Benefits and Concerns Related to the Implementation of Artificial Intelligence Technology in Enterprises Located in the West Pomeranian Voivodeship of Poland

Abstract

Artificial intelligence (AI) technologies are actively implemented in companies to support their operation. However, the adoption of AI technologies brings both benefits and concerns, which influence the decisions made by business managers. The aim of this article is to examine the benefits and concerns associated with the use of AI technologies based on the opinions of representatives of Polish enterprises located in the West Pomeranian Voivodeship of Poland. In order to conduct the study, a questionnaire was developed and a survey was carried out among representatives of companies operating in the West Pomeranian Voivodeship of Poland. Multivariate correspondence analysis was used as a research tool to analyze the collected data. It was found that a customized approach to the implementation of AI technology is needed, depending on the organizational context, market type, and company’s size. Furthermore, it was stated that respondents’ perception of benefits and concerns varies depending on the number of employees and sector in which the company operates. The results of the study may be of interest to companies’ leaders interested in implementing artificial intelligence technologies.

1. Introduction

Artificial Intelligence (AI) is a domain of computer science that deals with the development of intelligent computer systems capable of perceiving, analyzing, and responding appropriately to inputs [1,2]. AI and related technologies such as machine learning, natural language processing, deep learning, and others have made a strong entry into a world undergoing the fourth phase of the industrial revolution [3].

Currently, the use of digital solutions supporting, for example, the execution of repetitive tasks or improving customer service is a fundamental element of competitiveness and development of modern enterprises across almost all industries.

Technical solutions based on AI technologies are perceived as one of the main components of digital transformation implemented by companies worldwide. This is confirmed by research results concerning the level of AI adoption and utilization in enterprises. According to Eurostat reports [4], in 2024, 13.5% of enterprises operating in the EU and employing at least ten people used some kind of artificial intelligence technology. An increase of 5.5% compared to 2023 was noted. The highest share of such enterprises was recorded in Denmark (27.6%), Sweden (25.1%), and Belgium (24.7%). Conversely, the lowest level of AI usage in business was observed in Bulgaria (6.5%), Poland (5.9%), and Romania (3.1%). A more in-depth analysis indicates that the most frequently used AI technologies included text mining (6.9%), followed by natural language generation (5.4%) and speech recognition (4.8%).

One of the most popular tools for assessing AI development dynamics is the Stanford HAI Global AI Vibrancy Tool [5]. The study performed with the use of this tool covered only 36 countries worldwide and was based on 42 indicators grouped into eight pillars, including i.a. research and development, economy, education, policy, and governance. According to the ranking published in November 2024 (based on data from 2023), the top five countries with the highest AI development dynamics are: the United States, China, the United Kingdom, India, and the United Arab Emirates. Poland placed as low as 24th in the ranking.

The global race to explore artificial intelligence is accelerating. In a report prepared by Stanford University [6] it was shown that the current leader in AI—the United States—still dominates in developing top-tier AI models (where 40 new models were generated in 2024). Meanwhile, 15 new AI models were produced in China, while only 3 were developed in Europe. This situation is mainly caused by the level of investment in artificial intelligence technologies. According to the mentioned report, the United States invested $109.1 billion in private AI technologies, China invested $9.3 billion and also launched a National AI Investment Fund with an initial capital of $8.2 billion. The European Union, together with the United Kingdom, attracted €9 billion in private AI investments. Therefore, it can be concluded that competition among the world’s largest players currently maintains a status quo in this area.

KPMG in Poland, part of a global network of independent advisory and audit firms, has been conducting annual studies titled Business Digital Transformation Monitor since 2021. The report published in 2025 [7] placed particular emphasis on the examination and use of AI by Polish entrepreneurs. The report was based on a Computer-Assisted Telephone Interview (CATI) survey conducted in March 2025, involving 180 respondents representing small, medium, and large enterprises (under condition that companies employ at least 10 people). It was revealed that the percentage of enterprises declaring any degree of AI technology implementation in 2025 almost tripled compared to 2024—from 28% to 82%. This demonstrates a significant increase in interest in these technologies. However, Polish entrepreneurs approach this topic cautiously, as more than half of AI implementations are relatively limited, though with an indication of rapid adaptation if expectations, such as improved work efficiency, are met.

Based on the literature review, it was found that the benefits and concerns related to AI implementation by enterprises located in different regions of Poland, including West Pomeranian Voivodeship, have been analyzed to a limited extent. Therefore, it is reasonable to explore practitioners’ opinions on these issues, considering factors such as company size, scope of operations, number of employees, and other.

The following research questions were formulated:

• what are the benefits and concerns for specific sectors and types of businesses, particularly those located in the West Pomeranian Voivodeship in Poland?
• what benefits and concerns are most important from the perspective of representatives of companies located in the West Pomeranian Voivodeship in Poland?

The aim of this article is to examine the benefits and concerns associated with the use of AI technologies based on the opinions of representatives of Polish enterprises located in the West Pomeranian Voivodeship. A survey questionnaire was developed, and opinions from representatives of enterprises operating in the analyzed region were collected. Multiple correspondence analysis (MCA) was used to analyze the collected data. Based on the results of presented pilot study, the conclusions were formulated.

The article includes Section 2, which presents the results of the literature review on the benefits and concerns of AI implementation in enterprises. Section 3 describes the research methodology. Section 4 contains the research results with a detailed analysis of benefits and concerns, considering various aspects. In Section 5 the achieved findings are summarized.

2. Literature Review

Artificial intelligence technologies encompass a wide range of solutions [8,9]. These include technologies that inter alia analyze written language, generate written or spoken language, convert spoken language into machine-readable formats, recognize objects or people based on images, automate processes, or support decision-making, enable process automation through repetitive task execution, machine learning [10]. In Poland, these technologies are implemented by various enterprises, including small, medium, and large companies.

AI technologies provide significant support for businesses, particularly in process automation and decision-making. Their implementation requires specific changes in companies’ organization and task structures, which may explain the relatively low level of AI adoption in Polish enterprises [11]. The research results show that relatively small number of companies use AI tools [12,13]. This is often linked to insufficient confidence among company managers regarding the benefits of AI adoption. In most enterprises, decisions about acquiring specific AI technologies are made by managers [14], sometimes also these activities are initiated by employees [15]. This may be influenced by limited financial resources and insufficient technological capabilities within companies. Enterprises that decide to implement AI typically acquire it from specialized AI service providers who focus on developing and deploying business software that automates processes and improves workflow within the organization. The choice of provider and specific AI technology depends, among other factors, on the company’s characteristics [16].

