Improving the Efficiency of Intellectualisation Processes in Enterprise Management Systems

Abstract

Modern management requires the highest level of analytics and the optimisation of business processes with a low risk of poor management decisions. These are essential since rapid changes in the financial world and the external environment can have critical effects. The direction of a company’s growth and the effectiveness of its management systems depend directly on the quality of intellectualisation. This study aims to develop a new methodology for studying the criteria and results of the intellectualisation processes to achieve the highest efficiency in company management systems. This study used sociological and empirical methods to find intellectualisation efficiency criteria. These criteria were then used to analyse the intellectualisation process in ten Russian companies. The correlation analysis method revealed a close relationship between the intellectualisation integral indicator and company performance over time. The results showed that the intellectualisation efficiency criteria are intellectualisation indicators in human resource management systems as well as computer-aided and automated management systems. In addition, it was found that company performance depends on the intellectualisation integral indicator, the human intelligence and artificial intelligence synergy, as well as on the efficiency of using artificial intelligence in business processes.

1. Introduction

The history of the world’s civilization, culture, and economy is based on the discoveries of human intelligence (HI): fire, the wheel, the steam engine, the printing press, the computer, and the Internet. All of these things show that human knowledge and skills have grown to a level that was not possible before. The introduction of artificial intelligence (AI)-based information technologies allows people to grow and develop in all aspects of their lives. The incredible synergy that exists between HI and AI demonstrates how useful AI can be; however, it does not replace people. Nevertheless, it is becoming increasingly significant in the ongoing development of new technologies. In addition, AI is crucial for the purposeful achievement of goals.

Even though modern businesses and society have access to innovative intellectual tools, such as computer-aided systems, software, and robotics, they still deal with difficulties and obstacles that have never been seen before. A few examples are global pandemics, scarcity of resources, economic and digital wars, cybercrime, climate change, and geopolitical unrest. The unstable external environment, intense digital competition, and the high probability of losing competitive advantages force businesses to develop their internal business processes. Other limitations include human cognitive biases and stereotypes, the inability to thoroughly search through large databases, coverage of all probabilistic factors, functional fixity, and so forth. Under these conditions, it is crucial to employ the capabilities of intelligent machines to compensate for human intelligence. AI combined with HI provides the maximum advantage, including in the nuances of thinking, reacting, social communication, and leadership.

For modern companies, management must reorganise their business procedures and integrate AI into them to remain competitive. When HI and AI can collaborate successfully within a business process, intellectualisation and management as a whole can achieve greater success. Regardless of the business process, there is a need to improve the following areas:

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demand forecasting;

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customer service;

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communication with suppliers;

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innovation and product quality improvement;

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warehouse space optimisation and on-time delivery.

Various studies have tried to tackle the issue of artificial intelligence (AI) in the automation and optimisation of business processes. Thus, the studies by C. Mills and I. Abeysekera confirm that the use of AI for automatic sales forecasting allows enterprises to determine optimal production and sales volumes, reduce the remnants of unrealised products, and improve production planning [1,2]. The studies by W. V. D Aalst indicate that AI makes it possible to improve the efficiency of inventory management and optimise the processes of ordering and delivering goods, as well as predict the demand for them [3].

At the same time, according to A. M. Subramanian, the use of AI can significantly increase the efficiency of business processes, reduce decision-making time, and lower labor costs. However, the successful implementation of the intellectualisation of enterprise management systems requires a clear action plan. It is also important to identify the necessary resources, including specialists with relevant knowledge and skills [4].

In terms of the effects that HI and AI bring, there are some examples of key concerns for prescient managers to consider:

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the speed and adaptability of highly intelligent solutions;

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the accuracy of calculations;

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the objectivity of analytical results;

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the adaptability to a changing external environment.

Intelligent company management systems have a number of benefits for companies. These benefits include:

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efficient financial performance and risk monitoring;

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highly effective decisions on how to manage production and technological facilities;

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quick receipt of accurate information on the operation of structural divisions;

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more optimal knowledge content and final product and service quality;

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lower labour intensity and optimisation of all business processes, from business planning to marketing and logistics.

In the era of increasing global risks, some issues are more relevant than ever in terms of research and application. Thus, there is a need to search for opportunities and prospects for enhancing the intellectualisation efficiency of management systems in modern companies. In addition, companies should restructure their business processes using the HI and AI synergy.

The previous research on intellectualisation efficiency in enterprise management systems has both theoretical and managerial contributions. From a theoretical point of view, previous research has broadened the understanding of how AI can be applied to improve both business processes and the efficiency of enterprise management. The existing studies provide an opportunity to learn about new technologies and approaches to solving problems in various areas of a business. From a managerial point of view, the research provided practical tools and methods for the implementation of intellectualisation in the enterprise management system. This study can help improve decision-making processes and optimise business processes, thereby leading to an increase in the efficiency of enterprise management [5].

This article aims to create a methodology for researching a company’s management intellectualisation system and ultimately implementing it in company management. The methodology will consist of three management systems:

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a company intellectualisation integral indicator;

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the HI and AI synergy;

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a set of business processes optimised by the distribution of AI.

The methodology’s practical significance is twofold:

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the identification of the criteria for the effectiveness of management system intellectualisation;

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the determined intellectualisation-induced growth factors in a company’s performance.

1.1. Literature Review

1.1.1. The Formation of Artificial Intelligence as a Part of Business Processes

There are numerous modern studies devoted to the intellectualisation of business management systems. These studies offer a variety of authorial approaches to this concept’s core and various evaluations of its constituents, HI and AI. Researchers note that it is crucial to significantly change the rules of the market game before strengthening the role of intelligence in management. Management needs a new strategy of thinking and leadership, called Quantum Intelligence, to keep up with these rules and adapt to them correctly. When implemented, this strategy could increase management adaptability and help the company stand out in a changing competitive environment [6].

Intellectual capital is studied as a type of management in today’s economics publications. Some researchers closely relate intellectual capital to the process of managing knowledge and consider it a way to increase surplus value. Companies need to be able to create and store knowledge, then use it to improve business processes and incorporate it into the company’s culture. Knowledge also multiplies and increases in value, and the products of previous knowledge, such as patents, copyrights, trademarks, licencing agreements, and franchising agreements, can protect future cash flows produced by new knowledge [7]. Management intellectualisation shows that companies need scientists and analysts who know how to use AI to turn event data into useful information [3,8].

According to sources, intellectual capital has an impact on economic actors’ motivation to attract, generate, and sell new knowledge and technology [9]. Human capital includes each person’s knowledge and learning processes, whereas structural capital is the formalised intellectual outputs of the staff (software, databases, copyrights) [4]. Since it requires special skills, the intellectualisation of management systems is also regarded as the transformation of knowledge into new fields of knowledge, innovative products, and information technologies [10]. The strategy for a company’s sustainable development and its financial performance is determined by the leadership’s intellectual capabilities and motivational management resources [6].

