Public dissemination of information on Artificial Intelligence in companies. Design of measurement indicators

ABSTRACT

Introduction:  The dissemination of information about artificial intelligence stands as a fundamental pillar to generate trust and encourage the responsible adoption of this technology in the business field.

Objective:  The objective of this study is to design a tool that allows measuring and comparing behaviors regarding the dissemination of information about artificial intelligence that companies offer through their websites.

Methodology:  To this end, an exploratory study has been carried out on all types of open access documents published in the period 2021-2025, in the area of Business Economics, in the WOS database on information related to different parameters of artificial intelligence.

Results:  Therefore, a set of fifty indicators has been established, distributed as follows among the proposed parameters: seven for machine learning, nine for employment, eight for reliability, fourteen for innovation, five for transparency, and seven for sustainability.

Conclusion:  It is concluded that this study provides a valuable tool for understanding and improving the way companies communicate about artificial intelligence, which is essential for building trust and promoting its responsible use.

KEYWORDS:
Disclosure of Information; Artificial intelligence; Machine learning; Reliability

RESUMEN

Introducción:  La difusión de información sobre inteligencia artificial se erige como un pilar fundamental para construir confianza y fomentar la adopción responsable de esta tecnología en el ámbito empresarial.

Objetivo:  El objetivo de este estudio consiste en el diseño de una herramienta que permita medir y comparar los comportamientos sobre difusión de información sobre inteligencia artificial que las empresas ofrecen a través de sus sitios web.

Metodología:  Para ello, se ha llevado a cabo un estudio exploratorio sobre todo tipo de documentos de acceso abierto publicados en el periodo 2021-2025, en el área de business economics, en la base de datos WOS sobre la información relativa a diferentes parámetros de la inteligencia artificial.

Resultados:  Por tanto, se ha establecido un conjunto de cincuenta indicadores, distribuidos del siguiente modo entre los parámetros propuestos: siete para aprendizaje automático, nueve para empleo, ocho para fiabilidad, catorce para innovación, cinco para transparencia y siete para sostenibilidad.

Conclusión:  Se concluye que este estudio proporciona una valiosa herramienta para comprender y mejorar la forma en que las empresas comunican sobre la inteligencia artificial, lo que es esencial para construir confianza y promover su uso responsable.

PALABRAS CLAVE:
Divulgación de información; Inteligencia artificial; Aprendizaje automático; Fiabilidad

1 INTRODUCTION

In recent years, voluntary disclosure of information has experienced a significant boom, due to the benefits it brings and the growing demand for transparency (Domínguez; Álvarez; Sánchez, 2010). In its early days, the dissemination of strategic information focused solely on communicating the corporate mission (Campbell; Shrives; Bohmbach‐Saager, 2001). However, disclosure has evolved to cover a wider range of topics, including corporate strategy, corporate governance, corporate governance policies, and adherence to good governance codes, among others (Morales Parada; Jarne Jarne, 2022). In addition, the disclosure of sustainability information is a tool that companies use to demonstrate their commitment to a sustainable future and their social responsibility (García-Benau; Bollas-Araya; Sierra-García, 2022). Likewise, investors' need for quality sustainability information that allows them to assess companies' social and environmental performance has prompted companies to improve their disclosure practices (Martín Zamora et al., 2019). On the other hand, given that disclosure on environmental, social, and governance (ESG) factors influences the cost of capital, it is logical to observe growing interest from stakeholders(Tsang; Frost; Cao, 2023). At the same time, there has been a shift in the medium of dissemination, moving from traditional annual reports to corporate websites (Domínguez; Álvarez; Sánchez, 2010), perhaps due to the high effectiveness of this channel as a tool for communicating the company's corporate reputation (Morales Parada; Jarne Jarne, 2022).

On the other hand, today we are witnessing exponential technological progress, marked by rapid advances in hardware and software and by the convergence of various Technologies (Henriques; Pereira, 2024). As a result, a radical transformation in business models has emerged, characterized by the integration of digital technologies in all areas of business, a phenomenon known as “digital transformation.” This has revolutionized working models, enabling offshoring and remote working, unlike in the last century, when data storage was limited and expensive (D’Almeida et al., 2022). Likewise, artificial intelligence (hereinafter AI) has emerged as the driving force behind digital transformation, marking a new milestone in this fourth industrial revolution. This refers to the design of algorithms and computational models that enable machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem solving (Zailani et al., 2017). The term “AI,” which combines human creation with the capacity for independent reasoning, still lacks a universally accepted definition (Henriques; Pereira, 2024). It is also a rapidly growing field of study that is expanding its presence into many businesses and areas of research.

AI is an essential factor in business profitability, which is why companies are increasingly turning to this tool to reap its benefits (Enholm et al., 2022). Thanks to AI and 4.0 technologies, companies can optimize their processes, improve the efficiency of their teams, and obtain real-time information that allows them to anticipate potential problems and reduce downtime (Martínez et al., 2024). This intelligent system is also radically transforming business management, forcing companies to rethink their innovation strategies to adapt to this new technological environment (Haefner et al., 2021). Furthermore, the rapid implementation of this technology in personnel management is creating injustices that demotivate employees and increase turnover (Robert et al., 2020). According to the Spanish Association of Accounting and Business Administration (AECA) (2024), the growing implementation of these intelligent algorithms has created a need to ensure their reliability and transparency. Therefore, companies must demonstrate to the outside world that their algorithms are fair and impartial in order to generate confidence in solutions based on this technology.

Given this need for transparency, many companies are already disclosing sustainability information due to pressure from investors to have this information and Directive 2022/2464/EU. These companies disclose information on various non-financial matters, such as social responsibility (Pache Durán et al., 2022), digitization policy (Arias-Abelaira; PacheDuran; Rodriguez-Ariza, 2023) and ESG factors (Briozzo, 2024); but there is a gap in the information disclosed regarding the intelligent algorithms implemented in companies. For this reason, the following research question arises: do companies disclose quality information about AI on their websites?

