Service leaders need to start thinking like product teams

For years, the role of a service leader was to run an efficient operation. Success meant building great teams, managing queues, improving response times, and helping agents resolve customer issues consistently. AI is changing what's possible in service.

Today's service leaders are increasingly responsible for how AI agents, people, workflows, knowledge, and business systems work together to resolve customer needs. As AI makes service easier to configure and adapt, service becomes something leaders can actively build and improve over time.

In turn, customers aren't thinking about the departments, handoffs, or tech stack behind the scenes. They expect the same qualities from service that they expect from great products: experiences that feel intuitive, connected and continuously improving. 

This places CX leaders at one of the most exciting and pivotal moments in service -- they have the ability and control to build more custom, bespoke, and seamless experiences that truly reflect the character of their brands. And at the same time, as this new level of control becomes available, ways of working are shifting too – moving closer to work that looks and feels like that of a product team. 

I see four ways CX leaders can put these principles into practice:

1. Leverage a uniquely deep understanding of customers and their needs

It’s undoubtable that CX teams have always been some of the closest to real time feedback and experiences of customers, but what they can do with that understanding is now much more impactful on the way service is built. Decisions about which experiences to create, how workflows should operate, and where AI agents  step in depend on knowing what customers need and where they encounter friction.

That means:

• Understanding the job to be done. What is the customer actually trying to accomplish? Someone asking to reset a password ultimately wants access to their account.

• Identifying the critical moments. Where does the current experience meet customer needs, and where does it fall short? Look for moments when customers get stuck, repeat themselves, or encounter unnecessary friction.

• Designing around those needs. Consider how AI agents, people, and systems can work together to create experiences that solve customer problems while reflecting the unique character of the business.

BritBox offers a good example. As the streaming service expanded globally, it wanted to ensure customers received the same white-glove, personal experience when they needed help as they had come to expect from the BritBox brand. Working with marketing and creative teams, BritBox used Zendesk's custom tone of voice capabilities to design a virtual agent with distinctly British mannerisms: witty, polite, and approachable. The team also tailored the experience for different markets, preserving the personal touch customers valued while making support more accessible at scale. Customer satisfaction reached a record 89 percent.

2. Ship, learn, and improve

Building a capability is only the beginning. Every release should teach you something. Teams put new capabilities into use, observe how people use them, and use those insights to guide the next iteration.

In service, a team might test different paths for a delayed order, compare when an AI agent should involve a specialist, or refine a workflow as policies change. Each release creates another opportunity to improve the experience.

Over time, those improvements create opportunities that previously weren't practical. As more capabilities become available, teams can deliver new experiences without rebuilding from scratch.

Earlier this year, we launched our ‘A/B Testing’ feature in Forethought by Zendesk’s ecosystem to help CX leaders execute the level of testing and experimentation great customer experience now requires. Customers can test versions of AI Agent policies against each other, revealing the policy which will yield the best results for the business.

3. Measure the impact

Every capability should have a clear purpose, and every improvement should have a way to measure whether it worked.

Did customers reach resolution more quickly? Did repeat contacts decrease? Was less effort required? Did specialists spend more time on work that benefited from their expertise?

Those answers shape what gets improved next. Every interaction becomes another source of learning.

4. Use service insight to improve the wider business

Customer journeys often reflect the way businesses are organized. Sales, support, billing, and marketing may each have their own systems, processes, and customer interactions. AI creates an opportunity to give service a broad view of how customers interact across the entire business, revealing patterns that might otherwise go unnoticed.

That broader view reveals patterns that are valuable beyond the service organization. A billing question may point to confusing pricing. Repeated delivery inquiries might highlight gaps in proactive communication. Frequent product questions can uncover opportunities to improve onboarding or simplify the product itself.

With greater visibility into these patterns, CX leaders can help shape improvements across the business, working with other teams to address the underlying causes of customer friction. Service becomes a continuous source of insight that helps the entire business improve.