Rather than customers manually comparing dozens of options, AI agents can interpret preferences entered by the customer to do research, assess trade-offs, and find the most relevant choices. As a result, product experience will increasingly depend on the quality of your product information, including technical specifications, availability, pricing, sustainability attributes, customer feedback, questions and answers, and characteristics related to experiences such as comfort, fit, or ease of use. To succeed in this game, you need to ensure that your products are described in ways that both humans and AI agents can understand.
Especially in a digital context, product experience is commonly looked at from a technical perspective. In this post, I want to broaden the approach to also include physical and sense-related experiences as we discuss how agentic AI will affect the product experience and commercial success.
Key takeaways
- Agentic AI will transform product experience by shifting the focus from how products are presented to human shoppers to how they are understood, evaluated, and recommended by AI agents acting on a customer’s behalf.
- Rich data is needed to make sure agents can find your products as consumers more often start the product journey in an AI tool with context-related requirements.
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Agentic AI is relevant to all aspects of the product experience and needs to include the technical as well as sensory side to give a full picture.
The technical side of product experience in agentic commerce
Product experience in digital has been built on having rich technical data. This becomes even more important with agentic AI. Looking at the past, rich product data was needed to help customers make comparisons or to create suggestions based on personas. In the AI era, product data has to become broader. That’s because customers are often beginning their product journeys in an AI tool and asking context-related questions that might get very detailed and specific.
For example, if a customer asks an AI shopping assistant to find running shoes suitable for marathon training, with sufficient cushioning, extra arch support, waterproof materials, and a wide toebox – all at a budget under €150 – the AI can only succeed if your product data contains those specific attributes in a consistent and searchable format.
That means that basic information like product name and price is not enough. Looking at our running shoe example, the AI needs data about cushioning type, energy return, terrain suitability, fit, weight, materials, durability, customer reviews, sizing, and even performance characteristics. With rich product data, AI can compare options, explain trade-offs, personalize recommendations, and narrow down choices much like an in-store specialist would. Without it, the AI may return irrelevant or incomplete suggestions, leading to a sub-standard customer experience. At worst, your product won't even end up in the AI comparison if the necessary information isn't available. This means it’s crucial to make sure your product information is AI-friendly.
And of course, having this data is useful for humans as well. When you have a good data baseline, it supports traditional product experience, like how to support the customer journey and help them find products on your website, while also preparing for AI use.
How can companies prepare for AI discovery?
In Europe, fully autonomous agentic commerce is not widespread and mostly seen in closed betas or specific tools. But it’s only a matter of time before this changes. Europe’s regulatory environment is also becoming clearer in this regard.
This means that now is the time to start preparing for the future where AI systems will increasingly act on behalf of customers by researching products, comparing options, making recommendations, and eventually completing purchases. If you invest now in product information management, inventory visibility, customer data governance, and AI-ready commerce infrastructure, you’ll be better positioned to capture demand as customers use AI tools for shopping.
Part of the process of staying ahead in this shift is deciding what standards to use. For example, Google's UCP (Universal Commerce Protocol) is an open-source standard that allows AI agents, online stores, and payment systems to "speak the same language". If you want to be part of Google UCP for example, you need to take into account the standard and start working now to be found in the tool. Google is not the only option of course, and different platforms have different capabilities. Making the decision now about which ones you want to leverage will help you secure your position in the market.
The role of marketplaces
Being able to easily gather and unify product data from sources such as PIM (Product Information Management) systems is critical for preparing for agentic commerce. That’s because AI agents rely on comprehensive information to evaluate, recommend, and purchase products on behalf of customers.
This becomes even more important when you’re selling through partner marketplaces such as Amazon, where product content, availability, pricing, attributes, reviews, and marketplace-specific requirements are often spread across multiple systems. By ensuring the flow of data from PIM platforms and marketplace channels, you can be sure that AI agents have access to consistent product information regardless of where a customer is shopping.
In Finland, local services also need to be taken into account. The Synkka GS1 system provides broad and deep data for products. It’s not a silver bullet that will solve all challenges related to agentic AI and product experience, but it’s often a good starting point for companies looking to be more AI ready.
Why the sensory side of product experience matters in agentic commerce
When preparing for agentic commerce, product experience extends beyond technical considerations such as data quality, APIs, and system integrations. AI agents will increasingly shape how customers discover, evaluate, and interact with your products, making it important to capture and communicate the full product experience, including sensory and physical attributes.
For example, when recommending a running shoe, an agent may need to understand not only specifications such as weight, cushioning, and durability, but also how the shoe feels during a long run, the type of runner it suits, and even qualities such as comfort, responsiveness, or fit.
The sensory side of things is best captured specifically through customer feedback, product reviews, and images. Pure product information rarely provides enough depth for the customer – they look for the experiential side in reviews. AI should be able to extract that data in the future and incorporate it into recommendations and reviews. Ensuring this information is available is a great way to make your products stand out, meaning reviews and feedback should be seen as a crucial part of extended product information.
As a result, you need to think about product experience on a high level, ensuring that both technical product data and sensory aspects of ownership and use are available to support more personalized AI-driven decisions.
Vincit has a proven track record of helping companies get ready for agentic AI in commerce. Contact us to discuss how to make your products discoverable in AI.
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Riku Kärkkäinen,
Business Area Lead, Vincit Commerce
Eveliina Salomaa,
Senior Consultant