A key part of the success of management strategies is the ability of managers to think critically and rationally and evaluate their own management decisions. Forward-thinking leaders are more likely to come up with novel solutions. Indeed, the ideas and approaches that were successful in the past may not be the same in the future [11]. Researchers examine the effects of intellectualisation criteria on the business processes of companies, for instance, staff competency in new knowledge and practical skills, as well as the standard of the educational process [8].

Scientists discovered a link between a person’s creative abilities and intelligence as early as 1964 [9]. It was found that people with low intelligence tend to lack creative thinking. People with average intelligence generally have an average level of creative thinking. At the same time, there is no mutual influence between high intelligence and creative thinking. Nevertheless, developed creative thinking is always an indicator of high intelligence [12].

As competition in the digital business space heats up, companies should maximise the efficiency of their financial, energy, and time flows if they plan to maximise their return on investment. In this case, the combination of human resources (HR) with hardware and software is critical. The authors consider employee tacit knowledge management to be the primary competitive advantage [13].

1.1.2. Research on the AI Effectiveness in Enterprises

The main purpose of using AI is increasing the productivity and efficiency of enterprise management. The study by A. Zebec shows that the use of AI in the management system will help companies reduce costs, enhance the quality and accuracy of decision-making, increase productivity, and improve customer satisfaction [14]. According to S. Al Mansoori, AI can help in forecasting and risk analysis, allowing companies to make better decisions and prevent some problems in the future [15]. In addition, AI can be essential in automating human resource management processes, providing more accurate forecasting of demand for products and services, as well as improving marketing and advertising solutions [16]. Another article outlines a sociological approach to examining how human, relational (social), and structural capital, among other variables, affect business productivity and competitive advantages [17]. Corporate capital (the company’s philosophy), technological capital (procedures and programmes for creating added value), and innovative capital (intellectual property (IP), intangible assets) are all examples of structural capital.

J. McCarthy, the creator of AI theory and a co-author of the first time-sharing system and computer resources (the forerunner of the Internet), defined AI as “scientific and technical knowledge on the development of, in particular, intelligent computer programmes” [12]. The importance of science in the intellectualisation of management systems cannot be overstated. Science can interact with the management system (medium codification) and society (low codification) because it is a process of creating fundamental knowledge and applied developments with a high degree of codification [13].

To manage and optimise business processes, modern businesses increasingly rely on intelligent information systems that can adjust their parameters in response to internal tasks and external factors without human intervention. Hence, business process cognition and productivity, as well as overall corporate efficiency, are improving [18]. Technology, in particular, has proven to be useful in knowledge management. The process of combining HI and AI has begun to take shape. The human factor is responsible for 70% of the success in knowledge management processes because it is both the source of knowledge and the means of disseminating it. The factor of processes that play the role of physical knowledge management accounts for 20% of the total. The remaining 10% is assigned to the factor of technologies that enable people to implement processes and make knowledge available anywhere and at any time [15]. These technologies have helped management processes become more affordable, standardised, and capable of better serving individual needs. AI capabilities can elevate management to a new level, significantly accelerating information processing and management decision-making [15].

Researchers view the robotisation of business processes as the automation of service tasks that replicate the work previously performed by humans. The use of robotics has experienced strong growth in a variety of fields, including digital forensics, finance and audit, and industry. The introduction of robots has reduced operating costs by 30–50% [19]. Robotic process automation involves software agents that simulate a human’s journey through a series of computer applications while performing business process tasks [17].

AI, which consists of software, computer hardware, and databases, is defined as a combination of tangible (hardware) and intangible (software) company assets [18]. To determine how AI and intangible assets affect the capitalisation of businesses, industries, and the national accounts system, the authors use a model that explains what happens when the economy invests in both tangible and intangible assets [18]. Scholars emphasise that HI must reorient itself towards enhancing the emotional and social aspects of its engagement given AI’s growing importance in business as a performer of analytical and thinking tasks. People strengthen the elements of responsibility, sensitivity, trust, and mutual workplace assistance by offloading analytical and cognitive tasks to artificial technologies [20].

Alongside this, scholars believe that AI can impact companies in three ways:

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automation and robotisation of administrative, financial, and bureaucratic activities;

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support for managers in detecting the interpretation of hidden data models;

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the emotional state of staff and customers [21].

The authors explore the use of AI to increase the efficiency of management systems in digital commerce and finance at different stages of business processes. According to the research, AI benefits are as follows:

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higher customer service quality;

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higher supply chain management efficiency;

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operational efficiency;

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strengthened product quality control;

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optimised costs [22].

The researchers’ focus is on the role of software and hardware architecture in the management of marketing processes. There are three identified participants: administrators, managers, and users. Software benefits are the following:

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settlement mechanisms and procedures for the product delivery routes become automatic;

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advertising transaction costs can be calculated;

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overall business management efficiency is higher [23].

The available publications study the role of software in the change of IT infrastructure in the management of information systems [24]. Some studies address the effect of software function design on the introduction of new business models [25]. Research also covers the role of artificial neural networks in modelling the human nervous system, processing and analysing signals, building clusters, and making predictions about how industries will grow [26].

The institutionalisation of management standards has a significant impact on company revenues, operational efficiency, and capitalisation. Indeed, the standardisation of business processes creates a positive image of the company and helps to increase the number of loyal customers [27]. Nowadays, scientists are trying to reveal the connection between HI and AI, as well as how HI and AI thinking diverge and converge depending on the entrepreneurial process [28].

The critical analysis of research related to enterprise management system intellectualisation and its efficiency has shown that AI can significantly improve the efficiency of companies. Nevertheless, the introduction of AI into enterprise management systems requires significant investments in technology and personnel training. In addition, it is important to take into account the ethical and social aspects of using AI to minimise risks and negative consequences.

1.2. Problem Statement

Currently, there is an urgent need to investigate the increase in the intellectualisation efficiency of enterprise management systems. The rationale for the research in this field are as follows:

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the need to improve efficiency;

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the demand for automation and optimisation of management processes;

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the development of new technologies in the field of artificial intelligence and machine learning that can be applied in enterprise management;

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an increase in the volume and complexity of data to process before making informed decisions;

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the need to predict and analyse market trends and consumer behaviour;

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the positive effect on the quality and accuracy of managerial decisions.

The optimisation of all data can make it possible to increase the efficiency of enterprise management systems. Therefore, an enterprise may see accelerated information processing, improved quality of analytical conclusions and decisions, and reduced possibility of errors in the system. Data optimisation can also reduce information processing costs and improve the competitiveness of an enterprise in the market.

