What I Do
- Define knowledge objectives and "direction" — what matters, why, and for whom
- Model knowledge flows: capture → refinement → linking → retrieval → reuse
- Design taxonomies: tags, metadata, states (draft / published, active / dormant)
- Create Maps of Content and layered navigational structures
- Establish continuity mechanisms — reviews, audits, pruning, resurfacing
- Build lightweight governance: what gets stored, where, and how it evolves
Outputs
- Knowledge architecture diagrams and governing rules
- Metadata schema + status lifecycle definitions
- Maps of Content and navigation layers
- Knowledge capture templates (meeting notes → insights → actions)
- Audit and cleanup protocols
Where It Helps
- Preventing institutional memory loss during transitions and handovers
- Faster onboarding — new team members navigate, not excavate
- Increasing reuse of insights across projects and time
- Making "thinking work" visible, transferable, and auditable
How This Connects to Enterprise Transformation
- S/4HANA programmes: design governance, decision retention, preventing knowledge erosion across multi-year rollouts
- AI & Automation: structuring the inputs AI needs — clean context, clear taxonomy, defined retrieval paths
- Cloud Architecture: knowledge as infrastructure — traceability, operating models, architectural decision records
Proof & Demonstrations
Live
Writing on Knowledge Systems
Published presentations on knowledge sharing in the digital workplace and unified knowledge system design.
View on Blog →
Coming Soon
Knowledge Lifecycle Diagram
An interactive diagram showing how knowledge moves from capture through to retrieval and reuse.
Coming Soon
Public Map of Content
A navigable MoC: "Knowledge Work in the AI Era" — linking concepts, frameworks, and decisions.