AI and Retail Pricing:

what really falls under the umbrella of artificial intelligence

One word, three realities

A category manager comparing two pricing solutions hears the same claim from both sides: “powered by AI.” Most of the time, they have no easy way of knowing what that actually entails.

In reality, three very different technologies lie behind this term in retail pricing:

• Traditional statistical methods, which have been in use for decades, that apply a fixed formula to data (mean, threshold, management rule).
• Machine learning, which trains a model to learn a relationship from historical data—for example, how demand responds to a price change.
• Generative AI: language models capable of producing text, explaining concepts, and summarizing—but which, by their very nature, do not “know” a price—they must be connected to a source of truth.

These three building blocks do not serve the same purpose, do not require the same amount of effort to build, and—most importantly—do not carry the same level of risk when they fail. Confusing them is like evaluating a supplier based on a single word, rather than on what they actually do.

Traditional statistical methods: the foundation—not always AI in the strict sense

A moving average of sales over the last four weeks, a reorder point, a rule stating “if the competitor’s price drops by more than 5%, match it”—these are statistical methods or business rules that are applied in a fixed manner. They do not change on their own: a human defines them, and they remain the same as long as no one modifies them.

Classifying these methods under the label “AI” isn’t entirely wrong, but it’s often more a marketing choice than a precise technical description. A management rule, no matter how useful it may be, doesn’t learn anything: it simply executes what it’s been programmed to do.

This foundation remains essential. A pricing system without business rules—such as minimum margin thresholds, regulatory constraints, and product line consistency—would generate recommendations that are technically optimized but commercially nonsensical. The issue is not to pit rules against AI, but to determine which of the two performs which function within a given system.

Machine learning: a model that learns, not one that merely repeats

Machine learning is changing in nature: instead of applying a predefined formula, a model is trained on historical data to learn a relationship that no human has explicitly defined line by line.

In retail pricing, the most telling example isprice elasticity: how demand for a product reacts to a price change, product by product, store by store. A machine learning model learns this relationship from historical sales and price data—it was never given this relationship as a rule; it deduced it from the data.

This represents a true departure from traditional statistics: the model can capture complex relationships (interactions between price, seasonality, and competition) that no manual formula could cover exhaustively. But this power comes at a cost: a machine learning model does not spontaneously explain its reasoning, and its quality depends entirely on the data on which it was trained.

Generative AI: Excellent at Formulating Content, but Blind to Default Pricing

Generative AI—language models like ChatGPT or conversational assistants built into software—is currently the most visible technology, and the one most misunderstood when it comes to pricing.

A general-purpose language model excels at rephrasing a prompt, summarizing a dashboard, or answering a question in natural language. However, by design, it has no reliable, up-to-date knowledge of the actual prices in a catalog: it generates the statistically most plausible answer, not the most accurate one.

What makes generative AI useful for pricing, then, is not its ability to “know” a price, but its ability to interact with data and rules that are, in fact, accurate —a conversational assistant connected to a retailer’s actual product database, not to its general training memory.

The Chart: What Each Brick Actually Does

None of the three families replaces the other two. A good pricing architecture combines them, with each one focusing on what it does best.

To put it this way: a robust pricing platform doesn't have to choose just one of these three components—it coordinates them, with each one focusing on what it does best, under a governance structure that defines who approves what.

"Agent washing": when "AI" becomes a marketing ploy

Since the rise of “AI agents”—systems that are supposed to act autonomously, not just answer a question—a new trend has emerged among some software vendors: renaming an existing chatbot, rule-based automation, or RPA tool as an “AI agent,” even though its internal mechanics haven’t changed one bit.

Gartner has given this phenomenon a name:“agent washing.” The firm’s assessment is harsh—of the thousands of vendors that currently claim to use agent-based AI, only a handful are said to offer truly autonomous and proven capabilities.

According to Gartner, of the thousands of vendors currently using the term “agent-based AI” in their marketing messaging, only about 130 actually offer true agent-based AI—the rest engage, to varying degrees, in “agent washing” (rebranding existing chatbots or automation tools). This observation, combined with unforeseen costs and often lacking governance, largely explains why more than 40% of enterprise agent-based AI projects are expected to be abandoned by the end of 2027.

For a category manager or pricing director evaluating a tool, the question to ask is therefore never “Is this AI?”—almost everyone will answer yes—but “What exactly does this system learn, from what data, and who validates its recommendations?”

The Booper Approach: Humans are in the driver's seat; AI never sets a price on its own

This confusion between the three components has significant implications for pricing: an incorrectly set price has a direct and immediate impact on margins, price perception, and customer trust. That is why Booper has taken a clear stance since its inception: a platform that combines AI, business rules, and governance, where AI makes recommendations and runs simulations, but never makes decisions or takes action on its own.

At Booper: an assistant that helps you decide, not one that decides for you

Booper's AI Center —the "BOOPER AI Assistant"—provides natural-language responses on pricing, competitive matching, promotions, and monitoring, with pre-filled question suggestions to guide the user. It uses generative AI connected to the retailer's real-time data, not an isolated model that invents a plausible answer.

Behind him, the GENIUS Link module demonstrates a well-defined machine learning task: it uses NLP (natural language processing) to match a retailer’s products with tracked competitor SKUs, even when the product names differ—displaying a confidence score rather than a binary answer.

This philosophy aligns with the one documented among Booper’s clients. At Barbotteau Group, a leading player in the French Caribbean, the roadmap presented during a webinar co-hosted with Booper follows a four-step path: Excel → rule-based logic → hybrid AI → automation. It’s never a direct leap to “all AI”—it’s a gradual progression, where each step adds a building block without removing human control from the previous one.

How to Tell the Difference Between a Genuine AI Pricing Project and a Simple Rebadge

1. What does the system learn, and from what data? A true machine learning solution can specify the training data (sales history, prices, seasonality). A marketing-driven solution remains vague on this point.
2. What does it actually produce? A quantified recommendation with a confidence level, or a generic text-based response? The nature of the output often reveals which library is actually being used.
3. Who approves changes before they affect an actual price? A system that changes a price without human approval carries a very different level of risk than a system that makes a recommendation and waits for approval.
4. What happens when it makes a mistake? A vendor that doesn't have a clear answer to this question probably hasn't established a true governance framework for its AI—whatever form it may take.

Checklist before buying an "AI-powered" tool:

• Can the vendor specify the actual technology used (statistics, machine learning, generative AI) rather than a generic term?
• Are the recommendations explained, or are they simply presented as a take-it-or-leave-it result?
• Does a human approve a recommendation before it affects an actual price?
• Was the system trained using data specific to your brand, or is it still a generic model?
• Is the term “agent” or “agentic” supported by concrete evidence, or just by a slide?

Want to get a clear picture of what your pricing tool actually does? Spend 30 minutes with our team to determine—based on your specific situation—what’s driven by machine learning, business rules, and marketing hype. → Let’s schedule a meeting.

FAQ

Do you still have questions? Here are the answers to the most frequently asked questions on this topic.

‌

To learn more about this topic: AI that decides vs. AI that executes, where to draw the line, why generative AI gets prices wrong, and data quality as the glass ceiling for AI pricing. For more on agentic AI applied to pricing, see also our article “Agentic Pricing: Definition and Examples.” To deploy a governed AI pricing solution for your product catalog, check out our MPS platform.