Consequently, the purpose of this study is to develop a new method for analysing company management system criteria and outcomes. The study also aims to deepen understanding of how effective the intellectualisation of company management systems can be. The study’s hypotheses are the following assumptions:

• To assess the effectiveness of interconnected control system intellectualisation, an intellectualisation integral indicator must be introduced;
• Company performance is the result of company management systems’ intellectualisation processes and is directly dependent on the intelligence integral indicator;
• Company performance is directly dependent on factors such as the synergy of interaction between HI and AI and the efficiency of AI distribution across business processes.

2. Materials and Methods

This study’s methodological framework applies sociological and empirical analysis methods to the company as a structure that comprises three management systems: HR management system, computer-aided and automated systems (Figure 1). All studies took place in 10 large Russian enterprises of various industries: telecommunications and IT, electric power, transport, banking, retail, digital trade, metallurgical production, logistics, and agriculture. A sample of companies from different industries was selected to increase the overall representativeness and generalisability of the results. This approach made it possible to obtain more accurate and representative data on intellectualisation effectiveness in enterprise management systems and make more generalised conclusions and recommendations. In addition, a sample of companies from different industries can help to identify differences in intellectualisation effectiveness in enterprise management systems of different sectors. These data can be useful for comparing and analysing the results.

2.1. Empirical Analysis

The intellectualisation of management systems is defined as a systematic process of increasing the qualitative and quantitative involvement of HI and AI in a company’s business processes. The quantitative expansion of intelligence involvement is due to the increase in several intelligence indicators:

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The number of intellectually equipped employees (system 1): forward-thinking leaders, full-time scientists, employees with special, tacit knowledge, creative abilities, and a high level of proficiency in AI systems; the number of standardised business processes; the number of registered IP rights; the cost of their motivational fund and investment.

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The amount invested in digital platforms, databases, software, pipelines/networks, and computer technology for business process automation (system 2).

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The amount invested in autonomous AI and automatic technical systems and mechanisms, such as robots and neural networks (system 3).

The depth and organicity with which HI and AI interact (synergy), as well as the effectiveness with which AI is decomposed into business processes at the company, determine the quality of management system intellectualisation.

To prove the efficacy of control system intellectualisation processes, this study introduces criteria for evaluating the processes (indicators). The sum of these indicators will be considered an intellectualisation integral indicator (

Table 1. Integral indicator of management systems intellectualisation I = (X1, …, X15).

Indicator	Formula	How Intellectualisation Efficiency Is Affected
System 1. Management, staffing, and motivation system. HI
Forward-thinking, system-minded, and leadership ability of managers, X1	X1 = The number of forward-thinking managers/the total number of managers	A forward-thinking leader’s intelligence is a powerful asset for reorganising a company’s management and combating competition. The ability to isolate important elements from the process, analyse them, and restructure them can result in a quick and high-quality solution. Intellectualisation success can be increased by the careful selection of the most optimal procedures for a company’s system management as well as employees who are able to perform a particular task more effectively.
The innovation level of management decisions, X2	X2 = The number of innovators/the number of managers	Managers with innovative thinking continuously improve business processes, bringing in fresh ideas and involving their employees in the process. They can save time and resources, reduce the number of process failures, and increase the number of loyal customers.
New and implicit knowledge that employees produce and transfer, X3	X3 = The number of employees with tacit and new knowledge/the total staff	The knowledge that extends beyond the typical professional knowledge is the foundation of business processes. The number of profitable deals, the number of attracted resources, and the amount of time saved can all increase through knowledge of partners and customers, personal communications and useful connections, authority, and accumulated experience.
Employee proficiency in AI systems, X4	X4 = The number of AI proficient employees/the total staff	The depth of cognitive interaction between a person and an intelligent machine depends on the AI proficiency level. There is no benefit to business process intelligence regardless of how advanced the software is if the employee does not know how to use it.
The cognitive and creative activity of the staff, X5	X5 = The number of employees with creative thinking and high analytical abilities/the total staff	Employees with high levels of creative intelligence have a strong internal motivation to succeed and find innovative solutions to any problems that arise. These employees are independent and not reliant on external evaluations when it comes to generating new ideas.
The share of bonuses to staff for an intellectual product in the wage fund, X6	X6 = Value of intellectual contribution bonuses/bonus fund	Motivation is an essential component of any HR system. The increase in the proportion of intellectual product premiums in the premium fund indicates a rise in HI’s contribution to the surplus product.
Institutionalisation of management standards, X7	X7 = The number of business processes based on instructions, standards/all business processes	Management system standardisation improves quality by ensuring compliance with uniform business standards and template technologies developed by business experts.
Scientists’ share of the staff, X8	X8 = The number of employees engaged in science/the total number of employees	Scientists have a habit of thinking analytically and can objectively evaluate business procedures. R&D, which is the result of their efforts, can serve as a resource, a method of transforming business processes, and/or a final product with high knowledge intensity.
IP registered, X9	X9 = The number of registered patents/the number of inventions	By securing the ownership rights to IPs, the company legitimises the new knowledge and technologies it has developed, securing its competitive advantage and the right to use them in actual business operations.
HI investment, X10	X10 = Training and qualification growth investments/Total personnel investments	Investments in courses, advanced training, and the search for and adaptation of talented employees all contribute to the company’s intellectual resources.
System 2. Computer-aided control system. AI + HI
Computer technology and database investments, X11	X11 = Computer technology investments/total investments	Computers are machines that can deploy AI and connect to the Internet, allowing for office automation and easy data sharing.
Software, pipelines and networks investments, X12	X12 = Software investments/total investments	Conditions for a high return on investment include: - The use of licensed software and its proper configuration for business processes, - Software prioritisation as appropriate: customer base management, financial flows, security protection.
Online servers, platforms, marketing communication channels investments, X13	X13 = Digital platform investments/amount of investments	The growth of benefits from the integration of hardware and application solutions, as well as the growth of effects from market participant interconnection and information exchange. Sales growth.
System 3. Automated control system. AI
Autonomous automated device and robot investments, X14	X14 = Robotics investments/amount of investments	Investing in the speed of business decisions and processes, releasing HR from routine operations, and shifting HR’s focus to more global tasks. Large-scale data processing. Achieving operational, flexible control of business efficiency.
Artificial Neural Network (ANN) investments, X15	X15 = ANN investments/amount of investments	Illegal transaction detection, risk calculation, customer behaviour analysis, and identification of the possibility of purchasing a product. Consumer demand forecasting. Client advising.

Source: Prepared by the author.

).

Below is the list of companies that use industry-specific software in addition to standard software and applications, such as customer base management (CRM), bookkeeping (1-C, Wave, Xero, Sage Business, etc.), business process optimisation (BRM), innovation management (Question Pro), and HR management (Cleverstaff, BambooHR). The kind of software these businesses use depends on the business sector they are in (

Table 2. AI’s Effect on the Business Efficiency in the Companies.

