With new agentic capabilities becoming the norm, retail is facing a major shift. In our new Reaktor Talks video, we discuss what this means for brands and retailers.
What's happening in the retail landscape is an evolution of ecommerce, or even omnicommerce. We're moving toward agentic commerce, powered by the rise of AI and agentic capabilities.
It's genuinely exciting for businesses and consumers alike. For businesses, it means operating in new, more innovative ways with greater efficiency. For consumers, it means connecting with the brands they love, discovering products, and comparing offers in a completely frictionless way. It's relevant for any retailer or brand – whether you're a premium label, a high-volume discounter, or a marketplace.
Agentic commerce is an arc where the definition can be quite wide, and what can be done technologically is very modular. So anyone can benefit from its evolution.
A new way to express what you want
What's really changing for consumers can express their needs. Until now, they were largely stuck describing product attributes when they wanted to buy something. Today, they can express their needs in natural language with desired outcomes in mind.
Take someone preparing for a marathon. They can simply say: "Hey, I'm running in London in two months and I need fast running shoes to help me finish in under four hours." The cognitive load of browsing websites, comparing offers, and reading hundreds of reviews can be partially or fully offloaded to a personalized AI assistant, leaving the consumer with the power to decide when and what to buy. And when they're ready, they can do so with an almost zero-click checkout. That's one of the defining benefits of agentic commerce.
Getting started: the challenge for brands and retailers
For brands and retailers, the first challenge is knowing whether they even exist in these new AI assistants and getting reassurance that they do. But once that's confirmed, the next question is: does that presence align with their brand? Is their brand image respected? Is there any dilution of their core message?
This is where generative engine optimization comes in, specifically the auditing and insights phase. But as a parallel track, a safe and practical starting point for anyone, from a premium brand to a cross-category retailer to a marketplace, is to self-audit the quality and richness of their product content, beginning with the product catalog in a text-based format.
The key question: does your content include the keywords and attributes that large language models (LLMs) value today? Going back to our marathon runner example, claiming that a running shoe is "propulsive" may not be enough. What does that actually mean? Has the brand conducted tests? What's the measurable energy return, expressed as a percentage?
Consumers may not frame their search that way, but a brand has scientifically valid methods to measure and prove performance. Passing that information to an LLM is powerful – it also lowers the risk of hallucinations from those systems. We believe the brands that invest in this area will have a clear edge over their competitors.
Looking inward: internal optimization
Businesses can also look inward at what they can optimize internally. Many have low-stakes, low-risk tasks and processes ripe for automation. We're already seeing this: more mundane tasks are moving toward full automation, building out agentic workflows and driving greater overall efficiency.
Further out, mid- to long-term, we foresee brands forming alliances and partnerships with marketplaces and other players to achieve even greater operational efficiency. A brand and a marketplace that compete today could become collaborators tomorrow.
Consider this: a consumer wants to buy a product from a brand, but the brand is out of stock. Today, that consumer drifts to a marketplace, and the brand loses the sale. In an agentic framework, two agents, one from the brand and one from the marketplace, can communicate in real time. The brand's agent can check the marketplace's inventory, capture the sale in the appropriate channel, and settle a referral fee between the two parties. For the consumer, it's a seamless, zero-click experience – no jumping between websites.
Premium and luxury: bridging the human moment
For brands in the premium or luxury tier, there are also significant opportunities, particularly in bridging the gap between the machine-to-machine nature of agentic commerce and the emotional, high-touch experiences those brands are known for.
Imagine a consumer expressing a high intent to buy a luxury watch. Their personal AI assistant knows where they are, checks whether a nearby store is available, and books an appointment, or signals that a personalized in-store experience awaits them. The consumer then walks in and gets exactly the kind of emotionally rich, high-value experience that defines the brand. That's agentic commerce working in concert with omnicommerce, not replacing it.
Two tracks, run in parallel
This is a big shift, and it's easy to feel overwhelmed. But we recommend brands, retailers, and marketplaces adopt a dual-track approach and run both in parallel.
The first track: start self-auditing how you exist today in AI assistants from the consumer's point of view. Are your brand and products showing up? What's your reputation in those systems?
The second track: on the product content side, whether that's data, text, images, videos, or rich content, there's clear potential to start investing now. Sanitize, structure, model, and enrich your data so it's LLM-friendly, as early as possible.
We're particularly excited at Reaktor to keep working with our customers on agentic commerce as a genuinely gigantic opportunity. Technologically, a lot is already in place, whether you're shaping new shopping experiences for consumers or optimizing what you do internally. And what's really exciting? Imagination is the limit.
