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Enterprise AI Architects

AI readiness assessment tool: find what will stall AI in production.

An AI readiness assessment is a structured review of whether an organization's data, platform, security, governance and people can support AI workloads in production. This free tool is the self-assessment version.

Thirty-five statements across seven dimensions, scored 0 to 3. Free and private: your answers never leave your browser. Based on our AI readiness assessment checklist. For interviews and a review of what is actually running, see our AI readiness assessment service.

0 · Absent
Nothing in place
1 · Ad hoc
In pockets, undocumented
2 · Defined
Documented, partly used
3 · Operating
Used, measured, improved
Strategy and leadership
0 / 15

  1. An AI strategy exists that links AI investment to specific business outcomes, not to technology adoption for its own sake.

  2. A named executive is accountable for AI outcomes and has budget authority.

  3. AI priorities are reflected in the annual planning and budgeting cycle.

  4. Success metrics are defined for AI initiatives before they start, and results are reported.

  5. Leadership has agreed a position on risk appetite for AI, including what the organization will not do.

Data foundations
0 / 15

  1. The data needed for priority use cases is identified, and its owners are known.

  2. Data quality for those sources is measured, with known issues documented.

  3. Unstructured content (documents, knowledge bases, tickets, transcripts) is inventoried, accessible, and has clear ownership.

  4. Access permissions on source data are well defined and can be enforced downstream, including in retrieval systems.

  5. Data classification and lineage allow you to know what sensitive data an AI system can reach.

Platform and architecture
0 / 15

  1. There is an approved way to access foundation models (for example through Azure OpenAI, Amazon Bedrock, Google Vertex AI or direct provider APIs) with enterprise terms in place.

  2. Model access is centralized through a gateway or equivalent, with logging, quotas and routing.

  3. Shared services exist for retrieval, vector storage, evaluation and observability, rather than each team building its own.

  4. A reference architecture defines standard patterns for common AI workloads, such as RAG, agents and document processing.

  5. AI workloads can move from experiment to production through a defined path, with CI/CD, environments and version control for prompts and models.

Security
0 / 15

  1. AI-specific threats are included in threat modeling, including prompt injection, data leakage through model outputs, and insecure tool use, for example using the OWASP Top 10 for LLM Applications and MITRE ATLAS.

  2. Controls prevent sensitive data from being sent to unapproved AI services, and shadow AI use is visible.

  3. Agents and AI applications use scoped, least-privilege credentials and cannot exceed the permissions of the user they act for.

  4. AI applications are tested adversarially before production, proportionate to their exposure.

  5. AI incidents are defined and covered by the incident response process.

Governance and risk
0 / 15

  1. An AI policy and acceptable use standard exist and are known to staff.

  2. An inventory of AI systems exists, including third-party products with embedded AI, and is kept current.

  3. AI systems are risk-tiered, and review effort is proportional to tier.

  4. Release gates require evaluation evidence and named approval for higher-risk systems.

  5. Governance aligns with a recognized framework, such as the NIST AI RMF or ISO/IEC 42001:2023, and with applicable regulation.

Talent and operating model
0 / 15

  1. The organization has, or has access to, the core skills: AI architecture, AI and ML engineering, data engineering, evaluation, and AI security.

  2. Roles and decision rights for AI are clear between central teams and business units.

  3. There is a structured intake for new AI ideas, with a defined path to delivery.

  4. Builders and users receive role-appropriate training on AI capabilities, limits and policy.

  5. Production AI systems have named owners and funded teams to operate them after launch.

Use-case portfolio
0 / 15

  1. Candidate use cases are collected in one place and assessed consistently.

  2. Use cases are prioritized by value, feasibility and risk, not by who asked loudest.

  3. Each priority use case has a business owner, a defined metric and a baseline.

  4. The portfolio balances quick wins with a smaller number of higher-value, harder use cases.

  5. Pilots have explicit criteria for moving to production or stopping.

About this assessment

What does this AI readiness assessment measure?

It scores seven dimensions that decide whether AI reaches production in a large organization: strategy and leadership, data foundations, platform and architecture, security, governance and risk, talent and operating model, and the use-case portfolio. Each has five statements scored from 0 (absent) to 3 (operating), so each dimension totals 0 to 15.

How should I interpret my AI readiness score?

Read the dimensions, not the total. A dimension at 0 to 5 is a blocker: priority use cases that depend on it will stall or carry unmanaged risk. 6 to 10 is workable for pilots and low-risk production with specific gaps to close. 11 to 15 is a strength to build on. Fix the lowest dimension that your priority use cases depend on first.

Is my data stored or sent anywhere?

No. The assessment runs entirely in your browser. Your answers are saved only on your own device so you can come back to them, and nothing is sent to us unless you choose to book a briefing and share your results.

How accurate is a self-assessment?

It is a good first read, but self-scores tend to be generous where a team owns the area and harsh where it does not. Score with evidence, ask people in different functions to score independently, and compare. A facilitated Architecture Assessment adds interviews and a review of what is actually running.

Want an outside view of your scores?

Bring your results to a 30-minute briefing. A senior architect will pressure-test them and name the gap to close first.

  • Vendor-neutral advice. We resell nothing.
  • Senior architects on the call, not a sales team.
  • Mutual NDA available before we talk specifics.

Book with Kelsey

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