Due to the lack of previous studies and based on the analysis carried out by AriasAbelaira et al. (2023) on the disclosure of information on digitalization, in which AI is considered a parameter of this, we have decided to address this knowledge gap by developing an index that allows us to evaluate the effectiveness of companies' disclosure actions on AI through their websites. The aim is to create a tool that enables companies to improve their reputation, strengthen stakeholder confidence, and position themselves as leaders in the field of ethics and responsibility in AI. Therefore, the objective of this study is to design a tool that measures and compares the behavior of companies in terms of the dissemination of information on AI through their websites. To this end, the study is structured as follows. Following this introduction, a review of the literature on the disclosure of information on sustainability and AI is carried out. Next, the methodology used is presented. Finally, the results and conclusions obtained are discussed.

2 LITERATURE REVIEW

2.1 Disclosure of information on sustainability

The disclosure of information is a strategic issue that companies must address carefully, considering the potential benefits and risks involved (Domínguez; Álvarez; Sánchez, 2010). Some of the advantages of disseminating information include improving the company's image, increasing investor confidence, and increasing the liquidity of securities. Despite its advantages, the generation and disclosure of information entails costs that, in certain circumstances, may outweigh the benefits obtained (Gray; Radebaugh; Roberts, 1990). In this sense, the dissemination of sustainability information is a mechanism through which companies communicate their commitment to sustainability and social responsibility, thereby promoting transparency and accountability (García-Benau et al., 2022). Likewise, corporate information, both financial and non-financial, helps investors understand management decisions, reduces information asymmetries, increases capital market confidence, and encourages greater foreign investment (Martínez‐Ferrero; Ruiz‐Cano; García‐Sánchez, 2016).

As mentioned above, companies disclose information on various non-financial matters, such as social responsibility, digitalization policy, ESG factors, or, in line with the objective of this study, AI. Furthermore, with Directive 2022/2464, the disclosure of sustainability information by organizations has become increasingly common (European Parliament and Council of the European Union, 2022). Currently, the government has passed a law that promotes transparency and accountability in companies, improving the quality and verification of sustainability information and updating the criteria for company size to simplify their obligations. In this regard, Directive 2022/2464 expands and deepens sustainability information, which is crucial for reliability and transparency verified by auditors (Ministry of Economic Affairs and Digital Transformation, 2024).

However, more and more studies highlight the fundamental role of the Internet as a means of communication between companies and anyone interested in their performance (Alonso Almeida, 2009). Communication and dialogue foster mutual trust by recognizing the integrity of the other (Bolton; Katok; Ockenfels, 2004). In this sense, transparency becomes a key factor, as trust increases as the company shares not only financial information, but also information relevant to all its stakeholders (Alonso Almeida, 2009). According to the Dictionary of the Spanish Language, the term transparent is defined as “the body through which objects can be seen clearly.” When transferring this concept to the business world, transparency refers to the participation of managers in the management of the company, decision-making processes, and the accurate disclosure of its financial situation. In this context, transparency is equivalent to information; likewise, it is essential to disclose the actions that management and the board of directors are carrying out, as well as the results obtained (Muñoz Paredes, 2005). Although communicating this information to the market used to be a complicated and costly task, the internet has greatly simplified this process, allowing boards of directors to share it quickly and efficiently on a global scale (Alonso Almeida, 2009).

2.2 Artificial intelligence

Over the years and with the advancement of new technologies, AI has become immensely valuable, evolving into an indispensable tool (Tobarra et al., 2021). It has also established itself as a booming field of study, extending its influence to a wide range of disciplines and businesses (Shaik et al., 2022). AI is one of the main technologies to emerge from the digital transformation (Wang et al., 2023). In just a few years, AI has driven a technological revolution with exponential growth and transformative capacity, but its adoption and development depend fundamentally on reliability and transparency (Ministry for Digital Transformation and Public Service, 2024).

Thanks to AI, simulation for intelligent diagnostics has reached a new level of complexity, enabling accurate, real-time management of industrial processes (Li, 2022). In addition, this tool has radically transformed several industries, offering greater efficiency, improved decision-making, better customer experiences, cost reductions, and competitive advantages (Masoodifar; Arslan; Tekeoğlu, 2023). Therefore, the objective of this system is to optimize processes, reducing costs, improving the quality of products and services, and facilitating decision-making based on accurate and timely data(Jan et al., 2023). Likewise, the convergence of Industry 4.0 and AI is revolutionizing industrial processes, accelerating the adoption of digital technologies (Vergara Villegas et al., 2021).

According to Rajesh et al. (2023), one branch of AI is machine learning, which enables machines to improve their performance in specific tasks such as optimizing production processes. In addition, Regulation (EU) 2024/1689 of the European Parliament and of the Council of June 13, 2024, laying down harmonized rules on AI, addresses the functioning of the internal market by establishing a uniform legal framework, specifically for the development, placing on the market, putting into service, and use of AI systems in the European Union (European Parliament; Council of the European Union, 2024). This regulation seeks to leverage AI to improve labor efficiency while mitigating its risks in processes such as hiring. Its objective is to promote the innovation of reliable, human-centered systems, ensuring citizen protection and fundamental rights. On the other hand, the Artificial Intelligence Strategy 2024, published in May 2024, reinforces Spain's position at the forefront of technology and promotes the use of Spanish and co-official languages in AI systems, favoring their adoption in the Administration and in the business sector (Ministry for Digital Transformation and Public Service, 2024). This strategy prioritizes social consensus on the use of AI to lead in its ethical, transparent, and reliable development, emphasizing sustainability for the responsible use of resources.

In line with the above, the design of the index for this study takes into account a number of dimensions, such as machine learning, boosting employment, ensuring reliability, establishing measures to support innovation, requiring transparency with regard to high-risk AI systems, and using these systems in a sustainable manner. These dimensions were selected because they reflect the most relevant aspects of AI in the current context, as established in Regulation 2024/1689 and the Artificial Intelligence Strategy 2024.