Company	Sector	AI (Software and Devices)	The Impact on the Growth of Business Process Efficiency
a	Telecommunications	Rack servers Data Storage Centres LTE networks, 5G	Efficiency growth in a wide range of business loads, from the network to decentralised databases. Inspection of cell towers. Increased speed and flexibility of scaling on a single platform and data reliability. Support of engineering calculations, creation of high-precision network models.
b	IT	ITIL, ITSM platforms 5G networks	Troubleshooting and failure management to keep the product quality high. Configuration management to support infrastructure management. Measuring the quality and completeness of service while taking its demands into account. Financial management: bringing business process financial support to balance.
c	Electric power industry	Censore-Monitoring SCADA	Timely informing employees on the status of reports, graphs, and charts. Dispatching control and data aggregation for remote station inspection. Field devices can be communicated via wired and wireless networks without the need to adhere to any specific protocols. Short-term predictions of the amount of electricity used by electrical systems. Active energy balance calculations.
d	Transportation	Rail-Tariff Rail-Info Digital platforms	Freight tariff calculation. A documentary regulatory framework that regulates the accounting and transportation of goods. Customers can book tickets online.
e	Banking, financial services	AML Insighter	Client credit history and solvency monitoring; control and combating money laundering, tax avoidance, and terrorist financing.
f	Trading	Internet services and marketplaces MySklad, Poster, EKAM, Prime. Automated ticketing	Trade and warehouse operations automation, warehouse and trade accounting, cash register support, integration with barcode scanners, and cash registers. Client newsletters. Customer base maintenance.
g	E-commerce
h	Production sector (metallurgy)	Magmasoft Electronic steel guide Metallurgical calculator	Analyses of metal casting operations in real-time for quality assurance and adjustments. The availability of a database with information about the chemical and physical properties of more than 300 types of steel and metal alloys. To facilitate technologists’ work, foundry modelling and 3D modelling are used. When parameters are available, it is possible to calculate the pipe mass and length.
i	Logistics	TMS UIS Maxoptra Cyber Log	Delivery planning and control, as well as cargo sorting Route planning with consideration for traffic congestion and cargo volume characteristics. Single information platform for the live information exchange between customers, cargo carriers, and freight forwarding companies.
j	Agricultural industry	Farm at hand Software Machinery Guide Software Scout Pro Agri Botix Agri Vi	The monitoring of fieldwork and harvesting. New information on crop cultivation methods, pest control, and plant diseases. Agricultural land visualisation and analysis. Product quality assurance. Planning and monitoring of the usage of all resources. Weather conditions monitoring. Inventory control. Reporting.

Source: A survey of company managers.

).

Table 3. Company Gross Revenue and Gross Costs (Billion Dollars) in 2018–2021.

	2018		2019		2020		2021
	Gross revenue	Gross costs	Gross revenue	Gross costs	Gross revenue	Gross costs	Gross revenue	Gross costs
a	0.0074	0.0043	0.0073	0.0039	0.0076	0.004	0.008	0.0041
b	0.0034	0.0015	0.0036	0.0015	0.0034	0.0015	0.0004	0.0017
c	0.048	0.034	0.049	0.0342	0.051	0.036	0.052	0.036
d	0.077	0.057	0.106	0.0848	0.12	0.097	0.083	0.064
e	0.035	0.0182	0.037	0.0187	0.0385	0.02	0.035	0.0167
f	0.02	0.0092	0.023	0.0107	0.026	0.013	0.03	0.0164
g	0.003	0.0013	0.0034	0.0015	0.0067	0.0032	0.008	0.0043
h	0.0097	0.005	0.0123	0.007	0.0118	0.0061	0.0126	0.0065
i	0.0015	0.0009	0.0014	0.0007	0.0013	0.0006	0.0014	0.00067
j	0.001	0.00057	0.0012	0.0007	0.0012	0.0007	0.0014	0.00074

Source: Respondents’ financial statements.

shows data on gross revenue and costs in 2018–2021.

Based on data on gross revenue and gross costs, company performance in 2018–2021 and an average annual indicator were calculated (

Table 4. Company Performance in 2018–2021.

	2018	2019	2020	2021	͡  EE
a	0.725	0.86	0.9	0.93	0.85
b	1.2	1.4	1.2	1.45	1.3
c	0.41	0.44	0.43	0.42	0.43
d	0.35	0.25	0.24	0.39	0.3
e	0.92	0.98	0.92	1.1	0.98
f	1.17	1.14	1.0	0.83	1.04
g	1.3	1.2	1.08	0.86	1.1
h	0.91	0.78	0.93	0.95	0.89
i	0.67	1	1.15	1.09	0.98
j	0.75	0.7	0.7	0.9	0.76

Source: Data in Table 3.

).

2.2. Sociological Study

The sociological study took place from 1 February 2022 to 15 February 2022. The survey involved 100 top and middle managers (10 from each company). They provided confidential statistical data on their companies’ gross revenues and gross costs from 2018 to 2021 (Table 3). Company performance was evaluated using these data.

Respondents completed the questionnaire for the period 2018–2021 using the formulas and rationale in Table 1 and answered the following questions:

• What types of AI programmes and devices are used in your company? What impact do they have on business optimisation?
• What is your company’s AI distribution percentage across key business processes?
• How would you rate the distribution’s success over the last three years? (0–1).

If you define synergy as the organic connection of the elements of a system that produces an effect many times greater than the effect of the individual elements, how would you rate the level of HI & AI synergy in your company over the last three years? (0–1).

3. Results

The intellectualisation of management systems has the potential to introduce expert management into the business process of a company. Based on how well HI and AI work together and how well they complement each other, communication with suppliers improves, and procurement transactions accelerate. The elimination of human error and the improvement of the overall product and service quality are both possible outcomes of production automation.

Manufacturing automation is a factor of modern scientific and technological development that allows people to change their nature and increase their creative potential [29]. When AI is integrated into production management and financial accounting, it becomes possible to:

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make predictive diagnostics of manufacturing processes;

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accurately calculate the actual manufacturing cost and determine deviations from the planned values;

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find a variety of financial indicators.

Intellectualisation boosts productivity and sales. Algorithms analyse sales, aggregate customer data, create digital profiles, research market conditions, and identify competitive threats. The use of intelligent technology helps the company become more customer-oriented. This is especially true in digital business, where AI improves business process insight and quality [30].

When employed correctly, AI has the potential to be a powerful functional calculator, structuring the flow of data and turning it into a final answer. The benefits of AI for logistics business processes are as follows:

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accurate accounting of warehouse space;

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optimised stocks and an increase in their trade turnover;

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an accurate assessment of product demand, resulting in more on-time deliveries and lower transportation and procurement costs.

