Integration of Artificial Intelligence Technologies into Design-Thinking Processes in the Development of Managerial Decisions as a Factor of Enterprise Sustainable Development

Abstract

Sustainable development is one of the most significant and meaningful trends in the world. However, the process of implementing the Agenda for Sustainable Development Goals, approved on 25 September 2015, reflects the ability to achieve no more than 20% of stated goals, which requires a solution that will allow for accelerating the pace of a resilient transformation. The objective of this paper is to substantiate the role of artificial intelligence technologies in ensuring effective ESG (environmental, social, governance) transformation in the process of developing managerial decisions aimed at achieving sustainable development of the enterprise by integrating into design-thinking. The research methodology is based on scientific papers on sustainable development, digital technologies, artificial intelligence, and design thinking. Analytical data from various world rankings, as well as reviews of sustainable development practices and digitalization of companies, were employed. General scientific and special methods of cognition were used to conduct the study. As a result, the role of artificial intelligence in the processes of the sustainable transformation of modern companies was identified and justified. This is reflected in the proposed algorithms and models for the development and implementation of a sustainable development strategy.

1. Introduction

Today, one of the global trends that is particularly important for all national states is sustainable development, the vector of which is aimed at ensuring a synergistic effect of activities based on the interaction of three components, or the so-called ESG-principles, namely: economic efficiency, environmental safety, and social responsibility. In particular, the importance of this trend is confirmed by the increasing scientific and practical interest in sustainable development, both in individual enterprises and in countries, expressed in the growth rate of scientific publications on the subject. Thus, according to the Google Scholar system, if the query “sustainable development” for the period before 2000 resulted in 253,000 works, then for the period up to 2025, this number will have increased to 1,710,000 [1].

Sustainable development appeared as a separate topic of research interest in the middle of the 20th century. The impetus for this origin was the definition presented in 1987 in the scientific report “Our Common Future”, according to which sustainable development was the development in which the current generation of people meets their needs without compromising the opportunities of future generations [2].

Over time, the concept has evolved in the elaboration and specification of researchers’ positions on their conceptual approaches and principles of sustainable development, the emergence of new challenges, the real practice of implementing ESG principles, including in various spheres, as well as solutions to debating points. For instance, cleavage in regards to views has been on what should be sustainable and what should be developed; what is the link between sustainability and sustainability; how to link environment and development, and over what period; how it is possible to overcome existing environmental constraints and preserve living standards; how to ensure resource accessibility for everyone and other [3,4,5,6,7,8,9].

Without going into a deep etymological analysis of the concept of sustainable development, since we do not have to justify or refute any of the existing definitions, we note that with no common approach to the essence and content of the definition under consideration, however, there is a consensus among the researchers on, first, the high importance and urgency of sustainable development in today’s world; second, on the need to ensure a close implementation and interconnectedness between all ESG components; third, on the undisputed fact that not realizing the concept of sustainable development will lead to a global catastrophe and will deprive the next generations of the future, given the speed and frequency of the impacts on nature and the environment.

The Main Sustainable Development Goals (hereinafter: SDGs) for the world community for the short term were defined by United Nations member states in 2015, which was reflected in the so-called Sustainable Development Agenda 2030 as “The overall plan for peace and prosperity for people and planet now and in the future” [10].

In general, the plan for global well-being is divided into 17 directions, and completing each of them is expressed through a corresponding goal that also has several sub-goals. Thus, there are 17 SDGs comprising the elimination of poverty and hunger; ensured health and well-being, high-quality education; conservation of marine and terrestrial ecosystems; reduction of inequalities; rational and efficient resource exploitation; as well as other assignments [10].

However, analytical data show that the progress achieved within the remaining five years (by 2030) allows expecting the implementation of no more than one-fifth of those declared goals, as was noted in particular by the United Nations in its report, prepared in June 2024 [10]. Thus, according to an assessment of progress in achieving the SDGs, no more than 17% of the SDG targets are on track, while only two of the 17 goals—eradicating poverty and ensuring access to affordable, reliable, sustainable and modern energy sources for everyone—are not characterized by stagnation or deterioration (with the first goal is not characterized by progress either).

In the context of identifying and studying factors that have a negative impact on the ability to implement tasks on the trajectory of sustainable development, researchers highlight the consequences of geopolitics [11,12] the COVID-19 pandemic [13,14], lack of funding [15,16], “bottlenecks” in the management of human resources [17] and human capital [18], as well as a number of others.

It is logical to state that, in addition to the factors having a negative influence on the dynamics of achieving the SDGs, there are factors whose activation will accelerate sustainable development processes and reduce the impact of threats. As the analysis of recent publications on this subject shows, the leading directions indicated by researchers as catalysts for the transition to sustainable development are: the creation of different options of collaborations, combining the efforts of participants in processes and expanding the geography of ESG-activities [19,20,21] intensification of processes for development and implementation of innovations in priority areas [22,23,24,25,26] as well as the introduction of digital technologies [27,28]. It is digitalization, according to analysts, that should be a key trend in the short term in sustainable development practices.

The positive impact of digitalization on sustainable development can be confirmed by the fact that the leading countries in achieving the SDGs are also in the top various rankings for different aspects of digitalization, as reflected in

Table 1. Ranking of leading countries by progress in achieving the SDGs.

