Artificial intelligence is transforming both the way consumers search for products and the way retailers manage their businesses.
A shopping journey that previously involved a search engine, several e-commerce sites, and various product listings can now begin with a conversation with an AI. Consumers can ask the AI to search for a product, compare several offers, or help them choose based on their own criteria. With the rise of AI agents, some of these tasks could gradually be handled by the AI itself.
For retailers, this trend presents a twofold challenge. Their offerings must remain visible and relevant when AI systems are involved in the shopping journey. At the same time, their organizations already have AI-enhanced solutions to improve their forecasting, pricing, and operational decisions.
Agent-based AI is part of this transformation. It is no longer used solely to generate information or recommendations: it can pursue a goal and carry out a series of steps within a defined framework.
How does this trend affect the retail industry? How does it change the relationship with consumers? And what conditions must retailers meet to take advantage of it?
What is agent-based AI?
Agent-based AI refers to artificial intelligence systems capable of pursuing a goal with a certain degree of autonomy.
Generative AI typically responds to a request made by the user. An agent can go a step further: interpret a task, determine the steps needed to carry it out, draw on various sources of information or tools, and adapt its actions to the situation at hand.
The difference, therefore, lies less in the ability to produce a response than in the ability tolink several steps together to achieve a result.
In a retail context, a traditional system can, for example, identify an anomaly in sales or generate a forecast. An agent can use this information to gather the necessary context, query various systems, and then determine how to handle the situation.
This approach does not mean that the agent replaces the specialized software or models already used by the retailer. It operates in an environment where AI is already widely integrated into business tools.
Predictive AI, Generative AI, and Agent-Based AI: What Are the Differences?
Predictive AI is primarily designed to anticipate a phenomenon. In the retail sector, it can be used, for example, to forecast demand or detect certain behaviors.
Generative AI produces or synthesizes information. It can explain a situation, summarize various data points, answer a question, or generate content.
Agent-based AI incorporates a logic of action. It can use various capabilities to achieve a goal and, when its level of autonomy allows, carry out multiple steps without waiting for a new instruction from a human.
These approaches are therefore more complementary than competitive.
How is agent-based AI transforming the customer journey?
One of the most significant changes is taking place directly on the consumer side.
For a long time, the digital shopping journey has been built around a relatively predictable sequence of steps: conducting a search, reviewing several results, visiting different websites, reading product descriptions, comparing prices, and then making a choice.
Conversational interfaces are beginning to change this dynamic.
From Keyword Search to Conversational Search
Consumers no longer necessarily need to break down their needs into a series of requests.
They can directly express a complex intention: searching for a product suited to a specific use, staying within a budget, comparing certain features, or prioritizing quick availability.
AI can then handle part of the research and pre-selection process even before the user arrives at a retailer’s website.
This trend is already evident. Deloitte reports that visits from ChatGPT and other conversational interfaces now account for 15 to 20% of referral traffic for some retailers. The firm also cites estimates suggesting that AI agents could be involved in up to 25% of global e-commerce sales by 2030.
The challenge of e-commerce is therefore no longer limited to optimizing a website’s internal search engine or appearing in the results of a traditional search engine. Part of the discovery process could gradually shift toward new intermediaries.
From Assistance to Delegation
Conversational search is only the first step.
Today, consumers can ask an AI to search and compare. Agent-based logic makes it possible to envision a greater degree of delegation: monitoring a deal, verifying multiple criteria, or handling certain steps necessary to complete a purchase.
The user journey gradually evolves from “help me find” to “help me choose,” and then potentially to “complete this task for me according to my criteria.”
Industry leaders are already anticipating this transformation. In its 2026 Global Study, Deloitte states that nine out of ten retail executives Respondents expect AI to be used more and more in place of search engines. Half also anticipate that the current multi-step purchasing process will be redefined by 2027, in favor of interactions that are more AI-centric.
New intermediaries between retailers and consumers
This trend could change the relationship between retailers and their customers.
