Your innovation data may be more important than your AI strategy
Almost every company now uses AI in some form, and most are already running agents. What separates the organizations getting real return from the ones running experiments is usually the quality of the context they can feed those systems.
Innovation and R&D groups sit on exactly that kind of context. Portfolio history, market signals, technical assessments, and stage-gate decisions all carry the institutional knowledge that makes AI output worth acting on. When those inputs are connected, the effect shows up where it matters most: better hit rates on new products, and sharper judgment about which bets to place.
In this on-demand session, Wellspring walks through how AI technologies have matured, what that shift means for growth innovation practices, and how two years of investment have shaped the roadmap ahead.
What you will take away:
- Why cross-functional innovation data is the prerequisite for getting value from AI
- Real examples of next-generation innovation practices that AI now makes possible
- How a stronger innovation function raises AI value for the rest of the enterprise
- Where Wellspring is investing in AI for the mid- to long-term