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A conceptual framework for using AI to support and scale human decision-making with data-driven insights.
AI-assisted decision-making denotes the purposeful use of artificial intelligence to support human decision processes. It combines data-driven models, explainability and governance to improve decisions, mitigate risks and scale expertise. Transparency, accountability and measurable evaluation criteria are essential for safe and trustworthy adoption.
Percentage of correct recommendations compared to expert judgments.
Average time from input to provided recommendation.
Share of recommendations accepted or overridden by humans.
A system combines symptom data and risk models to suggest priorities for treatment resources.
AI models prioritize suspicious cases and provide explainable cues to analysts.
Price recommendations delivered with business constraints and explanations; decision remains with the pricing team.
Define objectives and success criteria
Provide data infrastructure and integration points
Develop, validate models and implement explainability mechanisms
Run pilot with human oversight and metrics
Establish governance processes and move to production