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Evaluate your organization's readiness for AI adoption across six dimensions.
Read through the lesson, mark it complete when the concept is clear, then move to the next lesson in the sequence or jump back to the module map.
Organizations that deploy AI without assessing readiness fail 70% of the time. Not because AI doesn't work — because the organization isn't prepared to use it. Readiness assessment prevents expensive false starts.
Data — Do you have sufficient, clean, accessible data? AI systems are only as good as the data they're trained or prompted with. Assess: data volume, quality, accessibility, and governance.
Technology — Does your tech stack support AI integration? Assess: API access, data pipelines, deployment infrastructure, security posture.
Process — Are your target processes well-defined enough to automate? Assess: process documentation, consistency, exception rates, handoff points.
People — Do your teams have the skills and mindset to work with AI? Assess: technical literacy, change readiness, executive sponsorship, AI champions.
Governance — Do you have policies for responsible AI use? Assess: privacy, security, compliance, ethical guidelines, audit trails.
Budget — Can you fund both implementation and ongoing operations? Assess: initial investment, operational costs (API fees, infrastructure), total cost of ownership.
Rate each dimension 1-5. Total below 18 means foundational work before AI deployment. 18-24 means you're ready for pilots. 25-30 means you're ready to scale.
Data quality — the most common blocking issue. AI amplifies data quality: good data makes AI excellent, bad data makes it confidently wrong.
Process ambiguity — if humans can't consistently execute a process, AI can't automate it reliably. Standardize before automating.
Change management — the technical deployment is usually easier than getting people to actually use it. Plan change management from day one.