Key takeaways

• Agentic commerce is moving from concept to early operational reality
• Consumer adoption may be uneven, but commerce stacks still need to prepare now
• The core challenge is orchestration across product data, pricing, payments and fulfillment
• Brands need governance as well as speed in how they deploy AI-led commerce

The most important shift in digital commerce today is not simply personalization. It is the emergence of machine-assisted buying journeys. Forrester has already highlighted how commerce services providers are rewriting long-standing strategies around AI-enabled shopping experiences, while Gartner predicts that by 2028, 60% of brands will use Agentic AI to deliver streamlined one-to-one interactions. At the same time, Gartner warns that more than 40% of Agentic AI projects will be canceled by 2027 due to unclear value, inadequate controls or rising costs. That combination of optimism and caution is exactly what makes this topic strategically important.

The agentic shopper is already appearing in fragments: AI-guided search, recommendation engines, conversational product discovery and assisted checkout flows. Forrester’s recent work on agentic commerce and agentic payments suggests adoption is still uneven, but the behavioral shift has begun. Customers are becoming more comfortable with AI acting as an intermediary between intention and transaction.

“The agentic shopper is not a future persona. It is an emerging buying behavior that is already exposing which commerce stacks were built for search, and which were built for decisions,” said Kristina Rogers, Chief Growth Officer and Global Head of Retail, CPG and Luxury at HCLTech.

Why the stack matters more than the interface

This is where many retailers and CPG brands remain underprepared. The market is moving from automated support to more autonomous, transaction-capable experiences. But agentic commerce will only scale if the underlying commerce stack can expose trustworthy product data, live availability, pricing logic, payment options and fulfillment rules in a form that AI systems can understand and act on.

This makes architecture a competitive issue. A commerce environment that still relies on fragmented catalogs, inconsistent pricing logic or weak inventory accuracy will struggle in an agentic model. AI does not solve those weaknesses. It surfaces them faster. That is why experience platforms such as HCLTech X and HCLTech’s Responsible AI approach in retail and CPG become relevant here. The former helps orchestrate content, commerce and engagement in one layer, while the latter addresses the trust, explainability and governance questions that will become more visible as AI moves closer to the moment of purchase.

Agentic commerce will also change service

The other implication is that the buying journey and the service journey are becoming harder to separate.

HCLTech’s work with a leading UK technology retailer illustrates this well. Supporting a transformation across five brands, HCLTech helped migrate the retailer from legacy virtual assistants and live chat tools to a Copilot-powered contact center built on Microsoft Dynamics 365 and CCaaS.

The result was not only a more modern customer support model, but a more connected one: 55% of interactions are now resolved through self-service, intent recognition accuracy doubled and 350 agents were brought onto the new platform with integrated escalation paths and better operational visibility. That matters in agentic commerce because machine-assisted shopping does not remove the need for human-centered service. It raises the standard for it. When AI helps initiate the transaction, brands still need fulfillment continuity, contextual escalation and reliable issue resolution built into the same operating flow.

“If your commerce stack can’t explain your product, validate availability and complete the handoff to fulfillment in real time, it is not ready for agentic commerce,” said Rogers.

Preparing for the next buying interface

The shift toward agentic commerce is likely to happen unevenly, but the architectural implications are already clear. Brands need to prepare for a world in which discovery, comparison and transaction are increasingly influenced by AI systems acting on behalf of customers.

That raises the importance of product data quality, fulfillment logic, service continuity and governance. A commerce stack that cannot support those elements in a structured, trusted and machine-readable way will become harder to surface, harder to recommend and harder to transact through. That risk does not depend on mass consumer adoption arriving all at once. It grows as soon as machine-assisted journeys begin to shape how products are found and evaluated.

For Retail and CPG brands, the priority now is readiness. The strongest position will belong to organizations that can support AI-mediated buying without losing control of trust, pricing integrity or customer experience. In that environment, agentic commerce is not just a new interface. It is a test of how robust the underlying commerce model really is.