Wayfair CTO on Bringing AI Into the Virtual Living Room

Optimization plays a central role in business growth, whether it’s through adopting new technologies, rethinking operational workflows or introducing innovative products and services.

Amid all the technologies out there today, businesses looking to unlock greater optimization capabilities are increasingly turning to artificial intelligence (AI). In retail and eCommerce alone, AI and generative AI applications are reshaping the way consumers shop, businesses operate and supply chains function.

“The speed at which AI capabilities have advanced, from text models to multimodal and reasoning agents, has been remarkable,” Fiona Tan, CTO at Wayfair, said during a discussion for the PYMNTS series “When Chatbots Go Shopping: How GenAI Is Shaking Up the Retail Status Quo.”

The AI revolution is just getting started. The potential for ongoing improvements is vast, as generative AI continues to evolve and integrate more deeply into core eCommerce operations.

At Wayfair, AI isn’t just a tool; it’s part of the home goods and furniture platform’s fabric. Tan described how AI is integrated across teams rather than being siloed into a separate function. “Our machine learning scientists work alongside engineers and product managers to tackle customer problems and operational challenges,” she said. “AI is just one of the tools we use to solve these issues.”

Generative AI’s accessibility has further democratized its use across the organization. “It’s no longer confined to the tech team,” said Tan. “To fully leverage generative AI, we’ve needed to focus on change management across the organization. It’s not just about the technology but also about enabling people to use it effectively.”

How AI Is Transforming Online Shopping

Wayfair’s integration of AI is not new. Tan said the company began exploring traditional AI and predictive models over a decade ago to address complex eCommerce challenges.

“One of the first applications was around our marketing channels,” said Tan. “We used predictive models to autonomously adjust our marketing spend and improve attribution accuracy.”

The company’s focus on AI was, in part, driven by the unique challenges of selling home furnishings online. These early investments laid the foundation for Wayfair’s advanced use of AI today.

“The home category is emotional, style-based, and lacks the benefit of well-known brands,” Tan said. “Coupled with supply chain complexities and the difficulty of returns, we had to push the envelope on AI and machine learning to create effective solutions.”

With the advent of generative AI, Wayfair has elevated its capabilities, especially in customer-facing applications. One example is the Decorify feature, which uses generative AI to inspire customers with hyper-personalized visualizations. “Customers can upload a photo of their room and see it styled in various ways,” Tan said. “We use text-to-image generation to create these visuals and then match them to real-world products in our catalog.”

“The conversion rate from customers who use this feature is significantly higher,” she added. “It bridges the gap between the generated world and the real world, making shopping an inspiring and seamless experience.”

Generative AI Takes Center Stage in Retail’s Future

AI has also strengthened Wayfair’s relationships with its suppliers. Through predictive insights and generative content, the company helps suppliers showcase their products more effectively.

“We’ve always shared insights like predictive winners with our suppliers,” said Tan. “Now, with generative AI, we can create more personalized content that represents their products better, fostering a win-win partnership.”

Despite AI’s transformative power, Tan emphasized the importance of keeping a human in the loop for critical applications. “We’ve always ensured human oversight in areas like customer service or catalog management,” she said. This approach balances innovation with quality control, ensuring that AI-generated outputs meet Wayfair’s high standards.

“We’ve learned that while custom models excel in certain areas, generalized models can sometimes outperform them, depending on the use case,” she said.

However, Tan noted that in some areas — like generative imagery for customer inspiration — a hands-off approach can work. “Customers are surprisingly forgiving of minor inaccuracies, like slightly askew table legs, because the overall benefit outweighs the imperfections,” she said. “It’s about finding the right balance.”

As for the future, Tan sees endless possibilities. “We’re excited to lean further into multimodal capabilities and explore how generative AI can inspire customers in new ways,” she said. “There are exciting times ahead, and we’re just scratching the surface of what’s possible.”