The benefits of AI for document flow include improved office work quality, and fewer errors in document preparation, reporting, and auditing. AI allows companies to analyse customer data, spot fraudulent transactions, and defend businesses against cybercrime and financial losses due to the introduction of neural networks and external surveillance systems. Thus, AI increases service quality and customer loyalty. Intellectualisation improves personnel management’s effectiveness, motivation, and control. Software solutions include online interviews, technical task generation, and gamified neurobiological tests.

The aforementioned software solutions enable HR managers to confirm applicants’ knowledge, thereby completing the recruiting task, i.e., finding the ideal employee position. At the same time, managers who think globally can apply AI to:

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assess and forecast employee performance;

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plan staff and retain the most talented employees;

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provide staff incentives;

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optimise working hours and ensure labour productivity growth [31].

A well-organized business process means that HI and AI can interact closely and effectively (Figure 2). This interaction excludes uneconomical expenditure of incoming resources, as well as loss of physical and mental strength transferred to the finished product. In addition to preventing process participants from being uncoordinated, it eliminates information asymmetry within the company, unclear deadlines and stages of implementation of management tasks, and frequently repeated mini-processes rather than one optimised process. At the same time, the interaction between HI and AI coordinates task deadlines and the frequency of errors, improves employee productivity, and enhances the efficiency of business intelligence. The collection, disclosure, and comprehension of the precise application of raw data are the main factors influencing the quality of analytics in the value chain [32].

The primary cause of a company’s loss of competitive advantage is chaotic business processes (Figure 3). Business processes are sporadic, capacities are idle, transport routes are not optimized, deliveries fluctuate due to force majeure, and demand is unstable because of supply disruptions and information asymmetry. Employees are either overburdened with unnecessary tasks or lack all necessary information. This leads to a lack of competencies. There is a significant waste of extra energy, which does not help to establish communication components. The absence of automation causes the processes to involve an excessive amount of human labour. This unnecessary involvement introduces new potential sources of error into the process of resolving management responsibilities.

The company incurs significant losses as a result of inaccurate risk assessment, forecasting, and calculations of financial indicators. On the other hand, excessive automation of processes is another factor that reduces the success of business processes. This is especially true in situations where social interaction, reputation, and trust are important. The return on managerial resources is heavily influenced by employees’ tacit knowledge and the level of mutual support within the company.

The lack of effective control over employees’ work is another factor in the low efficiency of business processes. When there is an imbalance in control over a person, the work process itself turns into the target of control. Control automation is the implementation of highly precise software for personnel performance, integrating motivation and tasks to proactively and efficiently organise workers’ cognitive and creative resources. Likewise, it is essential to monitor the efficient collection, storage, and utilisation of data [33].

It may be useful to consider the mechanism of well-established business processes based on HI and AI synergy and its effective distribution across business processes. A bank may serve as an example in this case (Figure 4). Competent staff members conduct automated customer base monitoring based on strict adherence to regulatory standards. Accounting systems display relationships with partners, give detailed information about customers, and perform identification monitoring. Analysts audit information about untrustworthy clients using powerful probabilistic and phonetic algorithms. Chatbots and robo-advisors interact with clients by consulting, making financial recommendations, conducting transactions, and transferring money.

To assess the risks of non-payment, credit scoring employs numerical statistical methods. AI monitors customer behaviour and, based on the Central Bank’s criteria for suspicious transactions, blocks them, preventing tax evasion and money laundering. The bank conducts a quarterly analysis of corporate activity based on software scoring models. Staff performance can be evaluated using tracking systems. Information theft is prevented by the use of specialized software by security personnel to conduct external security surveillance inside offices. AI can spot threats and find fake projects.

It is significantly easier to generate reports while adhering to national standards, cross-check data between files, consolidate departmental data, calculate indicators, and transmit information to the tax service and the Central Bank when competent accountants use software packages.

AI that interacts organically with HI but is introduced unevenly into the chain of business processes or is not fully utilised reduces the return on management system intellectualisation. For example, a company may have automated resource procurement, operational processes, and sales, but its analytical and forecasting system depends solely on human error. In this case, there is a very high likelihood of budget planning and market research errors, which will affect pricing and revenues.

The following table of indicators shows the intellectualisation degree of the management systems in ten Russian companies. These data were obtained through the analysis of the questionnaires filled out by respondents (

Table 5. Intellectualisation Integral indicator for Management Systems of Russian Companies in 2018–2021 (I).

a				b				c				d				e				f				g				h				i				j
2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021	2018	2019	2020	2021
HR Management System intellectualisation sub-index, I1 I1 = x1 + … + x10
5	5.5	6	6.2	7	7.5	7	7.6	4	4.5	4.6	4.5	4	4.5	4.5	5	8	8.2	8	8.7	5	5	4.6	4	5	4.65	5	4.4	3	2	3	3	2	3.5	4	4	4	4.3	4	5
Computer-aided control system intellectualisation sub-index, I2 I2 = x11 + x12 + x13
0.4	0.5	0.8	0.7	0.7	0.7	0.65	0.8	0.3	0.3	0.1	0.1	0.1	0.1	0.2	0.2	0.6	0.6	0.6	0.7	0.2	0.1	0.1	0.2	0.7	0.8	0.6	0.6	0.1	0.1	0.1	0.2	0.1	0.3	0	0	0.3	0	0.3	0.2
Computer-aided control system intellectualisation sub-index, I3 I3 = x14 + x15
0.4	0.4	0.1	0.1	0.2	0.2	0.1	0	0.1	0	0	0	0	0	0	0	0.2	0.2	0.2	0.1	0.1	0.1	0.1	0	0	0.2	0	0	0	0	0.1	0.1	0	0.1	0	0	0	0	0	0
Integral indicator, I I = I1 + I2 + I3
5.8	6.4	6.9	7	7.9	8.4	7.75	8.4	4.4	4.8	4.7	4.6	4.1	3.5	3.4	4.7	8.8	9	8.8	9.5	5.3	5.2	4.8	4.2	5.7	5.65	5.6	5	3.1	2.1	3.2	3.3	2.1	3.9	4	4	4.3	4.3	4.3	5.2

Source: Respondent questionnaires.

).

The higher the first system’s intellectualisation sub-index (maximum 10), the more effectively HI is used in business processes. The best company is one whose top levels of management are 100% forward-thinking and has system-minded leaders who innovate. The employees in these companies are 100% loaded with new and tacit knowledge that helps them think and be creative. The employees are entirely motivated by the outcomes of their intellectual efforts. All the company’s employees work on scientific research and put it into practice, and all the planned R&D publications are registered. Every year, intangible assets grow. Every single management procedure takes place under established regulations and guidelines.

