What is an AI readiness checklist?

An AI readiness checklist is a structured assessment tool that helps organisations evaluate whether they have the data, infrastructure, skills, and processes needed to successfully adopt AI. It identifies gaps and priorities before you invest in AI projects, reducing the risk of failed implementations.

Who should use this checklist?

The checklist is designed for CTOs, CIOs, VPs of Engineering, and AI/ML leads who are evaluating whether their organisation is ready to adopt AI. It is also useful for business leaders building a case for AI investment and need a structured framework to assess readiness.

How long does the assessment take?

Most teams complete the checklist in 1-2 hours. It is designed to be completed collaboratively by a cross-functional team including IT, data, operations, and business stakeholders so you get a comprehensive view of organisational readiness.

What does the checklist cover?

The checklist covers four domains: Data Audit Criteria (data quality, accessibility, governance), Infrastructure Review (compute, storage, MLOps tooling), Skills Assessment (team capabilities, training needs), and Process Maturity Scoring (workflow automation readiness, change management capacity).

Is the checklist really free?

Yes, the AI Readiness Checklist is completely free. AINinza provides it as a resource to help organisations make informed decisions about AI adoption. There is no obligation to purchase any services after downloading.

Free Resource

AI Readiness Checklist — Free Download

Assess your data, infrastructure, team capabilities, and process maturity before investing in AI. Used by 500+ engineering leaders to make confident AI adoption decisions.

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What the Checklist Covers

Four assessment domains with specific, actionable criteria. Complete the checklist with your cross-functional team in 1–2 hours to get a clear picture of your AI readiness.

Data Audit Criteria

Data is centralised or accessible via APIs
Data quality standards are documented and enforced
Sensitive data is classified and governed by policy
Historical data is available for model training (6+ months)

Infrastructure Review

Cloud or on-prem GPU compute is available or budgeted
CI/CD pipelines exist for application deployments
Monitoring and logging infrastructure is in place
API gateway or service mesh supports new integrations

Skills Assessment

Team includes or has access to ML/AI engineering expertise
Data engineering capacity exists for pipeline development
Business analysts can define success metrics for AI use cases
Leadership has baseline AI literacy for decision-making

Process Maturity Scoring

Target workflows are documented and standardised
Decision-making criteria are explicit (not tribal knowledge)
Change management processes exist for new tool adoption
KPIs are tracked and reviewed regularly for target processes

Download the Full Checklist

Enter your details below and we'll send the complete AI Readiness Checklist to your inbox as a downloadable PDF. No strings attached.

What You'll Get

40+ assessment criteria across 4 domains
Scoring rubric to quantify your readiness level
Gap analysis template to identify priority investments
Action plan framework for closing readiness gaps
Industry-specific benchmarks for comparison

Frequently Asked Questions

Need Help With Your AI Readiness Assessment?

If your checklist reveals gaps, AINinza's AI Readiness Assessment service provides a hands-on evaluation with expert recommendations and a prioritised action plan.

Download the Checklist Talk to an AI Advisor