With this in mind, AI is a tool that can revolutionize business processes. It is capable of combining machine learning with job creation, reliability, innovation, transparency, and sustainability. In addition, AI covers one of the sections of the framework for the disclosure of information on Corporate Digital Responsibility (hereinafter, CDR) (AECA, 2024). According to the opinion issued by the AECA (2022), CDR involves the legal and ethical use of data and digital technologies to protect people's rights and establish a bond of trust in relation to the usefulness, security, and efficiency of the technology used. Furthermore, like Regulation 2024/1689 and the Artificial Intelligence Strategy 2024, which establish the importance of reliability in the development and use of AI, the framework for the dissemination of information on CDR seeks to provide guidelines for companies to effectively report on their responsibility in the use of reliable AI (AECA, 2024). The need to communicate the reliability of the AI used is crucial to building trust. Therefore, companies need to show that algorithms and data are used responsibly and impartially (AECA, 2024). Open and honest communication can strengthen the relationship between companies and consumers, building trust and loyalty. In this context, it is essential to develop specific indicators that enable institutions to measure and manage the risks associated with AI implementation, as well as assess its contribution to innovation and operational efficiency.

3 METHODOLOGY

The objective of this study is to design a tool that allows for the measurement and comparison of companies' behaviors regarding the dissemination of information about AI on their websites. The main goal will be to promote greater transparency, encourage best practices, and contribute to the development of more reliable and responsible AI. To this end, an exploratory study has been carried out on all types of open access documents published in the period 2021-2025 in the field of business economics in the Web of Science (WOS) database on information relating to different AI parameters: (1) Machine learning, (2) Employment, (3) Reliability, (4) Innovation, (5) Transparency, and (6) Sustainability; these are milestones derived from the extensive precursor literature (Bankins; Formosa, 2023; Kuzior; Sira; Brożek, 2023; Pai et al., 2022), in addition to being addressed in the European Union Regulation drafted by the European Parliament & Council of the European Union (2024) and in the Artificial Intelligence Strategy 2024 developed by the Ministry for Digital Transformation and Public Service (2024). Furthermore, these criteria for identifying documents were selected based on the need to ensure that they are closely linked to the field of business economics, reflect the latest research trends, and, finally, are open access. WOS was chosen because it is a global database that indexes the most rigorous and internationally cited academic publications (Gong et al., 2019). By focusing on this recent period, we ensure that our results are current and reflect the latest trends in this field.

Table 1 below details the results obtained from the search for each parameter and the number of final documents selected after applying exclusion criteria (documents not directly related to the topic).

Table 1
Results of the literature Search process

Parameter	Search equation	Initial documents	Final documents
Machine learning	“machine learning” AND “Artificial intelligence” AND “Business” AND “Deep learning”	465	153
Employment	“Employment” AND “Artificial intelligence” AND “Business”	59	29
Reliability	“Reliability” AND “Artificial intelligence” AND “Business”	86	18
Innovation	“Innovation” AND “Artificial intelligence” AND “Business”	557	141
Transparency	“Transparency” AND “Artificial intelligence” AND “Business”	74	16
Sustainability	“Sustainability” AND “Artificial intelligence” AND “Business”	248	65
TOTAL

• Source: Prepared by the authors

4 RESULTS

In order to carry out the analysis, a measurement index was constructed to evaluate the transparency of companies in their AI practices. The aim is to provide a tool that allows them to improve their reputation, strengthen the trust of their stakeholders, and position themselves as leaders in the field of ethics and responsibility in AI. The development of indices is one of the central techniques of content analysis, allowing for an in-depth study of corporate data (Ortiz; Clavel, 2006). Diffusion indices are one of the most relevant instruments for measuring the transparency of information provided by companies in a specific sector or country (Bonsón; Escobar, 2004; García Meca; Martínez Conesa, 2004). In order to construct the index for our study, we have based ourselves on methodologies used in the construction of indices in other fields, such as digitization (Arias-Abelaira; Pache-Duran; Rodriguez-Ariza, 2023) nd social responsibility (Nevado Gil; Gallardo Vázquez; Sánchez Hernández, 2013), among others. The purpose of the index is to verify whether or not the following indicators are disclosed by companies' websites regarding machine learning, job creation, reliability, innovation, transparency, and sustainability in the use of AI.

4.1 Machine learning

For the parameter “Machine learning,” a search was conducted in the WOS database for the concepts: “Machine learning,” “Artificial intelligence,” and “Business.” The search equation was refined for all types of open access documents from the main collection for the years 2021-2025 in the area of business economics. Given that the search yielded a large number of documents, it was decided to include the term “deep learning.” If we break down machine learning, deep learning is a current and exciting field of machine learning, i.e., it is the supervised machine learning model with the best performance and lowest investment (Dargan et al., 2020). Of the 465 studies obtained, those that did not address the four concepts above, and were therefore not related to machine learning and artificial intelligence in companies, were discarded. Therefore, the analysis to obtain the indicators was carried out with 153 articles. Given the large number of articles from which we obtained the indicators, we selected the most relevant ones to justify them, opting for a total of 22 articles. This selection was based on criteria of quality and relevance, prioritizing those articles published in high-impact and prestigious journals in the field of study. The choice of high-level sources guarantees the robustness and validity of the selected indicators. After this exhaustive review of the literature, seven key indicators were selected to evaluate machine learning with AI in companies. These indicators were selected based on their theoretical relevance, their measurability, and their frequent use in previous research. They will now be subject to a detailed justification.

Currently, AI relies heavily on machine learning and, in particular, deep learning, which raises important ethical and governance considerations that must be addressed by companies (Camilleri, 2024; Janiesch; Zschech; Heinrich, 2021). AI, through machine learning, is also revolutionizing business marketing by generating strategic insights that optimize customer understanding and opportunity identification (Birim et al., 2024; Zhu, 2022). Furthermore, deep learning makes it possible to unravel the complexity of financial data, optimizing investment strategies (Chen; Ye; Huang, 2021; Liu et al., 2024). Consequently, the following items are proposed: (A1) “Reports on the introduction of machine learning models into marketing” and (A2) “Reports on solving financial problems through machine learning”.

Machine learning is essential for detecting anomalies in user behavior and preventing insider threats (Abd-Alhalem et al., 2024; Wang; Sun; Zhou, 2023) Furthermore, given the complexity of business relationships, machine learning provides sophisticated tools for evaluating and assessing strategic aliances (Lee et al., 2023; Li; Liu; Wang, 2022). In addition, this technology allows for a more accurate and personalized assessment of working conditions (Fan et al., 2023; Zhang; Zhang, 2023). The following items are proposed: (A3) “Reports on the detection of internal threats through machine learning”, (A4) “Reports on companies' business alliances through machine learning” and (A5) “Reports on the use of machine learning models for the assessment of working conditions”.

