DEVELOPMENT OF A DATA-DRIVEN TRENDWATCHING MODEL IN THE EUROPEAN BUSINESS ENVIRONMENT: THE «AGENTIC AI» CASE
Keywords:
business trend matrix, digital transformation, foresight, innovation monitoring, open data integration, strategic business adaptation.Abstract
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.
JEL: M15, O31, L86.
References
Adesina, A. A., Iyelolu, T. V., & Paul, P. O. (2024). Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights. World Journal of Advanced Research and Reviews, 22(3), 1927–1934. https://doi.org/10.30574/wjarr.2024.22.3.1961
Ajah, I. A., & Nweke, H. F. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), Article 32. https://doi.org/10.3390/bdcc3020032
Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), 1993–2009. https://doi.org/10.1108/MD-07-2018-0754
Asiri, A. M., Al-Somali, S. A., & Maghrabi, R. O. (2024). The integration of sustainable technology and big data analytics in Saudi Arabian SMEs: A path to improved business performance. Sustainability, 16(8), Article 3209. https://doi.org/10.3390/su16083209
Chen, Y., Li, C., & Wang, H. (2022). Big data and predictive analytics for business intelligence: A bibliographic study (2000–2021). Forecasting, 4(4), 767– 786. https://doi.org/10.3390/forecast4040042
Dvulit, Z. P., & Maznyk, L. V. (2024). The role of business analytics in the era of big data: New opportunities for managerial decision-making. Management and Entrepreneurship in Ukraine: the stages of formation and problems of development, 6(2), 152–165. https://doi.org/10.23939/smeu2024.02.152
Falahat, M., Cheah, P. K., Jayabalan, J., Lee, C. M. J., & Sia, B. K. (2023). Big data analytics capability ecosystem model for SMEs. Sustainability, 15(1), Article 360. https://doi.org/10.3390/su15010360
Filipova, L. (2022). Business intelligence systems: Modern development trends. Library Science. Record Studies. Informology, 18(1), 43–48. https://bdi.com.ua/web/uploads/pdf/CaC_№2_2023_Filipova.pdf
Jun, S.-P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69–87. https://doi.org/10.1016/j.techfore.2017.11.009
Kashchena, N., Ostapenko, R., & Velieva, V. (2024). Business analytics as a data processing tool. Economy and Society, (62). https://doi.org/10.32782/2524-0072/2024-62-14
Raju, S., Ravinder, D., & Kumar, N. S. (2024). The role of data analytics in forecasting business trend – A study. AIP Conference Proceedings, 2971(1), Article 040041. https://doi.org/10.1063/5.0195754
Spaniol, M. J. (2024). Organizing foresight tools. World Futures Review, 16(3), 261–276. https://doi.org/10.1177/19467567241262951
Tawil, A.-R. H., Mohamed, M., Schmoor, X., Vlachos, K., & Haidar, D. (2024). Trends and challenges towards effective data-driven decision making in UK small and medium-sized enterprises: Case studies and lessons learnt from the analysis of 85 small and medium-sized enterprises. Big Data and Cognitive Computing, 8(7), Article 79. https://doi.org/10.3390/bdcc8070079
Received: October 30, 2025.
Reviewed: November 30, 2025.
Accepted: December 16, 2025.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
