Everyone’s talking about AI in manufacturing. It’s in every keynote, every LinkedIn post, and every product pitch. But there’s a truth that too few are willing to admit: most manufacturers aren’t ready for it.
Not because the technology doesn’t work or isn’t powerful enough, but because the data isn’t.
AI can operate using publicly available information, the same kind of data that trains models like ChatGPT, but that approach comes with tradeoffs. The results can be generic, inconsistent, or out of sync with how a company actually works.
For manufacturers, that means losing the nuance and trust that define their processes, products, and culture. To achieve better, more reliable outcomes, AI needs to be fueled by internal data: the information that reflects a company’s specific mission, machines, and people.
Before making a plan to introduce AI pilots, copilots, or predictive models, manufacturers need to take a long look at what will be fueling them. For most shops, that means digging into existing processes and asking some uncomfortable, but essential, questions:
- Where does our data actually live?
- Can we trust it?
- Can we easily share it?
- Is it readable across systems or trapped inside silos so no algorithm can untangle it?
Many AI initiatives die before they even start. This isn’t a result of bad ideas, but bad data. Years of growth, mergers, and upgrades have created disconnected systems that can’t speak to each other. The result is a jumbled web of information: design data in one system, machine data over here, quality data over there, and production data locked in legacy systems that haven’t been touched in decades.
When that’s the foundation, AI doesn’t stand a chance.
The Hidden Cost of Bad Data
Every manufacturer knows the frustration of downtime or rework caused by missing or unreliable information. The same principle applies to AI. If you train a model on incomplete, inconsistent, or siloed data, it won’t deliver reliable or accurate insights. Rather than automating progress, it automates confusion.
The consequences of this “bad” data isn’t just financial; it’s cultural. Faulty AI recommendations can chip away at trust on the shop floor. Engineers lose trust in the systems they use. Managers question ROI. Eventually, AI is considered another overhyped experiment that didn’t live up to expectations.
That’s why, before adopting anything new, forward-thinking manufacturers are hitting pause on the pilots and focusing on what really matters: making sure the data is quality, accessible, and contextualized.
The Questions
Success starts with simplicity so before investing in new technology, every manufacturer, big and small, should ask these five questions about their data:
- Where does our data live? Map your resources: machines, sensors, ERP systems, quality records, spreadsheets or even paper notes. Understanding where data originates is the first step to managing it effectively.
- Is it readable? Data trapped in outdated formats or closed systems has no value. If it can’t communicate, it can’t contribute, so manufacturers may need to take action to make their data readable for the process(es) they plan to implement.
- Is it shareable? If information can’t move freely between departments, everything slows down. True digital transformation requires frictionless collaboration between systems and teams.
- Is it contextualized? Context is everything and numbers without context don’t tell a story. Just like you and I, AI needs to understand the relationships between variables (batch, time, material, operator, environment, etc.) in order to interpret data accurately.
- Can it be trusted? Data integrity matters as much as accuracy. Everyone must agree on a source of truth (what the data represents and where it comes from) or it won’t provide the results you're looking for.
While these questions may sound basic, they’re essential to a successful AI initiative. They transform data from a passive asset into a living, reliable system that supports better and faster decision making.
Data Cleanup is the Competitive Advantage
Fixing data may not sound as exciting as deploying a new AI tool, but it's the difference between success and failure. Cleaner, connected, and contextualized data enables AI to deliver meaningful and measurable value, whether it's through predictive maintenance, automated inspection, or real-time optimization.
Leading manufacturers are already proving this today. They’re using unified data to monitor performance across global facilities, reduce waste, and accelerate decision-making. Instead of relying on gut instinct or legacy knowledge, they’re using real-time insights to act with precision and confidence.
It’s important to remember that this transformation doesn’t require a complete digital overhaul. In fact, you can start small. Start with a single process line, a single dataset, or a single machine. Each improvement builds on the one before, creating a culture of data discipline that allows for scalable innovation.
Manufacturing has always been built on precision, and now, that mindset needs to apply to data. A strong foundation of organized, trustworthy information is what will separate AI success stories from the rest. When data is readable, shareable and contextualized, AI stops being an experiment and becomes an everyday advantage.
Before chasing the next big thing, it’s worth taking a step back and making sure the basics are solid. Because in the race to innovate, the manufacturers that slow down to fix their data will be the ones that end up moving the fastest.