No.	Country Leaders in Progress Towards SDG [29]	International Institute for Management Development (IMD). World Digital Competitiveness Ranking [30]	Information and Communication Technology (ICT) Development Index by Country 2024 [31]	Global Digitalization Index 2024 (GDI 2024) [32]
1	Finland (86.4)	12 (100)	2 (98.1)	4 (73.0)
2	Sweden (85.7)	5 (182)	15 (95.3)	3 (74.5)
3	Denmark (85.0)	3 (96)	9 (97.1)	5 (71.8)
4	Germany (83.4)	23 (104)	57 (87.8)	14 (63.4)
5	France (82.8)	20 (102)	44 (89.8)	15 (62.2)
6	Austria (82.5)	25 (70)	19 (94.3)	22 (57.3)
7	Norway (82.2)	10 (154)	25 (93.4)	13 (64.9)
8	Croatia (82.2)	46 (90)	45 (89.6)	38 (46.7)
9	United Kingdom (82.2)	18 (194)	23 (93.6)	11 (66.8)
10	Poland (81.7)	39 (160)	12 (95.8)	36 (47.8)

.

In particular, as is shown by the results of the correlation analysis of the submitted data, there is a sufficiently strong link between the position reached on the trajectory to the SDGs and the position in the rating for the development of information and communication technologies (0.5) and the transformation of society under the influence of digital technologies (0.8), which confirms that there is a significant impact of digitalization on sustainable development processes.

A similar relationship is demonstrated by the Boston Consulting Group study, according to which the country’s digital leaders (who have made significant progress in digital infrastructure or digital accessibility) have made 40% more progress toward achieving the SDGs than countries comparable to them in terms of their income [33].

Considering that today, business is one of the main drivers of social development, the focus of efforts in the field of sustainable development is rightfully shifting from the macro level to the micro level. Flexibility, focus on consumers and value creation for them, innovative activity, social responsibility, and environmental friendliness of the business become its integral components, including ensuring attractiveness for investors. Furthermore, it is justified to argue that the most important component of success in achieving the SDGs is the results of the sustainable transformation of individual enterprises and organizations, forming a sustainable ecosystem and contributing to its increase at meso- and macro-levels.

At the same time, the question remains open as to which stage of the ESG transformation of an enterprise should digital technologies be connected to obtain the best result. Digit is now widely used for data collection and information processing for ESG ratings. Digital technologies allow for optimizing several processes that are related to the SDG Agenda (e.g., robotization of documentation procedure, management of electricity loads in hybrid-type networks, use of lidar data, and new carbon-neutral technologies). However, this is not enough, as evidenced by the corresponding momentum toward achieving sustainable development goals.

Achieving high efficiency through the use of digital technologies can be obtained at the stage of developing managerial decisions aimed at ESG transformation of enterprises, which is the hypothesis of our study.

The purpose of this study is to substantiate the role of artificial intelligence technologies in ensuring an effective ESG transformation and to propose solutions for leveraging their advantages in modern enterprises’ practices during the development of managerial decisions aimed at achieving sustainable development. This involves integrating these technologies into the stages of design thinking to determine the most optimal ESG projects in the context of the chosen strategy.

The present paper is organized as follows. The introduction substantiates the research interest in the issues of sustainable development in the context of artificial intelligence technologies’ integration into design-thinking processes in enterprise managerial decision-making. Section 2 contains a theoretical analysis of the provisions of sustainable development digitalization, exploring the role of digital technologies in the ESG Agenda and the study of digital tools used for sustainable development by modern enterprises. Section 3 describes the research methodology. Section 4 presents the analysis possibility of artificial intelligence technologies used in design-thinking and managerial decision-making for ESG transformation. Section 5 introduces the model for developing a strategy for the integration of artificial intelligence technologies into the process of managerial decision-making and an algorithm for selecting sustainable development projects. Section 6 provides a discussion and conclusions of the research.

2. Theoretical Analysis of the Provisions of Sustainable Development Digitalization

2.1. Exploring the Role of Digital Technologies in the ESG Agenda

The modern world is characterized by high turbulence and volatility, and the continuous emergence of new challenges and threats. Thus, futurologists call the current stage of development SHIVA-world (Split, Horrible, Inconceivable, Vicious, Arising), which is characterized by chaos, crises, severe uncertainty, and ruthlessness, as well as rapid change in the market. In such conditions, the orientation toward sustainability is a crucial vector of adaptation to the requirements of an unstable world, promoting investment balance [34], reducing corporate financial risks and enhancing corporate transparency [35], and developing an innovative direction [36,37].

In this regard, sustainable development, despite the impact of various factors (for example, a pandemic, economic crisis, geopolitics), not only does not lose but continues to gain popularity both at the macro and micro levels, which cannot be associated only with the desire of enterprises to ensure investment attractiveness. Rather, the growing interest of investors in ESG-responsible companies is a consequence and confirmation of their desire to work effectively. After all, the implementation of ESG principles and the trajectory of sustainable development mean higher productivity and profitability, effective management, and motivated and loyal personnel, which together ensure an increase in goodwill, capitalization, and company value in the market. In particular, there is a high impact of ESG transformation on the activities of companies in developed and developing countries [38,39], while 95% of the world’s 500 largest companies have ESG programs.

As a result, many companies around the world have already adopted sustainability reporting standards. According to a study by Klynveld Peat Marwick Goerdeler (KPMG Impact), about 80% of 52 countries report on sustainable development (this is, for example, 5% more than in 2017) [40].

However, it is necessary to understand that the transition and subsequent operation on the principles of sustainable development is, as an organizational innovation, not a one-time event, but a long process requiring careful preparation, and incorporation into the map of the strategic development of the company and related resources. At the same time, the less information the enterprise management has about available potential, resources, and possibilities, the longer the process of ESG transformation will be and the more complicated its implementation and subsequent “maintenance”.