The second and third management systems’ intellectualisation sub-indices show the proportion of a company’s total investment in AI. Many areas of a company’s investment structure require financing, including production equipment, technological capacities, workshops, office and warehouse space, etc. Depending on the sector, the amount of money invested in AI can vary a great deal. It is naturally higher in knowledge-intensive industries and the service sector (telecommunications, IT, banks), and lower in manufacturing, agriculture, transportation, and trade sectors. According to the survey in this study, AI accounts for 80–90% of the investment made by telecommunications and IT companies (a, b), while only 10–30% of the investment is made in the fields of transportation, metallurgical production, logistics, and agriculture.

The study interviewed the leaders of ten large Russian companies in the telecommunications and IT, banking, transportation, electric power, trade, digital trade, manufacturing, logistics, and agricultural sectors. The interview revealed how their business processes use AI (Figure 5).

Managers claim that this representation of the decomposition of AI in business processes, as shown in Figure 6, accurately captures how effectively surplus product is produced.

Thus, the graph shows the respondents’ subjective opinions on the effectiveness of artificial intelligence distribution across business processes of enterprises for 4 years, from 2018 to 2021. The graph also demonstrates an increase in estimates every year in the field of trade. Therefore, we can assume that the introduction of artificial intelligence into the business processes of enterprises in this industry is becoming more and more effective. In addition, there have been stable indicators in the fields of electric power, digital trade, production, and logistics for all the studied years. This fact indicates that these areas have reached a certain level of development and are now functioning at stable levels of productivity and efficiency. The sphere of banking services and transport does not show the dynamics of change. At the same time, the respondents’ opinions made it possible to assess the level of synergy between human intelligence and artificial intelligence in the system. Synergy implies that the interaction of two components (in this case, human and artificial intelligence) leads to a more effective result than if each of the components functioned separately.

Figure 7 shows the respondents’ assessment of HI and AI synergy.

As a result, it was found that, in the fields of electric power, trade, digital trade, production, and logistics, respondents determined that there was a fairly high and stable level of synergy between human and artificial intelligence over all the years of the study. Thus, the empirical analysis of 10 companies shows that the company performance, synergy, and efficiency of AI distributed in business processes are interrelated indicators (Figure 8).

The correlation between the dynamics of the intellectualisation integral indicator and the economic efficiency of enterprises can be verified with the correlation coefficient analysis (the Pearson correlation coefficient or Spearman’s correlation coefficient). The analysis determines the degree of the relationship between two variables (in this case, the integral indicator of intellectualisation and the economic efficiency of enterprises). The results of this analysis reveal the presence or absence of a relationship between these variables.

The trends of the intellectualisation integral indicator and the company’s performance from 2018 to 2021 were compared to identify their correlation (

Table 6. The Comparison of Intellectualisation Integral Indicator (X) and Company Performance (Y).

Company, a (telecommunications sector)
	Y	X	Y − Y	X − X	(Y − Y)2	(X − X) 2	(Y − Y) (X − X)
2018	0.72	5.8	−0.13	−0.75	0.017	0.56	0.098
2019	0.86	6.4	0.01	−0.15	0.0001	0.02	−0.0015
2020	0.9	6.9	0.05	0.35	0.0025	0.12	0.0175
2021	0.93	7	0.08	0.45	0.0064	0.2	0.036
	$\stackrel{⌣}{\mathrm{Y}}$ = 0.85	$\stackrel{⌣}{\mathrm{X}}$ = 6.55			Σ = 0.026	Σ = 0.9	Σ = 0.15
					σ = 0.08	σ = 0.47	r = 1
Company, b (IT sector)
2018	1.2	7.9	−0.1	−0.2	0.01	0.04	0.02
2019	1.4	8.4	0.1	0.3	0.01	0.09	0.03
2020	1.2	7.75	0	−0.35	0	0.1225	0
2021	1.45	8.4	0.15	0.3	0.02	0.09	0.045
	$\stackrel{⌣}{\mathrm{Y}}$ = 1.3	$\stackrel{⌣}{\mathrm{X}}$ = 8.1			Σ = 0.04	Σ = 0.22	Σ = 0.095
					σ = 0.1	σ =0.23	r = 1
Company, c (electric power sector)
2018	0.41	4.4	−0.02	−0.2	0.0004	0.04	0.004
2019	0.44	4.8	0.01	0.2	0.0001	0.0001	0.002
2020	0.43	4.7	0	0.1	0	0	0
2021	0.42	4.6	−0.01	0	0.0001	0.0001	0
	$\stackrel{⌣}{\mathrm{Y}}$ = 0.43	$\stackrel{⌣}{\mathrm{X}}$ = 4.6			Σ = 0.0006	Σ = 0.04	Σ = 0.006
					σ = 0.012	σ = 0.1	r = 1
Company, d (transportation sector)
2018	0.35	4.1	0.05	0.2	0.0025	0.04	0.01
2019	0.25	3.5	−0.05	−0.4	0.0025	0.16	0.02
2020	0.24	3.4	−0.06	−0.5	0.0036	0.25	0.03
2021	0.39	4.7	0.09	0.8	0.0081	0.64	0.072
	$\stackrel{⌣}{\mathrm{Y}}$ = 0.3	$\stackrel{⌣}{\mathrm{X}}$ = 3.9			Σ = 0.0167	Σ = 1.09	Σ = 0.132
					σ = 0.065	σ = 0.52	r = 0.98
Company, e (banking sector)
2018	0.92	8.8	−0.06	−0.3	0.0036	0.09	0.018
2019	0.98	9.3	0	0.2	0	0.04	0
2020	0.92	8.8	−0.06	−0.3	0.0036	0.09	0.018
2021	1.1	9.5	0.12	0.4	0.0144	0.16	0.048
	$\stackrel{⌣}{\mathrm{Y}}$= 0.98	$\stackrel{⌣}{\mathrm{X}}$ = 9.1			Σ = 0.0216	Σ = 0.38	Σ = 0.084
					σ = 0.07	σ = 0.3	r = 1
Company, f (retail sector)
2018	1.17	5.3	0.13	0.4	0.017	0.16	0.052
2019	1.14	5.2	0.1	0.3	0.01	0.09	0.03
2020	1.0	4.8	−0.04	−0.1	0.0016	0.01	0.004
2021	0.83	4.2	−0.21	−0.7	0.044	0.49	0.147
	$\stackrel{⌣}{\mathrm{Y}}$ = 1.04	$\stackrel{⌣}{\mathrm{X}}$ = 4.9			Σ = 0.07	Σ = 0.75	Σ = 0.233
					σ = 0.13	σ = 0.43	r = 1
Company, g (e-commerce sector)
2018	1.3	5.7	0.2	0.2	0.04	0.04	0.04
2019	1.2	5.65	0.1	0.15	0.01	0.02	0.015
2020	1.08	5.6	−0.02	0.1	0	0.01	−0.002
2021	0.86	5	−0.24	−0.5	0.072	0.25	0.12
	$\stackrel{⌣}{\mathrm{Y}}$ = 1.1	$\stackrel{⌣}{\mathrm{X}}$ = 5.5			Σ = 0.122	Σ = 0.32	Σ = 0.173
					σ = 0.17	σ = 0.28	r = 0.9
Company, h (production sector)
2018	0.91	3.1	0.02	0.2	0.0004	0.04	0.004
2019	0.78	2.1	−0.11	−0.8	0.01	0.64	0.088
2020	0.93	3.2	0.04	0.3	0.0016	0.09	0.012
2021	0.95	3.3	0.06	0.4	0.0036	0.16	0.024
	$\stackrel{⌣}{\mathrm{Y}}$ = 0.89	$\stackrel{⌣}{\mathrm{X}}$ = 2.9			Σ = 0.0156	Σ = 0.93	Σ = 0.128
					σ = 0.06	σ = 0.48	r = 1
Company, I (logistics sector)
2018	0.67	2.1	−0.31	−1.4	0.096	1.96	0.434
2019	1	3.9	0.02	0.4	0.0004	0.16	0.008
2020	1.15	4	0.17	0.5	0.029	0.25	0.085
2021	1.09	4	0.11	0.5	0.012	0.25	0.055
	$\stackrel{⌣}{\mathrm{Y}}$ = 0.98	$\stackrel{⌣}{\mathrm{X}}$ = 3.5			Σ = 0.1375	Σ = 2.62	Σ = 0.582
					σ = 0.185	σ = 0.8	r = 0.98
Company, j (agriculture sector)
2018	0.75	4.3	−0.01	−0.2	0.0001	0.04	0.002
2019	0.7	4.3	−0.06	−0.2	0.0036	0.04	0.012
2020	0.7	4.3	−0.06	−0.2	0.0036	0.04	0.012
2021	0.9	5.2	0.14	0.7	0.0196	0.49	0.098
	$\stackrel{⌣}{\mathrm{Y}}$ = 0.76	$\stackrel{⌣}{\mathrm{X}}$ = 4.5			Σ = 0.027	Σ = 0.61	Σ = 0.124
					σ = 0.08	σ = 0.39	r = 0.99

