PAC considers that banking is at a critical inflection point, driven by the limitations of deterministic automation technologies. Traditional RPA and passive LLM wrappers struggle to address the complexity of legacy environments built on fragmented COBOL systems and manual reconciliation loops. These constraints render linear scaling of operations unsustainable and increase operational risk.
Agentic AI offers a structural shift by enabling autonomous, goal-directed execution. Rather than following rigid scripts, these systems can interpret ambiguous intent, decompose tasks, and execute complex, multi-step transactions across legacy APIs. This transforms operational models:
However, this transformation introduces material governance challenges. Probabilistic AI disrupts traditional deterministic risk frameworks, creating tension with stringent regulatory expectations. To address this, organisations must adopt a new risk paradigm:
The business case for agentic AI also requires a more sophisticated financial lens. Simplistic comparisons of software costs with headcount reductions are insufficient. Instead, organisations must holistically evaluate the total cost of ownership, accounting for infrastructure, model maintenance, and compute intensity. Success metrics should prioritise scalability, resilience, reduced error propagation, and improved employee and customer experience (e.g., faster dispute resolution and a lower cognitive burden).
Finally, execution strategy is critical. Banks must carefully manage external vendors to avoid long-term lock-in while retaining control of core architectures:
Recommended advisory: PAC Leadership Session – Financial Services Industry – AI Adoption
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