Development of a Data-Driven Trendwatching Model in the European Business Environment: The «agentic AI» Case

The article is devoted to addressing the issue of strategic business adaptation within the context of the digital transformation of the European economy. Traditional intuitive forecasting methods are losing effectiveness, necessitating a transition to data-driven approaches for the early detection of market opportunities. The aim of the research is the development and methodological substantiation of an authors’ model of integrated trendwatching, which combines web analytics tools with strategic planning methods. The proposed methodology is implemented through a three-tiered algorithm: 1) identification of «weak signals» using an adaptive algorithm for filtering Google Trends data; 2) analytical assessment via a developed business trend matrix that integrates SWOT analysis; 3) strategic modelling of development scenarios (Foresight). The model was tested on the emerging technological trend «Agentic AI». A comparative analysis of search interest dynamics was conducted for the period from December 2024 to November 2025 across key European economies (Great Britain, Germany, France), Eastern European countries (Poland, Ukraine), and the USA as a global benchmark. The research results demonstrate that the proposed model allows for not only recording the appearance of an innovation but also assessing the level of regional market readiness. A significant asymmetry in the perception of the trend between EU countries and the USA was revealed, opening specific «windows of opportunity» for European business. The practical value of the work lies in providing a unified toolkit for transforming «raw» data into verified business strategies.