In this context, digitalization is seen primarily as a tool of sustainable development that allows at each stage of the transition to an ESG-oriented company to accelerate processes, make them transparent and optimize resource consumption.

The relationship between digitalization and sustainable development, more precisely, the impact of digital technologies on the possibilities to achieve the SDG and ESG agenda, has been actively investigated over the last few years. At the same time, it is noteworthy that interest in both issues—digitalization and sustainable development—has quite similar dynamics, as it is reflected in Figure 1.

Today, digitalization is a sufficiently broad concept. It is interpreted as digital business; as an opportunity to create value, sustainable development, and prospects for business and society at large; as the use of digital data and technologies to change traditional processes or business models [42,43,44,45,46]. For this study, we will take as a basic definition that digitalization is a process of increasing the value of an object being changed through the application of digital technologies.

In general, the prevailing research opinion on the positive impact of digital technologies on achieving sustainable development goals is expressed as follows:

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Ensuring access to relevant information, including through the integration of digital platforms into the interaction of participants in sustainable development processes;

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Ensuring efficient use of resources, cost reduction, and optimization (as well as re-engineering) of processes related to the achievement of SDGs;

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Ensuring the participation of the community involved in sustainable development, taking into account the opinions and needs of different groups, which has a positive impact on social justice and equality;

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Ensuring high-quality monitoring and evaluation: digital technologies provide tools for more accurate monitoring of progress in achieving the SDGs; data collection and analysis systems allow real-time tracking of changes and policy adjustments;

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Creating incentives for innovative development to address sustainable development challenges.

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Developing partnerships and cooperation, sustainable ecosystems that allow the pooling of the necessary resources and competencies for more effective achievement of the SDGs.

At the micro-level, researchers note that digital transformation is an important component in the strategic orientation towards sustainable development [47,48], changing traditional management practices and business modelling [49,50], which promotes innovative development [51,52] and economic growth [53] of enterprises. The introduction of digital technologies has a direct positive influence on the rational use of internal potential [54,55] and also defines the possibility of “responding’’ to external challenges. The introduction of advanced IT (Information Technology) solutions in enterprises promotes the generation of new ideas and digital methods of work. As a consequence, there is an improvement in engineering, operation, and production processes, and the spin-over effects, which, for end customers, are various environmental and social benefits.

The positive impact of digitalization on enterprises’ transition to sustainable development is confirmed by scientific works, which present the results of an empirical study of digit on ESG transformation processes through increasing the capacity of enterprises for technological, market and management innovations [56], ensuring the implementation of the main components of sustainability [57,58], including medium and small enterprises [59], implementing a green course [60,61] and principles of closed-cycle economics [62], as well as other directions. There are also scientific studies that have documented a 15% reduction in CO₂ emissions from energy and construction, transport and manufacturing, agriculture, and services.

Experts of PricewaterhouseCoopers, one of the largest and most respected auditing and consulting companies in the world, note that the application of modern technologies can accelerate the achievement of at least 10 out of 17 SDG, formulated by the UN [63], which summarizes the current opportunity to achieve about 70% of the 169 indicators through increased use of digital tools.

Therefore, based on the existing empirical and practical results obtained, the researchers conclude that there is a clear positive impact and necessity of using digital transformation to promote the sustainable development of enterprises.

2.2. Analysis of Digital Tools Used for Sustainable Development by Modern Enterprises

At the same time, it is becoming legitimate to ask which digital transformations are currently the most effective and have the most positive effect in terms of ESG transformation.

The analysis of the research has revealed the following most “popular” digital technologies currently used in the context of ESG transformation, namely:

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Business intelligence systems that provide the necessary management information for decision-making, improving the quality of corporate governance [64,65];

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Personnel management systems that ensure not only the performance of necessary functions in this area, but also the quality of relationships with employees, the elimination of discrimination, and transparency of information for all [66,67,68];

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Procurement management systems that allow for the rational consumption of necessary resources and ensure transparency of all stages of the procurement process [69];

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Production process management systems that ensure the flexibility, adaptability, optimization, and self-organization of production systems [70,71,72,73];

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Platform solutions that provide a platform for communication and exchange of experience between enterprises and organizations, government authorities, and the expert community [74,75,76];

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Digital management systems focused on the organization of work with digital data, the formation of the “digital architecture” of the enterprise in accordance with strategic goals.

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Digital management systems focus on organizing work with digital data, forming a “digital architecture” of the enterprise according to strategic goals [77].

Among the main end-to-end digital technologies that provide solutions to assigned problems, regardless of the field of activity and functional area, today, are big data, digital twins, blockchain, Internet of things, as well as artificial intelligence, the use of which is currently capable of solving a wide range of ESG problems.

First of all, digital technologies make a significant contribution to solving environmental problems (E), which allows us today to talk about the development of the concept of the so-called digital circular economy, combining “traditional” principles of the circular economy and digital technologies [78,79,80]. In this context, for example, the Internet of Things is being considered as a means to increase supply chain transparency, real-time analytics, and greater control over transactions. Thus, smart sensors allow warnings about potential problems, and smart home systems allow the control of lighting and household appliances, control water supply, room temperature, and much more, which can contribute to energy saving and rational use of resources. In turn, blockchain technologies provide the ability to track a product from its origin, ensuring that all materials are accounted for over the full life cycle, increasing the level of reuse, recovery, and recycling. In particular, the practical implementation of this technology is realized in a digital product passport to track the product throughout its life cycle [81,82].

