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The Impact of Artificial Intelligence on Enterprise Decision-Making Process

Author

Listed:
  • Ernest G'orka
  • Dariusz Baran
  • Gabriela Wojak
  • Micha{l} 'Cwik{a}ka{l}a
  • Sebastian Zupok
  • Dariusz Starkowski
  • Dariusz Re'sko
  • Oliwia Okrasa

Abstract

Artificial intelligence improves enterprise decision-making by accelerating data analysis, reducing human error, and supporting evidence-based choices. A quantitative survey of 92 companies across multiple industries examines how AI adoption influences managerial performance, decision efficiency, and organizational barriers. Results show that 93 percent of firms use AI, primarily in customer service, data forecasting, and decision support. AI systems increase the speed and clarity of managerial decisions, yet implementation faces challenges. The most frequent barriers include employee resistance, high costs, and regulatory ambiguity. Respondents indicate that organizational factors are more significant than technological limitations. Critical competencies for successful AI use include understanding algorithmic mechanisms and change management. Technical skills such as programming play a smaller role. Employees report difficulties in adapting to AI tools, especially when formulating prompts or accepting system outputs. The study highlights the importance of integrating AI with human judgment and communication practices. When supported by adaptive leadership and transparent processes, AI adoption enhances organizational agility and strengthens decision-making performance. These findings contribute to ongoing research on how digital technologies reshape management and the evolution of hybrid human-machine decision environments.

Suggested Citation

  • Ernest G'orka & Dariusz Baran & Gabriela Wojak & Micha{l} 'Cwik{a}ka{l}a & Sebastian Zupok & Dariusz Starkowski & Dariusz Re'sko & Oliwia Okrasa, 2025. "The Impact of Artificial Intelligence on Enterprise Decision-Making Process," Papers 2512.02048, arXiv.org.
  • Handle: RePEc:arx:papers:2512.02048
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    File URL: http://arxiv.org/pdf/2512.02048
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