AI is reshaping the tasks inside a forecasting cycle — but the hardest problems remain process problems. Forecasting teams are being asked to move faster, cover more assets, and defend their numbers more rigorously. Drawing on patterns we see across biopharma forecasting organizations,, this session examines three recurring challenges across biopharma forecasting organizations: governance of assumptions and ownership, fragmented tool ecosystems and model evolution, and cycle timing that leaves no room for iteration. For each, we'll discuss emerging best practices and where AI practically helps.