Percentage of executives who report reaching the stage of development by use case area
SOURCE : BAIN GENERATIVE ARTIFICIAL INTELLIGENCE IN PHARMA SURVEY 2023 generate predictions and simulations , giving decision makers a comprehensive view of the company ’ s activities and insights .
To support a prioritized roadmap , industry leaders are also ensuring that they have the appropriate technical backbone in place . Many have signed contracts with multiple generative AI foundation model providers to experiment and understand the nuanced differences in their performance .
How to scale generative AI If pharma companies want to generate value from generative AI as fast as the technology allows , they need to ensure that the organization is ready . Companies can take a three-tiered approach to prime their operating model for generative AI at scale :
• Determine your strategic posture . Leading organizations will establish decision-making and funding models that prioritize high-return use cases . In particular , they will ensure that those use cases fit within their investment themes around bold bets for the future of the business . When it comes to funding , organizations often bill generative AI investments to IT , although they typically deliver the anticipated savings to the respective functional budgets . Winning companies won ’ t let these conflicts stand in the way of adoption . Instead , they will find ways to incentivize business unit leaders to invest in disruptive , value-generating AI initiatives .
• Lead through change . Generative AI at scale requires strong internal leadership and cross-functional alignment . The best companies will establish an organizational center of gravity with several executives who act as generative AI champions . The team holds the organization accountable to its roadmap and decision-making model .
• Build the foundations . In addition to the right technology , data , and models , generative AI at scale requires reorienting the organization to support big visions .
• Reshape your talent strategy . Many pharma companies continue to struggle with hiring data scientists for AI initiatives . Given the shortage of talent with generative AI expertise , companies that want to be included among the next generation of AI leaders will need to recruit aggressively now .
• Forge strategic partnerships . As they build up their stable of in-house talent , leaders will partner with external vendors for support . Consider , for instance , how Sanofi is using BioMap ’ s AI platform that converts proprietary data sets into biological maps of proteins to optimize its drug discovery process at scale , or how Bayer is working with Google to automate drafting and translating clinical trial communications in multiple languages .
• Engage on ethics and regulation . Data security , privacy , legal issues , and ethical considerations , such as biases in models ’ input and output , require a thoughtful approach from the start . While adhering to guidelines and regulations is paramount , industry leaders will go a step further with a companywide risk management approach , including guardrails that they continuously adjust to ensure safe deployment . For example , GSK has established an inhouse responsible AI team that brings together experts in engineering , philosophy , and policy to explore ethical and societal considerations and implement a framework for safe and ethical development . In addition to strict infrastructure and processes , AI users receive training to ensure proper practices .
Generative AI is already top of mind for most pharma companies , with 75 % citing it as a C-suite and board priority . And investors are watching closely to differentiate the pioneers from the followers .
As leadership teams move beyond experimentation into pilots and launches , they are thinking carefully about when and how to communicate their AI journey to investors . Those that can signal a structured , scalable enterprise-wide program , rather than a smattering of standalone initiatives , will reap the rewards in the next phase of AI . n
This article was written by Eric Berger , a Partner at Bain & Company in Boston , Robbie Sanding , a Partner at Bain & Company in Los Angeles , and KC George , a Partner at Bain & Company in San Francisco .
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