What it takes to make agentic AI work in retail

The Future of Retail: Unlocking the Potential of Agentic AI

As the retail industry continues to evolve, companies are turning to cutting-edge technologies like agentic AI to stay ahead of the competition. But what does it take to make agentic AI work in retail, and why is it so crucial for businesses to get it right?

In a recent episode of the Infosys Knowledge Institute Podcast, Prasad Banala, director of software engineering at a large US-based retail organization, shared his team's experiences in operationalizing agentic AI across the software development lifecycle. From validating requirements to generating and analyzing test cases, and accelerating issue resolution, Banala's team has been at the forefront of applying AI to drive business outcomes.

The Benefits of Agentic AI in Retail

Agentic AI, also known as autonomous or self-driving AI, refers to systems that can learn, reason, and make decisions without human intervention. In retail, agentic AI can be used to automate tasks such as inventory management, supply chain optimization, and customer service. By freeing up human resources from mundane tasks, agentic AI can help retailers improve efficiency, reduce costs, and enhance customer experience.

One of the key benefits of agentic AI in retail is its ability to analyze vast amounts of data and make predictions about customer behavior. For example, an agentic AI system can analyze customer purchase history, browsing patterns, and social media activity to identify trends and preferences. This information can be used to personalize marketing campaigns, improve product recommendations, and optimize inventory levels.

Operationalizing Agentic AI in Retail

So, how can retailers operationalize agentic AI in their businesses? According to Banala, it's essential to start by defining clear requirements and objectives. This involves identifying specific business problems that can be addressed through agentic AI, and developing a roadmap for implementation.

Once the requirements are defined, retailers can begin to develop and deploy agentic AI systems. This involves selecting the right technologies, training the AI models, and integrating them with existing systems. It's also essential to establish governance and human-in-the-loop review processes to ensure that the AI systems are functioning as intended and making decisions that align with business objectives.

Measurable Quality Outcomes

One of the key challenges of implementing agentic AI in retail is ensuring that the systems are producing measurable quality outcomes. This involves setting clear metrics and benchmarks for performance, and regularly reviewing and refining the AI systems to ensure they are meeting expectations.

Banala's team has developed a range of metrics to measure the performance of their agentic AI systems, including accuracy, speed, and customer satisfaction. By regularly reviewing these metrics, they can identify areas for improvement and make data-driven decisions to enhance the performance of their AI systems.

Real-World Applications

So, what does the operationalization of agentic AI in retail look like in practice? One example is the use of agentic AI in inventory management. By analyzing customer purchase history and browsing patterns, retailers can identify trends and preferences, and adjust inventory levels accordingly. This can help reduce stockouts, overstocking, and waste, and improve customer satisfaction.

Another example is the use of agentic AI in customer service. By analyzing customer interactions and preferences, retailers can develop personalized customer service scripts and respond to customer inquiries in a more effective and efficient manner.

Forward-Looking Thoughts

As the retail industry continues to evolve, the potential applications of agentic AI will only continue to grow. By operationalizing agentic AI across the software development lifecycle, retailers can unlock new levels of efficiency, productivity, and customer satisfaction. But it's essential to get it right, and to establish clear requirements, governance, and human-in-the-loop review processes to ensure that the AI systems are functioning as intended.

As Banala noted, "Agentic AI is not just a technology, it's a business strategy. It's about using data and analytics to drive business outcomes, and to create a more personalized and efficient customer experience." By embracing agentic AI, retailers can stay ahead of the competition and create a more sustainable and successful business model for the future.

Source: https://www.technologyreview.com/2026/02/19/1133324/what-it-takes-to-make-agentic-ai-work-in-retail/