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Assessment Key: 1 = Not in place, 2 = Informal, 3 = Partial, 4 = Mostly established, 5 = Mature.
Determines whether AI is connected to real business goals.
We have identified specific business problems where AI could create measurable value.
Leadership understands AI as a tool, not a complete solution by itself.
We have defined outcomes or success metrics for potential AI initiatives.
AI priorities are aligned with our organization’s strategic goals.
We have executive sponsorship for responsible AI adoption.
Assesses data quality, ownership, accessibility, and risk awareness.
Our data is organized and accessible to the teams that need it.
We have clear data ownership and stewardship responsibilities.
Our data is accurate, current, and sufficiently complete for analysis.
We understand where sensitive, confidential, or regulated data exists.
We have processes for cleaning, validating, and maintaining data quality.
Evaluates whether systems, security, and tooling can support AI adoption.
Our current systems can integrate with AI-enabled tools or workflows.
We have secure cloud, on-premise, or hybrid infrastructure options.
We have the technical expertise to evaluate AI platforms.
We understand the cybersecurity implications of AI tools.
We have a process for testing AI tools before broad deployment.
Measures workforce understanding, adoption capacity, and AI literacy.
Employees understand basic AI concepts, limitations, and risks.
Teams know how to use AI tools responsibly in their work.
Leaders can communicate the purpose and boundaries of AI adoption.
Employees are encouraged to question and verify AI outputs.
There is openness to changing workflows where AI adds value.
Identifies whether responsible AI policies, review, and accountability exist.
We have policies for acceptable AI use.
We have guidelines for privacy, bias, transparency, and human review.
We evaluate AI tools for legal, ethical, and operational risk.
We have a process for approving AI use cases.
We know who is accountable when AI-supported decisions create harm.
Assesses whether the organization can pilot, implement, and sustain AI change.
We have a structured process for piloting new technologies.
We train users before deploying new tools.
We collect feedback after implementation.
We measure whether technology adoption improves performance.
We have change champions or internal advocates who support adoption.