According to Gholami et al. (2023), machine learning enhances business intelligence, enabling more accurate and efficient decision-making. It also empowers organizations to make smarter, data-driven decisions (Khneyzer; Boustany; Dagher, 2024). In addition, machine learning models improve decision-making by facilitating data analysis for efficient management, sustainable innovation, and market adaptation, although they require consideration of ethical implications and the impact on the perception of internal company stakeholders (Gandía et al., 2025). Consequently, the following item is proposed: (A6) “Reports on the use of machine learning to achieve intelligent decision-making”.

On the other hand, machine learning allows for more accurate predictions of financial market trends by analyzing sentiment in corporate disclosures (Eachempati et al., 2021; Hu; Zhao; Khushi, 2021). It also allows for the prediction of future events in business processes, such as corporate bankruptcy, improving decision-making and risk management (Lombardo et al., 2022). Similarly, it improves the accuracy of predictions in various business areas, from logistics to sales (Ashraf et al., 2024; Belter; Hering; Weichbroth, 2023). The following item is proposed: (A7) “Reports on predictions related to the company through machine learning”.

The objective of these items is to analyze whether companies are transparent in the application of machine learning in various areas to solve problems, improve efficiency, and generate predictions.

4.2 Employment

For the parameter “Employment,” a search was conducted in the WOS database for the concepts: “Employment,” “Artificial intelligence,” and “Business.” The search equation was refined for all types of open access documents from the main collection for the years 20212025 in the field of business economics. Of the 59 studies obtained, those that were not related to AI employment in companies were discarded, so the analysis to obtain the indicators was carried out with 29 records. However, to complete and enrich the search for indicators for this parameter, other documents related to artificial intelligence in companies were used, such as the study by Arias-Abelaira et al. (2023), which uses AI as a parameter. After a thorough review of the literature, nine key indicators were selected to assess the transparency of AI employment in companies. These indicators were selected based on their theoretical relevance, their measurability, and their frequent use in previous research. They will be justified in detail below.

The rapid advancement of AI is radically transforming the labor market, automating tasks and displacing workers in various sectors, raising concerns about the future of employment and increasing inequality (Budhwar et al., 2023; Chen et al., 2024; Chen, 2023; Georgieff; Hyee, 2022; Jazdauskaite et al., 2021; Khalifa; Abd Elghany; Abd Elghany, 2021; Merola, 2022). In addition, significant advances in AI have raised questions about its potential to replace labor (Arias-Abelaira; Pache-Duran; Rodriguez-Ariza, 2023). Beyond automation, AI is also driving the creation of new jobs. Technological innovation and economic growth generated by AI are demanding specialized professional profiles, reshaping the labor market (Budhwar et al., 2023; Khogali; Mekid, 2023; Lazaroiu et al., 2024; Oncioiu et al., 2022). Consequently, the following two items are proposed: (E1) “Reports on staff replacement due to the increasing implementation of AI” and (E2) “Reports on the creation of new markets and/or employment opportunities due to the development of AI technology”.

AI also offers great potential for increasing business productivity and competitiveness. However, its impact on employment is complex and generates uncertainty (Ashrafi et al., 2023; Drydakis, 2024; Khalifa; Abd Elghany; Abd Elghany, 2021; Lazaroiu et al., 2024; Rožman; Oreški; Tominc, 2023). In addition, AI requires a highly skilled workforce. Education and training must evolve to equip people with the skills they need to thrive in an increasingly digitized and automated work environment (Giordano et al., 2024; Grădinaru et al., 2024; Semtner; Dzator; Nadolny, 2024; Uren; Edwards, 2023). The following two items are proposed: (E3) “Reports on the impact of AI on the company's work environment, including productivity, performance, employment, and/or skills” and (E4) “Reports on the comprehensive preparation required of workers for the implementation of AI”.

By implementing AI models, companies can more accurately assess employee job satisfaction and adjust their programs to train professionals who are in high demand in the industry (Jia, 2022; Zhang; Zheng, 2022). Likewise, effective adoption of AI can significantly improve employee well-being by reducing the workload of repetitive tasks and optimizing work processes (Yu; Xu; Ashton, 2023). Consequently, the following two items are proposed: (E5) “Reports on AI-assisted job satisfaction” and (E6) “Reports on improving the employee work experience with the adoption of AI”.

On the other hand, to fully leverage the potential of AI in talent management, managers need to be provided with tools and guidelines that enable them to interpret the results of turnover prediction models and make effective decisions (Chowdhury et al., 2023). In addition, AI is radically transforming the socio-technical systems of organizations. This technology is redefining work processes, organizational structures, and the skills required to succeed in the labor market (Moniz; Candeias; Boavida, 2022; Yu; Xu; Ashton, 2023). Likewise, the potential of AI to transform human resources processes is enormous. Through AI-based decision-making support platforms, organizations can optimize talent management and improve operational efficiency (Hajnić; Boshkoska, 2021). Consequently, the following items are proposed: (E7) “Reports on guidelines and training programs for HR managers on the use of AI”, (E8) “Reports on how AI is changing work practices and the organization of the company” and (E9) “Reports on the implementation of AI in the company's human resource processes”.

The purpose of these items is to analyze the disclosure of corporate information on the impact of AI on employment, workplace adaptation, employee well-being, and best practices for optimizing results.

4.13 Reliability

For the “Reliability” parameter, a search was conducted in the WOS database for the concepts: “Reliability,” “Artificial intelligence,” and “Business.” The search equation was refined for all types of open access documents from the main collection for the years 2021-2025 in the field of business economics. Of the 86 studies obtained, those that did not address the three initial concepts and were therefore not related to the reliability of artificial intelligence in companies were discarded. Therefore, the analysis to obtain the indicators was carried out with 18 articles. However, to complete and enrich the search for indicators for this parameter, the opinion issued by AECA (2024) was used, which details the relevance of AI reliability. After a thorough review of the literature, eight key indicators were selected to assess the reliability of AI in companies. These indicators were selected based on their theoretical relevance, their measurability, and their frequent use in previous research. They will now be justified in detail.

