Drawing on experience overseeing 1,600+ AI use cases across BNP Paribas, Adri shares how large-scale AI adoption is evolving from traditional ML and LLM deployments into selective, high-impact agentic transformation, while balancing risk, cost, and measurable business value.
• Quantify AI-driven commercial value through revenue uplift, cost reduction, operational efficiency, and risk mitigation frameworks embedded in financial reporting
• Identify where agentic AI delivers real enterprise impact—and where its application introduces unnecessary operational and model risk
• Redesign end-to-end business processes by orchestrating agentic workflows rather than layering AI onto legacy systems
• Mitigate operational risk across infrastructure, model, and system layers in probabilistic, non-deterministic AI environments