New ideas development and innovations adoption in enterprises may be impacted by various factors that can be analyzed using different theories. Among these theories diffusion of innovations theory, that seeks to explain how, why, and at what rate new ideas and technology spread [17], as well as Technology-Organization-Environment (TOE) framework, that considers technological, organizational, and environmental contexts [18], should be emphasized. The analysis of particular factors impacting the decision-making process related to innovations implementation in companies is crucial to understanding how enterprises can effectively adopt, manage, and sustain innovative solutions, as well as identifying barriers and enablers that shape successful innovation implementation and strategic growth.

It should be noted that adoption of AI may deal with benefits and concerns that may be perceived in a different way. The term “benefits” does not have a single universal definition, but it is often analyzed within the framework of social interaction theories, primarily Social Exchange Theory [19]. From this perspective, benefits are rewards, gains or positive outcomes (material or immaterial) that individuals receive in social interactions or social relationships as a result of exchanges with other social actors [20]. Benefits may also be defined as advantages on behalf of a particular stakeholder or stakeholder group [21]. Moreover, they may be perceived as all nonwage cash income and noncash compensation provided to employees, in addition to their normal wages or salaries [19]. In turn, concerns are considered as worried or nervous feelings about something, or something that makes feel worried [22]. Concerns may be related to privacy, bias, discrimination, the impact on employment and other aspects [23].

In the available literature, AI is perceived as a key driver of business transformation, enabling organizations to achieve a range of measurable and non-measurable benefits. Based on conducted analysis of published research and reports it could be stated that AI implementation brings diverse benefits depending on industry context, organizational maturity, and strategic objectives (

Table 1. Benefits of implementing AI technology [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48].

Benefits	Source
Operational efficiency and automation	[25,27,30,31,32,38,39,40,45]
Innovation and development of business models	[24,29,30,34,36,38,39]
Sustainable development and ethics	[26,28,34,37,42]
Customer service and consumer experience	[38,46,47,48]
Supply chain and logistics	[25,31,45]
Business value and company performance	[32,36,41]
Organizational culture and change management	[33,35,42]
Technological and implementation readiness	[24,26,35,40]
Sectoral application (services, public administration, logistics)	[43,44,45,46,47,48]

).

One of the most frequently cited advantages of AI is the improvement of process efficiency and task automation. Mao [25] and Wamba et al. [31] demonstrated that the use of AI in supply chain management leads to better resource planning, error reduction, and optimization of logistics flows. Similar observations are made by authors studying operation of small and medium-sized enterprises (SMEs), where AI supports operational management decisions and cost reduction [38,40].

AI technology implementations also foster innovation and the transformation of business models. Saleem et al. [24] highlighted the relationship between SMEs’ readiness for internationalization and the integration of AI with frugal innovation. Shemshaki [30], as well as Torres and Beirão [34] argued that AI enables the development of new services, products, and approaches to customer value, forming the foundation of data-driven growth strategies.

AI’s role as a tool supporting sustainable development was also emphasized. Kulkarni et al. [26], Sipola et al. [28], and Ahmad et al. [37] showed that AI technologies can enhance transparency and ecological efficiency while promoting ethical management practices. On the other hand, Rossi [42] stressed the need to develop ethical and regulatory frameworks accompanying AI implementation.

AI also significantly influences companies’ interaction with customers. Research results achieved by Puntoni et al. [48] and Mende et al. [47] indicated that these technologies reshape consumer experiences, affect emotions, and influence brand perception. Flavián and Casaló [46] analyzed AI’s role in service personalization and improving service quality, while Bhalerao et al. [38] described AI’s positive impact on customer service in SMEs.

The literature review also suggests that AI contributes to increasing the business value of organizations. Enholm et al. [36] and Wamba-Taguimdje et al. [32] showed that AI technologies support performance, competitive advantage, and measurable financial growth. The authors pointed out that this is possible due to, among other factors, integration of AI with decision-making processes, data analysis, and knowledge management.

The positive impact of AI on organizational culture was also highlighted. Ransbotham et al. [33] described how AI implementation strengthens cross-functional collaboration, promotes data-driven decision-making, and enhances organizational innovation. In contrast, Brynjolfsson and McAfee [35] emphasized the strategic importance of AI and the need to adapt work culture to the new technological paradigm.

Readiness to implement AI, considering potential benefits, has been examined by Saleem et al. [24], Kulkarni et al. [26], and Szedlak et al. [40]. They indicated that AI benefits are closely linked to the level of technical preparedness, organizational resources, and managerial awareness.

However, despite numerous benefits, implementing AI technologies also involves many challenges and concerns. These may be technological, organizational, or socio-ethical in nature, and their impact can significantly limit the effectiveness of AI adoption in enterprises. Scientific databases contain literature reviews addressing issues such as concerns, challenges, and barriers related to AI implementation (

Table 2. Concerns about implementing AI technology [49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79].

Concerns/Barriers/Challenges	Source
Ethical concerns	[49,51,52,53,54,55]
AI technologies are not useful in business	[50,56,57]
Incompatibility of AI technology with existing hardware, software, or systems	[52,58,59,60]
Difficulty accessing data or poor quality of data used by AI technologies and concerns about privacy breaches or data protection used by technologies	[51,52,55,61,62,63,64,65,66,67]
Lack of clarity on legal consequences related to the use of AI technology	[61,62,63,65,68,69]
Too high costs of implementing AI technology	[51,52,59,70,71,72,73,74]
Lack of human resources and knowledge about the use of AI technology	[53,75,76,77,78,79]

) [49,50].

Ethical concerns are often mentioned as barriers to implement AI. Lee et al. [49] and Paramesha et al. [51] highlighted the risks of bias, lack of transparency in AI system operations, and potential threats to human autonomy. These issues are particularly relevant in the context of recruitment, personnel management, and financial decision-making [54,55].

Another concern deals with the lack of recognition to use AI technology within enterprises. Urbani et al. [56] and Mahmud et al. [57] pointed out that managerial skepticism, low awareness of potential applications, and so-called “algorithm aversion” can effectively discourage investment in AI.