Characterized by its reliability, fairness, and transparency, AI is essential for realizing its full potential without compromising security (Alzubaidi et al., 2023). Recent studies have revealed a strong correlation between the implementation of AI in accounting and an increase in the reliability of results, which, coupled with the use of reliability tests in certain sectors, such as banking, suggests that AI, when used appropriately and subjected to rigorous evaluation, can be highly reliable (Fan et al., 2022; Мохаммед; Ваххаб, 2024). In addition, rigorous reliability and normality analyses performed on AI-managed data corroborate its accuracy and reliability, supporting its use in critical applications (Ajah et al., 2024; Almarashda et al., 2021; Xiong, 2022). Likewise, the results obtained in several studies confirm the reliability of AI in managing complex data and personalizing services, thus contributing to greater customer satisfaction (Alshibly et al., 2024; Chen, 2022; Latorre-Biel et al., 2021). Consequently, the following item is proposed: (F1) “Report on whether AI has a certain degree of reliability”. In addition, AI has enabled the development of a highly reliable system to improve decision-making support systems in economic management (Zhao, 2022). Similarly, the accuracy and reliability of AI-generated recommendations are crucial for informed decision-making (Akdemir; Bulut, 2024; Fonseca et al., 2024; Kumarappan et al., 2024). The following item is proposed: (F2) “Reports on the reliability of the AI-based decision-making support system for economic management”. On the other hand, ChatGPT, as an example of AI, offers interesting opportunities for business education, but its implementation must be careful, considering aspects such as reliability and the need for critical thinking (Vecchiarini; Somià, 2023). Consequently, the following item is proposed: (F3) “Reports on the reliability of using ChatGPT in business education”.

According to the opinion issued by the AECA (2024), it is essential to communicate clearly and effectively the reliability of AI solutions in order to gain user trust. In addition, it suggests possible content to be disseminated so that organizations can demonstrate their commitment to reliable AI, such as objectives and measures, policies, alignment with legal requirements and standards, such as ethical guidelines for reliable AI, governing bodies, and roles in the execution and supervision of AI development. According to Suárez Giri & Sánchez Chaparro (2024), to ensure the reliability of AI, it is essential to implement measures that promote transparency and bias mitigation, especially in sensitive areas where objectivity is crucial. Likewise, the growing impact of AI requires robust policies that guarantee the reliability and transparency of systems, mitigating risks such as algorithmic biases and unpredictable results (Dahal et al., 2023). Furthermore, the creation and maintenance of AI models require proactive management involving specialized roles in the technical and ethical direction of these projects, thus ensuring reliability and compliance with quality standards (Talukder et al., 2024). Taking into account the above information, the following items are proposed: (F4) “Reports on the objectives and measures to ensure reliable AI”, (F5) “Reports on policies to ensure reliable AI”, (F6) “Reports on ethical guidelines for reliable AI”, (F7) “Reports on the governing bodies responsible for overseeing the responsible development of AI” and (F8) “Reports on the roles in the implementation and oversight of AI development”.

These items aim to disseminate information about the reliability of AI regarding its data, algorithms, limitations, and results, as well as corporate commitment.

4.4 Innovation

For the “Innovation” parameter, a search was conducted in the WOS database for the concepts: “Innovation,” “Artificial intelligence,” and “Business.” The search equation was refined for all types of open access documents, from the main collection, from the years 20212025, and in the area of business economics. Of the 557 studies obtained, those not related to AI innovation in companies were discarded, so the analysis to obtain the items was carried out with 141 articles. As this was such a large number from which we obtained the indicators, we selected the most relevant ones to justify the indicators, opting for a total of 32 articles. This selection was based on criteria of quality and relevance, prioritizing those articles published in high-impact and prestigious journals in the field of study. The choice of high-level sources ensures the robustness and validity of the selected indicators. After this exhaustive review of the literature, 14 key indicators were selected to evaluate AI innovation in companies. These indicators were selected based on their theoretical relevance, their measurability, and their frequent use in previous research. They will now be subject to a detailed justification.

Investment in AI is essential for companies to innovate their business models in the digital age (Bustamante; Pérez; Del Pilar Escott-Mota, 2024; Jorzik et al., 2023; Naeem; Kohtamäki; Parida, 2024). This tool is transforming open innovation by facilitating external collaboration and optimizing process management to drive new business models (Bécue; Gama; Brito, 2024; Kuzior; Sira; Brożek, 2023). AI is also revolutionizing business management by driving innovation, optimizing operations, and improving decision-making (Campbell; Jovanović, 2024; Cui; Xu; Razzaq, 2022; Haefner et al., 2021). Consequently, the following items are proposed: (I1) “Reports on investment in AI with the intention of innovating its business models”, (I2) “Reports on the use of AI in open innovation management” and (I3) “Reports on whether AI has brought innovation to the management of the company”.

In addition, AI drives innovation management by facilitating cross-border knowledge and optimizing the identification of key factors (Hussain, 2023; Liu, 2022). This tool drives technological innovation by improving the efficiency, capacity, and competitiveness of companies (Gudigantala; Madhavaram; Bicen, 2023; Li; Wang; Wang, 2024; Liu et al., 2024). It is also revolutionizing the supply chain by optimizing processes, streamlining operations, and offering new economic opportunities (Braganza et al., 2022; Olan et al., 2022). On the other hand, governments must create balanced regulatory frameworks that encourage innovation in AI while protecting ethics and consumers (Beulen; Plugge; Van Hillegersberg, 2022; Manta et al., 2024). The following items are proposed: (I4) “Reports on the factors influencing AI in knowledge innovation management”, (I5) “Reports on the use of AI for technological innovation”, (I6) “Reports on new AI innovations in the supply chain” and (I7) “Reports on government implications for promoting AI innovation”.

By reducing costs, AI drives business performance by fostering innovation and optimizing productivity (Dancausa Millán; Millán Vázquez de la Torre, 2024; Samadhiya et al., 2024). Furthermore, innovation in AI is transforming business decision-making, enabling deeper analysis and accurate predictions (Coussement et al., 2024; Rajagopal et al., 2022). Likewise, to ensure responsible AI innovation, informed public debate and regulatory frameworks that address ethical and transparency challenges are crucial (Buhmann; Fieseler, 2023; Xing et al., 2023). On the other hand, AI optimizes the innovation process, driving efficiency and enabling companies to redesign their strategies (Bahoo; Cucculelli; Qamar, 2023; Roberts; Candi, 2024). Consequently, the following items are proposed: (I8) “Reports on the use of AI to drive business performance in terms of innovation”, (I9) “Reports on the impact of AI-based innovation on decision-making”, (I10) “Reports on responsibility in AI innovation” and (I11) “Reports on the benefits that AI can confer on the innovation process”.

