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November 21, 2025International Journal of Innovation Science0 citations

The transformative power of AI and its impact on business strategy, financial operations, and marketing decision-making: a case study method

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IGIoseb GabelaiaJHJacques Hendieh

Key Points

  • Integrating artificial intelligence enhances data-driven decision-making, improving business strategy and operations.
  • AI technologies significantly reduce operational costs and improve efficiency in financial processes.
  • The research utilizes case study analyses from three SMEs to explore AI's transformative capabilities and implications.
  • Findings suggest AI-driven models advance financial forecasting and risk management, supporting strategic agility in businesses.

Abstract

Purpose While artificial intelligence (AI) is widely integrated into modern enterprises, its concrete impacts on strategic agility, financial accuracy and marketing personalization remain under-researched. The purpose of this study is to bridge that gap by empirically assessing AI’s transformative role across business strategy, finance and marketing functions. Design/methodology/approach Drawing on a systematic literature review, the authors developed three hypotheses and examined them through a combination of case study analyses and a survey conducted within three small and medium-sized enterprises (SMEs). This approach clarifies how AI transforms these areas and what this means for the future of business continuity. Findings The results of this study demonstrate that integrating AI leads to more data-driven decision-making, thereby enhancing strategic planning and execution. Moreover, businesses with advanced AI capabilities display greater agility and adaptability. In financial terms, AI technologies simplify processes, reduce operational costs and improve efficiency. In addition, AI-driven models improve the accuracy of financial forecasting and risk management. Finally, AI technologies such as chatbots and recommendation systems enhance customer experience and satisfaction. Research limitations/implications The authors acknowledge several limitations. First, the case study analysis was limited to three SMEs and may not be generalizable to all industries or regions; however, it still provided substantial experiential insights. Second, the survey, despite a substantial sample size, relies on self-reported data, which may be subject to bias. This study assumes that the selected SMEs and their employees provided accurate and truthful information about their AI adoption and perceived business outcomes. It further assumes that the observed patterns and themes reflect broader trends in AI adoption across industries. Practical implications In financial terms, AI technologies simplify financial processes, reduce operational costs and improve efficiency. In addition, AI-driven models improve the accuracy of financial forecasting and risk management. Social implications From a societal perspective, this research supports a future in which AI facilitates more informed and efficient business practices. Economically, this study paves the way for innovations that foster productivity and profitability across industries, driving overall market competitiveness and progress. Originality/value This research offers valuable insights into AI’s transformative capabilities, emphasizing its significant impact on business strategy, financial operations and marketing decision-making. The findings are practical for businesses, offering recommendations for integrating AI technologies effectively to achieve a competitive advantage and pursue sustainable business growth.

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Cite This Study

Gabelaia et al. (2025) studied this question.

synapsesocial.com/papers/6924e3e6c0ce034ddc34eb98https://doi.org/10.1108/ijis-02-2025-0051
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