The Impact of Artificial Intelligence on Enterprise Decision-Making Process

• URL: http://arxiv.org/abs/2512.02048v1
• Date: Wed, 26 Nov 2025 14:45:16 GMT
• Title: The Impact of Artificial Intelligence on Enterprise Decision-Making Process
• Authors: Ernest Górka, Dariusz Baran, Gabriela Wojak, Michał Ćwiąkała, Sebastian Zupok, Dariusz Starkowski, Dariusz Reśko, Oliwia Okrasa,
• Abstract summary: 93 percent of firms use AI, primarily in customer service, data forecasting, and decision support.<n>The most frequent barriers include employee resistance, high costs, and regulatory ambiguity.<n>The study highlights the importance of integrating AI with human judgment and communication practices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Human Decision-making is Susceptible to AI-driven Manipulation [87.24007555151452]
  AI systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes.<n>This study examined human susceptibility to such manipulation in financial and emotional decision-making contexts.
  arXiv  Detail & Related papers  (2025-02-11T15:56:22Z)
• Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making [8.948482790298645]
  We examine how various decision-support mechanisms impact user engagement, trust, and human-AI collaborative task performance.<n>Our findings reveal that mechanisms like AI confidence levels, text explanations, and performance visualizations enhanced human-AI collaborative task performance.
  arXiv  Detail & Related papers  (2025-01-28T02:03:00Z)
• How Performance Pressure Influences AI-Assisted Decision Making [52.997197698288936]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions [1.024113475677323]
  We argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time.<n>First, we introduce a highly established computational framework that assumes decisions to emerge from the noisy accumulation of evidence.<n>Next, we discuss to what extent current approaches in multi-agent AI do or do not incorporate process data and models of decision making.
  arXiv  Detail & Related papers  (2023-08-29T11:27:22Z)
• Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations [62.997667081978825]
  Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information. We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
  arXiv  Detail & Related papers  (2022-04-25T02:47:25Z)
• AI Assurance using Causal Inference: Application to Public Policy [0.0]
  Most AI approaches can only be represented as "black boxes" and suffer from the lack of transparency. It is crucial not only to develop effective and robust AI systems, but to make sure their internal processes are explainable and fair.
  arXiv  Detail & Related papers  (2021-12-01T16:03:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.