In a traditional shopping experience, the retailer controls much of the discovery environment: its internal search engine, categories, product listings, recommendations, and content.
When a search begins in an AI interface, part of this mediation process shifts. The system can select, organize, or summarize the results even before they are presented to the consumer.
For retailers, a new challenge has emerged: being correctly understood by the machine even before being chosen by the consumer.
This assumes, in particular, that the information used to compare offers is reliable. Pricing strategy and the ability to actually meet demand can become particularly important when an agent is trying to choose among several options. This trend is detailed in our analysis on Agent-Based AI and the Challenges of Pricing and Inventory for Retailers
Why is data quality becoming even more strategic?
An agent can only properly compare products or brands if the information it has access to is sufficiently precise and interpretable.
An ambiguous product description, an outdated price, or incorrect availability information can change the way an offer is understood and presented.
Deloitte therefore recommends that retailers ensure their product and pricing data is accurate, accessible, and formatted for AI systems to read. Otherwise, certain offers could become less visible in these new shopping environments.
Data quality is not a new issue for the retail sector. However, agent-based AI can amplify the consequences of unreliable information.
Inaccurate data no longer just disrupts an internal analysis. It can directly affect how a product is found, compared, or recommended.
Product Data
Sales representatives must be able to understand exactly what is being sold and distinguish between product codes that are sometimes very similar.
The quality of descriptions, attributes, formats, variants, and technical information is therefore of the utmost importance. The more specific a consumer’s request becomes, the more the system must have sufficiently structured data to identify the corresponding product.
The price
Price remains an obvious factor for comparison. Its reliability becomes particularly important when AI is involved in the selection process.
The displayed price must correspond to the offer actually available to the consumer, accurately reflect the terms and conditions, and be sufficiently up-to-date.
Availability
The same principle applies to inventory.
A product may perfectly meet a consumer’s needs, but it loses all relevance if it is ultimately unavailable.
For retailers, this availability rests on a solid operational foundation: high-quality forecasting, visibility into inventory levels, and the ability to adjust supply as demand changes. We explore this topic in greater depth in our article on The impact of agent-based AI on product availability.
Can agent-based AI change how retailers are perceived?
Search engine optimization has long been a key component of digital visibility.
Retailers have learned to optimize their presence on search engines, marketplaces, price comparison sites, and their own e-commerce platforms.
AI interfaces now add a new layer.
Consumers may no longer simply ask which websites sell a product. They may ask directly which option best meets a specific set of criteria.
On a traditional results page, several brands appear, and the consumer does much of the comparison work on their own.
In a conversational environment, the system can perform part of this selection in advance.
Visibility may therefore depend more on the system’s ability to understand an offer and determine whether it meets the expressed intent.
Generative AI in Text Data Analysis
Generative artificial intelligence also expands the analytical capabilities available to retailers, particularly when it comes to data that has long been difficult to analyze on a large scale.
Customer reviews, product comments, online conversations, and product descriptions are an important source of information about consumer expectations and market trends. However, analyzing them has often been challenging due to the volume of data, the variety of languages, and the difficulty of correctly interpreting context and nuances.
Today, Large Language Models make this work easier. They can help summarize large volumes of content, identify recurring themes, interpret the context of a comment, and process multilingual data more easily.
For retailers, this information can be used, among other things, to better understand customers’ needs and pain points, identify emerging trends, or compare how different products are perceived.
This capability complements other uses of AI in retail: structured data helps improve forecasts and pricing decisions, while generative AI makes it easier to leverage some of the textual data related to products and consumers.
We discuss these applications in detail in our article on text data analysis using generative AI in retail.
Why Do Retailers Need to Step Up Their Use of AI-Powered Tools?
Retailers are significantly ramping up their investments in artificial intelligence, as it is no longer just a tool for automation—it is becoming a key driver of competitiveness and profitability.
