# AI Readiness Assessment Checklist

**Digital Solutions Delivery — AI Platform Architecture Studio**
digitalsolutiondelivery.com

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## How to use this checklist

Score each item: 0 (not done), 1 (partially done), 2 (fully done).
Total possible: 80. Score interpretation at the end.

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## Section 1: Data Readiness (20 points)

- [ ] Core business data is stored in a queryable format (not just PDFs or emails)
- [ ] You can identify the single most important data source for your primary AI use case
- [ ] Data access doesn't require manual export — it can be queried programmatically
- [ ] Data has consistent field naming and structure across records
- [ ] You can describe the data retention policy and how historical data is archived
- [ ] You have a clear understanding of what data is missing or incomplete
- [ ] Sensitive data is identified and has access controls in place
- [ ] You can produce a sample dataset without involving multiple departments
- [ ] Data quality issues (duplicates, blanks, format inconsistencies) are documented
- [ ] There is an owner for each major data source who can authorize AI access

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## Section 2: Tooling Readiness (16 points)

- [ ] Your team uses version control (Git or equivalent) for technical work
- [ ] You have an API or programmatic access to your core business systems
- [ ] Your infrastructure supports adding new services (not fully locked to a single vendor)
- [ ] You have a staging/testing environment separate from production
- [ ] Logging and monitoring exist for your current systems
- [ ] You can deploy a new web service without a 6-week change management process
- [ ] Your team has experience with at least one AI API (OpenAI, Anthropic, etc.)
- [ ] You have a budget approved for AI tooling and infrastructure

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## Section 3: Team Readiness (16 points)

- [ ] At least one team member can read and write code
- [ ] Someone on the team owns the AI initiative with clear accountability
- [ ] The team can describe the specific problem AI should solve (not just "use AI")
- [ ] Leadership understands that AI investment requires data preparation before results
- [ ] There is capacity to test, review, and iterate on AI outputs — not just deploy and forget
- [ ] The team can evaluate AI output quality for your specific use case
- [ ] You have a plan for what happens when AI output is wrong
- [ ] Team members who will use AI tools have been involved in defining requirements

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## Section 4: Process Readiness (16 points)

- [ ] The process AI will augment is documented (not just in someone's head)
- [ ] You can define "correct" output — there is a standard the AI output will be measured against
- [ ] Someone will review AI output before it affects customers or critical decisions
- [ ] There is a rollback plan if the AI system produces bad output in production
- [ ] The AI use case has a clear success metric that can be measured
- [ ] Edge cases have been identified — what happens in unusual inputs or data conditions
- [ ] The process has volume sufficient to justify automation (not just occasional use)
- [ ] There is a feedback loop for capturing when AI output was wrong

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## Section 5: Organizational Readiness (12 points)

- [ ] Executive sponsorship exists — someone at the leadership level owns the outcome
- [ ] Legal and compliance have been engaged on data use and AI output liability
- [ ] There is a realistic timeline expectation (months, not weeks, for production-grade systems)
- [ ] Budget is allocated for iteration — first deployment is rarely the final version
- [ ] AI investment is tied to a specific business outcome, not "we should have AI"
- [ ] The organization has had a candid conversation about which jobs AI will and won't change

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## Score interpretation

**64–80:** Strong foundation. You're ready for architecture design and implementation. Start with a strategy session to define the right first use case.

**48–63:** Partial readiness. Specific gaps need to be addressed before implementation. An architecture review can identify which gaps are blocking.

**32–47:** Early stage. Data and process foundations need work before AI investment will produce reliable results. Start with a readiness assessment.

**Under 32:** Not ready for AI implementation. Focus on data infrastructure and process documentation first. Return to this checklist in 6 months.

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*This checklist is a starting point, not a complete assessment. Context matters. A score of 64 in one domain profile may require different AI strategy than a score of 64 in another.*

**Questions? Book a free strategy call at digitalsolutiondelivery.com/contact**