ai
Capability-Based Planning for AI Crisis Preparedness
- Source
- arXiv — Computers and Society
- Published
- Last verified
- 20 Aug 2026
- Confidence
- Moderate
- Evidence
- Original document retained
- Reading time
- 1 min
- Country
- International
- Relevant to
- Research & Evidence, Policy & Regulation, Risk & Compliance, Technology & Data, Strategy & Planning
Executive summary
What happened, and why should leadership care?
Current government approaches to AI crisis preparedness are hampered by a 'predict-then-act' paradigm that struggles with the inherent unpredictability of AI. Experts and official reviews acknowledge that traditional likelihood-based risk assessments are unsuitable for AI-related risks. A new methodology is proposed, drawing on principles of decision-making under deep uncertainty, which focuses on capability-based planning rather than predictive risk ranking.
Why this matters
Why is this strategically important?
The development of robust and adaptable frameworks for AI crisis preparedness is crucial for ensuring governmental and institutional resilience in the face of rapidly evolving technological risks. Moving beyond traditional predictive models to capability-based planning offers a more practical approach to managing deep uncertainty, thereby strengthening national and international security postures against unforeseen AI-related challenges.
Key insights
What should be noted from the evidence?
- Traditional government AI preparations rely on a 'predict-then-act' model, ranking risks by likelihood and impact.
- AI's inherent unpredictability makes likelihood-based risk assessment ineffective, as expert timelines vary significantly and official reviews confirm this limitation.
- A methodological framework is proposed for AI crisis preparedness based on capability-based planning, adapted from defense and homeland security.
- The framework includes three components: a systematically sampled scenario library, a capability rating procedure against these scenarios using coarse, gated criteria, and a prioritization method (partially described in the abstract).
Evidence and confidence
How far can this assessment be trusted?
Moderate confidence. Provenance established; supporting evidence remains partial.
Analysis is prepared editorially by Aziz Shuaib Ausi. The original publication remains the authoritative record, and executive judgement remains entirely human.
Source
Where does this originate?
Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.
Read the original publication