Agentic AI in Retail:

Opportunity, Risk, and the Path Forward

As retailers accelerate AI adoption, agentic AI is emerging as a powerful lever for efficiency and profitability—provided it is deployed with the right data foundations and human oversight.

There’s tremendous excitement around leveraging agentic AI for real-world use cases to help brands reduce waste, optimize stock levels, and improve profitability. With clean data, even small data science teams can spend more time on strategic decision making and delegate the operational side to AI.

There’s a lot of momentum around AI-driven automation across planning, supply chain, customer support, and more. While AI agents offer incredible potential for rapid, autonomous decision-making that drives long-term growth, the biggest risk is poor decision-making based on incomplete or unstructured data. Prematurely replacing skilled teams with automated systems can backfire. The industry should proceed thoughtfully and focus on building strong digital infrastructure and clear, contextual communication layers – starting with structured data pipelines and a semantic layer tailored to retail.

Henrique Moyses

VP of Data Science, Crisp

Why Agentic AI Is Gaining Momentum

Avoiding the Risks of Over-Automation

As AI adoption scales, the biggest risk remains poor decision-making rooted in incomplete or unstructured data.