AI implementation often encounters technical problems, such as incompatibility with existing IT infrastructure. Rane et al. [52] and Micu et al. [58] emphasized that lack of integration with current software, ERP systems, or databases can generate additional costs and delays.

Data access and quality issues also represent a significant barrier in AI adoption in companies. As noted by Desouza et al. [61] and Tambe et al. [63], the effectiveness of AI systems depends on large, consistent, and up-to-date datasets. However, in practice many organizations struggle with fragmented data, poor quality, or lack of proper data management procedures. Additionally, concerns about privacy and data security arise, particularly in the context of processing sensitive information [61,65,67].

Legal and regulatory issues also pose a challenge for companies willing to implement innovations. De Bruyn et al. [68] and Duan et al. [69] pointed to the absence of clear legal frameworks regarding liability for decisions made by AI, which may discourage organizations from adopting these technologies, especially in regulated sectors.

Many organizations also mention excessively high AI implementation costs as a barrier. Authors such as Cao et al. [70] and Kumar et al. [72] noted that investments in infrastructure, training, and system integration can be prohibitive, particularly for smaller firms. High costs are often linked to uncertainty about return on investment and lack of experience in managing AI projects [51,73].

One more barrier is dealing with shortage of human resources and expert knowledge. Kar et al. [53] and Vărzaru [75] emphasized that the lack of specialists in AI, data analysis, or advanced technology project management significantly limits implementation capabilities. This problem concerns not only technical skills but also the ability to think strategically about AI in the context of business objectives.

The literature review clearly shows that the benefits and concerns of AI implementation are multidimensional, encompassing operational, strategic, and relational aspects.

Based on the conducted literature review, the following research gaps were identified:

• little is known about the opinions of representatives of companies located in the West Pomeranian Voivodeship in Poland on the benefits and concerns of implementing artificial intelligence technology.
• findings regarding the impact of aspects such as the status of the market in which the company operates, the type of owner, business sector, duration of company’s operation, and the number of employees on the perception of the benefits and concerns of implementing AI technology remain inconsistent.

This justifies the need to undertake this research.

3. Methodology

In order to examine the respondents’ opinions, a questionnaire consisting of two parts was developed. The first part of the questionnaire contained general questions identifying the profiles of the respondents participating in the study. The second part of the questionnaire contained detailed questions concerning the respondents’ opinions on the issues under study. The questionnaire contained single-choice and multiple-choice questions (18 in total). For several questions in the second part of the questionnaire, it was also possible to rate the proposed options using a Likert scale [80] from 1 to 5, where 1 was the least important aspect and 5 was the most important aspect.

The questionnaire in electronic form was shared with representatives of companies located in the West Pomeranian Voivodeship. The survey was anonymous and participation in it was voluntary. The survey was conducted online from 11 April 2025, to 31 July 2025. The survey was filled in by 57 representatives of companies from various industries located in the West Pomeranian Voivodeship in Poland. The respondents were mainly employees and department managers in companies.

Based on the analysis of the results, it was possible to identify the most significant benefits and concerns related to the implementation of AI technology in the companies located in West Pomeranian Voivodeship in Poland.

The obtained data were analyzed using multivariate correspondence analysis (MCA). Extensive literature on this topic, such as Abdi and Valentin [81,82], Greenacre and Blasius [83], Hwang, Tomiuk, and Takane [84], and others [85,86,87], provides detailed information on the theoretical foundations and practical applications of MCA. Furthermore, publications of Greenacre [88] and Hair et al. [89] emphasized the versatility of the method in various fields, such as sociology, psychology, and marketing.

The relationships between analyzed variables were verified. The data obtained were measured on a nominal scale, so the Pearson ${\chi }^2$ test of independence was used to assess the probability of rejecting the null hypothesis concerning the independence of the analyzed variables ($p < 0.05$). All analyzed variables met the necessary criteria for relationships and, therefore, were included in further analysis. To ensure clarity, the figures illustrating two-dimensional maps were created, allowing for a more detailed depiction of the relationships between the variable categories. Following the analysis of the data, conclusions were drawn.

However, it should be noted that presented research is a pilot study. The achieved results cannot be generalized to the entire population, as the analyzed sample is not representative. This study will be continued in order to examine the opinions of a larger group of respondents.

4. Results

Table 3. Variables used in the analysis (own elaboration).

Question Number	Question Area	Code	Code Description	Value
P2 *	area of business activity	1	local market	15%
		2	regional market	15%
		3	national market	19%
		4	international market	26%
		5	global market	25%
P3 *	company ownership status	1	state-owned (State Treasury)	6%
		2	municipal (local governments)	2%
		3	private (individuals’ ownership)	26%
		4	company/cooperative	43%
		5	foreign ownership	21%
		6	don’t know/hard to say	2%
P4 *	business sector	1	agricultural sector—agriculture, forestry, fishing, hunting, and mining	8%
		2	industrial sector—manufacturing, construction	26%
		3	service sector—trade, transport, communications, municipal services, healthcare, education, tourism, and culture	66%
P5 *	type of economic sector	1	agriculture, forestry, fishing	11%
		2	mining, quarrying	2%
		3	manufacturing	25%
		4	energy supply	2%
		5	water supply, water pollution, waste management	0%
		6	construction	9%
		7	trade, vehicle and motorcycle repair	9%
		8	transport, storage	25%
		9	hospitality, gastronomy	4%
		10	information, communication	8%
		11	financial consulting, insurance	9%
		12	real estate	2%
		13	professional, scientific, and technical activities	0%
		14	administration, services	4%
		15	public administration, defense, mandatory social services	0%
		16	education	4%
		17	public health, social work	6%
		18	arts, entertainment, recreation	0%
		19	other services	13%
		20	household (as employer), production and services for own needs	0%
		21	extraterritorial organizations	0%
P6 *	duration of company’s operation	1	<2 years	11%
		2	2–4 years	4%
		3	5–6 years	6%
		4	7–10 years	11%
		5	>10 years	64%
P7 *	number of employees	1	<10 persons	36%
		2	10–49 persons	15%
		3	50–249 persons	6%
		4	>250 persons	43%
P17 **	benefits from implementing AI technology	P17_1	reduced operational costs	3.74
		P17_2	shorter task completion time	3.89
		P17_3	error and risk reduction	3.79
		P17_4	increased availability (e.g., 24/7 services)	3.17
		P17_5	personalized services	3.28
		P17_6	decision-making process optimization	3.40
		P17_7	improved company image through innovation	3.25
		P17_8	environmental protection	2.87
		P17_9	other	2.51
P18 **	concerns related to implementing AI technology	P18_1	limited access to knowledge	2.75
		P18_2	limited access to technologies	2.83
		P18_3	need to possess trained staff	3.15
		P18_4	high costs of technology purchase	3.28
		P18_5	high operational costs, including training	3.34
		P18_6	complex decision-making process	3.28
		P18_7	prolonged procurement procedures	3.23
		P18_8	difficulty obtaining external funding	2.91
		P18_9	employee resistance, job loss due to automation	3.26
		P18_10	ethical dilemmas	2.72
		P18_11	privacy and data security risks	3.38
		P18_12	possibility of errors, dependence on technology	3.38
		P18_13	other	2.30