On the other hand, AI is transforming marketing by generating innovative strategies that revolutionize content production and optimize customer engagement (Awad, 2024; Islam et al., 2024). Furthermore, its adoption drives digital innovation in companies, especially through improved resilience and staff training (Saleem et al., 2023; Zeng; Li; Yousaf, 2022). Likewise, this tool facilitates sustainable innovation by identifying opportunities, optimizing processes, and enabling companies to adapt to environmental and market demands (Gandía et al., 2025; Govindan, 2022; Kolagar; Parida; Sjödin, 2024). The following items are proposed: (I12) “Reports on innovative marketing strategies with AI”, (I13) “Reports on AI adoption and links to digital innovation” and (I14) “Reports on the advantages of AI with sustainable innovation”.

These items aim to reveal whether the company is strategically adopting AI to drive business innovation in various areas, including responsible innovation and sustainability, and to assess the government's response.

4.5 Transparency

For the “Transparency” parameter, a search was conducted in the WOS database for the concepts: “Transparency,” “Artificial intelligence,” and “Business.” The search equation was refined for all types of open access documents from the main collection for the years 20212025 in the area of business economics. Of the 74 studies obtained, those that did not address the three initial concepts and were therefore not related to the transparency of artificial intelligence in companies were discarded. Therefore, the analysis to obtain the indicators was carried out with 16 articles. However, to complete and enrich the search for indicators for this parameter, the opinion issued by AECA (2024) was used, which details the relevance of AI transparency. After a thorough review of the literature, five key indicators were selected to assess AI transparency in companies. These indicators were selected based on their theoretical relevance, their measurability, and their frequent use in previous research. They will be justified in detail below.

One of the fundamental objectives for achieving transparency in AI is to guarantee the ethical use of data and promote clarity in decision-making (AECA, 2024). Likewise, the growing integration of AI in decision-making processes demands a transparent and ethical approach to data management, ensuring that AI systems are understandable and responsible in order to foster user trust (Booyse; Scheepers, 2024; Chowdhury et al., 2023; Grandi et al., 2024; Ryan et al., 2024). On the other hand, according to AECA (2024) , one of the possible contents to disclose so that the company can demonstrate its commitment to AI is to reveal specific information about high-impact algorithms. Furthermore, to ensure transparency and accountability in the use of AI, it is essential that organizations provide detailed information about the algorithms used, especially in areas such as pricing, where model transparency is crucial to building trust (Camilleri, 2024; Holmström; Hällgren, 2022; Xie, 2021). Consequently, the following items are proposed: (T1) “Report on transparency in AI decisionmaking” and (T2) “Report on the transparency of high-impact algorithms”.

On the other hand, the growing implementation of AI in investments demands a greater focus on the transparency of the models used, especially in areas such as price prediction, where transparency is crucial to ensure investor confidence (Hajek; Novotny, 2022; Sutiene et al., 2024). Likewise, the growing integration of this tool in the management of ESG factors demands a proactive approach to guarantee transparency and ethics in the development and use of these systems (Sklavos et al., 2024; Suárez Giri; Sánchez Chaparro, 2024). Furthermore, transparency is a fundamental pillar in the application of AI in supply chains, as it allows for identifying and mitigating risks, improving resilience, and strengthening consumer trust (Adamashvili; Zhizhilashvili; Tricase, 2024; Manning et al., 2022; Modgil; Singh; Hannibal, 2022; Singh; Modgil; Shore, 2024). Taking this information into account, the following items are proposed: (T3) "Reports on whether AI improves the transparency of the investment process", (T4) "Reports on whether they are using AI to improve their ESG performance and whether they are transparent about how they do so" and (T5) "Reports on the degree of transparency maintained and provided to all supply chain partners."

These indicators aim to assess how companies disclose information on the use of AI for the common good, promoting transparency in key areas such as investment and supply chains.

4.6 Sustainability

For the "Sustainability" parameter, a search was conducted in the WOS database for the concepts: "Sustainability," "Artificial Intelligence," and "Business." The search equation was refined for all types of open-access documents from the main collection, from the years 2021-2025, and in the area of business economics. Among the 248 studies obtained, those not related to the application of sustainability in artificial intelligence for businesses were discarded. Therefore, the analysis to obtain the items was carried out on 65 articles. Given the large number of indicators used to obtain the indicators, we selected the most relevant ones to justify the indicators, opting for a total of 24 articles. This selection was based on quality and relevance criteria, prioritizing articles published in high-impact and prestigious journals in the field of study. The choice of high-level sources guarantees the robustness and validity of the selected indicators. After this exhaustive literature review, seven key indicators were selected to assess sustainability with AI in companies. These indicators were chosen based on their theoretical relevance, their measurability, and their frequent use in previous research. A detailed justification is provided below.

AI is emerging as a key tool for driving corporate Sustainability (Balcıoğlu; Çelik; Altındağ, 2024; Sipola; Saunila; Ukko, 2023). Furthermore, this tool accelerates the transition towards a circular economy by facilitating the identification and promotion of products with sustainable characteristics (Roumeliotis; Tselikas; Nasiopoulos, 2023; Rusch; Schöggl; Baumgartner, 2023). Similarly, Bolesnikov et al. (2022) explore how fashion designers perceive AI as a tool for creating more sustainable collections. Consequently, the following item is posed: (S1) “Reports on the sustainable adoption of AI”. Furthermore, the growing importance of environmental sustainability demands that AI explore innovative and environmentally friendly business models (Gao; Liu, 2023; Jorzik et al., 2024). Moreover, AI is emerging as a strategic tool for companies seeking both environmental sustainability and talent retention (Ogbeibu et al., 2022; Sridhar et al., 2023). The following item is proposed: (S2) “Report on the growing importance of environmental sustainability with the adoption of AI”.

