In an agent-first retail world, behavioral data—not models alone—determines visibility, loyalty, and measurable P&L impact.

Retailers may “have AI somewhere,” but lack a behavioral data foundation that drives real P&L impact. Retailers see the highest ROI when AI models are connected to real-time product interaction signals: what shoppers touch, examine, hesitate on, or walk away from.

Those micro-behaviors drive tangible outcomes like better promotions, dynamic pricing, smarter inventory allocation, and optimized store labor. What these successes have in common is a strong behavioral data foundation, clear ownership between data teams and operators, and disciplined change management that embed AI outputs into daily decision-making rather than dashboards.

There are two clear signs that tell us a retailer’s AI story is still hype. First, there’s no link between models and unit-level execution. Insights don’t translate into actions at the shelf, store, or SKU level. Second, the data estate is fragmented or untrusted, making real-time decisioning impossible and eroding confidence across the organization.

Before deploying another model, two interventions are essential. First, it is important to rebuild the behavioral data architecture, so AI is trained on signals that reflect how shoppers actually behave in stores - not proxies or lagging metrics. Second, retailers must implement closed-loop decisioning, ensuring every AI insight triggers a measurable operational response and feeds results back into the system. Until those foundations are in place, scaling AI only multiplies noise and not value.

While retail AI agents are becoming the new front door to commerce, the retailers which have the most representative behavioral data signals that AI agents trust when ranking and recommending products will win. There are three primary steps retailers need to do in order to leverage their AI agents:

Leveraging Agentic AI in the retail environment is essential for staying competitive in an increasingly digital marketplace. By focusing on enriching first-party data, developing loyalty models that prioritize representation, and designing performance metrics tailored for AI-driven interactions, retailers can effectively harness the power of AI agents. These steps will not only enhance product recommendations but also deepen customer engagement, positioning retailers for success in the evolving landscape of commerce.

• Enrich first-party data with in-store intent signals,
• Create loyalty models based on representation instead of rewards
• Design performance metrics for agent-driven journeys where algorithmic preference replaces direct consumer browsing.