Towards an Improved Environmental Scanning Model Based on Artificial Intelligence Tools

Today, organizations face rapidly evolving socioeconomic and technological environments. Consequently, predicting future trends is increasingly difficult. As a result, there is a pressing demand for tools that facilitate the detection and tracking of emerging weak signals to help organizations make informed decisions and identify new opportunities. Environmental scanning, as a systematic methodological approach, enhances organizational decision-making efficacy by delivering strategic and timely insights. However, conventional environmental scanning faces limitations. This is mainly due to the rapid growth in data volume and complexity. This study addresses this challenge by proposing a conceptual model based on a theoretical approach that integrates artificial intelligence tools into the environmental scanning process. The focus is to improve the tracking, analysis, interpretation, and dissemination in order to enhance decision-making. The paper analyzes the revolutionary impacts of AI tools on the environmental scanning process, highlighting key features including machine learning (ML), predictive analytics (PA), natural language processing (NLP), and visualization tools. These AI tools will be expected to change the methods by which companies identify, analyze, process, and communicate information, consequently enhancing their capabilities to anticipate eventual trends and opportunities. The proposed framework shows significant promise, providing decision-makers with powerful tools for managing ambiguity and allowing informed decisions.