AI and pricing: what really falls under the umbrella of artificial intelligence
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Not all AI systems are created equal when it comes to pricing. Statistical rules, predictive machine learning, and generative AI: these three technologies are often lumped together, even though they address different needs and inform different decisions.
"Our tool uses AI" has become, in the world of pricing, a phrase that hardly means anything anymore. It could refer to a moving average programmed ten years ago, a real machine-learning model trained on millions of receipts, or a chatbot that rephrases a response without ever touching a price.
This guide clarifies three categories that are constantly conflated under the same term (statistical methods, machine learning, generative AI), explains why so many “AI” tools aren’t really AI, and sets forth the central premise that runs throughout this report: AI helps with decision-making; it does not make decisions on its own.
One word, three realities
A category manager comparing two pricing solutions hears the same pitch 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 pricing term:
- 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 and language models are capable of producing text, explaining concepts, and summarizing information, but since they do not inherently “know” a price, they must be connected to a reliable source of information.
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, and a rule stating “if a 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 person 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 of a marketing choice than an accurate technical description. A management rule, however 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 (minimum margins, regulatory constraints, product line consistency) would produce recommendations that are technically optimized but commercially nonsensical. The issue is not to pit rules against AI, but to determine which of the two does what in 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 pricing, the most telling example isprice elasticity of demand: 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 (such as 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.
in artificial intelligence spending in the global retail sector in 2024—a sector that is investing heavily—though this figure does not distinguish between proprietary machine learning and simple “AI” overlays applied to existing tools (IDC, Worldwide AI and Generative AI Spending Guide, 2024 V2).
Generative AI: Excellent at Formulating, But Blind to Default Prices
Generative AI—such as 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 an instruction, 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, therefore, 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.
| Brick | What She Does | What she doesn't do | Typical Usage Pricing |
|---|---|---|---|
| Statistics / Rules | Applies a formula or a fixed rule defined by a human | Does not learn on its own, does not adjust on its own | Margin Floors, Threshold Alerts |
| Machine learning | Learns a relationship based on historical data | Don't explain his reasoning on your own | Elasticity, forecasting, matching |
| Generative AI | Discuss, summarize, and explain in natural language | You can't know the actual price without being logged in | Chatbot, explanation |
To put it this way: a robust pricing platform doesn't have to choose just one of these three building blocks; it coordinates them—each 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 tendency 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.
Enterprise applications will incorporate AI agents specialized for specific tasks by 2026, up from less than 5% in 2025—a rapid and widespread adoption that makes it all the more necessary to distinguish a true agent from a marketing gimmick (Gartner, press release, August 26, 2025).
According to Gartner, of the thousands of vendors currently using the term “agent-based AI” in their marketing materials, only about 130 actually offer true agent-based AI, while the rest engage, to varying degrees, in “agent washing” (rebranding existing chatbots or automation tools). This observation, combined with unforeseen costs and a frequent lack of 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 acts alone when it comes to pricing
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 regarding pricing, competitive matching, promotions, and monitoring, with pre-filled question suggestions to guide the user. It uses generative AI connected to the retailer's actual data—not an isolated model that invents a plausible answer.
Behind him, the GENIUS Link module demonstrates a well-defined machine learning task: using NLP (natural language processing), it matches a retailer’s products with tracked competitor SKUs—even when the product names differ—and displays 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 process: Excel → rule-based logic → hybrid AI → automation. It’s never a direct leap to “all AI”; rather, 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
- 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.
- What does it actually produce? A quantified recommendation with a confidence level, or a generic textual response? The nature of the output often reveals which library is actually being used.
- Who approves changes before they affect an actual price? A system that modifies a price without human approval carries a very different level of risk than a system that makes a recommendation and waits for approval.
- What happens when it makes a mistake? A vendor that doesn't have a clear answer to this question probably hasn't established a real 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 identify—based on your specific situation—what falls under 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.
Three distinct categories: traditional statistical methods (averages, threshold rules), machine learning (models that learn a relationship—such as price elasticity—from data), and generative AI (language models that generate text or responses). Many commercial tools combine these three components under a single “AI” label. We explain how this works in detail in our article on how an AI pricing engine works.
