Infrastructure for Knowledge Article Auto-Generation

Knowledge article auto-generation requires a formally defined article schema — what sections a best practice article must contain, what a how-to guide structure looks like, what metadata fields are mandatory. This goes beyond L2 'documentation exists' to require documented templates that the AI can use as generation targets. The ps-km baseline has methodology frameworks and deliverable standards, but for auto-generation the article format itself must be explicitly defined so the AI produces consistently structured, usable output rather than free-form summaries.

Auto-generation of knowledge articles depends on systematic capture of source material: project retrospectives, meeting notes, client feedback, and solution documentation. These must arrive in the system with sufficient context metadata (project type, industry, phase, team) for the AI to generate appropriately tagged and categorized articles. Mandated project closure templates with required retrospective sections provide the systematic capture foundation. Without this, the AI generates from whatever fragments happen to be uploaded.

Generated knowledge articles must be tagged with taxonomy metadata to be discoverable — industry, service line, topic, methodology component. The ps-km baseline has a taxonomy schema at L3 that the auto-generation system can apply. Generated articles need to conform to this schema for consistent field population. However, the absence of formal ontology means the AI cannot automatically identify relationships between articles (this best practice is a prerequisite for that approach) — limiting output to standalone articles rather than linked knowledge structures.

Knowledge article auto-generation requires the AI to read source documents from repositories and write generated articles back to the knowledge management system. API access to SharePoint or Confluence for both read and write operations enables this workflow. The system can ingest project retrospectives, process them through the generation model, and publish structured articles without manual file handling. Binary format parsing (meeting notes in Word, deliverables in PPT) requires extraction pipelines but is achievable with available APIs.

Auto-generated knowledge articles reflect the state of firm methodology and project experience at the time of generation. As methodologies evolve and new project patterns emerge, old articles become misleading — a best practice article on 'ERP implementation' written from 2021 projects gives outdated guidance for 2024 cloud-native implementations. The ps-km baseline shows no systematic staleness detection or refresh cycle. At L2, articles are updated only when someone reports them as wrong, which may never happen for low-traffic content.

Knowledge article auto-generation requires the AI to access source documents from project repositories and deposit generated articles into the knowledge management system — a point integration workflow. Integration with PSA (to trigger article generation at project closure) or CRM (to link articles to client context) would improve coverage, but the core generation workflow functions with point connections to document repositories. The baseline confirms these repositories are standalone from CRM and PSA.