Source: Calculated by the author using data on the integral indicator and company performance. Y—the economic efficiency of the enterprise (dependent variable). X—the intellectualisation integral indicator (independent variable). Y − Y—the mean of enterprise economic efficiency. X − X—the mean of the intellectualisation integral indicator. (Y − Y)²—the squared deviation of enterprise economic efficiency from its mean. (X − X)²—the squared deviation of the intellectualisation integral indicator from its mean. (Y − Y) (X − X)—multiplied deviations of enterprise economic efficiency and the integral intellectualisation indicator from their average values.

).

Standard deviations (σ) in terms of X and Y are used to determine linear correlation coefficients (r). This demonstrates that the studied values were closely related to one another: the intellectualisation trends of company management processes determined the performance trends of the company. The HI and AI interaction system can reduce costs and improve company management systems by launching and developing business processes.

Table 7. The Intellectualisation Processes of Company Management Systems in 2018–2021.

	HI and AI Synergy	The Efficiency of AI Distribution across Company Business Processes	Average Company Performance, $\stackrel{⌣}{\mathbf{Y}}$	Average Annual Intellectualisation Integral Indicator, $\stackrel{⌣}{\mathbf{X}}$	The Linear Correlation Coefficient, r
a	0.9	0.95	0.85	6.55	1
b	1	1	1.3	8.1	1
c	0.7	0.75	0.43	4.6	1
d	0.6	0.5	0.3	3.9	0.98
e	0.99	1	0.98	9.1	1
f	1	1	1.04	4.9	1
g	0.99	1	1.1	5.5	0.9
h	0.98	1	0.89	2.9	1
i	0.99	1	0.98	3.5	0.98
j	0.8	0.8	0.76	4.5	0.99

Source: Prepared by the author during the research.

summarises data regarding how company management systems were intellectualised between 2018 and 2021.

Respondents’ assessments of HI and AI synergy factors, as well as the AI distribution in business processes, correlated with each other and with average annual company performance. The annual average Y in companies with high factor scores, such as a, b, e, f, g, h, and I, was in the range of 0.85–1.3. The average annual indicator Y in a company with an average factor j was 0.76. The indicator Y was 0.3–0.43 in companies with relatively low scores (c, and d), which was also significantly lower than in other companies.

Thus, respondents’ opinions on the factors of intelligence synergy and AI distribution in business processes correspond to the enterprise efficiency levels. High estimates of factors were accompanied by the high efficiency of enterprises, and low estimates of factors were accompanied by low efficiency. At the same time, the largest contribution to the average annual indicator Y was from the estimates of factors a, b, e, f, g, h, i, and the smaller contribution was from factor j. Enterprises with estimates of factors c and d had the lowest annual average Y. Therefore, to increase efficiency, these enterprises can pay attention to improving factors that respondents rated as low. In addition, the study confirms the importance of involving artificial intelligence in the business processes of enterprises and its impact on productivity at the macroeconomic level. The hypothesis of the productivity paradox was also confirmed. Thus, an increase in the use of AI does not lead to a decrease in employment; on the contrary, it can contribute to an increase in the number of jobs and economic growth.

4. Discussion

In light of global risks and the digital race, the competitive game’s set of rules is undergoing significant changes. AI is becoming essential, leading to changes in the role of humans in management systems. This is the starting point for the investigation of intellectualisation processes. It is impossible to ignore the importance of a flexible management approach built on HI: creative thinking and leadership skills [7]. As intellectual competition grows, it is more crucial than ever for businesses to implement knowledge management tools to encourage the growth of surplus value, thereby registering and protecting their intellectual property. The latter in this case includes management and production methods and technologies. Modern studies emphasise that HI, as a source for the transformation of cognitive resources into new information technologies, is at the heart of the intellectualisation of management systems [34]. The scope of the research also covers studies on the HI and AI interaction synergy and the aspect of AI distribution efficiency across various business processes for creating surplus value. Even if a company invests in cutting-edge software, connects to 5G networks and the newest online platforms, and hires highly intelligent people, it can still lose infested funds due to inefficient use of the intelligence. For example, the use may be excessive in some processes and insufficient in others. The control and maintenance of AI is therefore a task for humans.

The results of the study on increasing the intellectualisation efficiency in enterprise management systems are important in the context of analogous scientific research. The existing research also examines the impact of intellectualisation on the productivity of enterprises. Thus, the studies by A.C. Pereira and T. Ruppert confirm that the intellectualisation of enterprise management systems can significantly increase the productivity and efficiency of enterprises. The present study came to the same conclusion [34,35].

