This transcript has been lightly edited for clarity.
Amid disruptions, where are the biggest opportunities for improvements in customer service?
Services represent 60 percent of the $100 trillion global economy, and they are at a pivotal moment now. For service organizations to stay ahead, there are a few big opportunities they cannot miss. First is the realization that the era of incremental improvements is over.
Service institutions have a unique opportunity to leapfrog by leveraging AI- and gen-AI-led applications to completely rethink how they engage with customers.
A great example of an institution that has successfully embraced AI to provide seamless 24/7 customer service is a leading global digital bank in Greater China. It has managed to serve over 400 million individual clients and customers while having fewer than 5,000 employees.
The second opportunity is that service operations are no longer about efficiency—they are a source of competitive differentiation. The companies that succeed will be those that stop viewing service as a cost center and start treating it as a strategic asset, a competitive differentiator to create moments that matter.
The third opportunity, and my favorite one, is the magic formula that lies in combining the power of AI with human collaboration. Getting the right AI-human balance is critical for service institutions to stay ahead.
How can service leaders set bold aspirations and measure success?
Service leaders should create a bold aspiration, supported by a clear vision for their organization. And that should go beyond technology.
The vision should be grounded in clearly measurable targets, which can then serve as a catalyst for their organization to move past small-scale pilots.
Beyond efficiency and productivity, the institution should consider setting metrics that reflect customer and organizational pain points, such as customer satisfaction scores and other operational metrics.
How can service leaders use AI and gen AI tools for lasting impact?
We recently conducted a CXO survey, and there are a few lessons to be learned.
First is setting a bold, enterprise-wide AI-led vision—linked to the overall operational and servicing strategy, with clear financial outcomes.
The second is to have a business-backed view of how the institution would transform end-to-end, front-to-back domains to unlock financial value, with a clear diagnostic of what the key pain points are.
The third is to take a full-stack lens to the multiagent architecture by seamlessly blending gen AI with traditional AI, with advanced automation and digital.
Lastly is deploying AI agents and agentic architecture to reimagine complex workflows for the enterprise, where AI agents will be virtual co-workers who can plan, reason, and act on behalf of their human counterparts, with a well-defined job description for themselves.