https://taxonomy.eticas.ai/risk/organisational-readiness
Maturity: emerging
The risk that an AI system fails to deliver value not because of model performance issues but because it does not fit the organisational, technical, or human context into which it is deployed. This includes insufficient organisational capacity to absorb the technology, workflow incompatibility, interoperability failures with existing systems, and lack of sustainable maintenance capability.
This category is emerging. Assessment methods are still being developed. Definitions and subcategories may evolve.
Also known as: Integration Readiness · Deployment Readiness · Sociotechnical Fit
System type: ADM and LLM systems
Lifecycle stages: Pre Processing, Post Processing
| Framework | Reference |
|---|---|
| ISO/IEC 42001:2023 — AI Management System | A.4.2 Resources (human/competence) + A.4.3 Tooling + A.3 Internal organization |
| EU AI Act (Regulation 2024/1689) | Article 4 — AI literacy + Article 26 deployer obligations |
| AIUC-1 — AI Underwriting Company Standard | Assign accountability + E.10 Regulatory compliance + E.12 QMS |
| Council of Europe Framework Convention on AI (CETS No. 225) | Article 16 — Risk and impact management (capacity implied) |
| NIST AI Risk Management Framework (AI 100-1) | MAP function — context and deployment environment |
| NIST AI Risk Management Framework (AI 100-1) | GOVERN 2 — accountability structures + GOVERN 4 workforce |
| OECD AI Principles | Building human capacity & preparing for labour-market transition |
| Framework | Reference |
|---|---|
| IBM AI Risk Atlas | Non-technical → Governance + Skill gaps + Vendor risk |