Here, of course, it is important to note that there is also a flip side to the coin, expressed in the negative impact of digital technologies on the environment, for example, in increased CO₂ emissions [83]; however, as it is noted, it is insignificant and is mostly smoothed by the increase in environmental sustainability due to “digit”.

In the context of the social component (S), digital technologies are focused primarily on ensuring the transparency of information and its protection against cyberattacks and leaks [84,85]. In particular, Big Data provides a wide range of required information and ensures its validation [86]. Artificial intelligence technologies are at the core of corporate social responsibility (CSR), providing workplace safety monitoring, remote manipulations hazardous to employee health, creating knowledge-intensive jobs, and expanding opportunities for advanced skills through distance corporate learning [87,88].

If we talk about the component of corporate governance (G), then end-to-end digital technologies in this aspect are used to solve problems related to the provision of the necessary information and its analytics, which is successfully solved by Big Data technologies [89,90]. Modeling of complex systems for making informed managerial decisions is carried out based on a digital alter ego, which allows you to significantly reduce costs and find the “optimal” solution [91,92]. Artificial intelligence ensures the rational use of resources and optimization of all business processes, especially production and logistics, and allows you to “build” the most optimal development trajectory [93,94,95].

Of course, the examples given of digital cross-cutting technologies applied for sustainable development are far from exhaustive in the list of existing digital solutions for ESG transformation. In particular, we can note the role of virtual and augmented reality technologies, robotics, production technologies, and others that make a significant contribution to the implementation of the concept of sustainable development. In addition, this list is continuously expanding, taking into account the current dynamics of modern technological development.

However, without underestimating the significance and importance of all these digital cross-cutting technologies, we would like to pay special attention to the technology of artificial intelligence, which is directly correlated with our research goal and objectives. Moreover, as the above examples have shown, artificial intelligence is now being implemented in all aspects of ESG transformation: environmental, social, and corporate.

3. Materials and Methods

The methodology of the presented study was based on the concepts of sustainable development, strategic management, digital technologies, and design thinking. In the framework of this study, we employed general methods, including analysis and synthesis, deduction and induction, comparison, observation, generalization, modeling, and quantitative and empirical methods, namely correlation data analysis. Theoretical, historical analysis, and case methods were used as special research methods.

The research was based on outcomes of theoretical and empirical works of scientists, analytical reviews and scientific-practical conferences, analytical data, and international rankings.

The starting point of the study was the results of analytical data characterizing the level of sustainable development of national states in terms of achieving the SDGs, demonstrating a clear “time-lag” from planned indicators. Based on the justification of the role of digital technologies as a factor-catalyst of processes of transition to sustainable development, theoretical and historical analysis of the field of knowledge of digitalization and sustainable development is carried out, the practice of applying digital technologies within the framework of ESG transformation of enterprises, which allowed for justifying the special role of artificial intelligence. Developing the topic under investigation, based on existing theory and practice, the authors specified the role of artificial intelligence in design-thinking processes and presented a model of sustainable enterprise strategy, the most important stage, which is project selection, achieving the stated sustainable goals. Taken together, this ensured the validity and credibility of the main findings and results of the study.

4. Application of Artificial Intelligence for ESG Transformation

4.1. Analysis of the Application of Artificial Intelligence Technologies at the Modern Stage

Interest in artificial intelligence arose back in the middle of the last century (in 1950–1960), which was marked by the appearance of the first neural networks (SNARC, Mark I Perceptron, Eliza). After a calm period in the 80s of the 20th century (the so-called winter of artificial intelligence), the beginning of this century was marked by increasing interest in the subject of artificial intelligence and developments in this field, the pace of which increased exponentially. As a result, artificial intelligence has evolved quite rapidly from a “common-sense program” [96] to a “imitation system for collecting and processing knowledge and information and providing it to individuals” [97] and “technologies that perform and/or simulate human sensory or cognitive processes” [98]. Artificial intelligence began having a significant impact on the global economy. According to experts, the potential impact of artificial intelligence on the economy is $17–26 trillion per year; the majority—about 70%—comes from the implementation of traditional AI (machine learning, deep learning and advanced analytics), the rest amounting to $6–8 trillion per year—generative AI. It is justified that researchers call the current stage of development of society an era of artificial intelligence [99], characterized by systematic coverage of technological modernization [100].

The vast potential of artificial intelligence now allows it to be applied in different spheres and fields of human activity. For example, in the area of innovative development [101] or increasing operational efficiency [102]. Given the orientation of our work, we will consider the role of artificial intelligence in achieving the goals of ESG transformation.

Generally, researchers agree that artificial intelligence has a positive impact on the effectiveness of ESG. Thus, it is stated that artificial intelligence technology enhances the effectiveness of corporate governance in the field of ESG through the creation of an internal control system and a unified information environment [103]. The introduction of artificial intelligence also improves organizational change for sustainable development, which has an impact on optimal resource allocation and employee satisfaction [104]. It is also indicated to ensure the transparency of activities and the efficiency of decision-making [105], as well as the financial sustainability of ESG-oriented companies [106].

Undoubtedly, an interesting and topical modern application of artificial intelligence is the search for new energy-stable structures based on learned neural network models, as well as the creation of radically new technologies, CO₂ recycling, and capture-oriented so-called green transition energy materials. Experts and the business community have high hopes for technology in solving the climate issue. Google and the Boston Consulting Group predict that AI can help reduce up to one-tenth of all greenhouse gas emissions by 2030. To this end, in particular, the UN has initiated the AI Innovation Grand Challenge project aimed at identifying and supporting the development of AI-based solutions for combating climate change in developing countries [107].