*—values presented as percentage of responses (in question P5 it was possible to choose several options); **—values presented as arithmetic mean, the individual criteria were assessed by respondents on a scale from 1 to 5, where 1—not important, 2—slightly important, 3—moderately important, 4—important, 5—very important.

presents all variables and numerical codes, which were used in the study. These codes partly result from the numbering of questions in the questionnaire. The summary of survey results is shown in column “Value” (Table 3).

Two-dimensional perception maps were created using the MCA method. These maps should be interpreted by analyzing primarily the hidden dimensions represented by the axes of the graph, showing the main differences between categories. The position of points on the map provides information about the similarity of perceived elements: the closer they are to each other, the more often they co-occur in responses and the stronger their matchings are. Conversely, large distances indicate clear perceptual contrasts. Therefore, interpreting the entire map involves identifying which features or categories dominate on opposite sides of the axes, which makes it possible to name the dimensions and then understand the overall perception of the objects under study. In Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12 clusters of points relating to individual variables characterizing the surveyed entities are marked with colors.

The first issue analyzed is the subjective assessment of the expected benefits of implementing AI technology in relation to the core area of business activity of the surveyed companies (Figure 1).

Representatives of companies operating on the domestic market (P2:3) in particular pointed out that the significant benefits of implementing artificial intelligence in their organizations are related to reducing errors and risks (P17_3:5), reducing the company’s operating costs (P17_1:5), and shortening the time needed to perform tasks (P17_2:5) (marked in red in Figure 1).

In contrast, representatives of companies operating on the international (P2:4) and global (P2:5) markets perceive other significant benefits associated with the use of AI technology. These include increasing the company’s accessibility (P17_4:3), which may involve process automation and ability to provide services 24 h a day. In addition, respondents pointed out other elements that were not specified in detail in the survey questionnaire (P17_9:3) (marked in green in Figure 1).

Another issue analyzed is the respondents’ assessment of the expected benefits of implementing artificial intelligence technology in relation to the company ownership status (Figure 2).

Respondents representing entities owned by the State Treasury (P3:1) identified four key benefits. They believe that tailoring services to individual customer needs (P17_5:5), optimizing decision-making processes (P17_6:5), improving the company’s image through the implementation of innovations (P17_7:5), and protecting the natural environment (P17_8:5) are very important aspects that can influence the effectiveness and social responsibility of these institutions (marked in yellow in Figure 2).

On the other hand, employees of companies owned by local governments (P3:2) have different perception of benefits. They consider the optimization of decision-making processes to be insignificant (P17_6:1), while shorter task completion times are considered sufficiently important (P17_2:3) (marked in green in Figure 2).

In the case of employees of private companies (P3:3), shorter task completion times (P17_2:4), reduction in errors and risks (P17_3:4), tailoring services to individual customer needs (P17_5:4), optimization of decision-making processes (P17_6:4), and improvement of the company’s image through the implementation of innovations (P17_7:4) are considered as significant benefits (marked in blue in Figure 2).

Respondents employed in foreign-owned entities (P3:5) also recognize the benefits of implementing AI technologies, which they consider to be sufficiently significant. They pointed to increased accessibility of the company, e.g., through process automation and 24 h service provision (P17_4:3). In addition, respondents noted other elements that were not specified in detail in the questionnaire but which may also be relevant in the context of AI technology implementation (P17_9:3) (marked in red in Figure 2).

Figure 3 presents a subjective assessment of the expected benefits of implementing AI technologies in relation to the business sector of the surveyed companies.

Respondents representing companies operating in the agricultural sector, including agriculture, forestry, fishing, hunting, and mining (P4:1), perceived the benefits of implementing artificial intelligence as moderate. Reducing operating costs (P17_1:2), reducing errors and risks (P17_3:2), and tailoring services to individual customer needs (P17_5:2) were rated as insignificant (marked in green in Figure 3).

In contrast, respondents from companies operating in the industrial sector, including manufacturing and construction (P4:2), pointed to other benefits that they consider sufficiently significant. These include increasing the availability of the company (e.g., through process automation, providing services 24 h a day) (P17_4:3), tailoring services to individual customer needs (P17_5:3), and optimizing decision-making processes (P17_6:3) (marked in red in Figure 3).

Respondents from the service sector, including trade, transport, communications, municipal services, healthcare, education, tourism, and culture (P4:3), also perceive significant benefits from implementing AI. They pointed to the adaptation of services to individual customer needs (P17_5:4) and the optimization of decision-making processes (P17_6:4) as important aspects, which may indicate a growing need for service personalization and efficiency in customer service (marked in yellow in Figure 3).

The next step of the analysis covered a respondents’ assessment of the expected benefits of implementing AI technology in relation to the type of economic sector in which the majority of individual entities operate (Figure 4).

Respondents from the agriculture, forestry, and fishing sector (P5:1) perceive the benefits of implementing artificial intelligence as insignificant. In particular, they point to increased company accessibility (e.g., through process automation, 24 h service provision) (P17_4:2), optimization of decision-making processes (P17_6:2), improvement of the company’s image through the implementation of innovations (P17_7:2), and protection of the natural environment (P17_8:2) as minor benefits (marked in green in Figure 4).

On the other hand, respondents from the manufacturing sector (P5:3) view the introduction of AI as a source of sufficiently significant benefits, such as tailoring services to individual customer needs (P17_5:3), optimizing decision-making processes (P17_6:3), improving the company’s image through the implementation of innovations (P17_7:3), and protecting the natural environment (P17_8:3) (marked in red in Figure 4).