The study by V. Alcácer showed that companies that actively implement and use AI in their business processes have higher performance indicators and faster growth than companies that do not [36]. According to M. Antonescu, AI can also significantly increase the productivity and competitiveness of enterprises, especially in areas related to data processing and decision-making [37]. In addition, J. Lee notes that, as a result of the use of AI, the productivity of the enterprise increased by 25% due to a reduction in production time and defect frequency. It also made it possible to reduce the costs of warehousing and inventory management by 30%, since orders for the necessary materials can be made in a more accurate and timely manner [38]. Consequently, the present study correlates with the results of research on improving the efficiency of intellectualisation of enterprise management systems. It also confirms the significant potential of intellectualisation to improve the productivity and efficiency of enterprises.

It is also important to note that, regardless of how far science and technology have come, business process intellectualisation always begins with a human acting as a generator of new AI technologies for subsequent co-creation of added value. The cognitive and creative properties of the mind serve as an inexhaustible source of knowledge production on the development and implementation of AI in problem areas of management. These problem areas include situations when a miscalculation, procrastination, or subjectivity can result in a financial loss for the company. Indeed, it is difficult to argue against the fact that the management of both HR and AI is dependent on the following factors:

-

leadership qualities;

-

permanent improvement of the management resource of the company.

The latter is capable of involving talented personnel in business processes, motivating them, organising effective work, and controlling the impact of its actions [29,30].

Relationships with suppliers and customers, in addition to leadership qualities and managerial knowledge, play an important role in intellectualisation. The competitive advantage here is the gain from the use of employees’ tacit knowledge [21]. HR management is becoming more decentralised. In this case, top management delegates some of their authority to subordinates working in the field. These individuals have tacit knowledge concerning the features of the process as well as the behavioural priorities of partners. The combination of tacit knowledge and relationship-savvy capital with software and IP can make the company more competitive [37].

Supplementing AI with HI in an environmental way entails strengthening the latter’s emotional and social components. The intelligence of machines lacks sensitivity and indifference toward the performance of tasks by other process participants, as well as mutual workplace assistance. All of this should be combined with advantages unique to computer-aided technologies: high objectivity, speed, and consideration of a wide range of probabilistic factors [38,39].

This article has expanded the study of the management system intellectualisation through a synthesis of the studied sources. First, the study identified three management systems. Second, the paper described qualitative parameters of AI distribution in business processes, as well as how HI and AI interact with one another. Third, an integral quantitative indicator is presented for consideration. Fourth, there is a comparison of an integral quantitative indicator with the final intellectualisation outcome, i.e., company performance.

The following factors may be limitations of this study:

➢

limited time and availability of information on intellectualisation projects implemented in enterprise management systems;

➢

limited access to experts and companies involved in the implementation of AI in business processes;

➢

a limited number of sources used to reflect the aspects of this issue.

The plan for further research may be to deepen the analysis of concrete examples of the introduction of AI into enterprise management systems. Future studies may focus on the effectiveness of these projects and their impact on business processes. It is also possible to examine the experience and compare the methods of implementing AI in various industries and regions, to study the ethical and social aspects of using AI in business. Another possible issue is the motivation of artificial intelligence in the service sector with the help of tacit human knowledge.

5. Conclusions

The intellectualisation of company management systems is not limited to the incorporation of intelligent technologies and knowledgeable, creative employees. Intellectualisation is a balanced strategy for planning, organising, motivating, and controlling how effectively intelligence and other resources are used in all business processes of a company, including procurement, production, supply, logistics, analytics and calculations, innovation, security, and HR. The use of AI saves time and allows for the quick completion of tasks that previously required extensive HR efforts. Intelligence is responsible for the highest level of analytics as well as business process enhancements, both of which contribute to an increase in the company’s overall performance.

The increased involvement of intelligence in three management systems, namely HR management, computer-assisted systems, and automated systems, has contributed to the increased efficiency of the intellectualisation process in businesses. The growth of surplus value is driven by the fact that HI and IPs continue to increase in size. The primary business task of a successful company is to increase the number of forward-thinking and system-minded leaders, scientists, and personnel with unique, tacit knowledge, a creative approach to work, and a high level of AI proficiency.

Visionary companies devote most of their management strategies to increasing the engagement and inspiration of talented employees, thereby fostering a thriving internal culture of social interaction. Potential methods to achieve this include creating a pleasant workplace, encouraging intellectual work with high motivation, using alternative and flexible forms of cooperation, paying competitive salaries, and showing social responsibility to the staff. Another important prerequisite for increasing intellectualisation efficiency is an increase in the scale of AI involvement in management systems based on synergy with a human, i.e., generator of its creation and launch, and effective decomposition in business processes. The company raises the bar in business intelligence, resource optimization, response time to changes in the external environment, production rate, and finished product quality with more investment in business process automation and autonomous AI.

An empirical analysis of data from ten Russian companies reveals that company performance trends are due to both company intellectualisation process trends and managers’ assessments of HI and AI synergy and HI and AI distribution efficiency in business processes.

It was found that high scores for intelligence synergy and the use of AI in business processes suggest that the company skillfully uses AI technologies to optimise business processes and improve productivity, thereby increasing the efficiency of the enterprise. On the other hand, low scores of the factors indicate insufficient use of AI. This can lead to low efficiency and competitiveness of the enterprise. Therefore, the use of AI and its intellectualisation are important tools for improving the efficiency of business processes and the successful development of the enterprise.

The study also revealed that the interaction of human and artificial intelligence optimises costs and enhances the effectiveness of enterprise management systems. Thus, this interaction is a key factor in achieving high economic efficiency. To this end, it is crucial to continuously improve and optimise business processes using innovative technologies and tools.

The use of artificial intelligence and other new technologies can lead to a boost in productivity, create new business opportunities, and open new markets. Since the introduction of new technologies requires specialists, it can also increase the number of jobs. In addition, the use of AI can help companies improve the quality of products or services, reduce costs, and increase competitiveness. Nevertheless, the introduction of new technologies can also change some business processes and requires additional training of personnel.

Therefore, the study’s three hypotheses have been proven. The empirical assessment of the relationship between the growing involvement of AI in business processes and rising levels of productivity at the macroeconomic level, as well as testing of the productivity paradox hypothesis, are useful for researchers. Are the obvious benefits that come from incorporating AI into the business processes of companies reflected in the national GDP? How are the resources of intellectualisation distributed between companies that are ahead and those that are behind, and how does this affect economic growth? Is there a macro-level intellectualisation cycle? How quickly does the input of artificial intelligence manifest itself in productivity growth? The answers to these questions offer a wealth of opportunities for future research.