In a recent Manufacturing Business Outlook article, St. Onge Company’s Bob Swidarski argues that manufacturers are focusing too heavily on choosing the right AI tools instead of addressing the foundational issue: whether their operational data is accurate, complete, and trustworthy enough for AI to learn from effectively. Through examples ranging from warehouse capacity planning failures to ERP improvements in a lipstick manufacturing facility, Bob shows that operational problems are often rooted in inconsistent, outdated, or incomplete data rather than technology limitations. Ultimately, he concludes that successful AI adoption in manufacturing depends less on selecting the right platform and more on the disciplined, often overlooked work of maintaining clean and reliable operational data.
“The AI hype will continue. Feature sets will expand. Vendor promises will get larger. And underneath all of it, the unheralded work will remain the same: defining how work actually gets done, capturing what actually transpires, and maintaining the integrity of the data those systems depend on. Not because some transformation readiness framework demands it. Because no system, AI or otherwise, can learn from what has not been defined, optimize what has not been captured, or execute well on what has not been maintained.”
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