Respondents from the construction sector (P5:6) consider shorter execution times (P17_2:4) and environmental protection (P17_8:4) to be significant benefits, while reducing operating costs (P17_1:5) is perceived as a very significant benefit (light blue in Figure 4).

In the trade and repair of cars and motorcycles sector (P5:7), respondents identified increased company availability (e.g., through process automation, 24 h service provision) (P17_4:4) as a significant benefit. Shorter task completion times (P17_2:5) and a reduction in errors and risks (P17_3:5) are perceived as very important. When analyzing reducing a company’s operating costs, it should be noted that respondents’ opinions are divided—some consider this benefit to be important (P17_1:4), while others consider it to be very important (P17_1:5) (marked in yellow in Figure 4).

Respondents from the information and communication sector (P5:10) and public health and social work (P5:17) had similar opinions on the benefits of AI implementation, recognizing both increased company accessibility (e.g., through process automation, 24 h service provision) (P17_4:3) and other elements not indicated in the questionnaire (P17_9:3) as sufficiently important in their activities (marked in pink in Figure 4).

People employed in “other services” sector (P5:19) considered the following benefits to be sufficiently important: optimization of decision-making processes (P17_6:3) and improvement of the company’s image through the implementation of innovations (P17_7:3), but they did not identify other benefits (P17_9:1) (marked in dark blue in Figure 4).

Entities from the transport and storage sector (P5:8) perceived the reduction in company operating costs (P17_1:4) and the adaptation of services to the individual needs of customers (P17_5:4) as significant benefits, while shorter task completion times (P17_2:5) and the reduction in errors and risks (P17_3:5) were considered very important (marked in orange in Figure 4).

Figure 5 presents a subjective assessment of the expected benefits of implementing AI technology in the context of the duration of surveyed company’s operation.

Respondents from companies operating on the market for less than 2 years (P6:1) indicated that benefits such as improving the company’s image through the implementation of innovations (P17_7:3), optimizing decision-making processes (P17_6:3), tailoring services to the individual needs of customers (P17_5:3), increasing the company’s availability (e.g., through process automation, 24 h service provision) (P17_4:3), and protecting the natural environment (P17_8:3) were perceived as sufficiently important. However, respondents in this group considered that shorter task completion times as a result of AI implementation were insignificant (P17_2:1) (marked in red in Figure 5).

In the case of respondents from companies operating for 2 to 4 years (P6:2), a noticeable change in the perception of benefits was observed. Respondents considered the following to be significant effects of AI implementation: shorter task completion times (P17_2:4), reduction in errors and risks (P17_3:4), tailoring services to individual customer needs (P17_5:4), and improving the company’s image through the implementation of innovations (P17_7:4) (marked in blue in Figure 5).

On the other hand, representatives of companies operating from 7 to 10 years (P6:4), had a different approach to the benefits of AI implementation. They believed that all benefits not indicated in the survey form were irrelevant (P17_9:1) (marked in green in Figure 5).

Figure 6 presents a subjective assessment of the expected benefits of implementing AI technology in the context of the number of employees in the surveyed companies.

Respondents from companies employing between 50 and 249 people (P7:3) indicated that sufficiently significant benefits resulting from the implementation of AI include: increasing the availability of the company (e.g., through process automation, providing services 24 h a day) (P17_4:3), tailoring services to individual customer needs (P17_5:3), and protecting the environment (P17_8:3). In addition, respondents noted the importance of elements that were not mentioned in the questionnaire (P17_9:3), assessing them as sufficiently significant (marked in green in Figure 6).

Similar opinions were expressed by respondents from companies employing more than 250 people (P7:4), who also perceived these benefits as sufficiently important (marked in red in Figure 6).

Figure 7 presents a subjective assessment of concerns related to the implementation of AI technology in relation to the core business activities of enterprises.

Respondents from entities operating on the local market (P2:1) emphasized that concerns about elements not mentioned in the survey questionnaire (P18_13:3) were sufficiently important to them. They also pointed to significant concerns related to employee resistance to the possibility of job losses as a result of process automation (P18_9:4) (marked in blue in Figure 7).

On the other hand, respondents from companies operating on the regional market (P2:2) believed that concerns such as ethical dilemmas (P18_10:1) and other aspects not indicated in the survey (P18_13:1) were not relevant to their entities (marked in green in Figure 7).

Respondents from companies operating on the international market (P2:4) indicated a wider range of concerns related to the implementation of AI. They considered the following elements to be sufficiently important: limited access to knowledge about the use of technology (P18_1:3), limited availability of technology (P18_2:3), the need to possess trained personnel (P18_3:3), the complexity and multi-level nature of the decision-making process (P18_6:3), and the complex and lengthy tendering procedures related to the purchase of the technology (P18_7:3). Respondents from this segment also feared employee resistance related to potential job losses as a result of process automation (P18_9:3) (marked in red in Figure 7).

Figure 8 presents a subjective assessment of concerns related to the implementation of AI technology depending on the company ownership status.

Respondents from entities owned by the State Treasury (P3:1) pointed to a number of significant concerns that are important for their operations. These included: limited access to knowledge about the use of technology (P18_1:4), limited availability of technology (P18_2:4), difficulties in financing the costs of purchasing technology (P18_4:4), difficulties in financing the costs of operating technology, including training of employees (P18_5:4), a complex and multi-level decision-making process related to the implementation of technology (P18_6:4), complex and lengthy tender procedures (P18_7:4), difficulties in obtaining external sources of financing (P18_8:4), ethical dilemmas (P18_10:4), and threats related to privacy and data security risks (P18_11:4) (marked in purple in Figure 8).

Respondents from municipally owned entities (P3:2) also identified a number of very significant concerns, including difficulties in financing the costs of technology purchase (P18_4:5), difficulties in financing the costs of technology operation (P18_5:5), complex decision-making processes (P18_6:5), lengthy tendering procedures (P18_7:5), and difficulties in obtaining external sources of financing (P18_8:5). In addition, concerns about employee resistance resulting from the possibility of job losses due to process automation (P18_9:5) and the possibility of errors and dependence on technology (P18_12:5) were also significant (marked in green in Figure 8).

