10 questions. Know exactly where you stand with AI.
Think about the data you'd use for AI projects
Evaluates your organization across 10 dimensions - data quality, infrastructure maturity, team expertise, process readiness, leadership alignment, and more. Each dimension is scored and weighted to produce an overall readiness level with prioritized recommendations.
You need three things: clean, accessible data relevant to your use case; technical infrastructure that can support model deployment; and at least one executive sponsor who understands AI's limitations. Most companies overestimate their data readiness and underestimate the organizational change required.
Data quality. Most organizations have data spread across siloed systems with inconsistent formats, missing values, and no clear ownership. Fixing data foundations typically takes 2–4 months and is the single most valuable pre-AI investment.
Yes - especially with pre-trained models and APIs. A 50-person company can deploy AI-powered customer support, document processing, or demand forecasting using services like OpenAI, Google Cloud AI, or AWS Bedrock without building models from scratch.