Six ways agentic AI for retail transforms business in 2025

Discover how innovative retailers increase productivity and create value with agentic AI

This is an unprecedented time for the retail industry. Agentic AI for retail gives businesses the  opportunity to transform every aspect of their operations by embracing change and innovation. 

The newest front in the fast-changing world of AI involves agentic AI and the use of autonomous AI agents. Unlike predictive and generative AI which let retailers query data to forecast trends, make data-driven decisions, and create plans and strategies, agentic AI takes things to a new level with autonomous agents. An autonomous AI agent is an AI software program that performs tasks independently, makes decisions, and solves problems to achieve specific goals. These autonomous systems use advanced techniques to analyze data, adapt to new situations, and take actions with minimal human intervention. 

There are many areas within a typical retail organization where autonomy can provide powerful benefits but merchandising and category management are areas of immediate focus. That’s because they are data-intensive, process-driven areas rife with opportunities to dramatically improve workflows and decision-making speed and accuracy. Here are six real-world use cases where AI agents can help retailers work smarter, faster and more efficiently. 

New item evaluation and performance optimization with agentic AI for retail

New products are essential for retail growth, but they suffer from chronically high failure rates. AI agents help retailers solve the new product paradox by continuously monitoring performance metrics to identify potential issues and recommend preemptive corrective actions. Because an AI agent is “always-on,” performance issues  can be  quickly identified compared to legacy processes with infrequent reviews and slower reaction times. 

This process of continuous evaluation allows an AI agent to benchmark performance against similar items. This process helps identify factors that contribute to poor performance such as pricing compliance, promotion or poor on-shelf availability. Beyond simply identifying issues, an AI agent is like an enterprising co-worker who displays initiative and acts without being told what to do to achieve desired outcomes. The AI agent can automate corrective actions, such as optimizing promotions, adjusting prices, or even updating planograms to improve a new product’s odds of success, all while minimizing the need for human involvement. 

From a practical standpoint, imagine a retailer with several hundred stores launching a new product and experiencing initial success but then sales  plateau. An AI agent would be able to identify drivers of underperformance in select geographies due to factors such as shelf placement or ineffective promotion by benchmarking against similar successful launches. The AI agent can independently act within a scope determined by the retailer and appropriate human oversight to increase promotion frequency in underperforming areas while also collaborating with store operations to ensure proper shelf placement and, if needed, trigger alerts to correct out-of-stocks. 

Planogram compliance and category improvement with agentic AI for retail

AI agents are incredibly valuable in detecting planogram compliance and product availability issues that contribute to category underperformance. A typical situation where the need for this capability arises involves analysis of sales performance following a category reset. If a sales decline is observed, an AI Agent can quickly determine the root cause, and autonomously act to generate restock orders, recommend  assortment changes and promotional refinements. In addition, the AI agent can alert store teams of planned changes to ensure proper execution. 

A typical situation every retailer deals with involves planogram compliance following a category reset. An AI agent can identify misplaced or missing items and alert store employees to needed corrective actions. The AI agent can also identify post-reset promotions that fail to meet performance goals and take actions such as optimizing promotions for high performing products. This ability to quickly resolve non-compliance issues or improve promotional effectiveness are major advances in improving  category management. 

Sales analysis with agentic AI for retail

An AI agent can transform  how a  category manager conducts weekly sales reviews by automating data gathering, performance analysis and scenario simulations. The traditional weekly sales analysis process is incredibly  time-consuming, but AI agents can autonomously identify key performance drivers, uncover opportunities, and prepare actionable recommendations such as adjusting pricing, refining promotions, or alerting supply chain teams to current or anticipated product availability issues. 

The automation of mundane processes is a  key aspect of the AI agent value proposition. Category managers can focus less on data collection and more on executing strategic decisions, ensuring that their time is invested in impactful actions. For example, a category manager at a large retailer may spend four hours each week analyzing sales performance. An AI agent can perform the same analysis in minutes to highlight specific issues such as sales weakness caused by a competitive promotion and then suggest effective countermeasures like launching targeted promotions or adjusting pricing. 

This shift from manual reporting and insights discovery to automated insights and action-oriented workflows is a major breakthrough for  category managers and others involved in sales analysis who can devote more time to strategic planning. 

Promotion contribution analysis/optimization with agentic AI for retail

The speed and accuracy components of the agentic AI value proposition are again evident when it comes to the evaluation and optimization of promotions. An AI agent can identify inefficiencies such as product availability issues or excessive discounts, and autonomously recommend and execute optimal adjustments to promotion frequency, depth, and targeting. In addition, the AI agent can collaborate with pricing systems or create detailed reports for supplier negotiations. 

A practical example of what this looks like involves an underperforming promotion that contributes to excessive inventory and reduced margins. The AI agent can identify issues such as needlessly excessive  discount depths and poor targeting of promotional funds, and propose optimizing the promotional calendar, adjusting discount percentages, and reallocating resources to more profitable promotions. The result is a reduction in markdown losses and increased sales of better performing items. 

This is a huge win for retailers and demonstrates the powerful impact of intelligent promotion management made possible by a unified data platform, so retailers aren’t reliant on fragmented insights and spending on ineffective promotions. 

Competitive analysis with agentic AI for retail

Another use case where the value of an AI agent shines involves the often overlooked situation of competitive incursions in a local market. This can take the form of a new, expanded or remodeled store which poses a threat to loyalty, sales and customer traffic. An AI agent can proactively identify potential threats, forecast impacts on local stores, and recommend mitigation strategies. If desired, the AI agent can autonomously implement defensive strategies, such as adjusting prices or launching targeted promotions to safeguard market share and maintain customer loyalty. 

An AI agent brings a level of granularity to the competitive response that wasn’t previously available. It can evaluate potential sales impacts on key categories like fresh produce and private-label goods by analyzing data from previous competitor activity, running simulations and ensuring retention strategies are in place to appeal to at-risk shoppers. 

This proactive approach represents a major shift from traditional approaches which tended to be more reactive in nature, and consequently less effective because the competitive effect may have already been felt. 

Promotion planning and price elasticities with agentic AI for retail

The value of an AI agent is really felt in the area of promotions and pricing. When promotion planning is informed by pricing elasticities, an AI agent helps category managers, pricing analysts, and merchandising teams optimize promotional strategies. Integrating price elasticity insights ensures that promotional discounts are calibrated to boost demand without compromising profitability. 

As with the previously mentioned use cases, the AI agent can automate the selection of optimal price points and adjust promotions in real time, executing updates seamlessly across both online and offline channels to consistently deliver impactful results. This approach transforms promotional planning by addressing the limitations of traditional methods, which often rely on historical data and lead to ineffective promotions and profit erosion from needlessly aggressive discounts. 

These agentic AI use cases are a major advancement in the way work gets done inside the process-driven world of a typical retail organization. They also represent a powerful source of competitive advantage in an industry where winning is dependent on using increasing volumes of data more effectively to quickly make decisions with the highest probability of a successful outcome.