Rewolucjonizowanie procesów biznesowych – zwiększenie efektywności i rozwój poprzez wykorzystanie sztucznej inteligencji (AI)

Mateusz Mierzejewski; Madina Ravshan qizi  Dadajonova

doi:10.55225/pel.695

Authors

Mateusz Mierzejewski Krakow University of Economics Institute of Economics, Krakow, Poland https://orcid.org/0000-0001-8542-2373
Madina Ravshan qizi Dadajonova International School of Finance, Technology and Science, Tashkent, Uzbekistan

DOI:

https://doi.org/10.55225/pel.695

Keywords:

artificial intelligence (AI), Business Process Management (BPM), automation, decision-making, innovation, customer experience

Abstract

Artificial intelligence (AI) is increasingly embedded in the core operations of organizations, reshaping how work is organised, decisions are made and value is created. Yet, despite the proliferation of AI initiatives, many firms struggle to move beyond pilots and to translate technical capabilities into measurable performance gains. This article examines how AI implementation affects business operations and business process management (BPM), with particular attention to efficiency, growth and the emerging role of generative AI (GenAI). Conceptually, we synthesise recent research on AI-enabled BPM, human–AI collaboration and GenAI in operations and supply chains. Empirically, we conduct a secondary analysis of successive waves of large-scale surveys (w and related industry reports), focusing on the diffusion of AI and GenAI across functions, the breadth of deployment within organisations, and self-reported effects on cost, productivity and earnings before interest and tax (EBIT). The findings show that while AI adoption has become nearly universal and increasingly multi-functional, substantial financial impact remains concentrated in a small subset of “AI high performers” with advanced BPM-related capabilities. AI generates its strongest and most consistent operational gains in process- and information-intensive functions, and GenAI delivers sizable task-level productivity improvements that only translate into firm-level impact when organisations redesign workflows, invest in data foundations and manage human–AI collaboration. The article concludes with implications for theory and practice and outlines directions for future research on AI-enabled process transformation.

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