This refers to the practice of rebranding an existing tool (chatbot, rule-based automation, RPA) as an “AI agent” or “agent-based AI” without changing its internal mechanics. Gartner estimates that only a minority of vendors claiming to offer agent-based AI actually provide truly autonomous capabilities. We detail these actual levels of autonomy in our article onagentic AI pricing in retail.
No. Machine learning is trained on a retailer's own sales and price data to learn a specific relationship, such as price elasticity.
Generative AI excels at formulating, summarizing, and explaining in natural language, but by default it has no reliable knowledge of the actual prices in a catalog; it must be connected to data and business rules to be useful for pricing. We detail this calculation in our article onprice elasticity and AI.
Because they rely on fixed, predefined formulas (moving average, threshold, business rule) rather than on a model that learns and adjusts its own parameters based on the data. They remain a useful and reliable building block, but labeling them as “AI” is often more of a marketing decision than a technical one. We explore this phenomenon in detail in our article on why generative AI gets pricing wrong.
By asking what the system actually learns (what data, what model, what output), who validates its recommendations before they affect a price, and what happens when it makes a mistake. A system that cannot answer these three questions is likely just a facade. We go into detail about this checklist in our article on data quality, the true glass ceiling of AI pricing.
At Booper, it’s not a given: AI makes recommendations and runs simulations, while humans validate and oversee the process. The balance between what can be delegated and what must remain under human oversight depends on the stakes and the scale of the decision. We explore this balance in our article on “AI that Decides vs. AI that Executes” in retail pricing.
AI serves four purposes: forecasting sales, estimating elasticities, detecting anomalies, and recommending prices. It does not replace business rules or human decision-making. Start with a limited scope, measure the benefits, and then expand.
The difference between AI-based pricing software and traditional pricing software lies in how prices are set: the latter applies fixed rules, while the former predicts the impact of a change before recommending it. A traditional tool relies on a series of manually defined margin thresholds, coefficients, and alignments.
An AI solution learns from sales data, competitor prices, seasonality, promotions, and inventory levels to estimate price elasticities and volumes, then suggests the price that best meets the set objective (margin, volume, or price image). Business rules remain essential as safeguards, as does human validation. The mechanism is detailed in “How an AI Pricing Engine Works.”
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 on your catalog, check out our MPS platform.
The model is the easy part. A recommendation engine can be up and running in a few months; what takes years is the knowledge base, competitive matching, the rule set, explainability, and compliance. According to Exclaimer (2025), 71% of in-house developments are eventually abandoned, and 83% in regulated sectors: the breaking point is almost never deployment—it’s maintenance.
The hidden cost is a cost of continuity: an in-house tool relies on two or three people, and if they leave, the asset becomes a liability. The build remains useful within a narrow and stable scope, as a complement to a solution rather than a replacement for it. The deciding factor isn’t the team’s expertise; it’s their ability to last ten years.
Data and context are two distinct assets: data describes what happened, while context explains what we are allowed to do and what would be absurd. This context is implicit by nature: it has never been written down because there was never a need to do so as long as decisions were made by humans who shared it.
Four categories cover the essentials: price benchmarks, relational constraints, unwritten rules, and commercial objectives by category. Formalizing them provides lasting value, because the context outlasts the models: algorithms are replaced every two or three years, but a written context remains a company asset.
A business case for a pricing solution combines two types of return on investment that are completely different. Friction ROI refers to the hours saved: easy to measure, but capped. Decision ROI refers to the margin points gained from better-set prices: difficult to measure, and unlimited.
Confusing the two explains the gap identified by IBM in 2025: 66% of executives report productivity gains from AI, but only about one in five has met their return on investment (ROI) goals. The ROI of a decision isn’t measured by comparing “before” and “after,” but rather against a control group; otherwise, seasonal factors, inflation, or a competitor’s actions will be credited to the tool. The test to apply to a business case: if you remove all the “time saved” lines, is the project still profitable?