ARCH Lab · Free assessment

Organizational AI Readiness

Find out whether your company is ready to put AI to work, and where to start.

⏱ About 5 minutes20 questionsInstant results · works on your phone

Use-Case ClarityYou know which specific problems AI should solve and what success looks like.
Data & Security FoundationsYour data is accessible, accurate and protected.
Workflow & Process ReadinessYour processes are defined enough that AI can plug into them.
People & SkillsYour team has the skills and willingness to work with AI.
Governance & Responsible UseYou have rules for how AI is used, checked and disclosed.
Leadership SponsorshipLeaders are visibly committed, funding the work and setting direction.

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Organizational AI Readiness: questions

Rate each statement from 1 (strongly disagree) to 5 (strongly agree).

We have a short written list of specific tasks where AI could save time or money.
For each AI idea, we can say how we would know if it worked.
Our AI efforts are tied to a business goal, not just curiosity about the technology.
Our customer, job and financial data live in systems we can pull reports from.
Our data is accurate enough that we would trust a tool built on top of it.
We control who has access to sensitive customer and employee information.
A lot of our important information still lives in paper, texts or people's heads.
The processes we want to improve with AI are documented step by step.
We know which steps in our workflows are repetitive and rule-based.
We have changed a process in the past year based on a new tool, and it stuck.
At least one person on our team can set up and test AI tools without outside help.
Our employees are curious about AI rather than afraid of it.
We have given employees time or training to learn AI tools.
Most of our team would resist a tool that changed how they do their job.
We have written rules on what company or customer data may be entered into AI tools.
A person reviews AI output before it goes to a customer or drives a decision.
We know who is accountable when an AI-assisted decision goes wrong.
The owner or senior leaders personally use AI tools in their own work.
Leadership has set aside budget or time specifically for AI projects.
One named leader is responsible for our AI efforts.