What is an AI readiness assessment?

An AI readiness assessment measures how prepared an organization is to adopt and scale artificial intelligence safely and effectively. It evaluates the foundations AI depends on — strategy and governance, engineering and infrastructure, data, security and risk, and workforce adoption — and surfaces the gaps most likely to block or slow value.

Is the assessment anonymous?

Yes. The assessment runs entirely in your browser and requires no signup. Nothing is sent anywhere unless you choose to enter your email to receive a detailed breakdown.

What are the dimensions of AI readiness?

Strategy & Governance (owned strategy, acceptable-use policy, measured impact); Engineering & Infrastructure (ability to build, deploy, and monitor AI in production); Data Foundation (accessible, governed, high-quality data); Security & Risk (controls for data leakage, model risk, and AI governance); and Workforce & Adoption (skills, enablement, and day-to-day use).

What is a good AI readiness score?

Scores of 90+ are Leading, 75–89 Strong, 60–74 Developing, 40–59 At Risk, and below 40 Critical. Most organizations early in adoption land in the Developing-to-At-Risk range, typically held back by data foundations and AI-specific security controls rather than enthusiasm.

How do I improve my AI readiness?

Start with the lowest-scoring dimension. Common high-leverage moves: stand up an executive-owned AI strategy with an acceptable-use policy, close data governance and quality gaps, put controls in place to prevent sensitive data leaking into third-party AI tools, and invest in role-based enablement so adoption moves beyond pilots.

2 min · no signup · anonymous

AI Readiness Assessment

Measure your organization’s readiness to adopt and scale AI across five dimensions. Answer 15 quick questions and get an explainable 0–100 score, a letter grade, and prioritized recommendations — instantly, in your browser.

What is AI readiness?

AI readiness is how prepared an organization is to adopt and scale artificial intelligence safely and effectively. It depends on five foundations — strategy and governance, engineering and infrastructure, data, security and risk, and workforce adoption. Strength in enthusiasm rarely compensates for weakness in data governance or security controls, so a balanced view across all five is what separates durable AI advantage from stalled pilots.

Strategy & Governance:A clear, owned AI strategy with acceptable-use policy, funding, and executive sponsorship.
Engineering & Infrastructure:The platform, tooling, and delivery practices needed to ship AI features and agents reliably.
Data Foundation:Accessible, governed, high-quality data that AI systems can safely build on.
Security & Risk:Controls for model risk, data leakage, and AI governance so adoption doesn't create exposure.
Workforce & Adoption:Skills, enablement, and day-to-day adoption of AI across teams.

Strategy & Governance

A clear, owned AI strategy with acceptable-use policy, funding, and executive sponsorship.

We have a documented AI strategy with clear executive ownership and funding.

There is a communicated, enforced policy for acceptable and responsible AI use.

We measure the business impact of our AI initiatives against defined goals.

Engineering & Infrastructure

The platform, tooling, and delivery practices needed to ship AI features and agents reliably.

Our engineering teams can build, deploy, and monitor AI features in production.

We have the compute, platform, and MLOps/LLMOps tooling to run AI workloads reliably.

Our codebase has the tests, CI, and guardrails to adopt AI coding tools safely.

Data Foundation

Accessible, governed, high-quality data that AI systems can safely build on.

Teams can find and access the data they need, with clear ownership and cataloguing.

Our data quality is high enough to trust for AI use cases (accuracy, freshness).

Data governance, privacy, and lineage controls are in place for AI consumption.

Security & Risk

Controls for model risk, data leakage, and AI governance so adoption doesn't create exposure.

We have controls preventing sensitive data from leaking into third-party AI tools.

AI-specific risks (prompt injection, model misuse, hallucination) are assessed and owned.

We can audit how AI systems are used and demonstrate compliance to stakeholders.

Workforce & Adoption

Skills, enablement, and day-to-day adoption of AI across teams.

Employees have the training and support to use AI tools effectively in their work.

AI tools are widely adopted across teams in day-to-day work, not just pilots.

Leaders actively champion AI adoption and remove blockers to it.

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How the score is calculated

Each of the 15statements is answered on a 1–5 agreement scale and mapped to a 0–100 value. The three answers in a dimension are averaged into a dimension score, and the dimensions are combined into a single 0–100 score using relative weights — Strategy & Governance and Security & Risk carry the most weight because they most often gate safe AI scaling. The result is graded A (Leading) to F (Critical). Unanswered questions are left out rather than guessed, mirroring the ShipReady principle of never fabricating data.

Frequently asked questions

What is an AI readiness assessment?: An AI readiness assessment measures how prepared an organization is to adopt and scale artificial intelligence safely and effectively. It evaluates the foundations AI depends on — strategy and governance, engineering and infrastructure, data, security and risk, and workforce adoption — and surfaces the gaps most likely to block or slow value.
How is the AI readiness score calculated?: You answer 15 statements on a 1–5 agreement scale, three per dimension. Each answer maps to a 0–100 value; dimensions are averaged and then weighted (Strategy & Governance and Security & Risk carry the most weight) into a single 0–100 score graded A–F. Unanswered questions are omitted rather than guessed, so a partial result still reflects only what you actually answered.
Is the assessment anonymous?: Yes. The assessment runs entirely in your browser and requires no signup. Nothing is sent anywhere unless you choose to enter your email to receive a detailed breakdown.
What are the dimensions of AI readiness?: Strategy & Governance (owned strategy, acceptable-use policy, measured impact); Engineering & Infrastructure (ability to build, deploy, and monitor AI in production); Data Foundation (accessible, governed, high-quality data); Security & Risk (controls for data leakage, model risk, and AI governance); and Workforce & Adoption (skills, enablement, and day-to-day use).
What is a good AI readiness score?: Scores of 90+ are Leading, 75–89 Strong, 60–74 Developing, 40–59 At Risk, and below 40 Critical. Most organizations early in adoption land in the Developing-to-At-Risk range, typically held back by data foundations and AI-specific security controls rather than enthusiasm.
How do I improve my AI readiness?: Start with the lowest-scoring dimension. Common high-leverage moves: stand up an executive-owned AI strategy with an acceptable-use policy, close data governance and quality gaps, put controls in place to prevent sensitive data leaking into third-party AI tools, and invest in role-based enablement so adoption moves beyond pilots.

Glossary

AI readiness: The degree to which an organization's strategy, engineering, data, security, and workforce can support adopting and scaling AI to create value safely.
AI maturity model: A staged framework (e.g. ad hoc → developing → strong → leading) describing how AI capability deepens across an organization over time.
AI governance: The policies, ownership, and controls that make AI use accountable — covering acceptable use, model risk, data handling, and auditability.
LLMOps: The practices and tooling for deploying, monitoring, evaluating, and governing large-language-model applications in production.

Want AI readiness measured from real data?

This assessment is self-reported. ShipReady Metrics computes AI Readiness — and eight other domain scores — from the tools you already use, so your board sees evidence, not opinion.

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