A biopharmaceutical company wanted to launch an ambitious machine learning (ML) experiment as part of its corporate strategic initiative. The goal: to better understand ML and define an ML strategy to help bring new therapeutics to patients in less time. The entire project, which included multiple work streams, had a six-month timeframe from scoping and resourcing to execution. Due to the fast pace and high visibility, leadership knew that a senior project leader was essential to success. Our client engaged Point B to lead the initiative and help lay the foundation for an ML strategy that would accelerate discovery and innovation.
Let the "bake-off" begin
Point B brought agile project management methods and thinking to lead the initiative and manage its work streams in three clinical and operational use cases.
We took a "bake-off" approach to assessing our client's in-house capabilities and external needs in ML. We asked internal teams and selected external vendors to analyze and model data against the same clinical questions and success criteria. This friendly competition allowed us to assess the performance of potential ML partners and their capabilities, while also evaluating and enhancing our client's in-house analytics expertise. In the process, we assessed the state of our client's data, deepened our client's understanding of ML, and validated ML's strategic value.
Being agile—and thorough
Agility includes knowing when and how to spend time. We set the stage for success by conducting the right due diligence, getting the right subject matter expert perspectives, and giving people time to make key decisions. Knowing which issues to highlight, and when to escalate them, was also key to this fast-paced project. We kept issues minimal by instituting an operational strategy with status and governance updates, dashboards, and dedicated project managers and ancillary functions. We drove project management disciplines to include meeting facilitation, project planning, documentation, reporting and presentations. Our upstream and downstream communications strategy was key to sharing insights and building stakeholder buy-in.
Driving new speed to market
Point B led the ML project to rave internal reviews. Our next step? Developing the recommendations and roadmap for an ML strategy to align with our client's strategic business objectives. As such, it will address not only technology, but also critical changes in business processes and culture. Our client has a new understanding of ML's implications for the future and its role in achieving the most important goal of all—getting new and better therapeutics to patients in less time.