Use cases are multiplying across the entire value chain: demand forecasting, inventory management, product assortment optimization, marketing personalization, customer relations, and pricing. The goal is twofold: to improve operational efficiency while making faster and more accurate business decisions.
This trend is directly reflected in investment levels. According to the Boston Consulting Group, retail is now among the top three sectors accelerating their spending on AI. The net balance of retailers reporting an increase in investment in this technology has risen from 26% to 47% between the two periods studied by BCG.
This acceleration can be attributed in particular to increasingly tangible economic results. Pricing is a good example. According to BCG, retailers that have adopted solutions for AI-powered pricing solutions increased their gross profit by 5% to 10%, while achieving sustainable revenue growth and improving customers’ perception of value.
AI makes it possible to go much further than traditional pricing methods. Retailers can adjust prices at the product and store levels while simultaneously taking into account multiple factors: demand elasticity, competition, business objectives, changing costs, and consumer behavior.
The gains can also be significant in real-world scenarios. In an example presented by BCG involving a North American food retailer, the use of AI to transform the pricing strategy helped generate +4% to +5% in gross profit and +2% to +5% in sales starting in the first year, without raising average prices.
However, not all retailers have reached the same level of maturity. A 2026 study conducted by BCG and The Consumer Goods Forum indicates that 45% of the retailers surveyed are already in the process of large-scale AI deployment, while a comparable proportion is still in the early stages of adoption.
The question, therefore, is no longer simply whether retailers will adopt AI, but how quickly they will be able to integrate it into their strategic business processes.
As the benefits become measurable, AI is thus evolving from a technological experiment into a tool directly linked to growth, margins, and retailers’ ability to remain competitive.
Is brand and retailer loyalty being called into question?
The emergence of these intermediaries also raises the question of brand preference.
When a consumer asks an agent to find the best option based on a budget, intended use, or delivery time, the system may place greater weight on how well the offer meets those criteria than on brand recognition alone.
Deloitte reports that 81% of executives surveyed believe that generative AI could weaken brand loyalty by 2027, particularly by placing greater emphasis on the value or suitability of the offering.
This outlook does not mean that brands will lose their importance. Rather, it suggests that consumer preference may face greater competition from the ability of AI systems to quickly compare different alternatives.
Which areas of retail have already been transformed by AI?
Agent-based AI is attracting attention today, but it is entering a sector where artificial intelligence is already creating value.
McKinsey estimates that an AI-driven transformation of the entire value chain could generate 240 to 320 billion euros in economic value for the European retail sector over five years. In particular, the firm highlights the potential of retail merchandising, pricing, promotions, and the supply chain. Its analyses also show that gains generally do not come from using AI in isolation, but rather from the ability to deploy it across the entire organization.
AI in Pricing
Pricing is one of the areas where AI is already producing measurable results.
Pricing solutions powered by artificial intelligence can simultaneously incorporate a large number of variables: sales, competitor prices, costs, margins, price elasticity, seasonality, business rules, and changes in demand.
This capability enables retailers to move beyond pricing based primarily on static rules or on monitoring market trends. The models can analyze different scenarios and help teams strike a more precise balance between competitiveness, volume, and profitability.
The results observed may be significant. According to the Boston Consulting Group, retailers that have shifted their practices toward solutions that AI-powered pricing solutions have increased their gross profit by 5 to 10%, while sustainably improving their revenue and their customers’ perception of value.
This performance is not based solely on the algorithm. BCG also emphasizes the importance of a structured pricing team, processes that enable a rapid response, and a sufficiently integrated data platform.
In an environment where consumers and AI assistants can compare offers more easily, having a consistent pricing strategy and tools capable of executing it accurately becomes even more important.
To explore this topic further, we’ll explain how AI algorithms applied to pricing
AI in Demand Forecasting
Demand forecasting is another major area of application for artificial intelligence.
The models can analyze sales history, seasonality, promotions, events, and various explanatory variables to more accurately estimate future demand.
