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Exploring how document intelligence, data accuracy, and traceability are shaping AI adoption in manufacturing.
At the ARC Industry Forum 2026, Craig Resnick spoke with Chris Huff, CEO of Adlib Software, about the role of intelligent document processing, AI, and automation in supporting industrial digital transformation.
A central theme of the discussion was the challenge of unstructured and inconsistent data. Many industrial AI initiatives, Huff noted, struggle not because of the models themselves, but because of the quality of the inputs. Engineering documents, compliance records, and operational files often exist in complex formats that are difficult to standardize and validate. As a result, organizations face challenges in trusting the outputs generated by downstream systems.
Adlib’s approach focuses on what Huff described as an “accuracy layer” that sits alongside core manufacturing systems. Rather than replacing existing platforms such as PLM or quality management systems, this layer processes incoming documents—normalizing, extracting, and enriching data before it enters operational workflows. The goal is to convert unstructured content into structured, machine-readable data that can be reliably used by automation systems and AI applications.
The discussion also highlighted the operational burden associated with document-heavy workflows. Engineers and quality specialists often spend significant time reviewing documents against standard operating procedures and regulatory requirements. As Huff explained, “the biggest tax… is that they are having to stare at documents and compare those documents to regulatory compliance-oriented operating procedures.” He noted that much of this work can be automated, allowing human involvement to focus primarily on exceptions and decision-making scenarios.
Digital twins and simulation environments were another area of focus. Their effectiveness depends heavily on the quality of the underlying engineering data. Adlib addresses this by supporting the ingestion and normalization of a wide range of file types, including CAD drawings and PDFs, transforming them into consistent data products that can be used in pre-production and simulation environments. As Huff emphasized, the objective is to ensure that systems operate on “trusted data products” rather than fragmented document inputs.
Traceability and auditability also emerged as critical requirements for scaling AI. Huff described how post-processing capabilities enable organizations to track data lineage, including who interacted with the data, what changes were made, and how outputs connect back to source documents. In this context, he noted the importance of establishing “provenance… and the auditability back to the source document,” particularly for compliance and quality assurance use cases.
Looking ahead, Huff identified data governance as a key challenge for manufacturers in 2026. Many organizations, he noted, continue to invest in AI applications without addressing the quality and trustworthiness of their underlying data. He emphasized the need for a first-principles approach, where organizations focus on establishing reliable inputs before attempting to scale AI initiatives. As he summarized, being AI-ready means “taking complex documents and turning them into trusted data products.”
The discussion also touched on workforce considerations. Successful implementations, Huff suggested, involve incorporating domain expertise from existing teams rather than imposing new systems without context. As he noted, the most effective deployments include the workforce in the process, recognizing that “they know their process… better than anybody else.”
The conversation reflects a broader shift in industrial digital transformation, where the focus is moving beyond AI models toward the quality, structure, and traceability of the data that supports them. As organizations look to scale automation and analytics, the ability to transform complex documents into trusted data products is becoming increasingly central to operational success.
Watch the full discussion on YouTube or here: