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<p>Companies in dynamic markets face the challenge of processing large volumes of information early enough to make informed strategic decisions. Traditional methods of strategic foresight, such as trend studies, Delphi surveys, or manual horizon scanning, reach their limits due to constrained processing capacity, time delays, and cognitive biases. In this paper, we show how autonomous AI agents (Agentic AI) can overcome these limitations.</p><p>We present an architecture for a&#xa0;multi-agent system, comprising a&#xa0;Scout, a&#xa0;Validator, and a&#xa0;Synthesis agent. The Scout agent identifies relevant signals in unstructured data sources, the Validator agent assesses their plausibility, source quality, and potential biases, and the Synthesis agent consolidates the validated information into structured impact assessments that serve as a&#xa0;decision basis for the strategy team. This approach transforms strategic foresight from a&#xa0;one-off activity into a&#xa0;continuous, data-driven process.</p><p>We argue that Agentic Foresight has the potential to significantly increase the speed and accuracy of information processing and to improve management decision-making through continuous monitoring and iterative validation. At the same time, we discuss limitations such as hallucination risks, inference costs, and governance challenges. Human involvement remains essential for critical evaluation and ethical responsibility. Finally, we discuss the integration into existing strategy processes and outline perspectives for predictive models, cooperative multi-agent systems, and new application areas, providing companies with potential informational and competitive advantages.</p>

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Agentic Foresight: Potenziale autonomer KI-Agenten für die strategische Vorausschau in Unternehmen

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Sebastian Haugk,
  • Christian Leyh

摘要

Companies in dynamic markets face the challenge of processing large volumes of information early enough to make informed strategic decisions. Traditional methods of strategic foresight, such as trend studies, Delphi surveys, or manual horizon scanning, reach their limits due to constrained processing capacity, time delays, and cognitive biases. In this paper, we show how autonomous AI agents (Agentic AI) can overcome these limitations.

We present an architecture for a multi-agent system, comprising a Scout, a Validator, and a Synthesis agent. The Scout agent identifies relevant signals in unstructured data sources, the Validator agent assesses their plausibility, source quality, and potential biases, and the Synthesis agent consolidates the validated information into structured impact assessments that serve as a decision basis for the strategy team. This approach transforms strategic foresight from a one-off activity into a continuous, data-driven process.

We argue that Agentic Foresight has the potential to significantly increase the speed and accuracy of information processing and to improve management decision-making through continuous monitoring and iterative validation. At the same time, we discuss limitations such as hallucination risks, inference costs, and governance challenges. Human involvement remains essential for critical evaluation and ethical responsibility. Finally, we discuss the integration into existing strategy processes and outline perspectives for predictive models, cooperative multi-agent systems, and new application areas, providing companies with potential informational and competitive advantages.