Evidence-based AI adoption certification.

Foundation. Demonstrates baseline AI literacy, risk awareness and the ability to map your tasks to the right work mode.

• Complete the AI readiness diagnostic.
• Complete the basic AI literacy module.
• Map at least 5 professional tasks.
• Classify at least 5 tasks by work mode.
• Submit at least 1 evidence artifact.
• Complete at least 2 scenario assessments.
• Pass the basic risk & privacy awareness check.

Practitioner. You're using AI responsibly and productively in real role-specific work, with quality control.

• Complete a role-specific learning path.
• Map at least 10 professional tasks.
• Submit at least 3 evidence artifacts.
• Build at least 2 repeatable AI workflows.
• Complete at least 4 scenario assessments.
• Demonstrate output quality control.
• Pass reviewer / admin verification.

Specialist. You design multi-step AI tool chains with measurable improvements and credible oversight.

• Submit an advanced workflow chain.
• Show measurable productivity / quality improvement evidence.
• Complete advanced role-specific scenarios.
• Submit an implementation/escalation design for at least 1 task.
• Demonstrate risk management and human oversight.

Implementer. You can take an AI workflow from idea to working pipeline — including escalations, automation handoffs and verification.

• Submit an end-to-end implementation design (handoff to dev/automation).
• Show measurable rollout evidence on at least one workflow.
• Document risk controls and verification steps.
• Demonstrate change-management and adoption coaching.

Adoption Lead. You can guide a team's AI adoption, design training, and operate within governance constraints.

• Design a team-level AI adoption plan.
• Create enablement / training material.
• Complete a governance-aware scenario.
• Demonstrate ability to guide others.
• Submit adoption leadership evidence.