The benefit isn’t just about producing a more accurate forecast. AI also makes it possible to process volumes of data that are difficult to analyze manually, detect certain trends more quickly, and better incorporate multiple factors.
The quality of this forecast then directly affects inventory levels, replenishment needs, product availability, and the ability to prepare for a sales campaign.
In an environment of more volatile demand, the challenge is therefore to reduce the gap between what the retailer expects to sell and what it actually sells.
AI in the Supply Chain
In the supply chain, artificial intelligence also helps improve the ability to anticipate future developments.
Forecasting, inventory management, allocation, and replenishment can benefit from models capable of processing more signals and better identifying the risks of shortages or surpluses.
McKinsey estimates that AI could contribute to an improvement in EBITDA of 1 to 2 points in the supply chain and logistics of the European retail sector. In the food industry, the firm estimates that most of the potential value of AI will come, in particular, from frequent and incremental improvements related to supply chain efficiency, waste reduction, and better-targeted promotions.
This optimization is directly linked to the customer experience. A product recommendation made to the right customer creates no value if the product is not available when the customer wants to buy it.
AI in Merchandising
AI can also help merchandising and category management teams process large volumes of information more quickly, identify trends, and improve certain decisions regarding product assortment and allocation.
McKinsey correctly identifies retail merchandising as one of the areas with the greatest potential. The firm believes that AI can help improve 2 to 4 percentage points of EBITDA in this area.
In its support programs for merchandising teams, McKinsey also reports improvements of 2 to 3 percent in gross margin and 2 to 5% in revenue when technology, data, processes, and organizational structure are combined to improve the quality and speed of decision-making.
These results highlight a key point: the value of AI does not come solely from the model used. It also depends on its ability to be integrated into teams’ work processes.
AI in E-commerce
E-commerce, too, has undergone a profound transformation.
These applications already include personalization, product recommendations, internal search, behavioral analysis, content generation, and customer service.
The emergence of conversational interfaces has now broadened the scope of the topic: AI is no longer limited to the retailer’s website, but may potentially be involved earlier in the customer journey.
Retailers must therefore work simultaneously to ensure the relevance of their digital experience and the ability of their information to be correctly interpreted in these new environments.
What new challenges does agent-based AI pose for retailers?
Agent-based systems are not merely a new generation of technologies. They have the potential to alter several long-standing trade balances.
Maintain a direct relationship with the consumer
The more search and comparison are handled by AI intermediaries, the more important the issue of customer relationship management becomes.
If a consumer discovers a product and compares alternatives on a third-party platform, the retailer has fewer opportunities to showcase its brand, services, or loyalty program.
Be readable by AI systems
Retailers will likely need to consider two audiences at the same time: consumers and the systems that support them.
A product description must always be persuasive to a person, but the information it contains must also be structured well enough for a machine to understand what is being offered.
Aligning Sales Promises with Operational Reality
A recommendation based on an incorrect price or outdated availability can immediately lead to a poor customer experience.
The quality of the process therefore depends directly on the consistency between the reported business data and the operational reality.
Avoiding a New Technological Dependency
Retailers have already learned to navigate search engines, marketplaces, and social media.
Agent-based interfaces could become a new gateway to commerce.
Retailers will therefore need to determine what portion of customer discovery, customer engagement, and possibly the transaction itself they wish to entrust to third-party platforms.
What role do retail teams still play?
As tools become capable of handling a greater portion of the analysis, the human role is evolving.
Many teams still spend time searching for data, reconciling multiple sources, identifying significant anomalies, or performing repetitive tasks.
AI can reduce some of this workload and allow employees to devote more time to decision-making, exceptions, and strategy.
Business expertise therefore remains essential.
McKinsey emphasizes this point: retailers capable of creating value with AI will not only be those who adopt the tools the fastest, but those who manage to combine human judgment, technology, data, and new ways of working.
What are the main risks of agentic AI?
The ability to take action can speed up processes, but it can also amplify a mistake.
