Agentic AI in retail

Agentic artificial intelligence has the potential to fundamentally transform retail. This technological leap means that autonomous AI agents can execute tasks independently – from making decisions and developing strategies to taking real-world action. These AI tools do not merely assist humans; they are capable of carrying out activities end to end, entirely on their own.

What this means in concrete terms for customers, retail companies and other market players is the focus of this article series on agentic AI and agentic commerce. 

For consumers, agentic AI represents a profound shift in the shopping process, as AI can act autonomously on their behalf. Shoppers define their preferences and budgets and initiate the process. The AI agent then searches for suitable products based on these parameters, compares prices and independently executes orders and payments. Especially for recurring, functional purchases, this creates a clear gain in convenience, time savings and cost efficiency.

The technology is still at a very early stage, and many questions remain unanswered: in which shopping situations and for which products will such services actually be used? Which target groups will rely on agentic AI for shopping? And what role will trust in the technology and in its decision-making capabilities play in driving adoption? 

As customers increasingly delegate purchasing decisions to AI agents, retailers face a fundamental shift in their role. Purchase decisions are no longer primarily influenced by emotional messaging at the moment of purchase, but by structured criteria that consumers define for their AI in advance. Retailers are therefore no longer selling solely to people, but also to algorithmic proxies that evaluate products based on price, availability, reviews, delivery speed or predefined values.

To remain relevant, retailers must prepare to become visible, comparable and trustworthy for AI agents. Clean, machine-readable product data, transparent pricing, reliable delivery information and technical interfaces are basic prerequisites. At the same time, brand building remains crucial – but earlier in the customer journey: retailers and brands need to establish enough trust and recognition to become part of the preferences that consumers pass on to their agents. 

Agentic AI is still at an early stage, but it is no longer a distant vision. It is already being actively tested by retail platforms, technology providers and payment service providers. This new retail model is referred to as agentic commerce – a model in which autonomous AI agents manage the entire customer journey on behalf of consumers.

Major retail platforms such as Amazon and Walmart are integrating agentic functions directly into their ecosystems; search and AI platforms such as Perplexity are combining conversation with transaction; and payment providers such as Visa are building the infrastructure that allows AI agents to pay securely and autonomously.

The transition from traditional human-led shopping to autonomous, agent-based transactions raises fundamental questions for retailers. A hands-on perspective on this development is provided by Ravi Kumar, who, as a machine learning specialist at Dollar General Corporation, is leading a project on conversational AI and agent-based purchasing processes. He makes it clear that agentic AI goes far beyond classic forecasting or recommendation systems: autonomous agents can derive goals, make decisions, act and learn from outcomes – and thus actively intervene in purchasing and payment processes.

For retailers, this primarily means increasing pressure on prices and efficiency, but also new opportunities. Products must be discoverable and assessable by AI agents, and the demands on high-quality data management and technological excellence will continue to rise. At the same time, AI agents can unlock new customer segments and provide insights into real preferences that were previously difficult to access.

In retail, agentic AI is not only used by consumers; retail companies themselves can also deploy it as an active operational entity. Unlike traditional AI systems that support individual tasks, agentic AI is capable of pursuing higher-level goals and autonomously managing entire process chains. Instead of reacting to predefined rules, these systems act in a goal-oriented manner, learn from outcomes and continuously adapt their strategies.

In such a future scenario, AI agents could, for example, monitor inventory levels, optimise assortments, dynamically adjust prices, deploy marketing measures or independently handle service processes – importantly, not merely as recommendations for human decision-makers, but through direct execution. The role of human retail staff would shift from operational execution to oversight, control and strategic decision-making.