AI-Driven Data Analytics and Decision Intelligence Summit 2026

Many organizations build highly predictive machine learning models but struggle to turn those predictions into measurable business results. The problem is often in the decision systems rather than the AI model itself.

Decision systems and the failure modes arising from them are nothing new. Business rules, operational constraints and data limitations have long served to slow business results. But AI systems amplify these challenges by operating at scale, in real time and in environments where small design choices—like threshold settings, feature availability and data latency can have a big effect. These issues are often invisible during development, and improving model accuracy alone rarely solves them. Instead, better decision design is key to driving real-world outcomes.

This session explores how to close the gap between prediction and action. Attendees will learn practical strategies for designing decision systems that align models with business objectives, anticipate production challenges and deliver consistent results.

Join Gael Decoudu, Credit and AI Leader at Financial Services to discover how to turn predictive insights into business impact.

Key Takeaways:
- Why strong model performance doesn’t guarantee business impact.
- How decision design drives measurable outcomes.
- Common production failure modes and strategies to mitigate them.
- Practical methods to align models with business objectives.
- Measuring success through outcome-based metrics.