Pengfei Sun

While there's consensus around the significant potential in generative and agentic AI to transform CX in financial services, scaling these solutions is uniquely hard when anything short of 100% accuracy introduces real financial, regulatory and reputational risk – similarly felt at scale. In this discussion, explore how organizations can pursue AI innovation responsibly, whilst aligning with risk divisions and protecting brand credibility. Discuss:

• How peers identify and intervene when AI accuracy drops and determine where humans should stay in the loop - balancing risk mitigation with the efficiency gains automation promises

• Benchmark which governance, controls and operating models resonate with risk and compliance business partners, to align on how to enable scale, not stalling it

• Walk away with practical approaches for building AI solutions that deliver value, remain defensible to stakeholders and protect trust even when model perfection isn't possible

As AI becomes embedded in operations, financial services leaders face a defining question: how do you capture AI's productivity gains without undermining the human judgment your customers rely on?

This panel explores what it means in practice to calibrate the right level of human oversight as trust in AI matures. Panelists will share real-world case studies on how they've introduced AI into high-stakes operations while keeping agents empowered, customers protected and regulators satisfied.

Attendees will leave with:

● A clearer mental model for where AI should assist, where humans must lead, and where AI can own end-to-end

● Practical approaches to building trust in AI - both within your organization and with your customers

● Lessons learned from scaling AI in regulated, customer-sensitive environments