Respondents from companies operating in the private sector (P3:3) pointed to significant concerns regarding limited access to knowledge (P18_1:4), limited availability of technology (P18_2:4), the need to possess trained staff (P18_3:4), complex tendering procedures (P18_7:4), employee resistance related to possible job losses (P18_9:4), privacy risks (P18_11:4), and errors and dependence on technology (P18_12:4). Respondents also pointed to other concerns that were not included in the questionnaire (P18_13:3) and are considered sufficiently important (marked in red in Figure 8).

Respondents from companies and cooperatives (P3:4) considered concerns about the need to train staff (P18_3:3), complex tendering procedures (P18_7:3), and errors and dependence on technology (P18_12:3) to be sufficiently important (marked in yellow in Figure 8).

Respondents from foreign-owned companies (P3:5) emphasized concerns about threats to privacy and data security risks (P18_11:3) and errors and dependence on technology (P18_12:3) as sufficiently important (marked in blue in Figure 8).

The next step of the study presents a subjective assessment of concerns related to the implementation of AI technology in relation to the business sector (Figure 9).

Respondents from the agricultural sector, including agriculture, forestry, fishing, hunting, and mining (P4:1), expressed the opinion that concerns related to the implementation of artificial intelligence, such as limited access to technology (P18_2:2), difficulties in obtaining external sources of financing (P18_8:2), ethical dilemmas (P18_10:2), and other elements not included in the questionnaire (P18_13:2), were of little importance to them. However, problems related to difficulties in financing the costs of operating the technology, including the costs of training employees (P18_5:3) (marked in red in Figure 9), were perceived by this group of respondents as sufficiently important.

Respondents from the service sector, including trade, transport, communications, municipal services, healthcare, education, tourism, and culture (P4:3), emphasized that concerns about employee resistance resulting from the possibility of job losses due to process automation are significant (P18_9:4). In their opinion, concerns not indicated in the questionnaire (P18_13:3) were also sufficiently important (marked in green in Figure 9).

The study presents also a subjective assessment of concerns related to the implementation of AI technologies in relation to the type of economic sector in which companies operate (Figure 10).

Respondents from the agriculture, forestry, and fishing sector (P5:1) identified a number of sufficiently important concerns related to the implementation of artificial intelligence technology, including: the need for trained personnel (P18_3:3), difficulties in financing the purchase of technology (P18_4:3), a complex and multi-level decision-making process (P18_6:3), complex and lengthy tendering procedures (P18_7:3), difficulties in obtaining external sources of financing (P18_8:3), resistance from employees related to the possibility of job losses (P18_9:3) and ethical dilemmas (P18_10:3) (marked in light green in Figure 10).

Respondents from the mining and quarrying (P5:2), hospitality and catering (P5:9), as well as public health and social work (P5:17) sectors had similar significant concerns, including: limited access to knowledge about the use of technology (P18_1:4), limited availability of technology (P18_2:4), difficulties in financing the purchase of technology (P18_4:4), difficulties in financing the costs of operating technology, including employee training (P18_5:4), complex decision-making process (P18_6:4), lengthy tendering procedures (P18_7:4), difficulties in obtaining external sources of financing (P18_8:4), ethical dilemmas (P18_10:4), and threats related to privacy and data security risks (P18_11:4). Respondents from these sectors also pointed to other significant concerns that were not included in the questionnaire (P18_13:4) (marked in blue in Figure 10).

In turn, respondents from manufacturing companies (P5:3) considered ethical dilemmas (P18_10:1) and other issues not indicated in the questionnaire (P18_13:1) to be of little importance (marked in green in Figure 10).

Respondents from the education sector (P5:16) indicated that concerns related to difficulties in financing the costs of purchasing technology (P18_4:5) and threats regarding data privacy and data security (P18_11:5) are highly significant (marked in red in Figure 10).

Participants from the trade, motor vehicle and motorcycle repair (P5:7), as well as transport and storage (P5:8) sectors shared similar, significant concerns, focusing on employee resistance due to the potential loss of jobs resulting from process automation (P18_9:4) and other issues not specified in the questionnaire (P18_13:3) (marked in yellow in Figure 10).

Respondents from the information and communication sector (P5:10) considered limited access to knowledge regarding the use of technology (P18_1:2) to be of low importance (marked in pink in Figure 10).

Meanwhile, respondents from companies providing other services (P5:19) pointed to several concerns of low importance, including limited access to knowledge (P18_1:2) and limited availability of technology (P18_2:2). However, they noted the difficulties in financing the costs of purchasing technology (P18_4:3), the costs of operating technology, including employee training (P18_5:3), and difficulties in obtaining external sources of funding (P18_8:3) as sufficiently significant. Among respondents, there was a division in the assessment of ethical dilemmas (P18_10:2 and P18_10:3), with some practitioners considering these concerns to be of low importance while others indicated them as sufficiently significant (marked in purple in Figure 10).

Figure 11 illustrates the subjective assessment of concerns related to the implementation of AI technologies depending on the duration of company’s operation.

Respondents from companies operating on the market for less than 2 years (P6:1) indicated sufficiently important concerns regarding the introduction of AI, such as limited access to knowledge about the use of technology (P18_1:3) and limited availability of technology (P18_2:3) (marked in yellow in Figure 11).

Participants from companies operating for 5 to 6 years (P6:3) emphasized that concerns related to difficulties in financing the costs of purchasing technology (P18_4:5) and the costs of operating technology, including employee training (P18_5:5), are highly significant (marked in green in Figure 11).

Respondents from companies operating for 7 to 10 years (P6:4) noted that important concerns related to AI implementation were not included in the survey questionnaire (P18_13:4) (marked in blue in Figure 11).

Meanwhile, respondents from companies operating for more than 10 years (P6:5) considered the possibility of errors and dependence on technology (P18_12:3) to be a sufficiently significant concern (marked in red in Figure 11).

Figure 12 presents the subjective assessment of concerns related to the implementation of AI technologies in regard to the number of employees in the company.

Respondents from enterprises employing fewer than 10 people (P7:1) indicated that concerns about employee resistance related to the potential loss of jobs due to process automation (P18_9:4) are significant. Additionally, they emphasized that concerns not listed in the survey questionnaire (P18_13:3) were considered sufficiently important (marked in red in Figure 12).