As a rule, the main merit of artificial intelligence is usually the collection and analysis of big data in the context of green innovations and corporate social and environmental responsibility [108,109]. It also generates more complete and reliable internal and external corporate reporting in the field of sustainable development [110] and the implementation of ESG projects [111]. Thus, thanks to technology, it is possible to collect data gathered at different levels and stored in different databases and consolidate them into a single system in the necessary format, which provides easy access, unloading, and analysis. Artificial intelligence can find complex patterns in ESG transformation due to the ability to process poorly structured and ambiguously verified information, as well as to identify inaccuracies in data, thus improving the speed and quality of information preparation disclosure of the company.

By way of example, the practical implementation of these directions can be mentioned, in particular, the application of various solutions for ESG-rating, which are now provided as ready-made (for example, by Coriolis Technologies (Pune, India) or developed by enterprises themselves. For accelerating the processing and analysis of data, companies use various algorithms, such as UBS Wealth Management (Zurich, Switzerland), to perform due diligence based on them, while the Amundi fund (Paris, France) processes data on emerging markets and evaluates news tonality.

The X5 Group company (Moscow, Russia) in addition to using a unified automation system and accounting for ESG indicators, including energy consumption, waste generation, the number of materials transferred for recycling, labor safety indicators, charity, and others, structured in the form of a single dashboard, uses a demand forecasting system and a replenishment system, which ensures waste reduction. In addition, in a logistics company, artificial intelligence collects and processes information on the driving styles of each driver, forming optimal routes that ultimately focus on reducing CO₂ emissions. The tools of artificial intelligence used in Auchan (Croix, France) are also oriented to the optimization of storage and the rational allocation of resources. It is estimated that this will avoid more than 30,000 tons of additional carbon emissions from the disposal of expired products.

Henkel (Düsseldorf, Germany) has implemented AVEVA tools to optimize the supply chain to realize environmental components in its ESG transformation and their integration into a unified system, which allows the monitoring of energy consumption and reducing costs. This resulted in a 5–6% increase in resource efficiency at the production sites and an € 8 million reduction in costs [112].

PETRONAS (Kuala Lumpur, Malaysia) has implemented a unified information system for all its production facilities, which allows for faster and more efficient decision-making through data validation, optimizing business processes, and energy consumption [113].

Thus, as the examples above show, artificial intelligence is used quite actively for ESG transformation in environmental, social, and corporate aspects. However, in terms of corporate governance, the role of digital technology is generally reduced to servicing the management process, providing necessary data, automating, and generating appropriate reports in the required formats, which we believe is insufficient, limiting its ability to achieve sustainable development goals.

The foundation of corporate governance is a managerial decision that, based on an analysis of existing definitions, can also be the result of management activities aimed at ensuring the achievement of stated goals. Accordingly, the ability of the management system to ensure the achievement of the set strategic goals directly depends on the effectiveness and validity of the managerial decisions. Therefore, when projecting into the sustainable development plane, managerial decisions made primarily at the strategic level should directly correlate with the objectives of sustainable transformation and ensure the implementation of ESG principles. Whereas artificial intelligence, having the above-mentioned advantages, is able not only to form the necessary information basis for such decisions but also to provide an opportunity for accelerated and informed adoption of them.

4.2. Research on the Possibility of Using Artificial Intelligence in Managerial Decision-Making

Currently, there is a rather ambiguous opinion among researchers and practitioners regarding whether artificial intelligence technologies should be used in managerial decision-making processes. From the point of view of the proponents of such use, artificial intelligence makes it possible to reduce uncertainty in decision-making, identify potential problems, propose possible alternatives, reduce the negative influence and biased attitude of the person making the decision, and can also help in designing innovations and creating value [114,115,116,117]. Moreover, the large amount of data processed is a very valid source of information on which management relies [118,119].

On the other hand, artificial intelligence, based on historical data, is not able to identify independently ineffective and erroneous managerial decisions taken, and does not take into account the prevailing role of morality-ethical aspects and corporate values of the company and shows inadequacy in the face of ontological uncertainty [120,121,122,123]. There is also an elementary mistrust of management in this technology [124,125]. In addition, it is noted that currently, “weak” artificial intelligence is capable of solving only highly specialized problems. Strong artificial intelligence, capable of multitasking and having cognitive abilities and capabilities similar to humans, will appear no sooner than 2040.

However, despite the existing debate and discussion, today we see an intense diffusion of artificial intelligence tools into management practices in which technology takes over responsibilities traditionally performed by humans [126].

One such example is the solution Amazon Sage Maker Canvas, forming recommendations for demand management, advertising, procurement, and logistics [127]. Another example is the Morgan Stanley Wealth Desk platform, which is used to shape investment decisions, taking into account a wide range of different external and internal factors [128]. Unilever (London, UK) uses an online platform based on artificial intelligence that performs HR functions [129]. In 2022, Tang Yu, an AI-powered virtual humanoid robot, was appointed as the CEO of the company’s principal subsidiary, Fujian NetDragon Websoft (Fuzhou, China). Her functions included not only collecting and processing data but also approving management decisions with the right to sign management documents.

