arXiv:2606.10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis. We present \textbf{Trace2Policy}, whose core mechanism -- \textbf{EISR} (\textbf{E}rror-driven \textbf{I}terative \textbf{S}kill \textbf{R}efinement) -- maintains a human-readable rule document as its optimization target: each round executes the rules on a validation set, clusters errors by root cause into MISSING, WRONG, or CONFLICT types, applies targeted patches,
The proliferation of complex decision-making in enterprises, especially in highly regulated sectors, is driving the need for more efficient and auditable AI-driven automation tools.
This development allows for the systematic capture and improvement of tacit expert knowledge into explicit, machine-executable rules, directly impacting efficiency and compliance in white-collar work.
The process of codifying and refining expert decision-making in enterprises is significantly accelerated and made more robust through iterative, error-driven AI, moving beyond static rule-based systems.
- · AI software developers
- · Consulting firms specializing in process automation
- · Financial services sector
- · Legal and compliance departments
- · Manual process auditors
- · Traditional business process outsourcing firms
- · Companies slow to adopt AI automation
Enterprise operations become more efficient and less prone to human error in complex decision-making tasks.
The demand for human experts shifts from executing routine decisions to validating and refining AI-generated rules.
Entire industries relying on complex, tacit knowledge could see accelerated automation, potentially leading to significant workforce restructuring and new economic models for knowledge work.
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Read at arXiv cs.AI