The Impact of Artificial Intelligence on Enterprise Decision-Making Process

TL;DR

This study examines how AI adoption impacts enterprise decision-making, highlighting benefits, barriers, and organizational factors influencing successful integration across various industries.

Contribution

It provides empirical insights into AI's effects on managerial performance, decision efficiency, and organizational challenges in a diverse set of companies.

Findings

AI adoption is widespread, with 93% of firms using AI mainly in customer service and data forecasting.

AI improves decision speed and clarity but faces barriers like resistance and high costs.

Organizational factors and leadership are more critical than technological skills for successful AI integration.

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.