AI Maturity

The clarity and alignment of AI strategy with business objectives. Covers executive sponsorship, AI vision, investment planning, and strategic roadmapping for AI adoption.

The readiness of data assets to support AI and ML workloads. Covers data quality for AI, feature engineering, data labeling, training data management, and data pipelines for ML.

The practices and infrastructure for developing, deploying, and maintaining ML models in production. Covers experiment tracking, model training, CI/CD for ML, monitoring, and model lifecycle management.

Adoption and maturity of generative AI capabilities including LLMs, prompt engineering, RAG, fine-tuning, and AI-assisted workflows. Covers both internal productivity and product-facing GenAI.

Building and maintaining the human capabilities needed for AI. Covers hiring, upskilling, organizational structure, AI literacy, and building centers of excellence.

Ensuring AI systems are developed and deployed responsibly. Covers fairness, transparency, explainability, accountability, bias detection, and ethical governance.

The governance structures and risk management practices for AI systems. Covers AI policies, model risk management, regulatory compliance, audit trails, and AI asset management.

The compute, storage, and platform capabilities supporting AI workloads. Covers GPU/TPU infrastructure, ML platforms, experiment environments, and cost management for AI.

How AI solutions are adopted across the organization and integrated into business processes. Covers change management, user acceptance, trust building, and measuring AI business impact.

The organization's ability to explore and adopt emerging AI capabilities. Covers R&D, partnerships, proof of concepts, emerging technology tracking, and building competitive advantage through AI.