For years, ecommerce has been stuck in a transactional rut. Shoppers type in a keyword, scroll through product grids, maybe filter a few options, and if the stars align — convert. It’s a model built to manage massive product catalogs efficiently — and for a long time, that was enough. Or perhaps simply the most a shopper could hope for.
But today’s shoppers expect more than efficiency; they want experiences that understand their context, preferences and intent in the moment. They crave the kind of natural, human conversations we have in a store with a helpful associate.
But that’s finally changing.
Thanks to advances in generative AI and large language models (LLMs), we’re entering a new era of LLM-powered conversational commerce: one where shoppers can engage in real-time consultative dialogue with intelligent digital agents that truly understand their needs. Think about asking your favorite retailer’s AI assistant “What’s trending for spring?” or telling it, “I need a water-resistant jacket that looks good at work.” — and getting a thoughtful, personalized, human-like response.
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This isn’t just a chatbot with a pre-programmed script. It’s a new kind of retail experience — one that listens, reasons and responds with the kind of natural, contextual intelligence that feels human.
The challenge? Most brands aren’t ready for it.
Why? Because most retailers are still operating with systems built for transactional clicks, not conversational journeys.
Conversational Retail is More Than a Chatbot
To be clear, we’ve had “chatbots” for years. Most were glorified FAQ engines, able to route customers to order tracking pages or answer a frequently asked question. Helpful, sure, but far from conversational.
What’s different now is the rise of LLM-native platforms that can engage in rich, open-ended conversations. These systems don’t just pick from a list of canned responses. They dynamically interpret a shopper’s intent, combine that understanding with up-to-date product data, and generate insightful answers and product recommendations in real time.
And here’s where it gets even more powerful: these LLM-powered systems aren’t just conversational — they’re brand-specific. Retailers can now fine-tune large language models to reflect their unique voice, tone and values. That means a luxury fashion brand can sound elegant and refined, while a sneaker brand comes across as energetic and playful. These AI-driven conversations don’t feel generic. They feel like your brand, extending the personality and customer experience shoppers expect in-store to every digital interaction.
And most importantly, these LLM-powered systems should power the tools that feed the conversational shopping assistant, which shifts digital shopping from static keyword searches to dynamic, natural conversations, like saying, “I’m looking for something like this. What do you recommend?” And the results should delight the customer.
There’s less infinite scrolling and more guided discovery, and less self-serve and more assisted, human-like journeys. With LLMs powering the experience, customers can engage in fluid, back-and-forth conversations. These models ask follow-up questions, understand context and adapt in real time, delivering smarter recommendations and more personalized journeys at every step.
In essence, the store associate has come to the screen.
Why Most Retailers Can’t Keep Up
As promising as this sounds, most retailers aren’t equipped for it. Their current infrastructure is built for a world that is keyword-driven. This is not what the future looks like.
Here’s the reality for many brands today:
- They are focused on AI conversational assistants that are simply wrappers around ChatGPT or similar massive LLMs, without the appropriate tools for these systems to integrate with.
- Product discovery is hamstrung by a keyword search paradigm and does not take advantage of true LLM-powered search.
- Their brand voice is inconsistently applied, making AI-generated conversations feel generic or off-brand.
Building truly conversational retail experiences requires more than plugging ChatGPT into a website. It demands LLM-powered infrastructure that’s purpose-built for the retail commerce experience — systems that can ingest natural language, translate it into actionable queries, connect with live product and inventory data, and respond in a way that feels consistent with the brand’s unique tone and values.
What Retailers Should be Doing Now
If a more conversational and personalized experience is the future of commerce — and it is — what should brands be doing today to prepare?
A few priorities stand out:
1. Invest in an LLM-based operating system: ChatGPT is amazing, but it is not the engine that is going to power the personalized experience now possible with an LLM-based operating system. Retailers need to either build or partner to deliver a smaller LLM that is built for the speed of commerce and knows everything necessary about their customers and the products the retailer sells.
3. Define your brand voice for AI: Retailers need to codify how their brand speaks — its tone, vocabulary and values — so AI agents can respond in ways that feel authentically on-brand.
4. Start small, learn fast: Conversational commerce won’t be perfected overnight. Start with high-impact, low-risk use cases like guided product discovery, learn from customer interactions and real-time behavior, and scale from there.
A Personal Touch, Delivered by AI
Retail has always been about people. About understanding needs, offering guidance and building trust. Conversational commerce doesn’t replace that. It amplifies it in digital spaces where that personal touch has been missing for too long.
As AI reshapes how people shop, the brands that win won’t just be the ones with the best products or the slickest websites. They’ll be the ones that listen, understand and respond — in real time, in the customer’s language and in ways that feel natural.
The tools are here. The expectations are rising.
Now it’s up to retailers to catch up.
John Andrews is the Co-founder and CEO of Cimulate.ai, bringing decades of retail, ecommerce and digital transformation experience. A serial entrepreneur and industry leader, Andrews has built and scaled multiple ventures at the intersection of retail and technology, including leadership roles at Endeca, Oracle, co-founding Celect (acquired by Nike), and driving innovation to help retail brands unlock new revenue through AI-powered solutions.
