Why manufacturing's real AI opportunity is buried in its documents

Ask a plant manager why AI hasn't reached their operation and you'll usually hear a version of the same few things:

• No clear use case: Plenty of teams are waiting to see a proven application in their own environment before they commit budget or headcount. Stalled pilots and fuzzy returns make finance teams cautious, so the project never leaves the slide deck.
  
  
• No people to run it: The labor squeeze is real. Deloitte and The Manufacturing Institute project project up to 1.9 million manufacturing jobs going unfilled by 2033. Maintaining AI workflows takes time and expertise that stretched teams don't have to spare.
  
  
• No trust in the output: If nobody can explain why a system reached a decision, quality and compliance teams are right to be cautious about acting on it. In a plant where a wrong spec or a missed revision carries real consequences, "the AI said so" doesn't clear the bar.

There's a reason that sits underneath all of these, and it's the one that matters most.

AI runs on data. In manufacturing, most of that data is locked inside documents. Think about everything a single production order touches:

1. CAD drawings and their revisions
2. Bills of material and work instructions
3. Inspection reports and certificates of conformity
4. Delivery notes, purchase orders, and supplier declarations
5. The ERP entries that turn all of this into a live production order
6. The emails and sign-offs tying it all together

Most of that is scattered across shared drives, inboxes, ERP attachments and paper. It's a problem industry-wide, with Gartner putting unstructured data at 70% to 90% of all enterprise data and growing several times faster than structured data. It sits in storage, generating cost and risk while delivering nothing back.

You can't point AI at information it can't reach. And even once it's reachable, a model can't do much with a document it can't interpret. Before it can classify an invoice, match a delivery note to a purchase order, or surface the current revision of a spec, that content has to be captured, made available in a structured way, and understood in the context of the process around it. Skip that and even the best AI has nothing to work with.

The fix is to get all that content into one place and make sense of it. That's what an AI-powered intelligent content automation (ICA) platform is made for, and they’re relied on by leading manufacturers that once faced the challenges laid out above.

Here's how it works in plain terms: incoming documents get captured and processed as they arrive. For everything already held in systems like SAP or another ERP, or the PLM, CAD and quality system, the platform reaches in and works with the documents there. The AI reads each one, works out what it is, pulls out the data that matters, and ties it to the order, part or supplier it belongs to. Your teams get one place to reach all of it, so the information stops being locked away.

That solves the document problem, but it also does something bigger. Once that knowledge is connected, the platform can act on it across the whole business rather than inside a single department or system. For example:

• Releasing an invoice straight through to payment once it lines up with the purchase order and delivery note, so nothing sits in a queue waiting on a manual check
• Catching a price or quantity mismatch on an order confirmation before it reaches production
• Serving the current revision of a spec to whoever asks, so nobody builds to an out-of-date drawing
• Keeping quality records audit-ready, so a certificate of conformity is there the moment an auditor asks

But acting on documents is a bigger ask than reading them, and it only works if you can trust what comes out. A general-purpose AI assistant will answer the same question two different ways on two different days, and invent an answer when it doesn’t know. Fine for drafting an email, but not fine when the output posts an invoice. An ICA platform runs according to a set of strict rules, so the same invoice goes through the same process today as it did last month and gets the same result. Answers come from your own documents rather than the model's training data; every document keeps its permissions.

A concrete example always helps. SEW-EURODRIVE is a leading drive-technology manufacturer with around 22,000 employees and operations in more than 50 countries. It builds gear motors, servo motors, and the control systems that move everything from bottling plants to cable cars.

Inside the company, the volume of orders, delivery notes, invoices, and production documents had grown too large to process by hand. So SEW-EURODRIVE put a core AI component of its ICA platform to work on them: intelligent document processing (IDP).

IDP classifies and extracts data from 750,000 incoming customer documents every year, whatever the language or format, then routes the results straight into SAP and other connected systems. According to the company:

• Error sources have been largely eliminated
• Data that used to be inaccessible is now captured automatically
• Rolling the system out to a new country takes far less configuration than before
• As a result, the system can now show exactly what it identified in a document and why. That traceability is what turns cautious users into confident ones, and it answers the trust barrier head on.

Want to learn more? Explore the 6 trends driving digitalization in the manufacturing industry today.