Respondents from companies employing 10 to 49 workers (P7:2) highlighted a range of significant concerns, including: limited access to knowledge regarding the use of technology (P18_1:4), limited availability of technology (P18_2:4), the need for trained staff (P18_3:4), complex and lengthy tender procedures related to technology procurement (P18_7:4), employee resistance due to potential job loss (P18_9:4), threats to privacy and data security risks (P18_11:4), and the possibility of errors and dependence on technology (P18_12:4) (marked in blue in Figure 12).

Meanwhile, respondents from companies employing 50 to 249 people (P7:3) assessed that limited access to knowledge regarding the use of technology (P18_1:1) does not constitute a significant concern in the context of AI implementation (marked in green in Figure 12).

5. Conclusions

The article examines the benefits and concerns related to the implementation of artificial intelligence technologies, taking into account the opinions of representatives of enterprises located in the West Pomeranian Voivodeship in Poland. The study provided answers to the research questions posed. The identified research gap was addressed: the opinions of representatives of companies located in the West Pomeranian Voivodeship in Poland on the benefits and concerns of implementing AI technology were investigated, as well as the impact of aspects such as the status of the market in which the company operates, the type of owner, the sector and duration of company’s operation, and the number of employees on the perception of the benefits and concerns of implementing AI technology was revealed.

The most important conclusions from the analysis are presented in

Table 4. Summary of the analysis of benefits of implementing artificial intelligence technologies (own elaboration).

Criterion	Description
Market status	In enterprises operating on the domestic market, the benefits of implementing artificial intelligence include error and risk reduction, lower operating costs, and shorter process execution times. Respondents from companies operating in international and global markets primarily emphasize increased enterprise accessibility through process automation and the ability to provide services regardless of time and location. They also point out additional positive effects of technology implementation that were not detailed in the questionnaire, indicating more complex and individualized organizational experiences among these entities.
Type of ownership	Representatives of enterprises owned by the State Treasury indicate four key benefits: the ability to tailor services to customer needs, streamlining decision-making processes, enhancing the organization’s image through innovation, and actions supporting environmental protection. In entities owned by local governments, shortening task completion time is considered important, while improving decision-making processes is seen as less significant. In private companies, artificial intelligence is perceived as a factor that genuinely improves efficiency. Particularly important benefits include reducing working time, minimizing errors, increasing service personalization, supporting decision-making processes, and improving the organization’s image as modern. In enterprises with foreign capital participation, the benefits are assessed as sufficiently significant, including increased service availability and additional positive effects not specified in the study but observable in practice.
Business sector	In the agricultural and mining sectors, the benefits of implementing AI are assessed as moderate. Cost reduction, improved process quality, and service customization to meet specific customer needs are observed, but their significance is considered limited. In the industrial sector, artificial intelligence is perceived as a tool that enables greater enterprise accessibility, streamlines decision-making processes, and personalizes offerings. In the service sector, particular emphasis is placed on the ability to tailor services to individual customer needs and improve decision-making processes. Differences in emphasis occur across industries—for example, in construction, economic and environmental benefits are considered particularly important, while in transportation, speed of process execution and error minimization are highlighted.
Duration of company’s operation	In firms operating for less than two years, the benefits are assessed as moderate. Improvements in image, greater service availability, and enhanced organizational efficiency are noted; however, shortening working time is not perceived as a significant factor. In enterprises operating for two to four years, the benefits of AI adoption are clearly positive and include faster task execution, error reduction, greater personalization, and a favorable impact on image. In companies with seven to ten years of experience, respondents consider additional, unspecified benefits to be insignificant, which may indicate process stability but also a more conservative approach to change.
Number of employees	In medium-sized enterprises (50–249 employees), the benefits of implementing artificial intelligence are perceived as sufficiently significant, including increased accessibility, service personalization, and pro-environmental actions. At the same time, other values resulting from implementation, which were not included in the study, are also observed. A similar perception of benefits occurs in large companies employing more than 250 people, suggesting that larger organizations possess both the awareness and infrastructure necessary for the effective use of advanced technologies.

and

Table 5. Summary of the analysis of concerns regarding the implementation of artificial intelligence technologies (own elaboration).

Criterion	Description
Market status	In enterprises operating in the local market, the most significant concerns are related primarily to employee resistance stemming from the risk of job loss, as well as issues not included in the questionnaire. For companies functioning in the regional market, ethical dilemmas and additional unspecified factors are perceived as of low importance. In contrast, enterprises operating in the international market report a broad range of concerns, including limited access to knowledge and technology, the need for training, the complexity of decision-making processes and lengthy tender procedures, as well as employee resistance to process automation.
Type of ownership	In state-owned enterprises, a very high level of concern is observed, focusing on limited access to knowledge and technology, high implementation and operating costs, complex decision-making and tender procedures, difficulties in obtaining financing, ethical issues, and threats related to data security. Similar concerns occur in municipal entities, where employee resistance and the risk of errors and excessive dependence on technology are additionally emphasized. In the private sector, the most frequently indicated issues include limitations in access to knowledge and technology, the need for training, implementation procedures, employee resistance, and concerns about privacy and technology reliability. In companies and cooperatives, problems related to training and the complexity of procedures dominate, whereas in foreign enterprises, the main concerns are data security and technology reliability.
Business sector	In the agriculture and extraction sectors, key concerns relate to technology operating costs and the need for training, as well as the complexity of procedures and employee resistance. In the service sector, the most important issues are the risk of job loss due to automation and concerns not specified in the questionnaire. In the mining, hospitality, and public health sectors, there is a high level of concern regarding access to knowledge and technology, implementation and maintenance costs, administrative procedures, financing, ethical issues, and data security. In manufacturing companies, ethical issues and unspecified concerns are assessed as of low importance. In the education sector, concerns about technology purchase costs and privacy threats are particularly strong. In trade and transport, employee resistance plays a key role.
Duration of company’s operation	Companies operating for less than two years are primarily concerned about the lack of knowledge and access to technology. Enterprises functioning for five to six years emphasize the very high costs of implementing and maintaining artificial intelligence. Companies with seven to ten years of experience point to concerns not indicated in the survey form, which may suggest more individualized implementation barriers. In contrast, businesses operating for more than ten years mainly highlight the risk of errors and dependence on technology.
Number of employees	In companies employing fewer than ten people, the primary concern is employee resistance and additional, unspecified worries. In enterprises with ten to forty-nine employees, there is a wide range of concerns, including lack of knowledge, limited access to technology, the need for training, complex tender procedures, employee resistance, data security concerns, and the risk of errors. In medium-sized companies employing fifty to two hundred forty-nine people, lack of knowledge about technology is assessed as a minor concern, suggesting that these firms have developed internal competencies enabling the implementation of AI-based solutions.