The Dodo-Pizza company (Syktyvkar, Russia) introduced artificial intelligence into the procurement management system, which is implemented based on Microsoft Azure Machine Learning with the participation of Crayon, which provided significant savings for the company [130]. In general, as studies have shown, management has high hopes for the prospect of transferring coordination and control functions to artificial intelligence, which will free up time for solving non-standard problems [131].

Based on the analysis of the research work and practical examples, it is possible to highlight the following main areas of application of artificial intelligence in management processes:

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Automation of routine functions and data analysis, formation of management reporting;

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Assistance in the development of alternatives by providing data-intensive evaluation results;

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Development of prognostic models reflecting possible development prospects for the chosen option, taking into account the influence of external and internal factors;

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Automation of various management subsystems that make up a single enterprise management system.

By analyzing the practical cases and opinions of researchers, it is possible to aggregate the functions of artificial intelligence in the process of the development and implementation of managerial decisions, which Figure 2 reflects.

As you can see, artificial intelligence is now used in almost all stages of the development and implementation of managerial decisions, which confirms the opinion of researchers about the possibility of formalizing and optimizing this process based on algorithmization and the development of computer technology [132]. At the same time, if at the beginning of the integration of artificial intelligence technologies into the processes of development and implementation of management decisions, the role of substitute routine functions and formator of reporting was predominant; today, the importance of its expert function is growing, the results of which serve as a complement to management decision-making by the responsible person, for example in the form of a decision support system [133]. In other words, based on the classification of types of application of artificial intelligence tools proposed by Deloitte [134], there is now an expansion of the range of types of automation implemented, where liberation is summed with crushing and completion, when artificial intelligence not only performs routine work in collecting, analyzing, and processing information, but also offers various options for solving problems. In this regard, several researchers are already considering the problem of “humanizing” artificial intelligence and its ability to avoid human errors, which will ensure the complementarity of technology and humans in management decision-making processes [135].

Following this conceptual idea, many authors develop the idea of the possibility of artificial intelligence to complement the processes of strategy development, in which it is not only considered its already traditional fast data processing capabilities but also the provision of predictive models of company development, as well as the implementation of the role of the brain, which performs important managerial functions [117,136].

The inclusion of artificial intelligence technologies into the processes of development and implementation of management decisions should be carried out in synergy with modern management practices, methods, and tools, which will ensure the achievement of the best result and will respond to contemporary challenges. In particular, it is difficult to justify the use of artificial intelligence technologies in terms of efficiency if a company has paper management accounting or calculations are carried out manually. The example, of course, is significantly exaggerated, but it allows us to see the need for a “balance” of the applied “human” and digital management technologies.

One of these modern methods is currently the design-thinking method.

4.3. Artificial Intelligence in Design-Thinking

Although the first mention of this method appeared in the middle of the last century, the activation of research processes and application of the design-thinking method occurred relatively recently—at the beginning of this century, which once again confirms its essence as a method aimed at solving complex problems: as we said at the beginning, the modern stage of development has very harsh characteristics and requires considerable efforts to preserve and continue activities.

If we analyze existing approaches to the definition, then the design-thinking method is understood as the process of creating solutions (usually in the form of new products or services), with an emphasis on its cognitive-analytical component [137,138] and design work [139]. At the same time, the most important aspect of this method is its human-centricity, which focuses on the user’s needs, based on a deep “immersion” into his/her problems to find an appropriate solution.

Accordingly, it is this immersion in the problem that allows finding non-traditional solutions to even the most complex tasks, which has led to the dissemination of the method not only in various industries but also in different fields of activities, including management decision-making processes, which also contributed to the transformation of the vision of design processes not only as a creative-design activity but also as a modeling activity in general (design as the projection of anything), including the development of various scenarios of development of the object research.

Research shows that the leading industry in applying the design-thinking method is the field of information technology and communications (21.7%), which is very reasonable given its speed of development. Design thinking is also employed in service industries (19%), education (18%), professional scientific/technology activities, trade (8.8%), finance and insurance (6.8%), as well as manufacturing (4.8%) [140].

To characterize the demand for the method in various fields, we can also indicate companies that actively use it to solve their problems, which may be related to the development of new products and their appearance (for example, Apple (Cupertino, CA, USA), Bosh (Gerlingen, Germany), and conduct market research (for example, Procter & Gamble (Cincinnati, OH, USA)). At various stages of organizational processes, design thinking is applied in General Electric (Boston, MA, USA) and Google (Mountain View, MA, USA), Samsung (Seoul, Republic of Korea) and Netflix (Los Gatos, CA, USA), IKEA (Delft, The Netherlands), and other companies. The current trend is the penetration of design thinking into state-owned companies, which is related to the implementation of the customer-centric management concept.

Therefore, for modern business (and not only business), the design-thinking method allows for finding new development opportunities based on the identification of the best ways to meet the existing needs of stakeholders. Design thinking, as mentioned above, is a synergy of cognitive, aimed at understanding the problems of users or stakeholders, analytically, including obtaining all the necessary information to identify problems and generate ideas, as well as the design components that define the solutions.

Depending on the way each of these components will be implemented, the outcome and result of this process are directly found, which fact requires an appropriate resource base. It is obvious that in the “era of artificial intelligence”, one of the integral components is this technology.

It can be noted that today there are already research works confirming the positive effect of integrating artificial intelligence into the main stages of the design-thinking method expressed in the development of creative self-efficiency, reflexive and designer thinking [141], improvement of the development of ideas and results of prototyping [142], expansion of research stage capabilities and increase in efficiency of innovative teams, automation of prototyping and training stages, acceleration of design process, promotion of innovation, and provision of customer centricity [143].

