01
Organisational Intelligence
The capacity of an organisation to sense reality, remember what it knows, reason across functions, make timely decisions, act coherently, and learn from consequences.
The Framework
The AI Native Organisation Framework.
AI-native transformation is not a technology project. It is an operating-model redesign.
Core Concepts
Six ideas you’ll use across every engagement.
02
Organisational Unintelligence
The condition in which an organisation has abundant data, documents, dashboards, reports, meetings, and expert knowledge — but cannot turn them into timely, reliable, accountable intelligence.
03
Queryability
The ability of authorised users to ask meaningful questions of the organisation and receive timely, permissioned, cited, explainable answers from trusted sources.
04
Human Middleware
People manually bridging gaps between systems, processes, teams, and knowledge stores. AI-native redesign should stop wasting human intelligence on avoidable coordination debt.
05
Decision Rights
Clear rules about what AI may decide, recommend, draft, execute, escalate, or never touch.
06
Knowledge Operations
The discipline of maintaining organisational knowledge as a living operational asset: source quality, ownership, freshness, provenance, retrieval, permissions, and feedback.
The Maturity Model
Six levels. Each with a real-world signature, a characteristic risk, and a clear next move.
0
Level
Unaware
What it looks like
Sporadic individual experimentation. No shared norms, no inventory, no policy.
Main risk
Sensitive data leaks, hallucinated outputs in client work, regulatory exposure.
Next move
Publish a basic safe-use policy, inventory actual AI usage, run 3–5 low-risk pilots.
1
Level
Tool Adoption
What it looks like
Approved tools available. Individuals use AI for drafting, search, summarisation, analysis.
Main risk
Productivity gains are local; the organisation does not yet learn collectively.
Next move
Move from individual productivity to team-level workflow redesign, shared usage norms.
2
Level
Team Workflows
What it looks like
Selected workflows redesigned with owners, metrics, and guardrails. Pilots in flight.
Main risk
Pilots remain fragmented; no shared knowledge layer or governance pattern.
Next move
Create reusable patterns, evaluation rubrics, knowledge repositories, pilot governance.
3
Level
Knowledge Layer
What it looks like
Knowledge is governed, queryable, sourced. Retrieval works across the organisation.
Main risk
Operating model still mirrors pre-AI structure; decision rights unclear.
Next move
Build source ownership, provenance, retrieval quality, permissions, feedback loops.
4
Level
Operating Model
What it looks like
Roles, decision rights, incentives, procurement, and governance redesigned for AI work.
Main risk
Continuous audit, contestability, and renewal not yet institutionalised.
Next move
Redesign roles, decision rights, incentives, procurement, and governance around AI.
5
Level
AI Native
What it looks like
Knowledge, workflows, decision rights, governance, products, services, and learning loops are designed around human-led machine intelligence.
Main risk
Strategic complacency; missing the next paradigm shift in capability.
Next move
Institutionalise continuous audit, contestability, scenario testing, workforce renewal.