. It should be emphasized that differences in the perception of benefits and concerns related to AI implementation indicate the need for an individualized approach depending on organizational context, market, and company size.

The research results show that representatives of companies from West Pomeranian Voivodeship highly rated the shorter task completion time, as well as error and risk reduction, among the benefits related to AI implementation. The same viewpoint was expressed by Kirova and Boneva [29] who stated that reducing time and money by automating routine processes and tasks are seen as positives of AI by Bulgarian business representatives.

In turn, among the concerns related to AI adoption in companies, respondents highly assessed possibility of errors, dependence on technology, privacy and data security risks, and high operational costs, including training. Some of these aspects were also highlighted by Barsekh-Onji et al. [43] as challenges while adopting AI.

Selected recommendations for improvement of AI implementation in enterprises are presented in

Table 6. Selected recommendations for improvement AI implementation in enterprises (own elaboration).

Criterion	Description
Market status	Local market enterprises should focus on change management strategies aimed at reducing employee resistance, particularly by emphasizing job transformation rather than job loss. Clear communication and employee involvement in early stages of AI implementation are recommended. Regional market companies may benefit from developing ethical guidelines and monitoring mechanisms, even if these issues are currently perceived as less significant, to prevent future risks. International market enterprises should prioritize investments in knowledge acquisition and technology transfer, supported by structured training programs. Simplifying decision-making and tender procedures, as well as adopting standardized implementation frameworks, could reduce complexity and delays. Addressing employee resistance through reskilling initiatives is also essential.
Type of ownership	State-owned enterprises should streamline decision-making and tender procedures and establish centralized support units for AI implementation. Increased access to financing mechanisms and partnerships with research institutions could mitigate knowledge and cost barriers. Strengthening cybersecurity policies and ethical oversight frameworks is strongly recommended. Municipal entities should complement technological investments with targeted training programs and initiatives aimed at reducing employee resistance. Risk management systems should be introduced to limit errors and excessive dependence on technology. Private enterprises are advised to invest in continuous training and knowledge-sharing platforms while ensuring robust data protection and system reliability. Transparent implementation procedures can help reduce uncertainty and resistance. Companies and cooperatives should simplify implementation procedures and focus on developing internal training capabilities to build long-term competencies. Foreign-owned enterprises should prioritize advanced data security solutions and system reliability testing to address their key concerns.
Selected business sectors	Agriculture and extraction sectors should seek financial support or subsidies to offset operating costs and invest in practical, sector-specific training programs. Simplification of procedures and participatory implementation approaches may reduce employee resistance. Service sector organizations should address fears of job loss by promoting human–AI collaboration models and redefining job roles rather than eliminating positions. Mining, hospitality, and public health sectors require comprehensive support strategies, including access to expert knowledge, financial instruments, ethical guidelines, and strong data security frameworks. Manufacturing companies should maintain their current approach while monitoring ethical and emerging concerns to ensure long-term sustainability. Education sector institutions should prioritize cost-effective procurement strategies and strengthen data privacy protections to build trust among stakeholders. Trade and transport sectors should focus on change management and employee engagement programs to reduce resistance and improve acceptance of AI solutions.
Duration of company’s operation	Companies operating for less than two years should be supported through advisory services, incubators, and partnerships that improve access to knowledge and technology. Enterprises with five to six years of operation should conduct cost–benefit analyses and explore scalable or modular AI solutions to manage high implementation and maintenance costs. Companies operating for seven to ten years should adopt individualized implementation strategies, as their concerns suggest more specific, context-dependent barriers. Organizations with more than ten years of experience should focus on risk mitigation by introducing validation procedures, human oversight mechanisms, and contingency plans to reduce errors and overreliance on technology.
Number of employees	Micro-enterprises (fewer than ten employees) should emphasize transparent communication and basic training to reduce employee resistance and uncertainty. Small enterprises (10–49 employees) require comprehensive support, including training programs, simplified procedures, and guidance on data security and risk management. Medium-sized enterprises (50–249 employees) should leverage their existing internal competencies by focusing on advanced AI applications, process optimization, and strategic integration rather than basic knowledge acquisition.

.

It is also worth noting that the results of the study are limited to an analysis of the opinions of companies located in a single voivodeship. Another limitation is the relatively small number of respondents who participated in the pilot study. The difficulty in obtaining data for the analysis may have been caused by reluctance among company representatives to disclose detailed information about their operations.

These limitations highlight the need to continue research on a broader scale—both geographical and sectoral. Interregional comparisons and differences between micro, small, medium, and large enterprises will be considered in our future work. Sectoral differences and linking them to socio-structural factors, such as labor market characteristics, historical industrial legacies, or EU digital policy impacts will be discussed. Moreover, the contrast of responses coming from representatives of companies located in West Pomeranian Voivodeship with those from other Polish or EU regions will be taken into account. This would allow for a better understanding of how local economic conditions, the level of digitalization, and capital availability influence readiness to implement AI-based solutions.

Authors further research will focus on identifying specific barriers and strategies that could support effective AI implementation while minimizing business concerns. Qualitative interviews and analysis of focus groups behavior will be considered. Detailed analysis of concerns and benefits (e.g., economic or operational benefits, ethical concerns and other) and their validation using factor analysis will be carried out. Additionally, future research directions will involve analyzing the relationships within the collected data using other econometric methods, as well as developing a scientific model, framework for metrics, and examining theoretical frameworks, such as digital transformation capability models, socio-technical information systems, and man–machine interaction involving AI.

The findings may be of interest to enterprises considering AI adoption. The research results may be used by companies while developing strategies for AI implementation and policies that foster digital transformation and enhance competencies related to artificial intelligence. The identified differences in companies’ representatives’ perception of benefits and concerns, considering investigated criteria (market status, type of company ownership, business sector, duration of company’s activity, number of employees), may be useful in decision-making and searching for tailored solutions for AI adoption in enterprises. Moreover, the research results can serve as a reference point for regional authorities and institutions supporting digital innovation.