Building on the results of these works, we have aggregated and complemented the capabilities of artificial intelligence in terms of the main stages of the design-thinking process, which is reflected in Figure 3.

According to the presented figure, at each stage of the design-thinking process, the capabilities inherent in artificial intelligence contribute to the “incremental” formation of a product or solution. For instance, at the empathy stage, thanks to automated data collection, the use of machine learning algorithms for data analysis, and the application of Natural Language Processing (NLP), it becomes possible to gain a “deep” understanding of users. During the focusing stage, AI technologies synthesize the information gathered in the empathy stage and, by using algorithms such as K-means, DBSCAN, or LDA (Latent Dirichlet Allocation), group together user problems and needs, identifying existing “pain points”.

The development of the “right” idea is facilitated by generative AI technologies—for example, those based on Generative Adversarial Networks (GANs)—as well as machine learning algorithms that analyze successful solutions in similar areas and propose new ideas derived from them. When creating a prototype, AI can suggest solutions by running simulations and modelling various scenarios, which substantially increases the likelihood of obtaining a “working” product or solution.

At the testing stage, AI technologies such as machine learning, chatbots, and virtual assistants provide the necessary “feedback”, ultimately resulting in a product or solution that resolves the existing problem. Thus, incorporating AI technologies into the design-thinking process can significantly accelerate and improve the development of solutions while boosting both quality and user satisfaction.

It can be said that the interaction of design thinking and artificial intelligence is characterized by mutual positive influence. On the one hand, the design-thinking method increases users’ confidence in artificial intelligence technology, which is particularly confirmed by scientific research [144,145]. In addition, design thinking allows the expansion of the capabilities of artificial intelligence [146]. On the other hand, the effectiveness of the method depends on which data were used to analyze and study the pains of users, as well as on the number and options of generated solutions to the identified problems. It is obvious that in this area, artificial intelligence has many capabilities compared to human (i.e., information collecting and its processing), providing within the method a wide range of data and alternatives; thus ensuring the objectivity and reasonableness of the management decision-making process.

Thus, as the results of our research on the application of artificial intelligence technologies in the development and implementation of management decisions demonstrate, there is already a significant amount of research and analytical work. It has a high level of performance to ensure that its objectives are achieved, as is also confirmed by practice. While 20 years ago, artificial intelligence was considered only as a substitute for routine functions, today, it is a direct assistant, which not only provides the necessary information, and generates different solutions, but also develops predictions depending on the chosen option, as well as provides expert assessment.

Given the direction of our work, we can confidently assert that artificial intelligence tools will not only “accelerate”, but also, in synergy with modern management methods such as design thinking, ensure the development of “correct” development trajectories in the context of ESG transformation of enterprises and companies, which will directly affect the dynamics of achieving the SDGs.

5. Integration of Artificial Intelligence Technologies in the Process of Developing and Implementing Managerial Decisions: A Course for Sustainable Development

5.1. Process of Strategy with Integration of Artificial Intelligence Technologies

It was pointed out above that the initiation of ESG transformation processes should be carried out, first and foremost, at a strategic level. The course for sustainable development must be integrated into the company’s strategic objectives and reflected in its strategy. Therefore, already at the stage of defining the strategic development objectives of an enterprise, the orientation toward compliance with ESG principles should be taken into account, which will reflect relevant key success factors and key performance indicators in environmental, social, and managerial (economic) sections.

The model for developing a sustainable development strategy can be presented as follows (Figure 4).

Based on the results obtained earlier, it is possible to say that artificial intelligence plays a crucial role in the strategy of sustainable development of an enterprise at each stage, as shown in

Table 2. Integration of artificial intelligence in the stages of developing and implementing managerial decisions: a course for sustainable development.

Stage of the Process of Development and Implementation of Managerial Decisions in the Process of Strategic Sustainable Development	Methods and Tools Applied	Applied Artificial Intelligence Technologies
Identification of the sustainable development challenge	Method of design thinking Stakeholder mapping	Automation of data collection and analysis Recognition, generation, and processing of oral and written human speech
Definition of targets for the sustainable strategic development of an enterprise	Objective tree SMART-methodology Brainstorming method	Automation of data collection and analysis
Analysis of the external and internal environment from a sustainable development perspective	SWOT Matrix PEST Analysis SNW Analysis	Automation of data collection, processing, and analysis Quantitative and qualitative analysis
Determining the direction of sustainable strategic development	Brainstorming method SWOT analysis	Automation of data processing Automating the process of generating ideas while taking into account various factors
Defining available resources for the selected direction	Financial analysis Resources and opportunities analysis SNW-analysis Questionnaire Survey	Automation of data processing Quantitative and qualitative analysis Modelling
Development of a list of possible decision problems of sustainable development	Method of design thinking Expert assessment	Automation of data processing Automating the process of generating ideas, taking into account various factors Iterative and predictive modeling
Assessment of alternatives from a sustainable development perspective and development of a “sustainable” strategy	Brainstorming method “Stable” strategy map Balanced Scorecard (BSC)	Automation of the generation of ideas while taking into account various factors Iterative and predictive modeling
Formation of a portfolio for sustainable development strategy projects	Method of design thinking Analytic Hierarchy Process Method by T. Saaty Expert assessment	Automation of the testing ideas process by taking into account various factors Iterative and predictive modelling
Implementation of the sustainable development strategy	Budgeting Business planning Strategic map of sustainable development	Automation of data collection and analysis Automation of data processing
Monitoring and controlling the implementation of a sustainable strategy	Plan-fact analysis of sustainable development Key Performance Indicators (KPI) Analysis BSC	Automation of data collection and analysis Automation of data processing

.

