Generative AI’s Hype Is in Full Swing and Strong Use Cases for Biopharma Manufacturers Have Emerged

Generative AI became a hot topic in 2023, and is continuing to steamroll into 2024, with NVIDIA’s GTC conference feeling reminiscent of Steve Jobs announcing the iPhone in 2007 or, as the younger generation sees it, the industrial equivalent of a Taylor Swift Eras Tour concert. Its impact will be felt differently across manufacturing markets, with each industry having its own challenges and complexities that affect the deployment of generative AI. Biopharma, and the wider pharmaceuticals market, is an industry that can reap massive gains from using this new technology; however, its tight regulatory oversight and complex production processes make leveraging generative AI harder than simply installing it and turning it on, something that many companies providing generative AI applications are trying to lead manufacturers to believe.

The opportunities and applications of generative AI in the biopharma industry are wide, with the consistent development of new use cases. Below are three overarching, high-value examples:

• Quality Management Support: Quality is a key impact area for generative AI, with key functionality examples, including the automation of Root Cause Analysis (RCA) to support and rapidly speed Corrective and Preventive Action (CAPA) processes, the creation of Failure Mode and Effects Analysis (FMEA) documents, and comprehensive data analytics to reduce the likelihood of batch failure.
• Faster Drug Discovery: Generative AI supports the discovery of new drugs by synthesizing the massive data pool of academia to highlight promising research, alongside speeding the identification of new molecules. Once designed, AI can then support improved target patient selection to expedite clinical trials and, therefore, time to market.
• Effective Inventory Management: Adapting complex production processes in response to raw material shortages or changing product demand can be incredibly challenging. Generative AI planning tools can enable manufacturers to streamline their procurement processes, with the software highlighting potential supply chain/production disruptions and then recommending solutions to operators.

Manufacturers can employ several strategies to improve the success of their generative AI deployments such as the following:

• Create Clear Generative AI Deployment Plans: Companies need to deploy teams built from a range of critical business units such as Information Technology (IT), Operational Technology (OT), and Go-to-Market (GTM) bodies to pinpoint generative AI applications that can most effectively move the needle, alongside managing the deployment process and organizational expectations. These teams should identify both short- and long-term AI projects and not try to implement too much at once. Rather than getting bogged down in numerous pilot projects, manufacturers need to ensure a smooth 3 to 5-year deployment schedule across a few business-critical applications. Finally, this entire process needs to be driven from the C-suite and key AI advocates to facilitate stakeholder buy-in across the entire process from application selection to post-deployment.
• Build Close Partnerships with Technology Vendors: Picking the wrong technology solution partner can significantly set back digital transformation projects. Strong technology partners will be able to provide industry best practices and work hand-in-glove with them to ensure that generative AI applications are designed to meet the specific challenges. Due to the early stages of this technology and the high regulatory nature of the pharma market, deep AI technology partnerships should be prioritized over quick deployment, off-the-shelf solutions.
• Guarantee Clean Data to Work with: Manufacturers must have clean data to work with when they deploy new generative AI solutions. While off-the-shelf and synthetic data can serve to plug the gap, biopharma manufacturers will see the greatest success when using proprietary data, especially with the market’s rich and varied data sources. Getting clean data can be challenging due to the often-siloed nature of many companies’ data architecture and poor data collection methods; however, jumping in before establishing strong data lakes can massively complicate deployments and disenfranchise stakeholders. Bad data can lead to bias, hallucinations, or simply less valuable results and recommendations from the generative AI model, significantly reducing the effectiveness and success of deployments.

Overall, generative AI represents an exciting and impactful technology that will change the face of biopharma and life sciences manufacturing. However, manufacturers that want to be first movers with this technology need to ensure they have adequately weighed the risks and implement strong processes to ensure effective implementation, or risk doing more harm than good.