AI that decides vs. AI that executes: Where to set the pricing threshold
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Deciding and executing are two different things in AI pricing. Most reliable systems either carry out actions that have already been approved or make recommendations; they do not make decisions on their own in high-stakes situations.
The balance is struck by weighing the stakes and scope of each decision. Organizations that succeed with their AI projects are those that have established clear human oversight, not those with the most sophisticated model.
“Can we automate this?” is not the right question to ask about an AI pricing project. Technically, almost everything can be automated. The real question is: What should we automate, and what should remain in the hands of a human who bears responsibility for the decision?
This guide offers a simple framework for setting this balance (between an AI that carries out a pre-approved action and an AI that makes decisions on its own) and explains why so many ambitious automation projects end up being abandoned because this question wasn't asked early enough.
Two verbs that are often confused: "decide" and "execute"
In the marketing discourse surrounding “agent-based” AI, one word keeps coming up: autonomy. A system that would act on its own, without human intervention, from diagnosis to action. It sounds appealing on paper, but it’s risky in practice as soon as real stakes are involved.
It is important to distinguish between two verbs that are too often confused:
- Execute: Carry out an action that has already been approved, within a predefined framework (an approved price range, a competitively aligned rule that has already been adjudicated).
- Decide: Choose the course of action to take, without a human having approved this specific case before it takes effect.
Most serious pricing systems are designed for execution or recommendation—never for making fully autonomous decisions on high-stakes cases. This is not a technological limitation: it is a governance choice—and one that directly impacts the likelihood that an AI project will survive its first year.
What can reasonably be delegated to a system
Certain pricing decisions lend themselves well to supervised automation, within a strict and audited set of rules:
- Minor adjustments within an already validated range; a price that fluctuates by a few cents to remain in line with a competitive positioning rule that has already been determined by a human.
- Alerts and anomaly detection: flag a disruption, a suspicious price discrepancy, or a rule violation. Detection is not the same as decision-making: the system flags the issue, and a human makes the call.
- Data matching and structuring, linking a product to a competitor’s SKU, and cleaning up a database. A high-volume task with low per-unit stakes, where the cost of an occasional error is limited and correctable.
- Recommendations regarding low-stakes items, loss leaders with minimal profit margins, and categories where price differences do not affect the brand's overall image.
In all these cases, the common factor is not the technical simplicity of the calculation (some of these calculations are sophisticated) but the fact that the cost of an isolated error remains manageable and can be corrected quickly.
What Must Continue to Be Decided by a Human
Conversely, certain decisions should never be delegated entirely to a system, no matter how good the underlying model is:
- The prices of high-visibility products (KVI—Key Value Items)—those that shape the customer’s perception of price—where a mistake is easily noticed and remembered.
- Exceptions: a local event, an unusual concurrent news story, or a situation that the model has never encountered in its training history.
- Decisions that affect multiple departments—such as a price change that impacts the supply chain, the budget, or supplier relationships—should not be made in an automated silo.
- High-stakes, low-volume decisions—precisely those where automation would save the least amount of time, yet carry the highest risk.
In these cases, AI continues to play a decisive role (making recommendations, quantifying a scenario, simulating an impact), but the final validation remains a human decision—one that is documented and accounted for.
"High-performing" AI organizations (those that derive measurable value from AI) have implemented a formalized "human-in-the-loop" oversight process, compared with only 23% of other organizations; governance—not the model—makes the difference (McKinsey & Company, The State of AI in 2025, survey conducted in June–July 2025).
The Matrix That Determines the Cursor: Stake and Volume
A simple framework for determining, line by line, what can be delegated: cross-referencethe stakes involved in the decision (impact on profit margin, price image, and customer relationships) with its volume (number of SKUs involved).
| Location | Issue | Volume | Recommended cursor |
|---|---|---|---|
| Loss leader, marginal profit | Low | High | Automated execution, post-execution audit |
| Anomaly Detection / Alert | Variable | High | Automatic detection, human decision-making |
| Current Rate Recommendation | Medium | Medium | AI Recommendation, Rapid Validation |
| Strategic Price / KVI / Exception | High | Low | Systematic Human Arbitration |
To put it this way: the benchmark is never set in stone for an entire product category; it is determined on a case-by-case basis, depending on what the product actually entails, not on the sophistication of the model used to market it.
