Column
How to Successfully Scale Generative AI in Pharma
Go beyond the “ what ” of experimentation to the “ how ” of setting an organization-wide scaling strategy .
The generative artificial intelligence ( AI ) transformation is well underway in pharma . And pharma companies have high confidence in its value : Already , 40 % of executives say that they are baking expected savings into their 2024 budget ( see Figure 1 ), and 60 % have set targets for cost savings or productivity boosts , according to a recent Bain survey .
Nearly 60 % of executives say that they have moved beyond ideation and brainstorming to building out use cases . In fact , 55 % reported that they expected to have multiple proof-ofconcept or minimum viable product builds by the end of 2023 .
With companies large and small making significant headway in realizing the benefits of generative AI , what will separate the best from the rest ? Over the next three to six months , the companies that make the greatest progress will be the ones that move from isolated pilots to scaling winning use cases across the board . These leaders will pull away from the pack with an operating model that supports fast growth at scale and prioritizes the most valuable opportunities .
The state of AI in pharma Classical data science and machine learning are nothing new to pharma executives who have been investing in productivity enhancements for years , primarily in the drug discovery space . Bain research shows that 54 % of pharma companies have automated biomedical literature review solutions , and 46 % are using AI as part of their process to find potential disease targets .
Now , generative AI is broadening the aperture of use cases with new opportunities across the value chain . Biomedical literature review and preclinical research remain among the most popular use case areas , although we ’ re also seeing high investment in IT and competitive intelligence ( see Figure 2 ). Within these top areas , more than 60 % of executives , on average , say that they have at least a proof of concept in development , and around 10 % have already rolled out tools .
These early adopters have moved swiftly , often reaching a working pilot within about eight weeks . And already , many are seeing tangible value .
For example , within six weeks , one healthcare leader was able to develop a working pilot of an AI-enabled chatbot to help answer pharma reps ’ medical questions on a subset of its products . It significantly boosted contact center agent productivity by improving the number of issue resolutions per hour . Similarly , Eli Lilly estimates that it has saved around 1.4 million hours of rote human activity since 2022 through automation and technology . With further AI investments , it aims to reach 2.4 million hours by the end of the year .
Other leading pharma companies have made rapid gains in a range of areas , from research and development to support functions . One created an accurate model for clinical trial patient identification in a quarter of the time needed for previous machine learning models . Several have succeeded with generative AI tools that draft summaries of regulatory filing content or responses to regulator questions . Others have focused on chatbots for knowledge management , enabling employees to quickly query internal documents .
Percentage of executives who say that savings from generative artificial intelligence are being incorporated into 2024 budgets
Some have pursued commercial endeavors : For instance , companies are already piloting generative AI to streamline salesforce tasks , including dynamic content generation , and one company is using AI to draft custom ad copy according to US Food and Drug Administration guidelines .
The level , speed , and success of subscale experimentation has been impressive . But as early wins breed interest and energy across the organization , it ’ s increasingly critical for executives to shift from disconnected pockets of generative AI experimentation to an enterprise-wide program . Otherwise , the organization can trip over itself , becoming the bottleneck to its own potential .
Industry pioneers haven ’ t just built two to three proofs of concept . They have scaled those proofs of concept and encouraged adoption . They have also created thoughtfully structured backlogs with use cases throughout the company prioritized by how much value they bring and how practical they are to develop .
The best enterprise-wide roadmaps group use cases in thematic clusters , outlining the intent to evolve them over time . Leaders are starting with low-risk use cases and launching them in safe environments , with the ambition to test , learn , and gain confidence before going live with more mature , disruptive solutions . For instance , a company may prioritize an internal knowledge management chatbot before evolving it into an external-facing chatbot using similarly unstructured data . Or a company may begin with a patient-facing solution that relies on a human to mitigate risk , with the aim of eventually creating a fully automated version .
For example , Syneos Health , which has a multiyear deal with Microsoft to leverage OpenAI ’ s GPT technology , brought together a team of data scientists and business function leaders to create centralized , reusable machine-learning building blocks . In addition to working on a chatbot that can search across 400,000 clinician protocols , the biopharma company says that it is exploring applications across the value chain , from clinical trials to marketing platforms . Sanofi is also laying the groundwork for AI at scale by launching Plai , an app that harnesses internal data across the organization to
SOURCE : BAIN GENERATIVE ARTIFICIAL INTELLIGENCE IN PHARMA SURVEY 2023
70 ISSUE 1 | 2024 GlobalHealthAsiaPacific . com