AI that decides vs. AI that executes: Where to set the price
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Ines Amor
PhD in AI and Data Science
August 21, 2026
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 cases.
The balance is struck by weighing the stakes and scope of each decision. Organizations that succeed in 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 is worth automating, and what should remain in the hands of a human who bears responsibility for the decision?
This guide offers a simple framework for determining where to draw the line—between AI that carries out an action that has already been approved and 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 to approve this specific case before it takes effect.
Most serious pricing systems focus on execution or recommendations—never on making fully autonomous decisions on high-stakes cases. This isn’t a technological limitation: it’s 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 shifts by a few cents to remain aligned with a competitive positioning rule that has already been determined by a human.
- Alerts and anomaly detection —flagging a disruption, a suspicious price discrepancy, or a rule violation. Detection is not the same as decision-making: the system raises the red flag; the human makes the call.
- Data matching and structuring —matching a product to a competitor’s SKU, cleaning up a product database. A high-volume task with low per-unit stakes, where the occasional error has a limited cost and can be corrected.
- Recommendations regarding low-stakes references —loss leaders with marginal profit margins, 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 may be:
- 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 competing news story, a situation the model has never encountered in its training data.
- 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, 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, June–July 2025 survey).
The Matrix That Determines the Cursor: Stakes and Volume
A simple framework for determining, line by line, what can be delegated: cross-referencethe stakes involved in the decision (impact on 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 | Average | Average | 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 decision-by-decision basis, depending on what the decision actually entails, not on the sophistication of the model used to propose 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.
Enterprise agent-based AI projects are expected to be scrapped by the end of 2027—due to spiraling costs, unclear return on investment, and insufficient governance and risk control 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’s 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 draw the line.
Only a small percentage of consumers say they are willing to let AI make a purchase decision 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 it 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 judgment call—never an automatic process
GENIUS Price: An assistant that makes recommendations, a workflow that validates 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 calculates scenarios—but price approval remains a business decision, tracked within a governance workflow with defined roles and permissions (GENIUS Admin module), and is never an automated process without human oversight of high-stakes decisions.
This is exactly the point made 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 Cursor 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 performs tasks must have a written scope—not one that is merely implicitly understood by the team that configured it.
- 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 an occasional 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 simply “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 that falls within a range previously 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 the 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 always retaining 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 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 simply 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 consequence it can no longer handle.
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 spiraling costs, unclear return on investment, and insufficient risk control over what the system is allowed to do on its own—misconceptions that we detail in our article on common mistakes made by pricing teams when dealing with AI.
That is exactly what an explicit guideline, established from the outset, helps prevent—as the article emphasizes: a simple governance framework protects a project better 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 review. In between, the system continues to offer anomaly detection and standard pricing recommendations, which are validated by a human—an approach we detail in our article on pricing alerts and large-scale management.
The resulting benchmark never applies to an entire product category 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—but price approval remains a business decision, tracked within a governance workflow with roles and permissions defined via the GENIUS Admin module.
This is precisely the point confirmed by the pricing department at one of our clients in the food industry (1,700+ stores) 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 automated 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, far more of them are willing to accept a supporting 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 evident on the organizational side.
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 how data quality acts as a glass ceiling for AI pricing. To implement a traceable AI pricing governance framework for your catalog, check out our solution at Pricing Optimization Software.
Automating pricing doesn’t take decision-making away from humans; it eliminates the need for manual data entry. Two independent studies highlight the issue: AI-driven retailers see a 5 to 10% increase in gross margin (BCG), but 60% of AI projects not supported by AI-ready data will be abandoned by the end of 2026 (Gartner). The key difference between the two: a governance framework—including business rules, a designated owner, and a pilot category with a control group—established before automation, not after.
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.
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 cases.
The balance is struck by weighing the stakes and scope of each decision. Organizations that succeed in their AI projects are those that have established clear human oversight, not those with the most sophisticated model.