# What is KM4AI?

> KM4AI is an independent, vendor-neutral hub at the intersection of Knowledge Management (KM) and Artificial Intelligence.

It helps organizations and practitioners (a) prepare their knowledge assets so AI systems can use them reliably, and (b) apply AI to strengthen classic KM practices — capture, curation, taxonomy, search, sharing, and reuse.

- **HTML:** https://km4ai.org/
- **Markdown:** https://km4ai.org/md/what-is-km4ai.md
- **LLM index:** https://km4ai.org/llms.txt

## Why now

AI systems only perform as well as the knowledge they can retrieve and trust. Most organizations are data-rich and insight-poor: content is scattered, poorly tagged, permission-opaque, and stale. KM4AI exists to close that gap without locking teams into a single vendor stack.

## Two directions

### KM → AI

Prepare content for reliable AI use:

- Content quality and cleanup
- Metadata and taxonomy
- Chunking for retrieval
- Access control and permissions for RAG

### AI → KM

Use AI to strengthen knowledge practices:

- Auto-tagging and assisted curation
- Summarization
- Semantic search
- Expertise location

## AI Change Management

KM4AI also covers **change management** when AI tools, models, and versions keep shifting. An **AI transition team** leads adaptation before adoption; **star users** (typically ≥1 per team) help lightly so tools fit real work. The goal: maintain corporate knowledge *and* AI knowledge (agents, automations), so users stay productive instead of burning out.

See [AI Change Management](https://km4ai.org/md/ai-change-management.md) (HTML: [ai-change-management.html](https://km4ai.org/ai-change-management.html)).

## How to start

1. Read the [home summary](https://km4ai.org/md/home.md)
2. Read [AI Change Management](https://km4ai.org/md/ai-change-management.md)
3. Browse the [glossary](https://km4ai.org/md/glossary.md) (also [JSON](https://km4ai.org/data/glossary.json) / [CSV](https://km4ai.org/data/glossary.csv))
4. Follow the launch guides (markdown stubs; full guides forthcoming):
   - [Preparing a document repository for RAG](https://km4ai.org/guides/rag-readiness.md)
   - [Taxonomies and ontologies for LLM applications](https://km4ai.org/guides/taxonomies-ontologies-llm.md)
   - [Knowledge graphs 101 for KM teams](https://km4ai.org/guides/knowledge-graphs-101.md)
   - [AI-assisted content curation workflows](https://km4ai.org/guides/ai-assisted-curation.md)
   - [Measuring KM impact in AI projects](https://km4ai.org/guides/measuring-km-impact.md)
   - [Governance, provenance, and permissions](https://km4ai.org/guides/governance-provenance-permissions.md)
5. Explore the [tool directory](https://km4ai.org/md/tools.md) and [content knowledge graph](https://km4ai.org/data/knowledge-graph.jsonld)

## Tone and policy

Expert but plain-spoken. Practitioner-first, evidence-based, no hype. Vendor-neutral: when tools are named, they are presented comparatively. Open datasets under `/data/` are licensed [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).