THE POTENTIAL OF USING AI TECHNOLOGIES TO ENHANCE ENTERPRISE OPERATIONAL EFFICIENCY

Анотація

In the current conditions of a rapidly changing external environment, enterprises across various
sectors of the economy are finding it increasingly difficult to maintain optimal modes of operation
and development and to ensure operational efficiency. Today, within the context of the development
of new industrial paradigms (Industry 4.0, Industry 5.0, etc.), maintaining such optimal modes and
improving enterprise operational efficiency is based on the implementation of rapidly developing
technologies corresponding to new industries. Indeed, a company’s ability to achieve its objectives
while minimizing costs (time, money, effort) and maximizing outcomes – namely, product and
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service quality and profitability – through process optimization, efficient resource utilization,
flexibility, and the adoption of new technologies has become the foundation of enterprise
competitiveness. At the same time, one of the most promising modern technologies in terms of
generating a systemic effect from business processes is the potential of implementing artificial
intelligence (AI) [1, 2] at enterprises to enhance their operational efficiency, in particular through:
models of the functioning of process units and equipment; models for data and signal processing and
transformation; models for optimizing resource use processes; full or partial execution of routine tasks
performed by production personnel and managers (competences and professional activities), among
others.
In particular, the main areas of application of AI technologies at enterprises include, first,
visualization and clear presentation of aggregated data to support decision-making. Second,
knowledge extraction from data (for predicting work and generation of control actions) using methods
of mathematical statistics (regression, analysis of variance, discriminant, cluster, factor, and other
types of analysis) and machine learning (construction of rule bases, decision trees, application of
support vector machines, learning of association rules, etc.) [3, 4], which rely on the results of
modeling obtained using traditionally defined artificial intelligence methods [2]: biological processes
underlying human thinking (artificial neural networks), logical processes underlying human
reasoning (fuzzy inference systems), and evolutionary processes (swarm intelligence, genetic
algorithms, etc.).
At the same time, among the main tasks solved at enterprises through the application of AI
technologies to improve operational efficiency, the following can be identified:
- Automation of repetitive tasks – automation of routine processes, standardized and repetitive
operations, and the release of human resources for strategic initiatives as well as more complex and
creative tasks with higher added value;
- Real-time analytics – AI solutions enable the processing and analysis of large volumes of data
in real time, allowing enterprises to respond promptly to process changes, detect deviations from
standard operating conditions, and make timely management decisions;
- Predictive analytics and planning – enables forecasting of future values of key enterprise
performance indicators, product demand, and equipment failures, as well as efficient resource
allocation, thereby increasing the validity of decision-making and reducing costs, losses, and risks;
- Intelligent Process Automation (IPA) – IPA combines traditional robotic process automation
(RPA) with AI elements, enabling the automation of not only formalized but also partially
unstructured business processes that require data analysis, pattern recognition, or decision-making;
- Data-driven decision-making – in-depth data analysis aimed at identifying hidden patterns
and insights and generating recommendations, which reduces decision-making subjectivity and
increases accuracy and validity;
- Supply chain optimization – optimization of logistics processes, inventory management,
demand forecasting, and selection of optimal supply routes, which reduces costs, increases supply
reliability, and ensures continuity of production processes;
- Predictive maintenance – based on the analysis of sensor data, equipment parameters, and
failure history, AI systems are capable of predicting potential malfunctions before they occur,
enabling a transition from preventive to condition-based maintenance and reducing downtime and
repair costs;
- Personalization of workflows and customer experience – adaptation of processes to the
individual characteristics of users, employees, or customers;
- Energy management – AI systems analyze energy consumption in real time, forecast peak
loads, and recommend optimal equipment operating modes, contributing to reduced energy costs and
improved environmental sustainability of enterprises;
- Quality control using computer vision – automation of quality control processes through the
analysis of images and video streams, ensuring rapid and accurate defect detection, improving quality
consistency, and reducing losses;
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- Innovation in products and services – the use of artificial intelligence creates opportunities
for developing new products and services and improving existing ones; AI enables faster hypothesis
testing, analysis of customer needs, and reduced time-to-market for innovations;
- Market research – AI technologies are used to analyze large volumes of marketing data,
consumer behavior, feedback, and social media content, enabling deeper understanding of market
trends, customer preferences, and the competitive environment.
Thus, AI technologies have powerful capabilities and significant potential to substantially
improve enterprise operational efficiency. The use of AI technologies makes it possible to extract
models of domain-specific functioning from data, visualize and generalize them, and generate
recommendations, thereby supporting decision-making aimed at operational optimization and
achieving a greater systemic effect from business processes to enhance operational efficiency.
A promising area for further research is the study and consideration of the limitations of AI
technologies in enterprises across various sectors of the economy.