The Exponential Gain of AI-Powered Commerce: What the Gartner Hype Cycle 2026 Means for Retail

AI isn't simply adding another touchpoint to commerce. It's changing where the shopping journey begins.

Gartner’s Hype Cycle for Digital Commerce, 2026  report points to AI and agentic commerce as major forces reshaping customer behaviour, commerce technology and the architecture that sits behind the buying experience.

As AI platforms and AI assistants become part of product discovery, consumers find information, compare offers and narrow down their choices through conversation without necessarily starting with a retailer’s website or traditional search engine.

So, what do Gartner’s 2026 findings mean for retail destinations?

What Does AI-Powered Commerce Mean in 2026

The temptation with any rapidly developing technology is to wait until the market settles before investing. The Gartner Hype Cycle report itself, however, offers a more nuanced lesson.

The takeaway for retail operators isn't to adopt every emerging technology, nor is it to wait until each one reaches maturity. Instead, there is value in identifying practical applications that can be tested today, learning from how customers use AI and building the capabilities required for what comes next.

An important distinction, however, is that AI platforms and AI agents are at very different stages of development.

Some of the concepts mentioned in the 2026 report such as Agentic Buying and AI Checkout point toward a world in which AI may eventually take a much more active role in purchasing.

An agent could potentially move beyond recommending a product to evaluating alternatives and completing parts of a transaction on a customer's behalf. However, for now Agentic Buying and AI Checkout only offer a glimpse of where commerce could be heading, even if their broader adoption and impact are still developing.

How AI is Changing Retail Commerce

At the same time, other applications of AI are already much more tangible. AI agents in retail help shoppers discover stores, offers, dining, events and services through natural-language conversations.

For operators, those same interactions create something uniquely valuable: customer intent data. Retail teams can begin to understand not only what visitors do at a destination, but what they are actively looking for the brands, products, services and experiences behind their visit.

That means a seemingly simple question such as “Where can I find running shoes?” contains more than a request for directions. It can signal product interest and potentially purchase intent.

Every interaction helps retail destinations build a clearer picture of what visitors repeatedly look for, which questions go unanswered, and what remains difficult to discover or access.

For retail destinations, this creates an opportunity to treat AI not only as a customer-facing service, but as a new source of customer intelligence.

The Coniq AI Concierge

This combination of customer assistance and intent intelligence is central to the thinking behind the Coniq AI Concierge. It provides shoppers with natural-language answers grounded in the destination's own content, covering stores, dining, events, offers and facilities.

At the same time, conversations capture anonymous intent, helping operators understand what shoppers are actively looking for.

When a shopper signs in, that intent can become part of a consented customer profile and connect with the wider customer and loyalty experience.

In other words, the opportunity is not simply to put an AI interface in front of existing information. It is to create a loop in which better discovery generates better insight, and better insight enables more relevant retail customer engagement.

Optimizing Retail for AI-Powered Discovery

This report also shifts the focus to another important conversation: what happens when the interface between shoppers and commerce is no longer controlled entirely by the retailer?

Emerging concepts such as Answer Engine Optimization (AEO) suggest that product and destination information increasingly needs to be understandable not only to people, but also to the AI systems helping those people make decisions.

For retailers and retail destinations, this creates a new kind of visibility challenge.

While traditional SEO focuses on making information discoverable through search engines, AEO extends that challenge to AI-powered discovery. Information needs to be accurate, structured and accessible enough for AI systems to interpret it and surface it in response to customer questions.

For retail destinations, that puts greater emphasis on the quality of the information behind the experience. Store details, opening hours, offers, events, facilities and loyalty benefits need to remain accurate and accessible. After all, an AI experience can only be as useful as the information it has to work with.

Our Key Takeaways

• AI is becoming part of the commerce infrastructure, not simply another digital channel. The Gartner Hype Cycle for Digital Commerce, 2026 highlights the growing role of AI and agentic technologies across commerce. For retailers, the strategic question is increasingly where AI can create measurable value across the customer journey.
• Retailers can experiment today while preparing for more agentic commerce tomorrow. Concepts such as Agentic Buying and AI Checkout point towards a more autonomous future for commerce, but retailers do not need to wait for that future to begin learning. Practical AI applications already offer opportunities across discovery, customer service and shopper insight.
• AI agents create value for shoppers and operators at the same time. For visitors, conversational AI makes stores, products, offers and services easier to discover. For operators, those same interactions provide signals of shopper intent, helping teams better understand demand, identify experience gaps and create more relevant engagement.
• AI-powered discovery raises the importance of accurate, accessible information. As shoppers increasingly interact with AI systems, retail information needs to be understandable to machines as well as people. This makes strong data foundations and emerging practices such as AEO an increasingly important part of the retail technology landscape.