Balancing brand heritage and modern service with AI-powered customer experience.

Balancing brand heritage and modern service with AI-powered customer experience.

Heritage brands are often expected to move cautiously when it comes to tech innovation. Yet, Sealy has turned this assumption on its head.

Since revamping their CX with AI, Sealy has seen standout results – including customer satisfaction and team performance metrics well above industry norms. Perhaps most surprising: agent turnover and even sick leave have dropped, as smarter workflows help teams rest easy and focus on delivering standout service.

The story of how Sealy achieved these outcomes, and what it means for the future of customer experience, is one worth telling – first-hand! Michael Chen, Head of CX and Digital, Sealy, provides insights into the Sealy story here, with additional input from Mitch Young, Senior Vice President, APAC, Zendesk.

Sealy is a heritage brand known for its craftsmanship and longstanding reputation. What motivated Sealy to adopt AI in customer experience, and how did you overcome any initial skepticism internally?

Michael Chen: Sealy’s reputation for craftsmanship is built on comfort, support and disability, and we believe that premium standard should extend to every customer interaction. With that in mind, we adopted Zendesk AI to not just improve efficiency, but to set a benchmark as to what great customer service looks like for our industry.

When we started to roll out the AI, we expected some initial employee resistance and skepticism. However, adoption has been positive to date. We’ve been clear from the start that our approach is about amplifying the impact of our human agents and our team has been given AI assistants to help them to do their job better, instead of replacing them.

We firmly believe human empathy is not easily replaceable, and that human-first approach to AI has greatly helped us with adoption.

Serving diverse markets across Asia Pacific, what’s your strategy for maintaining Sealy’s brand promise while adapting to the unique needs of customers in each market?

Chen: While many customers across the region share similar expectations in terms of customer service, their preferred communication channels vary significantly. For example, Australian consumers primarily use phone and email, whereas in Singapore, Malaysia and Hong Kong they prefer WhatsApp. In Taiwan, Line is essential, while customers in mainland China engage mostly via WeChat.

Sealy’s strategy focuses on staying close to these evolving consumer behaviors and partnering with technology providers that enable us to quickly adapt. If a channel gains importance, we need to be ready to meet customers without compromising the consistency and quality of our brand experience. That’s why we turned to Zendesk.

The platform empowers our agents to craft faster and more natural responses on whichever channel our customers prefer. Previously, we relied heavily on macros and templates, which were efficient but often sounded robotic. With Zendesk Copilot, the language feels more human and dynamic, enabling our agents to focus on personalizing each message and resolving issues more effectively, all while preserving the seamless, premium experience Sealy is known for.

What measurable business results have you seen from using AI in customer care, and have you noticed any impact on employee mindsets or team culture?

Chen: Since getting started with AI, we’ve been using it to streamline both back-end administration and customer-facing communications. For example, AI automatically summarizes discussion points and action items after calls, reducing post call summary work from a couple of minutes to seconds. . Generative AI also helps agents draft responses faster, enabling them to focus on resolving issues more efficiently.

By automating these tasks, we’ve freed up time and energy for our team to focus on tasks that require human empathy and decision making, which has already delivered measurable business results.

First is the impact on our internal teams, because the repetitive and not so inspiring tasks have been taken care of by AI. Our agent’s job satisfaction and engagement has improved, turnover has dropped significantly, average tenure is at an all-time high, and unplanned leave has decreased.

For our customers, our CSAT scores currently sit at about 15% above the industry benchmark, demonstrating that increased efficiency and employee wellbeing are driving better customer outcomes.

How do you ensure AI enhances the human touch in Sealy’s service, and can you share any lessons learned in maintaining authenticity amidst digital transformation?

Chen: Being human is not going out of fashion any time soon, there will always be a degree of desire and preference for communicating with a real person.

For us, AI is not about doing the same with fewer people, but rather elevating our customer experience with the same amount of people. By automating routine, low-involvement tasks, we free our teams to focus on meaningful customer interactions where empathy and sound judgement are essential to uphold our premium brand promise.

A key lesson we’ve learnt is that authenticity requires combining AI efficiency with human ownership. While generative AI helps our agents draft responses faster, we always ensure agents review and personalize responses, so each interaction feels genuine and tailored. Transparency with customers about AI’s role has also been vital too in building trust, alongside ongoing training that helps agents leverage AI tools without losing their unique voice.

This balance allows us to drive operational efficiency and scale, without sacrificing the warmth and authenticity that define the Sealy customer experience.

From your experience supporting businesses through their AI journeys, what’s the key factor that enables some companies to scale AI successfully, while others struggle to move beyond pilot projects?

Mitch Young: There are three critical factors. Firstly, organizations must develop a clear and strategic approach focused on solving specific business problems. Without a clear purpose, projects can lose direction, fail to address real needs, and struggle to gain stakeholder buy-in – often stalling momentum.

Secondly, organizations must invest in the right infrastructure – robust data management, seamless integration with legacy systems, and scalable infrastructure – so that solutions can evolve and grow beyond initial use cases. This technical foundation enables AI projects to be agile and responsive to shifting business needs.

Thirdly, upskilling staff and fostering a data-driven culture play a fundamental role. Companies that succeed embed AI into everyday workflows, empowering employees with tools and training so they can fully leverage and trust new AI capabilities.

Ultimately, scaling AI isn’t simply about proving a concept, it requires strategic alignment, robust technology infrastructure, and effective change management. Together, these elements transform AI pilots into powerhouses that drive sustained value.