A practical readiness check covering data, controls, human oversight and ongoing operations.
Responsible AI can become abstract very quickly. Policies and principles matter, but at some point a project team needs to answer a more practical question: are the foundations actually strong enough to proceed?
I have been using a simple 12-question readiness check to make that discussion more concrete. It is not a certification or compliance assessment. The aim is to expose obvious gaps before a use case moves from experimentation into operational use.
The 12 questions
Data ownership
Is there a clearly accountable owner for the data used by the AI use case?Data classification
Is the information classified, and is its use permitted in the proposed AI platform?Data quality
Is the data sufficiently accurate, complete, timely and fit for the intended purpose?Access control
Are permissions limited to the people, systems and services that genuinely need access?Data lineage
Can you reasonably explain the main source, movement and downstream use of the data?Purpose and value
Is the business problem clear, and is there a measurable reason to use AI?Risk assessment
Has the use case been assessed for privacy, security, legal, ethical and operational impact?Approved platform
Is the AI platform approved, and are supplier, data handling and retention arrangements understood?Human oversight
Is meaningful human review defined for consequential or material outputs?Testing and evaluation
Have quality, failure modes, hallucination or bias risk and acceptance criteria been considered?Monitoring
Are owners, metrics, incident paths and review triggers defined for ongoing use?Shadow AI controls
Are users given clear guidance on approved tools, prohibited data and escalation paths?
Looking beyond one overall score
An overall readiness score is useful, but it can hide where the real weakness sits. I have therefore grouped the same questions into four dimensions:
Data Foundations
Risk & Controls
Human Oversight
Monitoring & Operations
This makes the result more useful in practice. Two organisations can have the same overall score while having very different problems: one may have weak data foundations, while another may have reasonable data controls but little monitoring or human oversight.
What happens next?
The assessment deliberately produces only three overall outcomes: Ready, Ready with Controls, or Further Assessment Required. The useful part is not the label itself; it is identifying which dimensions and individual questions need attention before scaling the use case.
As business impact increases, the strength of the assessment, evidence and oversight should increase with it. A low-impact internal productivity use case does not need the same governance treatment as an automated decision affecting customers.
Try the live assessment
https://huggingface.co/spaces/rakeshranderia/responsible-ai-readiness
View the source
https://github.com/rakeshranderia/responsible-ai-readiness
Related framework
https://github.com/rakeshranderia/data-governance-framework
The broader model behind the work remains: Data Governance → Trusted Data → AI Readiness → Responsible AI → Measurement → Scale.
Top comments (0)