Back in July 2023, researchers at the McKinsey Global Institute estimated that gen AI could unlock between $60 billion and $110 billion a year in economic value for the pharmaceutical and medical products industries, boosting productivity and innovation in domains across the industry’s value chain—from the way new treatments are discovered to how they are marketed and administered by physicians. Six months later, McKinsey experts dug deeper into those numbers, uncovering more than 20 use cases with the greatest potential for near-term impact.
Now, with gen AI use cases proliferating across the business community, we decided to find out how much progress life science organizations have made in capturing this value. In late summer 2024, we surveyed more than 100 pharma and medtech leaders responsible for driving their organizations’ gen AI efforts. All respondents report having experimented with gen AI, and 32 percent say they have taken steps to scale the technology. But only 5 percent say they have realized gen AI as a competitive differentiator that generates consistent and significant financial value (Exhibit 1). Nonetheless, companies remain optimistic about gen AI, with more than two-thirds of respondents saying they plan to significantly increase investment in the technology (Exhibit 2).
Why do so many life science organizations struggle to realize results from their gen AI deployments? And what are the minority of top performers doing differently? This article reveals the most common pitfalls life science companies are facing—and offers solutions that can help organizations move from pilot purgatory to driving real business value at scale.
The key challenges to scaling gen AI in life sciences
Based on our survey and our experience, we have identified five key areas that pose challenges for life science companies attempting to realize company-wide value from gen AI: gen AI strategy, talent planning, operating model and governance structure, change management, and risk (Exhibit 3).
Challenge 1: Ambiguous, shortsighted, or nonexistent enterprise gen AI strategy
About 75 percent of respondents say that their organizations lack a comprehensive vision for gen AI or an intentionally designed, strategic road map with clearly defined success measures linked to business priorities. Instead, they tend to proceed in a decentralized manner, use case by use case. This instinct to capture short-term value through experimentation, coupled with the federated/function-led structure of many life science organizations, explains many of the challenges organizations encounter when it comes to scaling.
McKinsey research has found that digital transformations seldom succeed unless C-suite leaders are aligned around a business-led road map. Without an intentional strategic posture toward gen AI—whether a top-down mandate or a coordinated enterprise road map driven by a center of excellence—individual business units are left to navigate the ever-evolving technology landscape on their own, pursuing a multitude of new use case ideas that, no matter how compelling, often fail to add up to a strategy that delivers actual value.
Challenge 2: Lack of talent planning and upskilling
At most life science companies, the existing pool of tech talent presents a traditional tool kit for IT, data science, and product development. Unfortunately, traditional approaches to tech talent are unable to deliver the quality and performance of enterprise-grade solutions needed for gen AI, for example, agent-based architecture, model validation, large language model (LLM) operations, and the fine-tuning of models. But only 6 percent of survey respondents report having conducted a skills-based talent assessment to determine how to evolve their talent strategy into one that considers gen AI priorities.
Prompt engineering has emerged as a key gap, especially for more complex gen AI applications. One life science company, for example, was attempting to use gen AI to draft regulatory documents, only to discover that prompt engineers required a unique combination of regulatory domain knowledge and engineering rigor to craft scalable prompts that generate submission-ready output—a specialized necessity that made the role especially challenging to fill.
Challenge 3: Loosely defined operating model and governance
One common challenge leaders face is creating the right operating model for gen AI transformation, often choosing between one of two extremes. At one end of the spectrum is a highly decentralized approach, in which the organization simultaneously launches multiple use case pilots. While this allows companies to move fast, it also leads to quality, cost, and sustainability challenges and creates operational silos that inhibit the sharing of knowledge and the ability to capture cost synergies. At the opposite end is a top-down approach, with centralized decision-making and a phased rollout of use cases. This approach can be slow and often frustrating, destroying momentum.
One company swung between the two. It began its gen AI efforts by launching 1,500 different use cases. When that proved unwieldy, company leaders imposed a top-down governance structure that led to a different set of issues, constricting the innovation pipeline with projects requiring an arduous approval process that stretched some two to three months.
