AI Decision Quality Assessment™
Measure whether employees and leaders make better, safer and more defensible workplace decisions when AI contributes evidence, recommendations, summaries or analysis.
Explore the assessment
Does AI make decisions better — or just faster?
Prompt quality may improve an AI output, but a well-prompted answer can still be wrong, incomplete or inappropriate for the decision context. Decision quality depends on what happens after the AI output appears: whether the evidence is integrated properly, assumptions are tested, alternatives are weighed and the person remains accountable for the outcome.
- Integrate AI evidence with human expertise and context
- Test the assumptions behind an AI-generated recommendation
- Weigh credible alternatives rather than settling on the first answer
- Retain ownership and accountability for the final decision
Speed is not the same as quality
AI adoption is moving faster than the evidence that it improves decisions. Two failure patterns show up repeatedly in AI-assisted work.
Decisions made too fast
The AI output is treated as the answer rather than an input.
- Accepts the first AI-generated recommendation
- Treats fluent language as evidence
- Skips testing of alternatives
- Automation bias and premature closure
Decisions made too slow
Useful AI evidence is discounted or duplicated by hand.
- Repeats AI-supported analysis manually
- Distrust prevents genuinely useful evidence being used
- Excessive escalation for low-risk decisions
- Lost value from AI adoption
How the AI Decision Quality Assessment works
Participants respond to realistic workplace situations where AI contributes evidence, analysis or a recommendation to a real decision.
Workplace AI decision
A commercial, people, client or governance decision includes AI-generated evidence or advice.
Evidence integration
The participant decides how to combine AI evidence with human expertise, context and judgement.
Behavioural evidence
Response choices reveal how thoroughly assumptions are tested and alternatives considered.
Decision quality profile
Reports identify strengths, risks, development needs and coaching priorities.
What the assessment measures
The framework can be tailored to the target roles and decision types. These six dimensions provide the core model.
Evidence Integration
Combines AI-generated and human-sourced evidence rather than defaulting to whichever is easiest to obtain.
Assumption Testing
Identifies and checks the assumptions an AI recommendation depends on before acting.
Alternative Consideration
Weighs plausible alternatives instead of accepting the first AI-supported option.
Stakeholder Impact Assessment
Considers who is affected by the decision and how, before it is finalised.
Decision Defensibility
Can explain and justify the final decision if it is later questioned.
Outcome Ownership
Takes responsibility for the decision rather than attributing it to the AI system.
What a decision-quality scenario might examine
Example only — not a live scored item.
You are reviewing an AI-generated recommendation to change the allocation of specialist staff across three client projects. The model predicts improved utilisation and margin, but the summary does not explain which historical data were used. One project director supports immediate implementation because a quarterly target is at risk. Another warns the recommendation may overlook client-specific knowledge and recent scope changes. You must advise whether the proposed allocation should proceed this week.
What the response reveals
- Whether missing evidence is identified before acting
- Whether the decision is time-bounded rather than delayed indefinitely
- Whether stakeholder concerns are weighed against commercial pressure
- Whether the participant retains ownership of the final call
Sample AI decision quality profile
Reports combine an overall profile with scale-level narratives, risk flags and coaching recommendations.
Integrates AI evidence well and defends decisions clearly. Development should focus on weighing a wider range of alternatives before closing out a decision.
Scores and descriptors are illustrative. Norms, benchmarks and interpretive claims should be based on evidence for the relevant assessment version, population and intended use.
How this differs from adjacent assessments
AI literacy assessment
Measures knowledge of AI concepts and tools. It does not show whether someone reaches a sound decision when AI evidence is imperfect.
Human–AI Collaboration Assessment
Examines how people work with AI across a task. This assessment focuses specifically on the quality of the resulting decision.
A governance audit
Reviews policies and controls. This assessment examines whether people demonstrate the decision-making behaviours those controls depend on.
Psychometrically informed and evidence-led
Recognised standards inform the design and use of this assessment. They are not presented as proof that a particular version has already been validated — reliability, validity, fairness and benchmark evidence should be developed through piloting and appropriate use.
ISO 10667-1 & 10667-2
International standards addressing responsibilities, procedures and quality considerations when assessment services are used in work settings.
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ISO/IEC 42001:2023
The international AI management-system standard, covering governance, policies, accountability and risk management.
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NIST AI Risk Management Framework
A voluntary framework helping organisations govern, map, measure and manage risks associated with AI systems.
OECD AI Principles
International principles promoting innovative and trustworthy AI, including transparency, robustness and accountability.
SIOP Principles
Professional principles addressing job relevance, validation, fairness and responsible use of employment assessment procedures.
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Standards for Educational & Psychological Testing
Widely recognised guidance on validity, reliability, fairness, score interpretation and appropriate assessment use.
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Independent psychometric expertise for AI-assisted decisions
RWA brings over 25 years of assessment design experience to measuring how people make decisions when AI is part of the evidence base.
Bespoke capability frameworks
Assessment content is aligned with the organisation’s real decisions, roles and risk profile.
Realistic behavioural evidence
Participants make decisions in realistic scenarios rather than rating their own confidence.
Independent expertise
Occupational psychology and assessment design expertise applied to an emerging capability question.
Responsible claims
Evidence requirements are documented without overstating what a pilot dataset can establish.
Explore related RWA AI assessment services
Build a Decision Quality Assessment around your real decisions
Discuss target roles, decision types, reporting needs and evidence requirements with Rob Williams Assessment.
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AI Decision Quality Assessment FAQs
What does the AI Decision Quality Assessment measure?
Whether people integrate AI evidence with human expertise, test assumptions, weigh alternatives and remain accountable for the final decision.
Is this the same as an AI literacy test?
No. AI literacy concerns knowledge of AI tools and concepts. This assessment concerns applied judgement when AI changes the evidence, speed or apparent certainty of a real decision.
Who is the assessment designed for?
It can be configured for graduates, professionals, managers or leaders, with scenario complexity matched to the role.
Can it be used in recruitment?
Yes, where decision quality is relevant to the role, as part of a broader evidence-based selection process with appropriate validation.
How does it differ from the AI Decision Confidence Assessment?
Decision Confidence focuses specifically on whether trust in AI is well-calibrated. Decision Quality looks at the overall standard of the decision itself, including confidence but also evidence use, alternatives and defensibility.
Can the assessment be customised?
Yes. Scenarios, scoring and reporting can be aligned to role level, sector and organisational risk profile.
What reports are available?
Options include candidate reports, development reports, line-manager coaching reports and group-level dashboards.
How do we begin?
The first stage is to define the target population, decision types and evidence requirements. RWA can then recommend an assessment format and development process.