THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE SMARTIZATION OF ENTERPRISES

Abstract

The increasing role of Artificial Intelligence (AI) in enterprise transformation necessitates compre- hensive assessment models that capture the full spectrum of AI-driven smartization. This study introduces the AI-Enterprise Smartization Index (AIES-Index) — a structured framework designed to evaluate AI adoption across five key dimensions: AI adoption level, decision-making autonomy, explainability and transparency, operational efficiency, and sustainability contributions. Unlike existing AI maturity models, which primarily focus on strategic or financial aspects, AIES-Index integrates quantifiable metrics that measure AI’s real-world impact on business operations, decision-making processes, and sustainability efforts.

The study employs a multi-criteria weighted index methodology, where each category is assigned a specific weight based on its significance in AI-enabled smartization. A min-max normalization approach ensures com- parability across enterprises, allowing for objective benchmarking. To differentiate AIES-Index from existing models, a comparative analysis highlights its advantages in measuring AI adaptability, transparency, and ESG alignment, areas often overlooked in traditional AI capability frameworks.

The results demonstrate that AIES-Index provides a more holistic and quantifiable assessment of AI-driven smartization, incorporating both financial and non-financial metrics. The model emphasizes AI’s role in en- hancing operational efficiency, optimizing business processes, improving explainability, and driving sustain- able innovation. A key finding is that AI transparency and decision-making autonomy are critical factors influ- encing enterprise-wide adoption and regulatory compliance.

Future research will focus on empirical validation of AIES-Index using real enterprise data, enabling prac- tical applications across industries. Additionally, a sensitivity analysis will be conducted to assess the model’s robustness by evaluating how variations in specific indicators affect the overall AIES-Index score. These steps will ensure the model’s adaptability to dynamic business environments and sector-specific AI implementations.

The proposed AIES-Index framework serves as a valuable tool for enterprises, policymakers, and research- ers, offering a structured methodology to evaluate AI’s transformative impact on business processes while align- ing with modern governance, ethical, and sustainability standards.