Enterprise AI Readiness Framework
Automation· 2026
Enterprise AI Readiness Framework
EARF is an open-source engineering framework and CLI for evaluating AI application repositories for evidence of reliability, safety, security, evaluation, observability, privacy, governance, and operational readiness.
- The context
- Many organizations can build an AI prototype, but struggle to determine whether it is truly ready for production. Important areas such as reliability, security, governance, evaluation, observability, human oversight, and responsible use are often documented inconsistently or checked too late.
- What was shipped
- I am building the Enterprise AI Engineering Framework (EARF) to address this gap. EARF provides practical assessments, engineering checklists, evidence-based evaluation rules, reference implementations, and scoring guidance to help teams identify risks early and make AI systems more reliable, secure, and production-ready.
- Operator contribution
- I created EARF from scratch and I am leading it development, including its maturity model, engineering checklists, evaluation rubric, evidence requirements, reference implementation, and automated assessment approach. I define the framework’s focus on system-level AI readiness, design practical tests for reliability, governance, observability, and safety, and translate the results into actionable guidance that engineering teams can use to improve their AI systems.
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