Similarly, AI is a key tool for driving business sustainability through more informed decision-making (Ali et al., 2024; Bickley; Macintyre; Torgler, 2024; Pisoni; Molnár, 2024). t is also a catalyst for sustainability in supply chains, optimizing resources and reducing environmental impact (Kazancoglu et al., 2023; Onyeaka et al., 2023; Remondino; Zanin, 2022; Zaid; Farooqi; Azmi, 2025). On the other hand, AI-driven automation is a driver of sustainability, optimizing processes and generating value for companies (Aydın; Turan, 2023; Rowan et al., 2022; Tairov et al., 2024). Taking this information into account, the following items are proposed: (S3) “Informs about the role of AI in decision-making for sustainability”, (S4) “Informs about the contribution of AI in improving supply chain management” and (S5) “Informs about sustainability in process automation with AI”.

On the other hand, AI is a transformative tool for achieving the Sustainable Development Goals, empowering companies to make more sustainable decisions (Felfernig et al., 2023; Fonseca et al., 2024; Peng et al., 2023). Furthermore, the integration of this tool into sustainability reporting represents an innovation that allows companies to gain deeper insight into their environmental and social performance (Boloș et al., 2024; Vărzaru, 2022). Considering the above, the following items are proposed: (S6) “Reports on the implications of AI for achieving various Sustainable Development Goals” and (S7) “Reports on AI for the sustainable analysis of reports”.

These indicators aim to assess how companies disclose information about their strategies and the quantifiable impact of AI on building a more sustainable future and contributing to the Sustainable Development Goals.

Table 2 below shows the indicators proposed above.

Table 2
Index on the dissemination of information on AI provided by companies

PROPOSED ITEMS
Axis 1: Machine Learning
A1: Reports on the introduction of machine learning models in marketing.	Adapted from Birim et al. (2024); Zhu (2022)
A2: Reports on solving financial problems through machine learning.	Adapted from X. Chen et al. (2021); Y. Liu et al. (2023)
A3: Reports on insider threat detection through machine learning.	Adapted from Abd-Alhalem et al. (2024); J. Wang et al. (2023)
A4: Reports on companies' business alliances through machine learning.	Adapted from D. Lee et al. (2023); Y. Li et al. (2022)
A5: Reports on the use of machine learning models for the assessment of working conditions.	Adapted from X. Fan et al. (2023); Q. Zhang & Zhang (2023)
A6: Reports on the use of machine learning to achieve intelligent decision making.	Adapted from Gandía et al. (2025); Gholami et al. (2023); Khneyzer et al. (2024)
A7: Reports on business-related predictions through machine learning.	Adapted from Ashraf et al. (2024); Belter et al. (2023); Eachempati et al. (2021); Hu et al. (2021); Lombardo et al. (2022)
Axis 2: Employment
E1: Reports on staff replacement due to the growing implementation of AI.	Adapted from Arias-Abelaira et al. (2023); Budhwar et al. (2023); K. Chen et al. (2024); Z. Chen (2023); Georgieff & Hyee (2022); Jazdauskaite et al. (2021); Khalifa et al. (2021); Merola (2022)
E2: Reports on the creation of new markets and job opportunities due to the development of AI technology.	Adapted from Budhwar et al. (2023); Khogali & Mekid (2023); Lazaroiu et al. (2024); Oncioiu et al. (2022)
E3: Reports on the impact of AI on the company's workplace, including productivity, performance, employment, and/or skills.	Adapted from Ashrafi et al. (2023); Drydakis (2024); Khalifa et al. (2021); Lazaroiu et al. (2024); Rožman et al. (2023)
E4: Reports on the comprehensive preparation required for workers to implement AI.	Adapted from Giordano et al. (2024); Grădinaru et al. (2024); Semtner et al. (2024); Uren & Edwards (2023)
E5: Report on job satisfaction assisted by AI.	Adapted from Jia (2022); H. Zhang & Zheng (2022)
E6: Report on improving employee experience with AI adoption.	Adapted from Yu et al. (2023)
E7: Reports on guides and training programs for HR managers on the use of AI.	Adapted from Chowdhury et al. (2023)
E8: Reports on how AI is changing work practices and business organization.	Adapted from Moniz et al. (2022); Yu et al. (2023)
E9: Reports on the implementation of AI in the company's human resources processes.	Adapted from Hajnić & Boshkoska (2021)
Axis 3: Reliability
F1: Reports on whether AI has a certain reliability.	Adapted from Ajah et al. (2024); Almarashda et al. (2021); Alshibly et al. (2024); Alzubaidi et al. (2023); J. Chen (2022); C. Fan et al. (2022); Latorre-Biel et al. (2021); Xiong (2022); Мохаммед & Ваххаб (2024)
F2: Reports on the reliability of the AI-based decision support system for economic management.	Adapted from Akdemir & Bulut (2024); Fonseca et al. (2024); Kumarappan et al. (2024); Zhao (2022)
F3: Reports on the reliability of using ChatGPT in business education.	Adapted from Vecchiarini & Somià (2023)
F4: Reports on objectives and measures to ensure trustworthy AI.	Adapted from AECA (2024); Suárez Giri & Sánchez Chaparro (2024)
F5: Reports on policies that ensure trustworthy AI.	Adapted from AECA (2024); Dahal et al. (2023)
F6: Reports on ethical guidelines for trustworthy AI.	Adapted from AECA (2024)
F7: Reports on the governing bodies responsible for overseeing the responsible development of AI.	Adapted from AECA (2024)
F8: Reports on roles in executing and overseeing AI development.	Adapted from AECA (2024); Talukder et al. (2024)
Axis 4: Innovation
I1: Reports on AI investments to innovate business models.	Adapted from Bustamante et al. (2024); Jorzik et al. (2023); Naeem et al. (2024)
I2: Reports on the use of AI in open innovation management.	Adapted from Bécue et al. (2024); Kuzior et al. (2023)
I3: Reports on whether AI has brought significant innovation to business management.	Adapted from M. Campbell & Jovanović (2024); Cui et al. (2022); Haefner et al. (2021)
I4: Reports on the factors influencing AI in knowledge innovation management.	Adapted from Hussain (2023); Q. Liu (2022)
I5: Reports on the use of AI in technological innovation.	Adapted from Gudigantala et al. (2023); D. Li et al. (2024); J. Liu et al. (2024)
I6: Report on new AI innovations in the supply chain.	Adapted from Braganza et al. (2022); Olan et al. (2022)
I7: Reports on government implications for promoting AI innovation.	Adapted from Beulen et al. (2022); Manta et al. (2024)
I8: Reports on the use of AI to drive business performance in terms of innovation.	Adapted from Dancausa Millán & Millán Vázquez de la Torre (2024); Samadhiya et al. (2024)
I9: Reports on the impact of AI-based innovation on decision-making.	Adapted from Coussement et al. (2024); Rajagopal et al. (2022)
I10: Reports on responsibility in AI innovation.	Adapted from Buhmann & Fieseler (2023); Xing et al. (2023)
I11: Reports on the benefits that AI can bring to the innovation process.	Adapted from Bahoo et al. (2023); Roberts & Candi (2024)
I12: Report on innovative marketing strategies with AI.	Adapted from Awad (2024); Islam et al. (2024)
I13: Reports on AI adoption and links to digital innovation.	Adapted from Saleem et al. (2023); Zeng et al. (2022)
I14: Reports on the benefits of AI in sustainable innovation.	Adapted from Gandía et al. (2025); Govindan (2022); Kolagar et al. (2024)
Axis 5: Transparency
T1: Report on transparency in decision-making with AI.	Adapted from AECA (2024); Booyse & Scheepers (2024); Chowdhury et al. (2023); Grandi et al. (2024; Ryan et al. (2024)
T2: Report on the transparency of high-impact algorithms.	Adapted from AECA (2024); Camilleri (2024); Holmström & Hällgren (2022); Xie (2021)
T3: Reports on whether AI improves the transparency of the investment process.	Adapted from Hajek & Novotny (2022); Sutiene et al. (2024)
T4: Report on whether they are using AI to improve their ESG performance and whether they are being transparent about how they do so.	Adapted from Sklavos et al. (2024); Suárez Giri & Sánchez Chaparro (2024)
T5: Reports on the level of transparency maintained and provided to all supply chain partners.	Adapted from Adamashvili et al. (2024); Manning et al. (2022); Modgil et al. (2022); Singh et al. (2024)
Axis 6: Sustainability
S1: Report on sustainable AI adoption.	Adapted from Balcıoğlu et al. (2024); Bolesnikov et al. (2022); Roumeliotis et al. (2023); Rusch et al. (2023); Sipola et al. (2023)
S2: Reports on the growing importance of environmental sustainability with the adoption of AI.	Adapted from Gao & Liu (2023); Jorzik et al. (2024); Ogbeibu et al. (2022); Sridhar et al. (2023)
S3: Reports on the role of AI in decision-making for sustainability.	Adapted from Ali et al. (2024); Bickley et al. (2024); Pisoni & Molnár (2024)
S4: Reports on the contribution of AI in improving supply chain management.	Adapted from Kazancoglu et al. (2023); Onyeaka et al. (2023); Remondino & Zanin (2022); Zaid et al. (2025)
S5: Report on sustainability in process automation with AI.	Adapted from Aydın & Turan (2023); Rowan et al. (2022); Tairov et al. (2024)
S6: Reports on the implications of AI for achieving several Sustainable Development Goals.	Adapted from Felfernig et al. (2023); Fonseca et al. (2024); Peng et al. (2023)
S7: Report on AI for Sustainable Reporting Analysis.	Adapted from Boloș et al. (2024); Vărzaru (2022)

