Workflow fit
Is there a repeated, valuable workflow where AI has a real job to do?
AI workflow readiness check
Is your business ready for AI?
Answer a short diagnostic and see whether one of your workflows is ready for an AI prototype, needs cleanup first, or should start with simpler automation.
A business is ready for AI when at least one real workflow has clear inputs, repeated work, human ownership, measurable risk, and a review path. This check turns that into a practical readiness read.
Start the check
Run the AI workflow read
Nine answers get you a baseline result. No patient data, files, or private records needed.
Step 1 of 911%
Workflow
What workflow are you hoping AI can improve?
Pick the closest shape. The result is about workflow readiness, not whether AI is fashionable.
What this checks
The scoring is based on practical AI adoption patterns: useful AI starts with a repeated workflow, reachable inputs, clear failure cost, human review, and an owner who can test it.
Process clarity
Can the team explain the current steps and exceptions before automating them?
Data readiness
Are the inputs accessible, permissioned, and reliable enough for a prototype?
Risk control
What happens if AI is wrong, and which guardrails need to exist first?
Human oversight
Who reviews outputs, approves actions, and owns the final decision?
Implementation readiness
Is there a decision owner, feedback loop, timeline, and appetite to iterate?
Built from practical AI readiness signals
AI readiness is a workflow question first
The model reflects the same themes that show up across enterprise AI adoption and AI-risk guidance: map the workflow, govern sensitive data, measure failure modes, keep humans accountable, and prototype before platform spend.
- AI adoption research from McKinsey and Microsoft
- NIST AI Risk Management Framework
- OWASP LLM application risk categories
- AI management-system patterns from ISO/IEC 42001