Why So Many Agent-Based AI Projects Fail: It's Not the Technology, It's Governance
The strongest argument in favor of an explicit slider is not just the risk it helps avoid of setting a price incorrectly. It is also, quite simply, the survival of the project itself.
Agent-based AI projects in enterprises are expected to be abandoned by the end of 2027 due to spiraling costs, unclear return on investment, and insufficient governance and risk controls over what the system is allowed to do on its own (Gartner, press release, June 25, 2025).
The reason for failure is almost never “the model didn’t work.” Rather, it is that no one had defined in advance what the system was allowed to decide on its own, and the organization discovered the problem when an automated decision produced a result it could no longer accept without taking a closer look. A limit set from the outset—even a simple one—prevents this scenario far more effectively than a more powerful model.
What Customers Themselves Are Willing to Accept From AI: A Sign That Should Not Be Ignored
The same tendency toward caution can be seen on the consumer side, which indirectly sheds light on where retailers should set the bar.
Only a small percentage of consumers say they are willing to let AI decide on a purchase for them, even in low-stakes categories such as personal care or household products, compared with 31% who are willing to let it narrow down their choices for household products, and 28% for consumer electronics (Gartner, survey conducted in January 2026 among 322 U.S. consumers, press release dated May 27, 2026).
The parallel is clear: customers want AI to help them compare, find, and filter—not to make choices for them. Internally, when it comes to pricing, the equivalent is that a pricing team wants AI to help them analyze, simulate, and make recommendations—not to set a strategic price without a human having the final say. The same level of measured trust seen on the consumer side is also found on the organizational side.
At Booper: Decision-making remains a professional process, never an automatic process
GENIUS Price: An assistant that makes recommendations, a workflow that approves them
The GENIUS Price module combines pricing, bill of materials, alerts, product rules, and simulations with a built-in “conversational AI” assistant. It offers recommendations and quantifies scenarios, but price approval remains a business decision, tracked within a governance workflow with defined roles and permissions (GENIUS Admin module); it is never an automated process without human oversight of high-stakes decisions.
This is exactly the point highlighted by the pricing department at one of our clients in the food industry (1,700+ stores) regarding the Booper rollout: “Our challenge wasn’t to acquire a new tool, but to improve our ability to make consistent pricing decisions at scale. […] The approach proposed by BOOPER won us over with its ability to balance automation, governance, and decision-making control by business teams.”
Build Your Own Slider in Four Steps
- Map out the decisions, not the tools. List the types of pricing decisions (loss leader, KVI, promotion, exception) before deciding what to automate—never the other way around.
- Rate each one based on stakes and volume. Use the matrix above to rank them objectively, rather than deciding on a case-by-case basis depending on your mood at the time.
- Document what is delegated and to whom. A system that is put into operation must have a written scope, not one that is merely implicitly understood by the team that set it up.
- Conduct audits regularly, not just once and for all. A benchmark set in January may no longer be relevant by September; a new product, a new category, or a shift in the market can change the landscape.
Checklist before delegating a pricing decision to a system:
- Is the cost of a one-time error in this decision manageable and quickly correctable?
- Does this decision affect a product that has a significant impact on the retailer's price image?
- Is there a defined validation workflow, or does the automation take place without human oversight?
- Is the exact scope of what the system can do on its own documented, or is it merely “understood” by the team?
- Has this threshold been revised recently, or was it set once and never reconsidered since?
Want to find the right balance for your pricing? Spend 30 minutes with our team to map out your pricing decisions and identify what can be safely delegated. → Let’s schedule a meeting.
FAQ
Do you still have questions? Here are the answers to the most frequently asked questions on this topic.
"Executing " involves carrying out an action that has already been approved, within a predefined framework—for example, republishing a price within a range that has already been approved by a human. "Deciding" means choosing the action to take without a human having approved that specific case before it has a real effect on a displayed price.
When it comes to pricing, the article is clear: most reputable systems focus on execution or recommendations, never on making fully autonomous decisions on high-stakes cases. This is not a technological limitation; it is a deliberate governance choice, as detailed in our article on how an AI pricing engine works and its safeguards.
This distinction directly determines whether projects survive: according to McKinsey, 65% of “high-performing” AI organizations have implemented formalized human oversight, compared with only 23% of others; governance makes the difference, not the sophistication of the model.
