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Prompt — create an AI Maturity & Readiness Survey addressing the areas below

1

Leadership
Commitment

Evaluate leadership sponsorship and vision to support adoption.

2

Data &
Technology

Assess data readiness and infrastructure. Is it ready for high-throughput AI?

3

Existing
Capabilities

Identify talent gaps and existing pockets of AI success.

4

Cultural
Readiness

Analyze workforce willingness to embrace AI-driven transformation.

Inject elements of your own business scenario!

AI Maturity & Readiness Survey

Complete all ratings, then submit to receive a graded output and recommendations.

Rating scale: 1 = Not ready, 2 = Early, 3 = Developing, 4 = Strong, 5 = Advanced / optimized
1. Leadership Commitment
Executive leaders clearly communicate why AI matters.There is a visible business reason for AI adoption.
AI initiatives have active sponsorship, budget, and decision support.Leaders remove barriers instead of only approving ideas.
The organization has a defined AI vision connected to measurable value.Goals include outcomes such as productivity, quality, risk reduction, or revenue.
Leadership supports responsible AI governance and risk management.Ethics, privacy, security, and human oversight are considered early.
2. Data & Technology
Important data is accessible, accurate, and well documented.Teams can find and trust the data required for AI use cases.
Current infrastructure can support AI experimentation and deployment.Compute, storage, networking, and integration patterns are ready enough to begin.
Security, privacy, and access controls are in place for AI data usage.Data handling is not left to individual judgment alone.
The organization can monitor AI systems after release.Performance, drift, usage, errors, and business outcomes can be tracked.
3. Existing Capabilities
Teams already have AI, analytics, automation, or prompt-engineering experience.There are people who can turn ideas into working experiments.
The organization has examples of successful AI or automation projects.Existing wins can be reused as patterns.
Skill gaps have been identified and learning plans are available.Training is targeted to business roles, technical roles, and leadership roles.
Teams know how to move from prototype to production responsibly.There are practices for testing, documentation, deployment, and ownership.
4. Cultural Readiness
Employees are willing to experiment with AI tools in their work.People are curious rather than only fearful or resistant.
Teams feel safe reporting AI errors, concerns, or unexpected results.Transparency is encouraged instead of punished.
Cross-functional collaboration exists between business, technical, legal, and operations teams.AI is treated as an organizational change, not just an IT project.
Workers understand how AI will augment roles rather than simply replace people.Communication reduces fear and builds adoption.
Please answer every rating question before submitting.

AI Readiness Grade

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Grade

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