Are Retailers Ready for Agentic Commerce?

We saw the introduction and rise of agentic commerce in 2025 thanks to the massive adoption of AI. As it’s moved from concept to reality, AI agents are beginning to influence how products are discovered, compared and purchased.

As autonomous agents can influence the path to purchase in real time, many retailers are asking a critical question: Is our data, technology and operating model ready for this shift? Last year’s Black Friday offered a glimpse of what’s coming.

Salesforce data showed that across Cyber Week, AI and agents influenced roughly 20% of all global orders, driven by personalized product recommendations and conversational customer service. This marked an inflection point.

Retailers are no longer using AI simply to remove friction – they’re using it to turn every digital and in-store interaction into a differentiated, brand-building moment. From Digital Commerce to Autonomous Decisioning From a consultancy perspective, the last few years have been fascinating.

We’ve watched digital storefronts replace catalogs, order management systems replace manual fulfillment, and personalization engines enhance merchandising. Now, the next evolution is underway: dynamic, autonomous decision-making across pricing, promotions, fulfillment and service.

Many enterprise retail systems were designed around human judgment and static rules. Agentic AI challenges that foundation by enabling decisions to be made continuously, contextually and at machine speed. The opportunity isn’t just automation, it’s a fundamental rethinking of how commerce decisions are made across the entire customer lifecycle.

Delivering Business Impact Retailers we work with are seeing early value where agentic systems sit closest to revenue, margin and customer experience.

In my conversations with leadership, agentic AI can make the most impact across four main areas of the business: Pricing and promotion governance: Moving from static promotions to AI-driven offers that respond to inventory, demand signals, customer value and margin thresholds can deliver a more personalized shopping experience.

Don’t forget to enforce guardrails at scale. Order fulfillment optimization: The latest technology gives agents the ability to dynamically determine the most profitable fulfillment path in real time, balancing delivery promises, inventory availability and cost-to-serve.

Service interactions: Conversational agents can resolve issues faster by understanding customer intent, order history, warranty policies and loyalty status. These features reduce handle time and improve satisfaction. Some retailers have partnerships with third-party AI platforms so customers can purchase items directly via chatbot.

Loyalty and rewards: Personalized incentives driven by behavior, lifetime value and moment-based triggers (not blanket discounts) create stickier relationships and higher repeat purchase rates for more satisfied customers. It’s never been more important to release your data from silos and ensure you’re working with the most accurate information.

The common thread: agentic commerce drives impact where operational decisions intersect directly with customer experience. Data is the Real Readiness Test Agentic AI is only as powerful as the data it can access, interpret and act on in real time. Most retailers don’t have an AI problem – they have a data problem.

High-quality, well-governed enterprise data enables agents to operate with accuracy, trust and transparency.

This includes different types of data, for example: Customer data (identity, preferences, behavior, and lifetime value) Product and inventory data (availability, substitutions, and margins) Operational data (fulfillment constraints, service policies, and costs) Outcome data (conversion, margin impact, churn and satisfaction) With the right contextual data, retailers can determine the next best offer, experience or buying moment that is shaped by customer segments, purchase history, seasonality and channel constraints.

It’s personalization at its best. Outcome data then closes the loop. It allows organizations to understand what actually happened – e. g. , how a price change impacted margin, how inventory shifted during a promotion or how service automation affected retention. Without this feedback loop, agents optimize blindly. Put simply: AI doesn’t create intelligence.

Data does. Three Truths for Retail Leaders As agentic systems mature, there are a few realities executives must embrace. First, agentic commerce amplifies whatever foundation already exists. So if your data is fragmented, your outcomes will be fragmented. Second, implementing agentic capabilities is an operating model shift, not a technology upgrade.

Teams must rethink ownership of decisions across marketing, commerce, service and supply chain in order for it to be adopted and successful. Governance matters as much as innovation. Retailers must define which decisions can be automated, where humans stay in the loop and who owns the out