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AI Readiness Quiz

Assess your organization's preparedness to adopt AI and get a personalized implementation roadmap in under 3 minutes.

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Data Foundation

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Data Foundation

How ready is your data for AI?

1. How would you describe your organization's data quality?

Clean, centralized, and well-documented with established governance

Mostly organized but spread across multiple systems with some gaps

Scattered, inconsistent, or largely stored in spreadsheets/emails

2. Do you have access to historical data for training AI models?

Yes — 2+ years of structured, labeled data in accessible systems

Some historical data exists but it's not fully organized or labeled

Little to no historical data available or it's in unusable formats

3. How integrated are your data systems?

Unified data platform with APIs and automated ETL pipelines

Some integrations exist but manual data transfers are still common

Siloed systems with no integration — data lives in separate tools

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Technical Infrastructure

Is your tech stack AI-capable?

4. What is your current cloud/computing capability?

Cloud-native infrastructure with scalable compute (AWS/Azure/GCP)

Hybrid setup — some cloud services alongside on-premise servers

Primarily on-premise with limited cloud adoption

5. Do you have APIs or integration capabilities for AI services?

Yes — RESTful APIs, microservices architecture, CI/CD pipelines

Some API access but limited to specific vendors or tools

No APIs — most processes are manual or use closed platforms

6. How do you currently handle automation?

Extensive automation (RPA, workflow engines, scripted processes)

Some automation in specific departments (e.g., marketing emails, reports)

Almost everything is done manually — automation is minimal

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Talent & Culture

Does your team have the skills and mindset?

7. What AI/ML expertise exists in your organization?

Dedicated data science or AI team with hands-on project experience

A few tech-savvy employees experimenting with AI tools (ChatGPT, Copilot, etc.)

No AI knowledge in-house — we would need to hire or outsource

8. How does leadership view AI adoption?

Strong executive sponsorship with dedicated AI budget approved

Leadership is interested but hasn't committed resources yet

Leadership is skeptical or AI is not on the strategic agenda

9. How open is your workforce to AI-driven change?

Culture of innovation — teams actively seek new tools and efficiencies

Mixed — some eager early adopters, some resistant to change

Generally resistant — "we've always done it this way" mentality

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Use Case Clarity

Do you know where AI can help?

10. Have you identified specific processes AI could improve?

Yes — documented use cases with projected ROI and success metrics

We have some ideas but haven't formally evaluated them

Not sure where AI would fit — we just know we should do something

11. What is your primary AI goal?

Strategic advantage — predictive analytics, personalization, new products

Operational efficiency — automate repetitive tasks and reduce errors

Exploratory — we want to learn what's possible before committing

12. Do you have KPIs to measure AI project success?

Yes — clearly defined KPIs and baseline measurements established

We track general business metrics but nothing AI-specific

No formal KPIs — we would figure it out as we go

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Ethics & Governance

Are you prepared for responsible AI?

13. Do you have data privacy and security policies?

Comprehensive policies covering GDPR/CCPA, data retention, and access controls

Basic privacy policies exist but haven't been updated for AI use cases

Minimal or no formal data privacy framework in place

14. Have you considered AI bias and fairness in your industry?

Yes — we have guidelines for bias testing and model transparency

Aware of the issue but no formal processes to address it

Haven't thought about AI ethics or bias considerations

15. What is your risk tolerance for AI experiments?

We embrace experimentation with clear fail-fast frameworks and sandboxes

Open to pilots but need strong business cases before proceeding

Very risk-averse — we prefer proven solutions with guaranteed outcomes

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