should be the ones with the most potential for results.Scale determines whether an AI initiative will have a broader, more widespread impact on a business, which provides the window for a greater return on investment, says Majeed. By thinking through the implications of scale from the beginning, organizations can be clear on the magnitude of change they expect and how bold they need to be to achieve it. The boldest commitment to scale is when companies go all in on AI, as Sanofi is doing, setting goals to transform the entire value chain and setting the tone from the very top. Speed: Are we set up to quickly learn and correct course? Organizations that can rapidly learn from their data and AI experiments, adjust based on those learnings, and continuously iterate are the ones that will see the most success. Majeed emphasizes, “Don’t underestimate this component; it’s where most of the work happens. A good partner will set you up for quick wins, keeping your teams learning and maintaining momentum.” Sheeran adds, “ZS has become a trusted partner for AWS because our customers trust that they have the right domain expertise. A company like ZS has the ability to focus on the right uses of AI because they’re in the field and on the ground with medical professionals giving them the ability to constantly stay ahead of the curve by exploring the best ways to improve their current workflows.” Human-AI collaboration at the heart Despite the allure of generative AI, the human element is the ultimate determinant of how it’s used. In certain cases, traditional technologies outperform it, with less risk, so understanding what it’s good for is key. By cultivating broad technology and AI fluency throughout the organization, leaders can teach their people to find the most powerful combinations of human-AI collaboration for technology solutions that work. After all, as Majeed says, “it’s all about people—whether it’s customers, patients, or our own employees’ and users’ experiences.” This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.