Are Documents Breaking Your Digital Thread — and preventing successful AI workflows?

Introduction

This guide and self-audit is intended for anyone responsible for, or invested in, or strengthening their company’s digital thread, or is preparing their organization for AI-driven workflows.

Over the past decade, manufacturing companies have worked toward building a digital thread — a connected flow of product data across design, engineering, manufacturing, and service. Industry research from groups like NIST, Deloitte, McKinsey, Gartner, and MxD all point to the same outcome: when product data moves seamlessly between systems, companies operate faster, reduce risk, and improve quality. That vision has driven major investments in engineering and operations platforms such as CAD, PLM, MES, ERP, QMS, and other digital tools, designed to ensure every team is working from the same source of truth.

But despite this progress, a critical layer of information often sits outside the digital thread: documentation. For most, documentation is created with general-purpose tools like PowerPoint, Word, Confluence, or even locked in old PDFs.

You don’t have a connected digital thread if you have disconnected documentation. Information begins to drift from the source of truth as screenshots, revisions, and parts metadata become outdated.

This challenge is becoming even more important as manufacturers invest in AI workflows. While systems like CAD, PLM, and MES contain structured product data, critical operational knowledge still lives inside static documents filled with screenshots, annotations, copied metadata, and manually maintained instructions. These formats are difficult for AI systems to reliably interpret, validate, or act upon.

The consequences are significant. Poor documentation and knowledge transfer contribute to billions of dollars in manufacturing waste and operational errors each year. Estimates suggest the global cost of poor quality exceeds $1.6 trillion annually, while engineers can lose 30–40% of their time searching for or recreating information.

Even organizations with sophisticated digital infrastructure can still experience a break in the digital thread. The moment product information is copied into a static document, it begins drifting from the systems that define the product.

If you're investing in your digital thread or considering adopting AI, you can't ignore documentation. It is your greatest source of contextual data — the operational knowledge that explains how products are built, serviced, inspected, and maintained. But when that information lives in disconnected, manually maintained documents, it becomes difficult for both people and AI systems to reliably trust, interpret, and act on.

The truth is, people at your company still create documents every day, and they're not going to stop. So rather than wrestling unstructured context into rigid systems, build documents that are smart enough to plug into them.