Introducing our AI-Assisted Decision Quality Assessment. Can your employees combine AI-generated evidence with their own expertise to make better decisions — rather than simply accepting the AI recommendation?
AI-Assisted Decision Quality Assessment
Measure how effectively people make decisions when AI-generated recommendations, analysis or evidence form part of the decision process. RWA assesses whether people use AI as an input to judgement without outsourcing the judgement itself.
Explore AI Judgement Assessment
What is AI-assisted decision quality?
AI-assisted decision quality is the ability to reach an effective, proportionate and defensible decision when AI contributes information to the process.
It is not simply the ability to use an AI tool. Nor is it the tendency to agree with or reject AI recommendations. Strong decision quality requires a person to understand the task, evaluate the available evidence, recognise uncertainty, integrate relevant human and AI inputs and retain responsibility for the final decision.
The construct becomes particularly important as generative AI, predictive models and AI-supported analytics become embedded in professional work.
What does the assessment measure?
Evidence integration
Combining AI-generated information with other relevant evidence rather than treating the AI output as the complete evidence base.
Independent judgement
Maintaining an independent view and challenging recommendations when the evidence, context or consequences justify doing so.
Decision proportionality
Matching the level of checking, scrutiny and human involvement to the importance and risk of the decision.
Uncertainty management
Recognising when information is incomplete, conflicting or ambiguous and avoiding false certainty.
Contextual reasoning
Considering stakeholders, organisational context, constraints and consequences that may not be represented in an AI output.
Accountable action
Making a defensible final decision rather than treating the technology as responsible for the outcome.
Why AI decision quality matters
AI can increase the speed and breadth of analysis. Yet the same characteristics that make AI useful can create decision risk. Fluent outputs can appear more certain than their underlying evidence warrants. Recommendations can omit local context. Models can reflect weak assumptions, incomplete inputs or historical patterns that are inappropriate for the current decision.
The problem is therefore not simply whether the AI system performs well. Organisations also need to understand how humans behave around the system.
NIST’s AI Risk Management Framework similarly treats AI risk as a socio-technical problem involving human roles, responsibilities, oversight and organisational decision-making.
Good AI use is not the same as good AI decision-making
AI capability asks
Can the person use AI effectively, identify appropriate use cases and interact productively with AI tools?
Decision quality asks
When AI becomes part of the evidence, does the person reach a sound decision that remains proportionate, contextual and defensible?
How AI-Assisted Decision Quality can be assessed
RWA typically uses realistic workplace scenarios rather than asking candidates whether they believe they make good decisions.
A scenario may present an AI recommendation alongside incomplete evidence, conflicting stakeholder information, operational constraints or time pressure. Several courses of action may appear reasonable. The assessment differentiates stronger judgement from behaviour that over-relies on AI, ignores useful AI evidence or responds disproportionately to uncertainty.
- Situational judgement scenarios
- Best-and-worst response formats
- Interactive decision simulations
- Leadership and executive scenarios
- Graduate and early-career assessment
- Bespoke role-specific exercises
Examples of weak AI-assisted decision behaviour
Automation bias
Accepting an AI recommendation primarily because the system produced it.
Unnecessary rejection
Dismissing useful AI evidence simply because a human source is available.
Single-source decisions
Allowing one AI output to substitute for a broader evidence base.
Context neglect
Following a technically plausible recommendation that ignores relevant organisational or stakeholder circumstances.
False certainty
Treating a confident or fluent response as though uncertainty has been resolved.
Responsibility transfer
Using the AI recommendation as a reason to avoid accountability for the final decision.
Where this construct sits within RWA AI judgement
AI-Assisted Decision Quality is closely related to several other RWA constructs but has a distinct interpretation.
Information Credibility Evaluation
Focuses on whether the evidence itself deserves trust. Decision Quality focuses on what the person ultimately does with all available evidence.
Human Oversight Behaviour
Focuses on retaining meaningful human review and accountability within the process.
Escalation Judgement
Focuses on recognising when uncertainty or risk should trigger wider review.
Confidence Calibration
Focuses on whether certainty matches the strength of the available evidence.
Applications
Hiring
Assess candidates for roles in which AI-generated analysis or recommendations influence workplace decisions.
Leadership
Measure executive judgement where AI creates strategic, commercial or governance trade-offs.
Development
Identify whether employees need development in evidence integration, challenge or decision discipline.
Graduate assessment
Evaluate whether early-career employees can combine AI assistance with appropriate independent judgement.
Workforce mapping
Compare decision-quality patterns across functions, levels or teams.
AI governance
Provide behavioural evidence about whether governance principles translate into actual decisions.
Related RWA AI assessments
AI Judgement Assessment
The parent framework for AI-assisted workplace judgement.
Explore →
Escalation Judgement
Assess when people seek additional review.
Explore →
AI Risk Evaluation
Measure whether people recognise and evaluate AI-related consequences.
Explore →
AI Confidence Calibration
Measure whether trust and certainty match evidence quality.
Explore →
AI Capability Assessment
Assess wider practical AI capability and readiness.
Explore →
Leadership AI Assessment
Assess AI-related executive judgement and accountability.
Explore →
Frequently asked questions
What is AI-assisted decision quality?
It is the quality of a person’s decision when AI-generated information forms part of the available evidence.
Is this an AI skills test?
No. AI skills may support effective decision-making, but this construct specifically concerns judgement after AI enters the decision process.
Can it be assessed using SJTs?
Yes. Situational judgement methods are well suited to presenting realistic trade-offs and differentiating stronger from weaker decision behaviour.
Is AI decision quality relevant to leaders?
Yes. Leadership decisions often involve greater uncertainty, accountability, stakeholder impact and governance consequences.
Can the assessment be customised?
Yes. Constructs, scenarios, scoring and reporting can be configured around the role, sector and level of AI-related risk.
Measure the quality of decisions made with AI
Assess whether candidates, employees and leaders combine AI evidence with independent judgement, appropriate scrutiny and accountable decision-making.