Technology
AI Maturity
10 domains · 30 questions · based on contemporary best practice and leading research.
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Domains covered
10 areas
Strategy
Domain
The clarity and alignment of AI strategy with business objectives. Covers executive sponsorship, AI vision, in…
Data Foundation
Domain
The readiness of data assets to support AI and ML workloads. Covers data quality for AI, feature engineering, …
MLOps
Domain
The practices and infrastructure for developing, deploying, and maintaining ML models in production. Covers ex…
GenAI
Domain
Adoption and maturity of generative AI capabilities including LLMs, prompt engineering, RAG, fine-tuning, and …
Talent
Domain
Building and maintaining the human capabilities needed for AI. Covers hiring, upskilling, organizational struc…
Ethics
Domain
Ensuring AI systems are developed and deployed responsibly. Covers fairness, transparency, explainability, acc…
Governance
Domain
The governance structures and risk management practices for AI systems. Covers AI policies, model risk managem…
Infrastructure
Domain
The compute, storage, and platform capabilities supporting AI workloads. Covers GPU/TPU infrastructure, ML pla…
Adoption
Domain
How AI solutions are adopted across the organization and integrated into business processes. Covers change man…
Innovation
Domain
The organization's ability to explore and adopt emerging AI capabilities. Covers R&D, partnerships, proof of c…
Maturity scale
Ad hoc and reactive. No formal processes, reliant on individual effort.
Basic awareness and some repeatable processes emerging.
Documented standards and processes applied consistently.
Measured, monitored and controlled with quantitative targets.
Continuous improvement driven by data and innovation.
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