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SEPTEMBER 2026 I I-CONNECT007 MAGAZINE 15 "acceptable quality levels" that tolerated a planned percentage of defects, arguing that any tolerated defect rate becomes a self-fulfilling target. Crosby would likely embrace AI-driven defect prediction enthusiastically, precisely because it shifts quality left, catching a likely failure during design or early production rather than paying for it in the field. Computer vision systems that flag a solder joint or a weld before it fails inspection fit his philosophy exactly: The earlier the catch, the closer the cost gets to zero. But he'd push hard against a subtle risk of treating a model's confidence threshold as a new accept- able quality level, quietly tolerating a defect rate the algorithm doesn't flag rather than driving toward true prevention. For Crosby, the measure of quality was always the cost of nonconformance, and he'd insist AI systems report in those terms rather than abstract accuracy scores. Ishikawa: AI as a Democratized Fishbone Diagram Ishikawa built his reputation on making quality tools accessible to the entire workforce, not just engineers and statisticians. The fishbone (cause- and-effect) diagram and quality circles were both designed to provide frontline workers with a struc- tured way to contribute to root cause analysis. His principle of company-wide quality control held that the people closest to the work often see problems that no dashboard can capture. He would likely see generative AI's biggest contri- bution not in the algorithms themselves, but in translation: a natural-language interface that lets a machine operator describe a problem in plain terms and get back a structured cause-and-effect break- down, or that turns scattered shop-floor observa- tions into a properly categorized fishbone diagram without requiring a black belt to build it. His skepticism would center on exactly what AI tends to erode if used carelessly: the participa- tory, cross-functional discussion that made his quality circles work. A model that outputs a root cause without a team ever discussing it together produces an answer, but not necessarily buy-in, and not the tacit knowledge that only comes from people arguing over a whiteboard. Shingo: AI as Poka-Yoke at Scale Shingo's most durable contribution, poka-yoke, was about making mistakes physically impossible rather than catching them after the fact: A part can only be inserted one way, or a jig won't close if a component is missing. His related concept of Zero Quality Control argued that source inspection, built into the process itself, should make sampling inspection obsolete. AI-enabled vision systems and sensor fusion are close to a direct realization of that idea at a scale Shingo couldn't have built by hand: a camera that halts a line the instant a part is misaligned, a model that predicts a tool is about to drift out of toler- ance, and stops the cycle before a bad part is even produced. Shingo would push hard for AI to be embedded at the point of creation rather than the point of inspection; an error-proofing device, not a dashboard reviewed after the shift ends. The Common Thread None of these six would treat AI as a replacement for the discipline they spent their careers building. Each would use it to do what they already believed in, faster and at greater scale, including grounding decisions in statistical evidence, reducing variation, focusing effort, preventing rather than detecting, involving the workforce, and error-proofing the pro- cess itself. The risk they'd each flag, in their own language, is the same one quality management has always faced: mistaking a more sophisticated tool as a substitute for management commitment, root-cause thinking, and a culture that treats every defect as a problem worth understanding rather than a statistic worth tolerating. I-CONNECT007 Steve Williams is president of The Right Approach Consulting. He is also an independent certified coach, trainer, and speaker with the John Maxwell team. To read past columns, click here. T H E R I G H T A P P ROAC H