AI Readiness Assessment

The assessment covers six knowledge areas, drawn from the genAssess Core-6 knowledge areas model:

• AI Foundations — the fundamentals of how AI works and its core concepts.
• Application & Use Cases — recognising where and how AI can be applied in real tasks.
• Bias, Ethics & Risk — awareness of bias, ethical considerations, and the risks of AI use.
• Data Quality & Governance — understanding the role of data quality and responsible governance.
• Output Interpretation & Evaluation — judging and evaluating AI outputs rather than accepting them blindly.
• Human Oversight & Collaboration — knowing when and how to keep a human in the loop.

As AI reshapes how people work, organisations face a visibility gap: they cannot see how ready their workforce is to use AI well. This assessment is designed to close that gap, giving an objective measure of the knowledge that underpins AI readiness.

It is built with a rigorous, expert-led approach, taking into account:

• The genAssess Core-6 model — built on an expert model of AI readiness developed by Martyn Redstone through more than 900 hours of research, including academic study and interviews with TA leaders, CHROs, tech CEOs, and policy analysts.
• Aligned to global standards — designed to align with leading AI literacy and governance frameworks, including UNESCO, the OECD, the US NIST AI Risk Management Framework, and the EU AI Act.
• Sova's assessment science — built into the Sova platform with the psychometric rigour behind every Sova assessment, so results are fair, reliable and defensible.
• Knowledge, not confidence — it measures what people actually know, not how confident they feel or claim to be.

Format

• short multiple-choice format, 48 questions in total, eight for each of the six knowledge areas, taking circa 15 to 20 minutes. The assessment is untimed.
• with nearly 5,000 possible assessment forms, candidates receive comparable but varied question sets, controlling for item exposure while covering the same six areas.

What you get

• Scoring and bands — an overall AI Readiness score, plus a score for each of the six areas across five benchmarked bands, so you can see how developed someone's knowledge is relative to a comparison group.
• Manager report — a breakdown across the six areas, what the scores imply, and suggested interview questions, to probe each area.
• Candidate report — a breakdown across the six knowledge areas, giving the candidate useful self-insight.

It sits at the screening stage alongside the assessments you already run, feeds the interview, and carries into onboarding and development. It is one input among several, and never makes the hire on its own.

The AI Readiness Assessment is built on psychometric science, with strong early evidence.

• Expert-built model — the Core-6 is an expert model of AI readiness, with every question mapped to one of the six knowledge areas.
• Convergent validity — scores show strong convergent validity of r = 0.82 against an independent academic measure of AI literacy, the GLAT from Monash University.
• Fairness — fairness-tested for differential item functioning (DIF), sample of 289, to check that items perform consistently across groups.
• Reliability — [reliability coefficient to be inserted from the technical manual].
• Criterion validity — work to link scores to on-the-job performance is underway, being built with launch clients through live validation studies.

The AI Readiness Assessment brings together Martyn Redstone's domain expertise with Sova's assessment science.

Martyn Redstone is the founder of genAssess. With over 20 years in talent acquisition and the last decade in applied AI, Martyn now specialises in AI governance, compliance and risk. He is the architect of the genAssess Core-6 knowledge model, built from more than 900 hours of research into what AI readiness really means, and the author of the assessment's suggested interview questions.

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