Encoding the SME: AI for Accelerated Learning in Complex Operations, TAPPICon26

Across the pulp and paper industry, experienced engineers possess decades of intuition about how their mills behave; knowledge that is often trapped in notebooks, reports, and routine analyses. At the same time, a new generation of engineers is entering the workforce amid high expectations for data‑driven decision‑making. This paper explores how emerging generative and agentic AI systems can bridge that gap by encoding expert reasoning into repeatable analytical workflows and grounding decisions in both data and documentation.

We follow a scenario in which a senior subject‑matter expert’s investigative methods, historical comparisons, anomaly triage, and root‑cause frameworks are captured and codified within an AI‑assisted environment. A new engineer then leverages those encoded workflows alongside document‑aware AI assistants that surface standard operating procedures and troubleshooting guidance in context. The result is not automation that replaces expertise, but augmentation that scales it: the junior engineer learns how to think like the veteran, gains confidence in interpreting process data, and acts with informed autonomy. By humanizing AI adoption, anchoring it in mentorship and transparency, this approach demonstrates how mills can accelerate learning, preserve institutional knowledge, and cultivate the next generation of data‑empowered process leaders.

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