Before AI Can Help, the Data Has to Work: Lessons from a 140,000-Person Manufacturer

Key points

• Jabil, a global manufacturing company with over 140,000 employees and more than 100 sites across 30-plus countries, is standardising its technology on SAP Integration Suite, a platform that connects separate software systems so they share data automatically.
• The company accumulated roughly 25 years of disconnected, site-specific tools, spreadsheets, and manual workarounds, creating gaps that slowed decisions and disruption responses.
• Jabil's IT director frames the core rule simply: any innovation without simplification adds more complexity, not less.
• AI tools such as predictive supply-chain forecasting are next on the roadmap, but only once the shared data foundation is stable.

There is a common temptation in big organisations: buy a new technology to fix a problem, then buy another when the first one creates its own problems. After 60 years in business and expansion to more than 100 factories worldwide, Jabil, a contract manufacturer based in St. Petersburg, Florida, that builds products for more than 400 global brands, found itself deep in that trap.

The symptoms were familiar. Each site had its own tools, its own spreadsheets, its own ways of recording what was happening on the factory floor. Data lived in separate pockets, invisible to anyone outside that location. When a disruption hit one part of the supply chain, the rest of the network couldn't see it quickly enough to respond.

"We had limited ability to see issues early across plants, across regions," says Harish Manohar, SAP IT Director at Jabil, speaking in an episode of the Business Lab podcast, produced by MIT Technology Review in partnership with SAP. "This complexity created data silos, and in a way delayed decision-making."

Why didn't Jabil just add smarter tools?

Adding new technology on top of broken plumbing makes the plumbing worse. That was the lesson Jabil drew from its own history.

Manohar describes the company's response as a "simplify-first, then-innovate mindset." The principle is blunt: standardise the underlying processes before bolting anything new onto them. Jabil chose SAP, the German enterprise software company, as its core digital platform, and is using SAP Integration Suite, a tool that acts like a universal translator between different software systems, as the connective layer.

The goal is to retire the patchwork of site-specific applications and route as many processes as possible through one consistent system. Not every site is there yet. Some carry legacy software that dates back decades. Some operate in regulated industries that add their own compliance rules. Standardising across all of them means changing how people work without stopping the factories that never close.

What does this have to do with AI?

Everything, in Manohar's view. AI tools, whether they forecast demand, flag exceptions in a shipment schedule, or plan production runs, are only as good as the data they read. Feed them inconsistent, siloed, or out-of-date information, and their answers are unreliable at best and costly at worst.

"The backbone of any modern organisation is data," Manohar says. "Before organisations can optimise, automate, or apply AI, data needs to flow seamlessly across systems."

With a cleaner foundation taking shape, Jabil is now exploring predictive supply-chain insights and AI-driven planning tools. But those come second. The plumbing comes first.

The broader point travels well beyond manufacturing. Any organisation, from a hospital network to a retail chain, faces the same sequence: clean, connected data is the prerequisite, not an optional extra. Skipping that step and jumping straight to AI does not speed things up. It moves the problem somewhere harder to see.

Watch for: If your own workplace is being sold an AI tool as a fix for inconsistent reporting or missing data, ask what happens to the underlying data problem first. A good answer goes to the data. A bad answer goes straight to the demo.