Self-assessment

AI-Readiness Assessment

10 questions. Pick the answer closest to your org today. Score each 0–3. Add them. Read your band.

Bands: Novice (0–13), Operational (14–23), Strategic (24–30).

Awareness

Can leadership name what AI is doing in the org, and what happens when it's wrong?

1. How does your leadership team define "AI readiness" today?

We haven't discussed it formally.We treat it as adopting chatbots / productivity tools.We've scoped it as a capability we're building across the org.We have a written AI posture tied to business outcomes, owned by a named exec.

2. When an AI tool gives a wrong answer in your org, what happens?

Someone works around it; nothing gets recorded.The user reports it and we move on.We log it and review patterns quarterly.We track AI incidents like any operational risk — owners, post-mortems, trends.

3. What share of your managers could explain where AI is already used in their function — including the data it touches?

Hard to say; most couldn't.A few early adopters could name the tools.Most could name the tools; fewer the risks.Most could name tools, data sources, and risk owners.

Tooling

Are the tools real, wired in, and measured — or stuck in a tab?

4. How are AI tools introduced in your org?

Individuals sign up on their own cards.Teams pick tools and expense them.We vet and approve a short list centrally.We vet, approve, and integrate with security + procurement sign-off.

5. Do your AI workflows connect to real systems, or stay in chat?

Mostly copy-paste between chat and our apps.Some browser extensions / plugins.A few automations wired to internal data.Production workflows with monitored inputs/outputs and access controls.

6. How do you measure whether an AI tool is actually working?

We don't, formally.Vibes / user satisfaction.Usage counts and time-saved estimates.Defined success metrics tied to outcome + error-rate reviews.

Governance

Who owns the risk, and what guards the data?

7. Who owns AI risk and policy in your org?

Nobody specific.IT or security, reactively.A cross-functional owner with a charter.Named exec sponsor + cross-functional council with a review cadence.

8. What's your policy on data sent to third-party AI?

No policy; people figure it out.A generic "be careful" email.Written rules on what not to share.Written policy + technical controls (DLP / approved tools) + training.

Ethics

When AI touches people or decisions, who's accountable, and is it fair?

9. When you deploy AI that touches customers or decisions, how do you handle fairness/bias?

Not considered.We'd notice if there were complaints.We review for obvious bias before launch.We test for bias / disparate impact and document the call.

10. How is human accountability handled when AI is wrong?

Unclear — "the AI did it."The user who ran it.A manager signs off on AI-assisted decisions.Clear accountability map: human-in-loop per decision type, documented.