Intelligenzgestützte

Intelligenzgestützte describes systems, processes, or approaches that combine human intelligence with computational intelligence to augment decision-making. It emphasizes human oversight and accountability while leveraging data-driven insights from artificial intelligence and analytics.

It denotes a class of tools and workflows where AI generates insights, predictions, or recommendations and humans interpret, validate, or act on them. In this sense, intelligenzgestützte solutions are closely related to decision support systems and to human-in-the-loop AI.

Applications span many domains, including business analytics, healthcare, finance, manufacturing, and public administration. Examples include clinical decision support that suggests diagnostic possibilities, risk scoring for credit or insurance, anomaly detection in compliance, and predictive maintenance in manufacturing.

Benefits include faster analysis of large datasets, improved consistency, and the ability to surface patterns that might be missed by humans. Challenges involve model transparency, potential biases, data quality and privacy, and the need for clear governance and defined responsibility when decisions are made.

Implementation typically requires user-centered design, explainable AI techniques, ongoing monitoring and validation, and procedures for human oversight. Organizations must balance automation with accountability and ensure that users understand the AI's capabilities and limits.

The term reflects a broader AI philosophy that aims to augment human capabilities rather than replace them. It is used in industry and research to describe systems that empower humans to make better-informed decisions.