21.08.2026
Before enhancing or implementing an AI solution, we need solid evidence it beats the current process, and we should hold ourselves to that standard as builders. We frame this as two key questions to ask before any AI rollout — what outcome are we improving, and what proves the new approach wins?
Define the Outcome That Actually Matters
Consider an AI solution we built to quickly identify out-of-stock conditions for products on shelves, such as at military depots and grocery stores, and to prioritize which conditions to remediate first. The legacy process was manual and periodic: staff walked the aisles or ran scheduled counts, catching problems late and unevenly. Our model promised faster, more complete detection — but “faster and more complete detection” is a technical claim, not evidence that the outcome that actually matters, avoided lost sales or safeguarded readiness, improved.
Don’t Scope the Outcome Too Narrowly
A common trap in evaluating AI solutions is scoping the metric of success too narrowly, to just the immediate work in front of you. It was tempting to score the model one store, one depot, one SKU at a time. But a flagged stockout — the “precursor” — does not by itself mean total loss of sales or readiness for the period: a customer may come back later, or a unit may be replenished from a nearby store or depot. Scoping success to the individual site overstated the model’s value. Scoping it to the store network, or the depot’s broader supply chain, showed the real picture: how stockouts actually converted into consequential, unrecoverable loss.
Don’t Mistake the Metric for the Outcome
A second trap is focusing on a narrow intermediate metric instead of the essential outcome. In a recipe, an ingredient’s contribution depends heavily on the other ingredients and cooking processes, and we credit it only by how it contributes to the finished dish. Our detection model earns its keep the same way: only by how much lost sales or mission risk it actually prevents. The benefit of our solution depends on two things jointly — correctly identifying out-of-stock conditions and correctly estimating the consequent sales lost, or mission jeopardized, if action on that specific stockout is delayed. A model that’s excellent at the first and blind to the second can’t tell anyone which shortages deserve attention today versus next week.
Reward the Right Behavior
If we tuned our detection system purely to maximize the number of stockouts flagged or to minimize the time taken to flag them, we would flood remediation crews with low-consequence alerts and starve attention from the few that mattered. The correct intermediate metrics weren’t “stockouts flagged” or “average time to flag,” but something closer to faster remediation of high-consequence items and improved on-shelf availability where it counted — metrics that jointly point back to the final outcome.
Bring It Back to the Final Outcome
This discipline applies to AI solutions generally. Better model accuracy, faster processing, or stronger technical performance matters only if it ultimately improves the operational outcome. Our stockout model could out-detect its predecessor on accuracy or speed while still leaving the overall process slower, more expensive, or less effective for its ultimate purpose — if it wasn’t scoped to the right unit, tied to real consequences, incentivized correctly, and checked for what it broke elsewhere.
The bottom line:
In your pursuit of AI innovation, anchor to the final outcomes that matter, measure them broadly enough to capture the full impact, and compare them against the status quo. Never implement something simply because an intermediate or technical metric improved. Demand strong evidence that the final outcomes will be better.
