From tacit knowledge to codified AI-ready assets

iManage’s data-readiness argument highlights inconsistent metadata, poor document classification and fragmented repositories as structural problems. The remedial work includes normalization, taxonomy development and historical remediation. That is classic KM work, but the business case has changed: it now determines whether AI can produce reliable firm-specific outputs.

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