Notes on keeping what your company knows
Practical writing on knowledge retention, offboarding, and making enterprise AI work.
When an employee leaves, the documents stay but the reasoning walks out the door. Here is what that actually costs, why wikis and search do not fix it, and how to retain the knowledge instead.
Knowledge retention is how organisations keep what their people know when they leave. Here is a clear definition, why it matters, and how it differs from knowledge management.
A practical, vendor-neutral checklist for choosing knowledge management software: the categories, the criteria that matter, the questions to ask, and the red flags to avoid.
A practical knowledge retention strategy: identify at-risk knowledge, capture it before people leave, preserve it in a usable form, and govern access. With real examples.
A practical offboarding checklist to transfer an employee's knowledge before they leave, so the team keeps the context, not just the files.
Practical ways to prevent knowledge loss when employees leave: what to do before, during, and after a departure to keep critical context inside the company.
Tacit knowledge is the hardest to keep and the most valuable. Here is what it is, why it resists documentation, and practical ways to capture it.
What AI knowledge management is, how it works, and the governance pitfalls to avoid. A clear explainer for teams adopting AI on top of their knowledge.
Should you build your own AI knowledge base or buy one? A practical look at the real costs of building RAG infrastructure versus adopting a governed platform.
IT and DevOps teams lose critical knowledge in incidents, runbooks, and the heads of senior engineers. Here is how to capture and keep it.
Most AI initiatives stall on knowledge, not models. A practical framework to assess and improve your enterprise AI readiness across governance, permissions, and freshness.
AI without your context guesses. A knowledge layer gives every AI tool governed, current, permission-aware access to what your company knows, through RAG and MCP.
Confluence is great for authoring docs, and its AI helps within Atlassian. Here is where it falls short for cross-tool knowledge and retention, and how to fix it.