This week Chris Burchett, who runs generative AI at Blue Yonder, published a piece in Fast Company arguing that ethical AI has graduated from a slide in the corporate responsibility deck to a genuine strategic priority for supply chain leaders. I spend most of my working life inside distribution centers running Blue Yonder WMS, and I want to take that argument out of the boardroom and put it where I actually live: next to a wave that just released the wrong stock to the wrong dock.
The framing matters. Burchett's core point is that a smart business strategy treats ethical AI seriously by holding the system accountable for being fair, safe, and transparent. That sounds abstract until you connect it to a model that decides which orders get picked first, which carrier gets the load, or which associate gets routed to the slow zone all shift.
A black box is a liability, not a feature
The article is blunt about one thing: organizations need to avoid black-box algorithms and keep human oversight inside the decision loop. In a WMS context this is not philosophy. When a slotting model reshuffles your fast movers and your pick rate quietly drops, the first question your operations manager will ask is why. If the only answer is "the model decided," you have a problem that no dashboard will solve.
Burchett ties accountability directly to data quality, and this is where supply chain teams should pay attention. He notes that biased or unrepresentative training data can disadvantage certain groups, and gives the example of supplier selection drifting toward established, data-rich companies while smaller or developing-region suppliers get filtered out simply because they generated less history. The same failure mode hides inside warehouse models. A labor model trained mostly on tenured associates can systematically deny newer workers the assignments that would let them build speed. Burchett raises exactly this kind of unfair labor outcome, where an algorithm denies training opportunities to people who never get a fair sample.
The uncomfortable truth is that an unfair model and an inaccurate model usually come from the same place: data that does not represent the reality of your operation. Fix the data foundation and you address both problems at once.
Regulation is the floor, not the ceiling
One of the more useful arguments in the piece is that leaders should treat frameworks like GDPR and the EU AI Act as the minimum bar, not the finish line. The recommendation is to futureproof implementations by getting ahead of where regulation is clearly heading rather than scrambling to retrofit governance after the fact. For anyone who has lived through a compliance retrofit on a live WMS, that advice lands. Bolting explainability onto a model already running in production is far more expensive than designing for it from day one.
Burchett also makes the commercial case, and I think it is the part skeptical operators most need to hear. Responsible AI is framed not as a cost center but as something with measurable return, citing fuel savings in the range of 10 to 15 percent, the potential to eliminate roughly 95 billion dollars of logistics waste across the industry, and improved worker safety. I will not pretend those figures map cleanly onto any single site, and I would not promise them to a client. But the direction is right: a model you can trust, explain, and defend is a model people actually use, and a model people use is the only kind that delivers any number at all.
What this means on the floor
For teams running Blue Yonder, the practical translation is straightforward. Insist on explainability for any model touching labor, slotting, or order release, so that every recommendation can be traced back to inputs your supervisors recognize. Audit your training data for representativeness before you audit the model's accuracy, because the second problem is usually the first one wearing a disguise. Keep a human authorization step on decisions with real consequences for people or service levels. And choose AI partners who understand the specific nuances of supply chain work and the regulatory direction of travel, rather than generic tooling bolted onto a warehouse it does not understand.
None of this slows you down in any way that matters. It is the difference between an AI program that survives its first serious audit or its first contested labor decision, and one that gets quietly switched off after the first bad week. Burchett's argument is that the responsible path and the strategic path are the same path. From where I stand, on a warehouse floor, watching real decisions get made at machine speed, I think he is right.
Sources: Fast Company: https://www.fastcompany.com/91552851/ethical-ai-is-a-strategic-priority-for-supply-chain-leaders