Challenge 4: Underestimating the process rewiring required to drive scale
To succeed with gen AI, companies must integrate the technology across complex workflows to promote adoption and impact—a reality that highlights the need for effective change management. McKinsey has found that 70 percent of digital transformations fail not because of technical issues but because leaders ignored the importance of managing change. In fact, for every $1 spent on technology, $5 is required for change management to successfully drive capability building, adoption, buy-in, and value capture over time.
One company launched a center of excellence function to initiate a broad gen AI platform for a range of use cases but failed to communicate a compelling change story to accompany those initiatives. That failure, coupled with the lack of holistic, end-to-end planning and thinking, resulted in a collection of gen AI tools that almost no one ended up using.
Challenge 5: Inadequate understanding of risk
Gen AI introduces unique risks, from hallucinations and accuracy to bias and intellectual property protection. But 35 percent of survey respondents report that they spend fewer than ten hours with their risk counterparts, limiting the degree of collaboration with these crucial functions. This dynamic needs to evolve to scale gen AI. Successful scaling requires business leaders, technology teams, and risk management professionals to communicate from the outset; the absence of such collaboration can lead to issues being raised late in the game, when they are much more difficult to fix, or a lack of adherence to the risk and compliance guardrails that are critical to building trust in the organization.
One company, for example, spent several months developing an external-facing gen AI solution, only to be forced to withdraw the launch due to a lack of alignment with its digital, medical, and legal teams—which raised significant risk issues after the tool had been developed. This resulted in a severe setback for the gen AI team’s agenda, morale, and momentum.
The solution: A five-point plan to realize value from gen AI
Successfully scaling gen AI and capturing its value potential requires more than just a technological rollout. An effective gen AI strategy is fundamentally different from traditional tech projects. Given the rapid pace of innovation, a gen AI strategy must be dynamic, scenario driven, and focused on how to engage with the broader ecosystem. Scaling gen AI involves comprehensive change across the organization, encompassing strategy, talent, governance, and risk management.
Based on our experience, we have identified five key strategies to move from gen AI use cases to enterprise-wide adoption. These actions ensure that organizations not only experiment with the technology but also fully integrate it into their operations to drive measurable business value.
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Adopt a domain-driven approach. Successful AI strategy cannot be based on a slew of disconnected use cases, which often leads to fragmented efforts and missed opportunities. Instead, the focus must shift to domain-driven transformations, where gen AI is applied to fundamentally reshape critical areas of the business, such as the commercial, medical, or R&D domains. Thirty-eight percent of the life science organizations surveyed cite research as their leading strategic priority in their gen AI journey, followed by the commercial domain, at 28 percent (Exhibit 4).
This domain-driven approach ensures that gen AI isn’t just another tech solution but a core enabler of business transformation. Rather than focusing on technology for technology’s sake, organizations that prioritize domain transformations are better positioned to capture the full value of AI. Crucially, there is no such thing as a stand-alone gen AI strategy. The real focus should be on deploying gen AI to support broader business objectives, drive strategic goals, and create differentiation in the market. Organizations that view the technology through this business-first lens have found greater success in scaling AI initiatives.
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AI transformation encompasses more than just tech. Scaling gen AI isn’t simply a matter of implementing a new technology; it’s about rewiring the organization’s operating model and culture to support new AI-driven ways of working. This extends to talent strategies: the workforce must evolve beyond traditional IT data science roles to include new skills—AI engineering, large language model fine-tuning, and business translation—to bridge the gap between technical execution and business value capture. Without a comprehensive talent realignment, organizations will be less successful in scaling their gen AI efforts. Further, gen AI implementation needs to drive measurable value. This requires a clear up-front agreement on how value will be captured, say, through acceleration of time to market, productivity increase, or improved probability of success.
One life sciences company, for instance, launched an enterprise talent upskilling and planning program, with targeted initiatives for business and technical roles. The program also introduced dedicated gen-AI-focused leadership roles in critical functions to drive sustained organizational change. With the appropriate talent—and leadership—in place, the company’s gen AI initiatives proceeded much more smoothly than they would have otherwise.
