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“I’m going on a 5K run in the Swedish woods, and I need trainers suitable for the terrain. What shoes would you recommend?”
It might sound like a conversation you could overhear between a customer and a seasoned retail assistant. But it could also be an AI search query. And it represents a fundamental shift in how people discover and buy products.
The traditionally linear marketing funnel is evolving. The journey from inspiration to purchase, once scattered across videos, social media, and search engines, is now converging into a single, fluid conversation.
We are seeing this shift in real time. To succeed in this new era of search, brands must follow a three-step journey to get “AI agent-ready”.
Before an AI can work its magic, your core product data must be flawless. This goes beyond just having the right price. It’s about creating a foundation of trust and reliability that an AI agent can build upon.
Utilising Google Tag Gateway and Enhanced Conversions ensures your measurement is both accurate and privacy-safe. These tools are commercially essential — enabling agents to continuously identify your highest-value customers and efficiently scale your reach to find more like them.
Much like local inventory ads allow an AI assistant to tell a user, “By the way, you can pick this up right next door”, the right combination of tools tells the AI, “This is exactly the type of conversation we should be having”.
Merchant Center Excellence Report can audit and optimise your shopping feeds. This helps you benchmark against the competition and improve everything from title length and description quality to stock accuracy.
Finally, many retailers only list their most profitable products in their feeds. In an agentic world, that means you’re invisible for a huge range of queries. To maximise discoverability, make sure your entire product catalogue is available.
Case study: There are brands already embracing the shift. Retail giant Jysk recognised that incomplete data feeds were a barrier to further growth. Alongside agency s360, they used an AI-powered feed engine, which aggregated input like customer reviews into a format search engines could recognise.
Previously, a shopping feed was a fairly straightforward performance tool. You needed a product title, a price, an image, and you were mostly set.
Today, that’s just the starting point. An AI-powered agent needs more. It needs to understand use cases, customer loyalty benefits, and detailed product features to have a meaningful conversation with a potential buyer.
This requires a fundamental shift from disparate data outlets to a centralised hub. Whether it’s a product information management (PIM) platform or a setup in a cloud environment like Google Cloud, centralisation is crucial for maintaining consistency and quality control as you scale.
Once your data is centralised, large language models (LLMs) like Google’s Gemini can analyse your products and generate the rich, descriptive details that answer complex user needs.
They can build use cases, explaining why a shoe is good for a rainy run, and create better creative, generating high-quality images with backgrounds that fit a specific season or campaign.
And they can do it at a vast scale, across thousands of products.
Case study: Retailer Clas Ohlson used LLMs to generate and audit brand copy across thousands of product ads. The results were not only an increased click-through rate, but also a lower production time.
Finally, you need to be present across the whole customer journey. When a consumer can go from vague inspiration to a direct purchase in a single conversation on AI Search, your marketing efforts must be just as integrated.
Universal Commerce Protocol (UCP) acts as a common language between retailers and AI agents. It’s a set of rules that allows an agent to perform specific actions, such as:
- Finding the right items based on the user’s conversational query.
- Enabling a purchase directly within the AI chat interface.
- Accessing member-specific pricing and offers.
- Providing order status updates within the conversation.
We know that the real magic happens when you break down silos. 82% of journeys where people discover new brands, products or services include Google or YouTube.1
You can no longer afford to have a disconnect between the emotional story you tell in a brand campaign and the functional data in your product feed. They are now two sides of the same coin, feeding the same AI-driven engine.
And a huge part of that is video.If your brand isn’t present on platforms like YouTube, you’re invisible during this discovery phase. An AI agent might not recommend your running shoes if it has never seen them in the context of a trail-running video or a review by a trusted creator.
Your branding activities are no longer just about building long-term equity; they are about providing the essential data and context for AI to discover your products in the moment.
Case study: Brands that use AI-powered ads products, like Video view campaigns, Video reach campaigns, and Demand Gen, see an average of 17% higher return ad spend (ROAS).2 Retailer Ocado used YouTube and Demand Gen to drive direct conversion and new customer interactions.
Becoming ‘agent ready’ for the future of retail
The journey to becoming “agent ready” starts with perfecting your fundamentals, is accelerated by enriching your data with AI, and is realised by unifying your marketing strategy.
We believe the brands that thrive in this new era will be the ones who stop thinking in channels and start thinking in dialogues. It’s a move from being found to being truly understood.
The future of retail is agentic and conversational, and the time to prepare your brand to speak is now.
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