AI Readiness Assessment

This assessment evaluates four critical dimensions of AI adoption: Governance, Capability, Infrastructure, and Leadership Alignment.

What You’ll Get

Your readiness score across all four dimensions
A personalized profile showing where you are on the adoption journey
Specific next steps tailored to your situation
Optional: 15-minute results review with our team

Time Required

5 minutes

16 questions

No Sales Pitch

Just honest assessment and clear recommendations.

Start the Assessment

1. Governance Clarity

Does your organization have the frameworks to make AI decisions effectively?

1. Who currently "owns" AI decisions in your organization?

A. We have a clear AI governance board or committee with defined authority B. It's owned by IT or innovation teams, but authority is somewhat unclear C. Multiple stakeholders are involved, but ownership is fragmented D. It's not clear who makes final decisions about AI initiatives E. We don't have anyone formally responsible for AI strategy

2. How does your organization evaluate risk for AI initiatives?

A. We have a defined risk framework specifically for AI (privacy, bias, compliance) B. We use our existing IT/data risk framework and adapt it case-by-case C. Risk is discussed informally but not systematically evaluated D. Teams move forward without formal risk assessment E. We're not sure what AI-specific risks we should be considering

3. What happens when a team wants to pilot a new AI tool or approach?

A. There's a clear, fast approval process (days, not months) B. There's a process, but it can be slow and bureaucratic C. Teams mostly figure it out on their own and ask for forgiveness later D. It depends entirely on which leader is involved E. We don't have a consistent process

4. Which statement best describes your organization's approach to AI governance?

A. Governance is designed to enable innovation while managing risk B. Governance exists but sometimes feels like it slows things down C. We're still figuring out the right balance between speed and control D. Governance is mostly about compliance and saying "no" E. We don't really have governance yet—it's mostly ad hoc

2. Team Capability

Do your people have the confidence and skills to use AI effectively?

1. How would you describe your team's current AI capability?

A. Confident and actively using AI in daily workflows B. Some champions are strong; most others are still learning C. People are aware of AI but unsure how to use it in their roles D. Most team members feel overwhelmed or intimidated E. AI capability hasn't been a focus yet

2. What does AI "training" look like in your organization today?

A. Ongoing, role-specific enablement with reinforcement and support B. We've done some workshops or courses, with mixed results C. People learn on their own (YouTube, articles, trial and error) D. We talked about training but haven't implemented anything yet E. Training hasn't been prioritized

3. When your teams "learn" about AI, what happens next?

A. They immediately apply it to real workflows and build new habits B. Some people apply it; others don't change their behavior C. They understand the concepts but struggle to see practical applications D. The learning doesn't translate to actual work E. We haven't gotten to the application stage yet

4. Are different roles (managers, analysts, operations, etc.) getting different enablement?

A. Yes—enablement is tailored to what each role actually needs B. We're starting to differentiate, but it's not fully customized yet C. Everyone gets the same generic training D. We haven't thought about role-specific needs E. We're not at the enablement stage yet

3. Adoption Infrastructure

Can your organization scale what works, or are you stuck repeating pilots?

1. What typically happens after a successful AI pilot?

A. It gets standardized and rolled out to other teams or departments B. We document learnings, but scaling is slow or inconsistent C. The pilot team keeps using it, but it doesn't spread D. Success is celebrated, then everyone moves on to the next thing E. We're still in the pilot phase and haven't scaled anything yet

2. Can teams reuse frameworks, prompts, or processes from other AI initiatives?

A. Yes—we have shared templates, workflows, and best practices B. Some things are documented, but not systematically shared C. Each team builds from scratch D. We don't have a system for capturing what works E. We haven't thought about reusability yet

3. How do you measure AI adoption (not just deployment)?

A. We track usage, behavior change, and business outcomes B. We track some metrics, but they're mostly activity-based (# of pilots, tools purchased) C. We measure success anecdotally based on feedback D. We don't really measure adoption E. We're not sure what metrics matter

4. How coordinated are AI efforts across different parts of the organization?

A. Highly coordinated—there's visibility and shared learning across teams B. Some coordination, but departments often work in silos C. Each department does its own thing D. We don't know what other teams are doing with AI E. Coordination hasn't been a priority

4. Leadership Alignment

Does leadership actively enable AI adoption, or is it mostly bottom-up enthusiasm?

1. How engaged is your executive leadership with AI initiatives?

A. Actively sponsors and participates—AI is a strategic priority B. Supportive but mostly hands-off C. Aware but not deeply engaged D. Skeptical or waiting for proof before investing E. Not on their radar yet

2. Do leaders across your organization use a common framework to talk about AI?

A. Yes—there's a shared vocabulary and strategic framework B. Some alignment, but different leaders emphasize different things C. Every leader talks about AI differently D. Most leaders avoid the topic or defer to technical teams E. We don't have leadership-level AI conversations yet

3. How are resources (budget, time, people) allocated for AI initiatives?

A. Strategically allocated with clear priorities and sustained investment B. Allocated project-by-project, but without long-term planning C. Teams compete for limited resources D. Resources are tight; AI is done on the side E. No dedicated resources yet

4. How is AI adoption being led as a change management effort?

A. Proactively—with change champions, communications, and stakeholder engagement B. Somewhat managed, but could be more intentional C. Assumed people will just adapt on their own D. Not treated as a change initiative E. We haven't considered the change management aspect

Your Results

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