AI adoption maturity model

> I wrote this for internal reference while working on AI enablement at Medium. Dimensions of maturity Maturity is not a single score. The organization can be at different stages across different dimensions. The framework tracks five dimensions. | | 1. Explore | 2. Enable | 3. Integrate | 4. Optimize | | --- | --- | --- | --- | --- | | Governance & Policy | No policies. Usage is ungoverned. | Usage policy published. Approved tool list. Basic data classification rules. IP and attribution guidelines established. | Policies enforced via infrastructure and process. Exception process exists. Regular policy reviews. Function-specific guidelines in place. | Governance adapts to new tools and capabilities. Policies are evidence-based and continuously updated. Cross-functional governance council operates routinely. | | Tooling & Infrastructure | BYO tools. No org-level procurement. | Sanctioned tools provisioned for each function. Access controls in place. Procurement pipeline established. | Tools integrated into standard workflows. Configurations standardized. Cross-functional tool consolidation where appropriate. | Tool selection driven by measured impact. Rapid evaluation of new options. Budget allocation informed by usage and ROI data. | | Fluency & Skills | Self-taught. Skill varies widely. | Training resources available by function. Knowledge sharing sessions. Best practices documented. | AI fluency in onboarding for all functions. Cross-team learning is routine. Shared prompt libraries and workflow templates. | Fluency is a recognized competency across career frameworks. Advanced practitioners mentor others. Function-specific mastery paths exist. | | Measurement | No measurement. Anecdotal impressions only. | Basic adoption metrics (seat count, active usage). Pilot results tracked. | Quality and productivity signals tracked by function. Regular reporting to stakeholders. | Impact data drives decisions. Experimentation culture. ROI understood by function and tool. | | Workflow & Process | AI layered on top of existing process. No adaptation. | Pilots test AI in specific workflows. Initial guidance on reviewing AI-assisted outputs. | Workflows adapted for AI across functions (engineering: code review, testing; marketing: content review, brand voice; design: iteration, feedback; etc.). Shared patterns for common tasks. | Workflows designed with AI as a core component. Continuous refinement based on data. Cross-functional workflow optimization. | Assessing where we are Rate each dimension independently, per function. For each dimension, look at the stage descriptions and identify which set of signals most closely matches current reality. It is normal and expected to be at different stages across dimensions, and for different functions to be at different stages. References and Influences This framework synthesizes ideas from several published models, adapted for our context: - The Art of AI Maturity (Accenture) - Building Enterprise AI Maturity (MIT CISR) - What's your company's AI maturity level? (MIT Sloan) - maturity-model (GitHub company) - Enterprise AI Maturity (Microsoft)