AI Readiness Assessment
Evaluate your organization's readiness for AI adoption across six dimensions.
Why Readiness Matters
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.
The Six Readiness Dimensions
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.
Scoring Your Readiness
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.
The Most Common Gaps
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.