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Embedded AI Agents Move Retail from Pilots to Operational Performance

Embedded AI Agents Move Retail from Pilots to Operational Performance

Retailers' multi-year AI experimentation

Over the past several years retailers have been actively experimenting with artificial intelligence, running pilots and trials to understand how the technology can be applied across the business.

These efforts were not isolated to research teams; they involved various business units exploring how AI could change day-to-day operations.

Common AI tools deployed in retail

Retailers introduced a range of AI-driven tools as part of those experiments to address different operational needs.

  • Chatbots to assist customer interactions and front-line support
  • Copilots to aid staff with tasks and decision support
  • Analytics tools designed to surface insights and improve planning

Objectives behind early AI projects

The main aims of these deployments were to boost efficiency across processes and to enhance decision-making by providing faster, data-driven recommendations to teams at multiple levels.

Many pilots focused on proving value in measurable operational outcomes rather than purely technical showcases.

Shift from experimentation to operational impact

As adoption has progressed, the industry conversation is moving beyond testing isolated solutions toward measuring and scaling the operational impact of AI across core business functions.

Organizations are increasingly focused on how AI can be embedded into routine workflows to deliver consistent performance improvements.

Industry perspective from SAP

“What’s a little different this year than last year is that retailers are getting, I would say, more serious about AI,”

The remark came from Kristin Howell, who is part of SAP’s retail industry product management team, in comments to Supply Chain Management Review.

The path to realizing AI value

That increased seriousness reflects a recognition that meaningful AI benefits arise when intelligence is woven into core business processes rather than left in separate point solutions.

Delivering that value depends on having clean, real-time data flows and well-defined guardrails so AI can operate reliably and in line with business objectives.

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