How AI can support decision-making in drug development portfolios

For the pharmaceutical industry, AI can be a powerful tool to drive better-informed asset portfolio decisions.

In brief

• Making decisions about drug development portfolios requires synthesizing dozens of complex inputs, a process that can be made more efficient and accurate with AI.
• By harnessing AI, the R&D portfolio office can transform its role as a strategic partner in asset prioritization decisions.

When evaluating their drug development portfolios, pharmaceutical companies traditionally have engaged in a delicate balance of scientific research, patient impact, market considerations, internal constraints and, ultimately, human judgment, with no industry-wide leading practice to guide them. The traditional role of the R&D portfolio office has been to run simulations with dozens of complex inputs and then provide two- or three-dimensional visual aids and narrative detail to decision-makers in pipeline prioritization meetings. The ability of the portfolio team to support decisions is often impaired by poor-quality program data, long turnaround times for additional analysis by program teams, and analysis and visualization tools that require significant effort to update to answer new questions.

As the AI ecosystem matures, proactive portfolio and program offices are already working with functional leaders to enable future AI capabilities while enhancing operational performance in the short term. Forward-thinking R&D portfolio leaders in the space are taking actions that make them more AI ready, while also addressing current state operational problems:

• Increasing efforts to inventory and document the comprehensive set of R&D data – planning, operational, market and competitive data – used for leadership decision support and map out the needed connection points between this data to enable AI engines
• Focusing on data integrity efforts – quality, completeness and timeliness of data – and establishing reference data sets that can be used for AI training while also building leadership trust in the data 
• Taking a more active role in guiding the evolution of the timeline, resource and cost planning practices across the R&D organization

Having a modern R&D portfolio office that embraces AI will better equip firms to identify opportunities, predict challenges and guide strategic decision-making, ultimately speeding the delivery of innovative drugs to patients.

Yuri Chernyak and Abhishek Tondon contributed to the writing of this article.