Warehouse AI: Map the Decisions First, Then Clean the Data

On November 10, 2026, IIoT World published an article based on a CxO video series interview with Keith Moore, CEO of AutoScheduler.AI. It addresses a common obstacle to AI projects in logistics: the belief that data must be perfect before work can begin.

A note on the source: this is the viewpoint of a vendor's CEO, not independent research, and the article reports no performance figures. The text is also disclosed as summarized with AI tools and reviewed by the editorial staff. It should therefore be read as a set of methodological guidelines, not as proof of results.

The problem: endless cleanup

According to Moore, many companies introducing AI into their warehouses start with a huge data cleaning effort, often lasting years, which risks postponing operational improvements indefinitely. His alternative is to reverse the order: first map the decisions, then determine what data quality is actually needed to make them.

"Your data only has to be as perfect as the decisions you're going to make on it."

Five steps to map the decisions

The approach described in the interview breaks down as follows:

• catalog the data the plant or warehouse already has;
• document how decisions are made today;
• trace how data feeds each decision and what triggers a change in it;
• separate decisions that can be standardized across the network from those specific to a single site;
• rank decisions by impact against data readiness and automate from the top down.

The benefit is clear: data quality becomes a requirement tied to a specific decision, and therefore measurable and bounded, rather than a generic, open-ended goal.

Explainability to build operator trust

An experienced warehouse operator, Moore notes, will not blindly follow a machine's recommendation. For this reason, each application records the equations, constraints and trade-offs behind its decisions.

In the process described:

• before go-live, the platform shows its logic and asks the person configuring it whether they would handle the trade-offs differently, and the answer is recorded alongside the rules;
• when an operator questions a recommendation, the system walks back through the data along the decision flow and exposes its reasoning;
• the operator can challenge and correct the logic, producing a documented audit trail.

Domain-specific AI

Moore argues that general-purpose models from the large AI labs do not embed knowledge of physical constraints: equipment handling times, dock doors reserved for certain types of goods, the knock-on effect of a late truck on the entire plan. AutoScheduler.AI has instead verticalized its algorithms, combining machine learning with supply chain consulting expertise; the underlying platform was originally built for Procter & Gamble.

Why it matters for automation professionals

Even without endorsing the vendor's commercial claims, the method is transferable. In scheduling, simulation and digital twin projects, the temptation to wait for perfect data is the same, as is the risk of losing operator buy-in if the system is a black box.

Practical takeaways:

• start from a decision inventory, not a general data cleaning project;
• define, for each decision, the data quality that is strictly necessary;
• build physical constraints and operating rules into the model rather than delegating them to a generic system;
• record logic, assumptions and operator corrections to build trust and traceability.

How well this works in practice remains to be verified case by case: the article offers no data to prove it. As a criterion for kicking off a project, however, the sequence of decisions first, data second deserves attention.