AI Readiness Checklist Template
A structured checklist to assess whether your organisation has the data, infrastructure, talent, governance, and culture needed for successful AI adoption. Use this before launching any AI initiative to identify gaps and create an action plan.
Overview
What's included
Data Readiness
Data Readiness
Score each item: 0 = Not started, 1 = In progress, 2 = Fully in place
Data Availability
- We have identified the key data sources needed for our priority AI use cases ( /2)
- Data is digitised and stored in accessible systems, not locked in paper or siloed tools ( /2)
- We have at least 6 months of historical data for our primary use case ( /2)
- Data is available in machine-readable formats (CSV, JSON, database tables) ( /2)
Data Quality
- We have a data quality monitoring process in place ( /2)
- Key datasets have less than 5% missing values for critical fields ( /2)
- Data is consistently formatted with standardised naming conventions ( /2)
- We have a process for handling duplicates and inconsistencies ( /2)
Data Governance
- We have a data catalogue or inventory of key datasets ( /2)
- Data ownership is clearly defined for each dataset ( /2)
- We comply with GDPR/relevant data protection regulations ( /2)
- We have consent mechanisms for personal data used in AI ( /2)
Data Readiness Score: /24
- 0-8: Significant gaps — prioritise data foundations
- 9-16: Moderate readiness — address critical gaps before piloting
- 17-24: Strong readiness — proceed with confidence
Technology & Infrastructure
Technology & Infrastructure
Score each item: 0 = Not started, 1 = In progress, 2 = Fully in place
Compute & Cloud
- We have cloud infrastructure (AWS, Azure, GCP) or budget to provision it ( /2)
- We can provision GPU compute for model training if needed ( /2)
- Our network and security policies allow API calls to AI services ( /2)
Integration
- Our core systems have APIs for data extraction and integration ( /2)
- We have ETL/data pipeline tools in place ( /2)
- We can deploy new applications to production within days, not months ( /2)
Development Environment
- We have version control for code (Git or equivalent) ( /2)
- We have CI/CD pipelines for automated testing and deployment ( /2)
- We have staging/testing environments separate from production ( /2)
Security
- We have an information security policy that covers AI workloads ( /2)
- We can encrypt data at rest and in transit ( /2)
- We have access controls and audit logging for AI systems ( /2)
Technology Score: /24
- 0-8: Significant investment needed
- 9-16: Foundation in place — address gaps
- 17-24: Ready for AI workloads
Talent & Skills
Talent & Skills
Score each item: 0 = Not started, 1 = In progress, 2 = Fully in place
Technical Talent
- We have data engineers who can build and maintain data pipelines ( /2)
- We have data scientists or ML engineers (internal or partner) ( /2)
- Our software engineers are comfortable working with APIs and AI services ( /2)
- We have someone who understands MLOps and model deployment ( /2)
Business Talent
- Business teams can articulate their processes well enough to identify AI opportunities ( /2)
- We have product managers or business analysts who can bridge technical and business needs ( /2)
- Domain experts are available and willing to help validate AI outputs ( /2)
AI Literacy
- Leadership understands what AI can and cannot do ( /2)
- Employees in target business units have received AI awareness training ( /2)
- We have an AI champion or community of practice ( /2)
Talent Score: /20
- 0-6: Critical skills gap — invest in hiring or partnerships
- 7-13: Partial capability — upskill and partner
- 14-20: Strong talent base
Governance & Culture
Governance & Culture
Score each item: 0 = Not started, 1 = In progress, 2 = Fully in place
Governance
- We have an AI ethics policy or principles ( /2)
- We have a process for reviewing AI systems before deployment ( /2)
- We have clear accountability for AI decisions and outcomes ( /2)
- We have a process for handling AI incidents or failures ( /2)
- We conduct Data Protection Impact Assessments for AI processing personal data ( /2)
Culture
- Leadership actively champions AI and digital transformation ( /2)
- Teams are open to changing processes based on AI insights ( /2)
- We have a culture of experimentation and learning from failure ( /2)
- Cross-functional collaboration is the norm, not the exception ( /2)
- Employees trust that AI will augment their roles, not replace them ( /2)
Governance & Culture Score: /20
- 0-6: High risk — address governance before launching AI
- 7-13: Developing — strengthen governance in parallel with pilots
- 14-20: Mature governance and supportive culture
Overall Readiness Summary
| Dimension | Score | Max | Status |
|---|---|---|---|
| Data Readiness | 24 | Red/Amber/Green | |
| Technology & Infrastructure | 24 | Red/Amber/Green | |
| Talent & Skills | 20 | Red/Amber/Green | |
| Governance & Culture | 20 | Red/Amber/Green | |
| Total | ___ | 88 |
Instructions
How to use this template
Gather the right people
Involve representatives from IT, data, HR, legal/compliance, and the business unit planning to use AI.
Score each dimension honestly
Work through each checklist item as a team. Use 0/1/2 scoring and discuss disagreements to reach consensus.
Identify critical gaps
Focus on items scored 0 in high-impact areas. These are your blockers and should be addressed first.
Create an action plan
For each gap, define a specific action, owner, timeline, and resources needed.
Re-assess quarterly
AI readiness is not static. Re-run the checklist every quarter to track progress and identify new gaps.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
There is no hard threshold, but organisations scoring above 50/88 generally have enough foundation to begin pilot projects. The key is that no single dimension should be in the red zone — one critical gap can derail an entire initiative.
A thorough assessment typically takes 1-2 weeks, including stakeholder interviews, data audits, and a scoring workshop. You can do a rapid version in 2-3 days for a quick health check.
Ideally, run a high-level readiness assessment first to understand your constraints, then use the results to inform which use cases are feasible. A detailed assessment can then be done for specific use cases.
Data readiness is the most common bottleneck. Consider starting with AI use cases that require less proprietary data (e.g. document summarisation, chatbots) while investing in your data foundations for more data-intensive applications.
Yes. The checklist works at both organisational and departmental level. For department-level assessments, focus the data and talent sections on that team's specific context while keeping governance and infrastructure at the organisational level.
Related Content
Need a custom AI template?
Our team can build tailored templates for your specific business needs. Book a free strategy call.