AI in Pharma: Where, When, and How Executives are Considering it

The AI rush is on in pharma organizations. A recent report by McKinsey & Company estimates up to $110 billion annual potential¹ across 21 pharma use cases in R&D, clinical development, operations, commercial, and medical affairs. But how do executives feel about where AI is taking us, when results will arrive, and how organizations will get there?

New data² from a September 2024 report by our company show that an impressive 91% of life science executives say they believe in AI’s value (Figure 1), with only 7% reporting skepticism.

What’s more, pharma leaders are putting their beliefs into action. Three out of four respondents were either using, testing, or actively exploring AI in their operations to meet related goals. A third say they have solutions in development, and 21% have solutions already in place. These include a wide range of efficiency-enhancing use cases from endpoint identification, validation, and development, to product quality monitoring, to “freeing up human capital for strategic thinking,” as one leader said. Another 20% haven’t deployed AI facilities yet but are actively learning and exploring their options.

Top Barriers to AI Implementation

Despite the enthusiasm, a sizable gap between promise and reality exists. Figure 2 shows lack of expertise with AI and concerns around data accuracy rank among the top barriers to success for teams implementing AI. Though there is a perceived expertise gap, 57% say they do have expertise enough to implement their AI strategies when asked directly, see Figure 3.

Nearly half of the respondents name cost as a barrier. As a more risk-averse industry, pharma professionals require a high degree of confidence in the business benefit for any expenditure. However, as 26% of respondents say they have not yet considered incorporating AI solutions, the path from AI vision to results is not universal.

Where pharma is using genAI today

AI has strong potential business impact in analyzing and automating data-driven functions, according to the survey of N=142 life science professionals. These include “tasks that require time and effort but not critical analysis”—versus generating new ideas or making diagnostic conclusions.

Present-day GenAI rollouts fell into three groups:

• The first group is improved drug development. In expediting the identification of biomarkers and novel targets, “selecting the right molecules to move forward into preclinical development,” computational model development, and real-world data analysis were all included in this group. “AI can help us extract the hidden value of data,” said one respondent. In manufacturing, AI offers the promise of further improving efficiency, safety, quality, and other processes.
• The second group is clinical research. By assisting with needle-in-the-haystack tasks in therapy optimization, developing protocols, patient side-effect knowledge and management, and mining electronic medical records, AI could support key aspects of clinical research. “Risk mitigation” is another key use case, said one leader.
• And lastly, document generation and regulatory compliance submissions. Respondents gave examples of literature review and pharmacovigilance in this group, instances where typical processes are manual, slow, and error prone. Our company provides such a solution with results indicating AI can help pharma teams complete literature reviews up to 40% faster—with 90% accuracy for title and abstract screening, and more than 80% for summarization and data extraction.

What’s next

Clearly demand for AI is growing among life science companies, with leaders believing that AI will be essential in the next few years. Choosing where to apply AI’s many advantages, for many pharma organizations, is a work in progress, but one that is well underway.

Gaugarin Oliver is founder and CEO of CapeStart, Inc. whose award-winning MadeAi™ platform helps pharma organizations compete and win in the AI economy.

Sources

1. McKinsey & Company, “Generative AI in the pharmaceutical industry: moving from hype to reality,” January 9, 2024, as published https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality
2. CapeStart’s Life Science AI Research Report, September 15, 2024, as published here: https://www.capestart.com/wp-content/uploads/2024/11/CapeStart-GenAI-Report-Sept.-24.pdf