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Adopt an ecosystem approach. In the rapidly evolving AI ecosystem, an externally focused partnership strategy is critical. Given the speed at which AI technologies and methodologies are advancing, life sciences organizations should consider cultivating a network of low-cost, high-optionality partnerships. These partnerships can provide flexibility and give organizations the ability to quickly pivot and seize opportunities as they arise. Organizations should also establish clear “triggers” that indicate when it’s time to move from exploratory partnerships to larger strategic bets. This ensures that the business remains agile and can scale up or shift its AI investments based on real-time insights and market movements.
Engaging with the broader ecosystem—including academia, tech, and venture capital—is also essential to staying on top of the latest developments. Relying solely on internal capabilities is no longer enough to stay competitive in AI. A dynamic, externally focused lens ensures that companies stay ahead of the curve and capture the full value of gen AI innovations.
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Deploy a platform-driven approach from the outset. A platform-driven approach is key to ensuring that gen AI initiatives are scalable, sustainable, and reusable across various business domains. A scalable AI platform allows organizations to standardize infrastructure, data pipelines, and development processes, ensuring that each new use case builds on the previous one. This can also help reduce duplication of effort, encourage collaboration across business units, and foster consistency in AI performance across the organization. Moreover, a platform-driven approach ensures that AI models are not developed in isolation but are integrated into a unified framework, allowing them to be adapted and reused across various business domains. This not only reduces costs but also accelerates time to value, as insights from one domain can be applied to another.
One life sciences company found success by adhering to a mantra: “Slow down to speed up.” The company spent three months defining a detailed blueprint for insights and document platforms. This enabled the reuse of components within each platform, enabling rapid scaling across use cases.
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Embed risk management in the full product development life cycle. One of the common mistakes organizations make with gen AI is treating risk management as an afterthought or as an obstacle to innovation. In fact, risk management must be embedded throughout the entire AI product life cycle. Gen AI introduces unique risks—such as hallucinations, bias, data security, and intellectual property issues—which require careful oversight.
To ensure these risks are managed effectively, business leaders and risk and compliance functions should collaborate regularly. Organizations should establish clear governance frameworks early on and ensure that ethical guidelines are in place to address concerns about AI fairness, transparency, and accountability.
Given the high regulatory requirements in life sciences, organizations should place greater emphasis on risk management. One organization proactively identified the guardrails necessary to address evolving regulations (for example, the EU AI Act) and technology limitations (for example, the probabilistic nature of models). The organization established clear, responsible AI requirements, including mandatory observability, validation protocols, and human-in-the-loop guidelines, which were defined prior to the start of product development.
What a holistic transformation can look like
What does a successful gen AI initiative look like? Consider one life sciences company that recognized the gen AI opportunity early and embarked on a holistic transformation across domains. Company leaders convened a C-level task force to steer the overall gen AI strategy, set up governing bodies across the R&D, commercial, medical, and operations domains, and asked each domain to prioritize one use case with high-value potential for C-level sponsorship.
The company then ran proofs of concept with an eye toward scaling, using its early experiences to organize reusable components into domain-specific platforms. The technology and business teams partnered from the outset, ensuring that all gen AI solutions addressed priority business needs and helped drive the process changes needed to spark adoption and deliver value.
In the meantime, the company engaged ecosystem partners to bring in learnings and assets from across the life sciences industry and beyond and built stage gates to focus resources on partnered solutions that were ready to scale across therapeutic areas and geographies.
Leaders shaped a compelling change story focused on how gen AI solutions were intended to augment rather than replace employees, for example, by helping them deal with increasing workloads, and used change management teams to help drive a successful rollout. They provided white-glove support for initial users and deployed these early adopters as change ambassadors to build bottom-up momentum. Impact metrics were defined, tracked, and reviewed at regular governance meetings to ensure gen AI initiatives remained on track to scale and deliver business impact.
This kind of experience does not have to be an outlier. Leaders of life science organizations should understand that capturing the potentially transformative value of gen AI requires more than experimentation and individual use case deployment. It demands strategic integration into the organizational fabric. In the next chapter of the gen AI story, organizations should take an intentional approach to driving alignment with business strategy, scalability, and sustainability. This pivotal moment is an opportunity for life sciences leaders to lead transformative change, revolutionizing drug discovery and patient care, as well as driving meaningful bottom-line results.
