Stop Feeding Garbage to Your AI: Why 80% Accuracy in Document Processing is Now a Failure | SPONSORED
A fundamental change in expectations is reshaping how manufacturers evaluate artificial intelligence systems. The long-standing benchmark of 80% accuracy in document processing is no longer acceptable. In the context of AI-driven product and supply-chain decisions, this level of performance constitutes failure. When two out of every ten data points extracted from a critical document—such as a chemical batch certificate or a precision machining spec—are incorrect, the resulting AI recommendations become untrustworthy and potentially hazardous.
The shift is driven by how processed document data is utilized. Historically, extracted information was often reviewed by human operators who could spot and correct errors. Today, that data feeds autonomous or semi-autonomous systems that trigger orders, adjust production parameters, or schedule maintenance. An error rate that was once manageable through human oversight now introduces unacceptable risk into automated workflows. In regulated environments, it creates compliance exposure; in production, it causes material waste and unplanned downtime.
The Engineering Standard for AI-Ready Data
Industrial operations require engineering-grade data inputs. This demands a system that moves beyond template-based extraction toward deterministic validation. The process must include multi-layered verification: cross-referencing extracted values against known databases, applying business logic rules to check for physical impossibilities, and using multiple AI models to vote on ambiguous interpretations. For instance, a system validating a material certificate might check the extracted alloy number against a sanctioned supplier list, confirm the tensile strength falls within a possible range for that alloy, and use consensus from different processing models to verify a smudged lot number.
This validation layer transforms data extraction from a probabilistic guess to a verified fact. It ensures that only information passing all configured checks proceeds to downstream AI systems and business applications. Data that fails any validation is flagged for human review, preventing corrupt information from polluting the digital ecosystem. This approach treats data quality with the same rigor as material quality control.
Building the Infrastructure for Zero-Tolerance Accuracy
Achieving this standard requires a dedicated infrastructure layer focused solely on document intelligence. This layer must normalize inputs from any source—scanned paper, PDFs, CAD files, image formats—into a consistent, structured format. It must govern the data with clear provenance, tracking the origin of every value and the validations it passed. Most critically, it must be configurable to enforce the specific business rules and compliance requirements unique to each manufacturing operation.
Investing in advanced AI models without first investing in the data quality layer that feeds them is inefficient. The priority must be establishing a document processing pipeline engineered for near-perfect accuracy. This pipeline becomes the foundation upon which reliable predictive maintenance, autonomous supply chains, and closed-loop production optimization are built. Tolerating garbage inputs guarantees garbage outputs, regardless of how intelligent the system processing them claims to be.
Sponsored by Adlib Software
This article is based on the IIoT World Manufacturing Day session, “Preparing Your Data Layer for AI-Driven Product and Supply-Chain Decisions,” sponsored by Adlib Software. Thank you to the speakers: Chris Huff (Adlib Software), Anthony Vigliotti (Adlib Software), Sabrina Joos (Siemens), and Hamish Mackenzie (New Space AI).