Technically, yes, but only for low-stakes, high-volume cases: loss leaders with slim margins, minor adjustments within a range already approved by a human, where the cost of an occasional error remains manageable and can be quickly corrected.
When it comes to high-stakes decisions—such as KVI pricing and strategic products—as well as local exceptions, the article recommends consistently maintaining human validation: the cost of an error in these cases far outweighs the time savings achieved through full automation, a distinction we explore in detail in our article on agent-based pricing and its levels of autonomy.
This threshold is never fixed for an entire product category: it is determined on a case-by-case basis by weighing the stakes and the volume of each type of case, rather than by applying a single rule to the entire catalog.
According to the article, the reason for failure is almost never that the model didn't work technically. It is most often a matter of governance: no one had defined in advance what the system was authorized to decide on its own, and the organization discovers the problem when an automated decision produces a result it can no longer accept.
The scale of the phenomenon has been quantified: Gartner predicts that more than 40% of enterprise agent-based AI projects will be abandoned by the end of 2027, due to cost overruns, unclear return on investment, and insufficient risk control over what the system is allowed to do on its own—misalignments that we detail in our article on common mistakes made by pricing teams when dealing with AI.
The article emphasizes that this is exactly what an explicit guideline, established from the outset, helps prevent: a simple governance framework provides better protection for a project than a more powerful model that lacks proper oversight.
The method proposed in the article involves cross-referencing two criteria for each type of decision:the stakes (impact on profit margin, price image, and customer relations) and volume (number of SKUs involved).
The resulting matrix yields four profiles: a high-stakes, low-volume scenario—such as a strategic price or an exception—requires systematic human review; conversely, a low-stakes, high-volume scenario—such as a loss leader—is suitable for automated execution with post-hoc auditing. In between, anomaly detection and standard pricing recommendations are still proposed by the system but validated by a human—an approach we detail in our article on pricing alerts and large-scale management.
The resulting benchmark is never applied to an entire product category all at once: it is set on a case-by-case basis, and the article recommends reviewing it regularly, since a decision that makes sense in January may no longer be appropriate after the introduction of a new category or a change in the market.
No. The GENIUS Price module combines pricing, bill of materials, alerts, product rules, and simulations with a built-in conversational AI assistant that offers recommendations and calculates scenarios; however, price approval remains a business process, tracked within a governance workflow with roles and permissions defined through the GENIUS Admin module.
This is exactly what the pricing department at one of our clients in the food industry (1,700+ stores) confirmed regarding the Booper rollout: The approach proved compelling due to its ability to balance automation, governance, and decision-making control by business teams—rather than simply providing a new tool—a “co-pilot” approach rather than autonomous execution, as we discuss in our article on retail AI pricing, “From Co-Pilot to Agent-Based Systems.”
This architecture directly reflects the article's philosophy: never allow automatic execution without human oversight of high-stakes decisions, no matter how sophisticated the underlying AI assistant may be.
Very few consumers are willing to let AI make decisions entirely on their behalf. A Gartner survey conducted in January 2026 shows that only 11% of consumers are willing to let AI decide on a purchase, even in low-stakes categories such as personal care or household products.
However, many more are willing to accept a supportive role: 31% are comfortable with AI narrowing down their choices for household products, 28% for consumer electronics—a clear sign that customers want help comparing and filtering options, not to be replaced in the final decision, a distinction we explore further in the context of pricing in our article on what truly constitutes artificial intelligence in retail pricing.
The article draws a direct parallel with internal pricing: a pricing team similarly wants AI to help it analyze, simulate, and make recommendations, without setting a strategic price without a human having the final say. The same level of measured trust seen on the consumer side is also found on the organizational side.
A hybrid platform allows AI to make suggestions, business rules to set limits, and the team to validate them. BOOPER works this way: AI recommendations, product rules, and a validation workflow—all within the same platform. Make sure that, with any vendor, the rules take precedence over the model and that every decision remains traceable.
First, define the non-negotiable rules (minimum margins, price differentials, sensitive products); let the AI make recommendations within that framework; then determine who approves what based on the level of risk. The AI acts independently on reliable cases, while humans make the final decision on ambiguous cases.
To learn more about this topic: AI and pricing, what truly falls under the umbrella of artificial intelligence (pillar), why generative AI gets pricing wrong, and data quality as the glass ceiling for AI pricing. To implement an AI pricing governance framework as outlined at your catalog, discover our price optimization software.
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?