5.1.1. Dimensions of AI Readiness
💡 First Principle: AI initiatives depend simultaneously on direction (leadership), fuel (data), willingness (culture), machinery (technology), and rules (governance). A shortfall in any one can stop the whole effort, so readiness must be assessed across all of them, and the lowest score usually matters more than the average.
The exam guide names five critical readiness dimensions:
| Dimension | What "ready" looks like | Warning signs |
|---|---|---|
| Leadership alignment | Executives agree on AI's strategic role, priorities, risk appetite, and funding; an active sponsor owns outcomes | Competing executive agendas; AI framed as an IT project; sponsor disengaged after launch |
| Data quality | Relevant data is accurate, accessible, governed, and owned; quality is measured | Data in silos; unclear ownership; teams spend most time finding and cleaning data |
| Cultural preparedness | Openness to experimentation, tolerance for learning from failure, trust in data-driven decisions, low fear of replacement | Risk aversion, resistance to change, "not invented here," fear of job loss |
| Technical infrastructure | Cloud foundation, integration capability, security, scalable compute, and tools to deploy and monitor AI | Legacy systems without APIs; no path from prototype to production; security reviews blocking every deployment |
| Governance frameworks | Policies, risk classification, accountable owners, responsible AI processes, AI inventory | No policy; ad hoc approvals; shadow AI; unclear accountability |
These dimensions map closely to the AWS CAF perspectives: leadership alignment and business value to Business, culture and skills to People, governance frameworks to Governance, technical infrastructure to Platform, Security, and Operations. Data readiness cuts across several perspectives.
A readiness assessment typically combines stakeholder interviews, surveys, data and system inventories, and review of current AI efforts, then scores each dimension on a simple scale. The output is not a single number but a profile: strong on technology, weak on data ownership and culture, for example. That profile drives the investment plan (5.1.4).
Readiness is also assessed per initiative, not only per organization. A company may be ready for AI in marketing (good data, eager team) and not ready in claims processing (fragmented data, regulatory constraints).
⚠️ Exam Trap: When asked how to assess AI readiness, choose options that evaluate multiple dimensions (leadership alignment, data quality, culture, technical infrastructure, governance), not a technology inventory alone. When a scenario shows strong technology but weak sponsorship, data, or culture, the organization is not ready to scale.
Reflection Question: A bank has a modern cloud platform, a skilled AI team, and enthusiastic executives, but customer data sits in separate systems owned by different product lines with no sharing agreement. Which readiness dimension is the binding constraint, and what would you do before funding new AI initiatives?
