This article was based on the interview with Dan Russotto, General Manager at furniture.com by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
The retail landscape is in constant flux, a dynamic environment shaped by evolving consumer behaviors and rapid technological advancements. For furniture retailers, this presents a unique set of challenges. The sheer volume of products, coupled with the intricacies of managing data across multiple vendors and platforms, can be overwhelming. Enter agentic AI, a technology poised to revolutionize how retailers operate, optimize, and personalize the customer experience. Dan Russotto, General Manager at furniture.com, an innovative furniture aggregator, offers valuable insights into how his company leverages agentic AI to navigate these complexities and create a seamless shopping experience.
Furniture.com isn’t your typical retailer. They aggregate products from numerous brands, creating a centralized platform where customers can explore a vast selection. This approach, while offering convenience and choice, presents significant data management hurdles. Russotto explains, “Right now we’re pushing about 65 retailers on the site. When you think about it in terms of 200 geographies, 200 categories, that’s 40,000 distinct pages across two million SKUs.” This scale necessitates a technological solution that goes beyond traditional methods. Agentic AI emerges as the answer, providing the scalability and flexibility needed to manage this intricate web of information.
The Power of Agentic AI
Russotto highlights the core difference between traditional automation and agentic AI. “What we mean by agentic AI in this scenario is instead of mapping it precisely, you can tell the agent what your intent is and what the context is.” This shift from rigid, rule-based automation to a more intuitive, intent-driven approach is transformative. Instead of painstakingly mapping every data point, the AI agent understands the desired outcome and navigates the data landscape accordingly. This allows for greater adaptability and resilience in the face of changing data structures or website updates. For example, instead of specifying the exact location of product reviews within a data feed, the agent can simply be instructed to “find reviews for this product.”
Real-World Applications
The practical implications of this technology are significant. Russotto outlines two key use cases. First, product enrichment. “We get basic product feeds from each of our retailers, but that’s all it is. It’s basic.” Agentic AI empowers furniture.com to go beyond these basic feeds, extracting valuable information directly from partner websites, such as detailed care instructions, videos, and customer reviews, enriching the product listings and enhancing the customer journey.
Second, streamlining transactions. “Now, with the way that agentic AI is taken off, we’re actually able to take the transaction as well.” Imagine a customer selecting items from multiple retailers within their furniture.com cart. Behind the scenes, agents work tirelessly, executing purchases on the customer’s behalf across different platforms. This seamless integration simplifies the checkout process and eliminates the need for customers to navigate multiple websites and checkout systems.
Optimizing for the Future of Search
Russotto emphasizes the importance of optimizing for Large Language Models (LLMs), drawing a parallel to the early days of search engine optimization. “It’s very similar to what happened with Google 20, 30 years ago where you had to set up your page… to optimize for SEO so that Google could crawl.” Just as websites adapted to the nuances of search engine algorithms, they now need to cater to the specific requirements of LLMs like OpenAI. Furniture.com achieves this by utilizing tools like the Model Context Protocol server, which facilitates the seamless integration of their data with LLMs, ensuring their content is readily accessible and discoverable.
A Mindset Shift
The adoption of agentic AI requires more than just technological integration; it necessitates a fundamental shift in mindset. Russotto acknowledges the initial hesitation, saying, “Oftentimes, companies don’t know where to start.” Furniture.com’s approach involved comprehensive training, hands-on hackathons, and ongoing education to empower their team to effectively utilize these new tools. This commitment to fostering a culture of experimentation and learning has been crucial to their success.
Looking ahead, Russotto envisions a future where the customer experience is truly personalized. “Shoppers will create their own user experience through the LLM.” Imagine customers dictating their preferred interface, borrowing elements from their favorite websites, and populating it with products from furniture.com. This level of customization, driven by the power of AI, promises to reshape the retail landscape, empowering customers with unprecedented control over their shopping journey.
The journey into the realm of agentic AI may seem daunting, but the potential rewards are immense. Furniture.com’s experience serves as a compelling case study, demonstrating the transformative power of this technology. By embracing a mindset of continuous learning, investing in training, and strategically implementing agentic AI solutions, retailers can unlock new levels of efficiency, personalize the customer experience, and position themselves for success in the ever-evolving world of retail. The key takeaway is to start somewhere, experiment, learn, and adapt. The future of retail is agentic, and those who embrace this shift will be best positioned to thrive.
