AI that decides vs. AI that executes:

Where to set the benchmark for retail pricing

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.

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).

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.

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.

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 Marc Decremps, Pricing Project Manager / Transformation Department at Coopérative U (1,700+ stores), regarding the Booper deployment: “Our goal was not simply to acquire a new tool, but to improve our ability to make consistent pricing decisions on a large scale. […] The approach proposed by BOOPER convinced us with its ability to balance automation, governance, and decision-making control by business teams.”

Build Your Own Cursor in Four Steps

1. 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.
2. 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.
3. 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.
4. 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

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To learn more about this topic: AI and retail 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 a traceable AI pricing governance framework for your product catalog, check out our solution at Pricing Optimization Software.