Here’s a counterintuitive argument from a company running more than 100 manufacturing sites across 30-plus countries: the best way to get AI-ready is to stop adding technology. Jabil, the St. Petersburg, Florida-based manufacturer with 140,000-plus employees, has been deliberately shrinking its tech footprint before expanding it. Harish Manohar, SAP IT director at Jabil, puts it bluntly: “Any innovation without simplification is going to add more complexity.”
The company adopted what Manohar calls a “simplify-first, then-innovate mindset.” That’s not a slogan. It’s a response to a real problem — disconnected systems, site-specific tools, spreadsheets, and manual workarounds that create data silos. Those silos make it harder to spot problems early, coordinate responses across regions, and make decisions with confidence. For a company serving more than 400 of the world’s top brands, the stakes are high enough that integration gets priority over everything else. “The backbone of any contemporary or modern organization is data,” Manohar says. Before AI can do anything useful, data needs to flow seamlessly across systems.
The hard part is that Jabil’s sites don’t all look the same. Different levels of process maturity, legacy systems, localized workflows, and regulated businesses with extra compliance requirements all complicate standardization. Change processes and governance without disrupting operations — that’s the tightrope. The payoff, though, extends beyond technology. Integrated workflows give employees shared visibility into data, reduce manual reconciliation, and shift their work from chasing information to acting on insights. Jabil is specifically targeting real-time visibility into supply chain events, which Manohar says enables faster responses to disruptions and reduces operational risk.
What Jabil is doing mirrors a broader industry shift away from the assumption that AI solves messy data problems on its own. I’ve seen this story before: companies bolt machine learning onto fragmented infrastructure, then wonder why forecasts are useless. The honest answer — one that vendors rarely lead with — is that the data plumbing matters more than the model. Jabil’s next moves depend on that plumbing: predictive supply chain insights, intelligent exception handling, and AI-driven planning and forecasting. Manohar’s closing line sums it up: “Simplicity at scale is a very competitive advantage.” The question for every other global manufacturer is whether they have the discipline to strip things down before building them up.
