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The agentic AI revolution is here. Leading technology giants like Amazon,Google, Microsoft, Nvidia, and Salesforce are embedding agentic AI into their offerings, possibly signaling a major shift in intelligent automation.

AI agents can independently reason, execute complex tasks, and achieve targeted goals, unlocking efficiencies across many banking processes, including credit underwriting, treasury management, and fraud detection.This transition to agentic AI is a natural progression in banks’ automation journey. In many ways, agentic AI builds on and amplifies the foundation laid by machine learning, traditional AI models, and generative AI.

However, real-world agentic AI applications in banking are still uncommon and emerging. The primary factors to consider? Regulatory challenges, model-related risks, access and control requirements, privacy complexities, ethical considerations, and systemic biases. Add to this the lack of more mature standards for agentic AI tools and communications. Existing legacy systems and weak data integration protocols may only complicate deployment. Many banking processes will likely need a major overhaul to embed agentic AI, particularly in workflows that have a limited history of autonomy through robotic process automation frameworks, machine learning, or generative AI.

This analysis is prepared by Deloitte.