Exploration as a Service (EaaS) is a structured framework designed to assess and guide AI initiatives before they move into full‑scale development. A centralized Center of Excellence works closely with decentralized data science teams to evaluate new ideas through a standardized exploration phase. This creates transparency across the organization, helping identify overlapping efforts and align similar initiatives early on.
EaaS enables teams to validate promising concepts, accelerate development when conditions are right, or stop initiatives that lack feasibility, impact, or readiness. It also supports cross‑domain comparison of AI use cases, allowing teams to understand what already works in one area and explore whether it can be transferred to another. The result is a more efficient, aligned, and scalable approach to AI innovation.
In this session, we will walk through the EaaS process, share key lessons learned, and demonstrate how structured exploration reduces risk and improves ROI. Participants will leave with practical methods to streamline decision‑making, avoid unnecessary development effort, and focus resources on the AI initiatives with the highest strategic value.
- Artificial Intelligence
