AI Nativeness Assessment

About this assessment

This assessment measures whether AI has become native infrastructure in the enterprise or remains an experiment running alongside the real operating model. It is intentionally compact: 15 questions designed to surface the structural indicators that separate organizations using AI from organizations that are AI-native.

The assessment covers four domains. Workflow and Tooling evaluates whether AI capabilities are embedded in the tools people actually use, whether agents orchestrate multi-step workflows autonomously, whether standard context protocols (like MCP) govern agent-to-API communication, and whether AI agents participate in cross-provider service management. Data and Context evaluates whether proprietary data is continuously vectorized and accessible through RAG, whether feedback loops refine model behavior without manual retraining, whether multi-tenant isolation prevents context leakage, and whether context window lifecycle management prevents decisions on stale data. Governance evaluates whether deterministic guardrails prevent data egress at AI speed, whether non-human identity management governs agent credentials, whether the enterprise maintains continuous compliance with AI management frameworks like ISO 42001, and whether a structured platform governs the full AI use-case lifecycle. Culture evaluates whether architecture decisions mandate AI-first evaluation, whether performance metrics reward human-AI collaboration output, and whether teams practice responding to agentic AI failures.

The assessment is not exhaustive. It does not cover model fine-tuning, training infrastructure, or AI research capabilities. It focuses on the structural and cultural indicators that determine whether an organization can operate at AI speed or is still translating between human workflows and AI capabilities.