• Source: Prepared by the authors

6 CONCLUSIONS

Disseminating information about AI is essential for building trust with stakeholders and ensuring the shared success of its implementation. By communicating the benefits, challenges, and opportunities of AI in a clear and accessible manner, organizations can encourage the adoption of this technology and maximize its potential. However, the parameters that define AI are vague and constantly evolving, making it difficult to establish an exact definition. Furthermore, this study aligns directly with the principles of access to information and transparency in information science. This research helps to ensure that information about this influential field is understandable and accessible to the general public. The proposed items evaluate not only the effectiveness of dissemination, but also examine how information on AI can be clearer and more accurate, thus fostering a more open and equitable information ecosystem. Therefore, taking into account the Artificial Intelligence Strategy 2024, Regulation 2024/1689, and the opinion issued by AECA (2024), six parameters have been proposed: machine learning, employment, reliability, innovation, transparency, and sustainability. An index has been developed to measure the dissemination of information on artificial intelligence in companies. Consequently, a set of fifty indicators has been established, distributed as follows among the proposed parameters: machine learning (7), employment (9), reliability (8), innovation (14), transparency (5), and sustainability (7). This tool is intended to improve the content analysis of companies' websites in future research, facilitating the efficient evaluation of their level of dissemination on AI. Furthermore, the development of these indicators was based on an exhaustive study of the main articles for each paramete.

This study stands out as the first to develop an index specifically designed to assess the dissemination of information on AI in companies. The proposed indicators are extremely useful both for guiding the implementation of best practices and as a basis for future research in this field. In addition, this work contributes to raising awareness among companies of the importance of disseminating relevant information on AI. As a future line of research, we propose evaluating corporate websites to determine the extent to which companies disseminate relevant information on the parameters established in this study for AI. In addition, we propose establishing a process for periodically reviewing and updating the indicators to adapt them to technological advances and changes in the business environment. With regard to limitations, we can highlight that AI is a complex and constantly evolving field, which makes it difficult to create an indicator that covers all important aspects. Secondly, the indicator may not be equally applicable to all sectors, as the use of AI varies significantly between them. Finally, the definition of AI parameters may vary between different experts.

Acknowledgments:

Not applicable.

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• 

  
  
• 

  
  
• Financing:
  Not applicable.
• Ethical approval:
  Not applicable.
• Availability of data and material:
  Not applicable.
• Image:
  Extracted from Google Scholar platform.
• JITA:
  BD. Information society
• ODS:
  9. Industry, innovation and infrastructure

Edited by

• Editor guest:
  Gildenir Carolino Santos https://orcid.org/0000-0002-4375-6815

Data availability

Not applicable.

Publication Dates

• Publication in this collection
  27 Apr 2026
• Date of issue
  2026

History

• Received
  18 Mar 2025
• Accepted
  08 Sept 2025
• Published
  19 Sept 2025