Agentic AI Will Pick Products for Your Customers. Will It Pick Yours?

Imagine handing an assistant you credit card, a budget and a brief: replace my running shoes, same fit, better grip, before Friday. It comes back having chosen, paid and arranged delivery, and you never saw a product page. That is agentic shopping, and the only question that matters for a brand is whether the agent picked you or skipped you.

How soon will AI agents really be shopping for people?

Sooner than most brands are prepared for. According to Gartner, 60% of brands will use agentic AI for one-to-one customer interactions by 2028, with a third of generative-AI interactions involving autonomous agents. The capability is arriving fast, and consumer willingness is not far behind.

A recent study by Worldpay's research found Chinese consumers leading the charge, with roughly two-thirds of consumers already open to AI shopping. In contrast, 53% of French consumers expressed they would not feel comfortable letting AI buy on their behalf. They will let an agent suggest, not yet decide.

So what should you actually be doing about agentic AI right now?

Don't panic, be proactive. The mistake isn't moving too slowly. It's moving rashly, burning 2026 budget on speculative strategies while the landscape remains unclear. The brands getting it right are doing something quieter and far more useful. They're laying the groundwork now.

And that groundwork is entirely in our control. The window before mass adoption is the opportunity, the moment when being early costs you almost nothing and being late costs you the most. How do we do that? 

We make your data, content and products machine-readable. We structure your information so a system can actually understand it. We fix the rickety foundations while there's still no penalty for it. An agent can't choose a product it can't read. The brands that do this work today are the ones agents will recommend tomorrow, and that head start can't be bought back later.

What actually makes a brand machine-readable?

Clean, structured, complete product data. Accurate feeds, consistent attributes, sizing and materials expressed in fields a machine can parse rather than buried in a hero image. Specifications that are present, correct and unambiguous. The dull discipline of tidy product data is, it turns out, the entire qualification for the agentic shelf. This is the foundation we lay in AI and automation work: getting a brand's data into a state where a machine can confidently choose it.

The agent will not be charmed by your brand film or moved by your founder story. It reads fields. The brands that win the agentic era are not the ones with the loudest campaign. They are the ones whose data was in order before the buyer handed the decision to a machine. Tidy your feed now. The robot is not sentimental.