AI Foundations
Core mechanics, capabilities and limitations.
AI readiness explained
What is AI readiness at work?
AI readiness is the ability of people and organisations to use AI for appropriate work with sound judgement, safe data practices and accountable human oversight. It involves more than access to a tool or confidence in using one.
Start with the question you are trying to answer
An organisation can be ready in one respect and unprepared in another. Technology readiness asks whether systems, data and controls can support a use case. Workforce readiness asks whether people understand what the technology can and cannot do, can recognise risks and know when to intervene. Role readiness goes further: can someone apply that knowledge to the actual tasks and decisions in their job?
These questions call for different evidence. A shared knowledge assessment can establish a comparable baseline across candidates or employees. It cannot, on its own, show how a person will perform a particular job or whether an organisation has suitable governance.
A useful distinction
AI literacy is the knowledge needed to understand and use AI responsibly. AI readiness is the wider set of conditions needed to put AI to work. Core-6 measures the knowledge part of that picture.
The common knowledge baseline
Six questions behind responsible AI use
Application & Use Cases
Matching the right tools to worthwhile tasks.
Bias, Ethics & Risk
Recognising bias, impact and compliance hazards.
Data Quality & Governance
Data lineage, quality, privacy and safe handling.
Output Interpretation & Evaluation
Critical review, fact-checking and interpretation.
Human Oversight & Collaboration
Intervention, escalation and accountable collaboration.
The Sova-delivered genAssess assessment measures knowledge across these six areas in the same format across roles. Explore the Core-6 framework
What should an enterprise do with the result?
Establish the baseline
Measure common AI knowledge before assuming that tool usage, course attendance or self-reported confidence indicate understanding.
Interpret it in context
Connect results to the work people do, the tools they use and the potential consequences of error. A single score should never stand in for a role assessment.
Act and revisit
Choose learning, workflow support or additional selection evidence based on the gap you need to address. Reassess knowledge over time where appropriate.
For hiring, a consistent baseline can inform a selection process alongside interviews and separate work samples. For learning and transformation, it can show where foundational knowledge needs attention. For Article 4, it can contribute evidence within a broader AI literacy programme. None of these uses turns Core-6 into a role-specific practical test.
The in-market assessment
See the Core-6 assessment in Sova.
One common AI knowledge assessment for candidates and employees, delivered through Sova.