Vision & philosophy
Start with the operating question and pursue trustworthy progress.
Complete Core Framework
DownloadWorking draft · DOCX · Published 1 November 2026
Purpose
HAIOF focuses on how people and AI systems work together in everyday operations, not simply on which tools an organisation buys. It connects work design, authority, knowledge, governance, evidence, cost, measurement and human takeover readiness.
AI should increase organisational capability without reducing organisational control, knowledge or resilience.
Framework areas
Framework principles
Twenty practical rules covering outcomes, authority, evidence, knowledge, risk and complexity.
Maturity model
Six levels from unmanaged use to an adaptive Human-AI organisation.
Work classification
Eliminate, human-led, AI-assisted, AI-executed or deterministic automation.
Roles & responsibilities
Make ownership, recommendation, execution, approval, monitoring and escalation visible.
Knowledge engineering
Treat authoritative, current and executable knowledge as operational infrastructure.
Governance & risk
Apply the minimum governance that controls the real consequence of the work.
Metrics & transformation
Measure outcomes, total effort, quality, cost, risk and sustainable change.
Human authority and AI challenge
AI capability does not automatically create organisational authority. Material decisions require a clearly identified human authority. AI may challenge an instruction when material evidence shows that an assumption has changed, a boundary would be violated, risk has materially changed, or the action would no longer achieve the intended outcome.
Authority defines what may happen. Evidence determines whether the approved action still makes sense.
Evidence-based completion
HAIOF distinguishes Proposed, Authorised, Attempted, Executed, Verified and Closed. Work is not complete merely because execution was attempted.