Enterprise skill taxonomies are built on self-reported data that is 18 months stale and never updated. An AI skills intelligence system infers demonstrated capabilities from work artifacts: code commits, documents, presentations, project outputs. It maps the real skill graph of the organization and identifies internal talent before opening an external requisition.
Every major enterprise has a skills database. Almost none of them work. Self-reported skills data decays at 15 to 25 percent per year as employees acquire new capabilities through project work and never update their profiles. The result is that talent acquisition teams open external requisitions for skills that already exist inside the organization, paying $20,000 to $50,000 in recruiting fees per hire for capabilities they already employ. Internal mobility suffers not from lack of will but from lack of visibility.
Research from the World Economic Forum and Deloitte Insights (2024) identifies skills inference from behavioral data as the highest-leverage intervention available to CHROs trying to address talent gaps without expanding headcount. LLMs can analyze the artifacts employees actually produce: pull requests, design documents, client proposals, training completion records, and project retrospectives. By mapping demonstrated behaviors to a standardized skills ontology (ESCO, O*NET, or a proprietary taxonomy), the system builds a living skill graph that reflects what people can actually do, not what they last typed into a profile. Role-fit scoring across this graph enables internal talent sourcing that reaches candidates a traditional search would miss entirely.
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