Inaccurate information can lead to an inappropriate decision. Poorly defined objectives can result in local optimization at the expense of a broader objective. Certain unusual situations may also fall outside the scope of the system.
Agent-based systems also address security and access rights concerns. A system capable of accessing multiple applications or performing certain actions must have only the permissions necessary to carry out its task.
Autonomy must therefore advance alongside governance.
How Can You Prepare Your Organization for Agent-Based AI?
The transition to agent-based systems does not require an immediate transformation of the entire company.
A gradual approach can begin with tasks or decisions that occur frequently enough to create value, but are sufficiently structured to allow for effective oversight.
Retailers must then identify the necessary information, the relevant tools, and the role they wish to assign to the system.
Performance should not be evaluated solely based on technological criteria. Time savings, decision quality, impact on margins, revenue, availability, and productivity are also essential.
This is what the findings from BCG and McKinsey show: value does not come simply from using AI, but from integrating it into sufficiently robust tools, data, and business processes.
Retail Redefined by AI and New Shopping Journeys
AI is now involved on both sides of the business relationship.
From the consumer’s perspective, it is gradually becoming a new intermediary for searching, comparing, and potentially completing certain steps in the purchasing process.
On the retail side, it is already improving pricing, forecasting, merchandising, and supply chain management. Agent-based AI adds a new dimension by changing the way certain information can be used to achieve a goal.
These two transformations are related.
As consumers increasingly rely on AI systems for their searches, retailers must ensure that their product offerings, prices, and availability can be accurately understood.
At the same time, the accelerating pace of analysis and decision-making underscores the importance of tools that can leverage AI to improve the quality of pricing, forecasting, and supply chain management.
Agent-based AI does not, therefore, mean the end of the fundamentals of retail.
On the contrary, it can make them even more strategic.
Data quality, price consistency, forecast accuracy, and the ability to deliver on the promise of availability remain critical. What is changing is the speed at which this information can be leveraged and the growing role of AI in the journey from intention to decision to action.
For retailers, the question is therefore no longer simply whether they will use AI.
It is gradually becoming: How can we combine artificial intelligence, business expertise, and data quality to remain competitive in a business environment increasingly driven by algorithms?
FAQ on Agent-Based AI in Retail
What Is Agent-Based AI in Retail?
Agent-based AI refers to systems capable of pursuing a goal, leveraging multiple sources of information, and carrying out various steps with a certain degree of autonomy. In the retail sector, it can be applied to both the customer journey and certain internal processes.
What is the difference between AI and agent-based AI?
Artificial intelligence is already used in retail to forecast demand, optimize pricing, analyze consumer behavior, and detect anomalies. Agent-based AI adds the ability to pursue a goal across multiple steps and, where appropriate, take action within a defined framework.
How is agent-based AI changing the customer journey?
It can shift some of the consumer’s research and comparison work to AI-powered intermediaries capable of evaluating multiple criteria simultaneously and selecting specific offers.
What benefits can AI bring to pricing?
BCG reports that retailers who have adopted AI-powered pricing solutions have seen a 5 to 10 percent improvement in their gross profit, along with sustained growth in revenue and perceived value.
What is the potential of AI for the retail industry?
McKinsey estimates that transforming the value chain through AI could generate 240 to 320 billion euros in economic value for the European retail sector over five years. The potential varies by sector and depends heavily on companies’ ability to deploy AI at scale.
Why is data important for agent-based AI?
Because systems make decisions based on the information available to them. Inaccurate production, pricing, or inventory data can alter the way an offer is analyzed or recommended.
Will agent-based AI replace pricing or supply chain solutions?
Not necessarily. These solutions already use AI to generate optimizations, forecasts, or specialized recommendations. Agent-based systems operate within this environment and are themselves dependent on the quality of the data and the available business capabilities.
What are the main challenges of agent-based AI for retailers?
In particular, they address data quality, system interoperability, security, governance, traceability, and determining the appropriate level of autonomy.
