Biopharma leaders today are poised to capitalize on the power of artificial intelligence (AI) to speed delivery of new therapeutics and drive down fast-rising costs. But for many biopharma companies, there is a gap between the vision for AI and the present reality. They struggle to launch AI initiatives or capitalize on previous efforts.
Why? The answer centers on data. The vast volumes of data that biopharma companies generate and collect are not ready for advanced analytics, AI, or machine learning applications. Moving forward will require a revolutionary scientific data paradigm.
In this white paper, you’ll learn:
- The promise of Scientific AI in biopharma
- 3 primary obstacles to Scientific AI
- How to close the AI gap