As the table presented shows, the role of artificial intelligence in the process of development and implementation of managerial decisions in the aspect of sustainable development strategy of a company is not confined to just the automation of processes of collecting and processing necessary information. The capabilities it provides include generating possible alternative solutions, as well as creating models that allow for scenario analysis of possible solutions. In other words, in the process under consideration, artificial intelligence provides decision-makers with not only the necessary information but also options to choose from. This ensures transparency, objectivity, and breadth of information serving as a basis for managerial decisions and also minimizes the subjectivity and individual interest of the person making the decision. This assertion can be justified by the fact that the options offered by artificial intelligence technology are based on algorithms, the choice of which decisions is based only on the optimal ratios sewn into these algorithms. Thus, if a choice is made contrary to the optimal solution, satisfying some “personal” interest, it must be justified in terms of efficiency, which is not always possible.

At the same time, the most important stage in the process of developing a “sustainable” strategy is the selection of projects that are on the trajectory of achieving the goals of sustainable transformation.

5.2. Algorithm for Selecting Sustainable Development Projects Using Artificial Intelligence Technologies

In the course of analyzing the practice of using artificial intelligence, we have found that currently the status of this technology is gradually transforming from a substitute for routine functions into an immediate assistant, generating different decisions and development predictions depending on the selected option, also providing expert assessment.

This role is most “pronounced” in design-thinking processes, accelerating them and expanding the base of generated solutions, as well as improving the quality of prototyping.

From a sustainable development perspective, this possibility can be realized at the stage of selecting sustainable development projects by using a design-thinking method that focuses on forming a project pool in a given context, the best way to achieve sustainable development goals.

An overview of the project selection algorithm for sustainable development strategy is presented in Figure 5.

As shown in the figure, artificial intelligence can be applied at all stages of designing and selecting sustainable development projects. We have previously identified the role of technology in design-thinking stages (Figure 3). In this case, we focus on the possibility of implementing a more effective selection of sustainable development projects based on several criteria. In the first instance, it is the project’s compliance with sustainable development goals and its effectiveness, feasibility, and reality for implementation. In addition, to implement a sustainable development strategy, it is necessary to select a certain number of projects from the total set of generated options sufficient to achieve the company’s sustainable development goals.

However, in our opinion, the crucial role of artificial intelligence technology is to form a pool of sustainable development projects that are balanced according to the criteria of ecology, social responsibility, and corporate governance. In other words, a portfolio of sustainable development projects should be developed that will ensure synergies and the best results in achieving the goals. To this end, the projects under consideration must be complementary, not contradictory, and synchronized in terms of timing and resources. This is the selection of projects that can provide artificial intelligence technologies.

5.3. Modeling the Processes of Selection of Sustainable Development Projects Based on the Integration of Artificial Intelligence Technologies

Our statement about the possibility of effective selection of sustainable projects naturally determines the matter of the applied methodology underlying it, whose algorithm must be implemented in artificial intelligence technology.

As such, on this basis, we consider the method of analyzing hierarchies by T. Saaty [147], which provides the opportunity to make effective managerial decisions using criterion indicators.

The selection of “sustainable” projects is based on an expert assessment of the importance degree λ of the project P in relation to its contribution to achieving sustainable development goals and fulfilling the condition for supplementing projects with ESG components, taking a value from 0 to 1 in such a way that the sum of the importance assessments is 1.

An expert assessment of the project’s importance may include quantitative and qualitative indicators characterizing it from different perspectives. Considering the heterogeneity and different significance of the assessment criteria, it is necessary to bring the indicators to a single scale by ranking the existing criteria by their weighting coefficients, which can be obtained by the analytical method and the method of expert assessments. In general, the transformation formulas are:

$$Q=\left\{\begin{array}{cc}{\displaystyle \frac{N}{K}},& IF N<K\\ 1,& IF N>K\end{array}\right.,$$

(1)

where Q is the value of the indicator transformed to the interval from 0 to 1; N is the original value of the indicator; K is the proportionality coefficient.

The numerical evaluation of criteria is the result of the calculation of the interval value on a weighting factor, reflecting the degree of importance of each indicator:

$$E=QP or E=P\times \left\{\begin{array}{cc}{\displaystyle \frac{N}{K}},& IF N<K\\ 1,& IF N>K\end{array}\right.,$$

(2)

where E is an estimate of the indicator; P is a factor weight.

As a result, the expert assessment of the importance level of the project in achieving sustainable development is:

$$\mathsf{\lambda }={\sum }_{i=1}^k{E}_i+{\sum }_{j=1}^m{E}_j$$

(3)

where λ is an expert assessment of the level of importance of a project in achieving sustainable development goals; Ei is an assessment of the first quantitative measure of the project; Ej is an assessment of the j-th qualitative measure of the project.

The characteristics of sustainable development projects according to the selected criteria are summarized in a table of initial data.

The summary of evaluation results is presented in

Table 3. Initial data for the assessment of sustainable development projects.

Alternatives	Environmental Criterion EC	Social Criterion SC	Corporate Governance Criteria GC
P1	λec1	λsc1	λgc1
P2	λec2	λsc2	λgc2
…	…	…	…
Pn	λecn	λscn	λgcn
Σ	1	1	1

.

After an expert assessment of the contribution of each project alternative to achieving sustainable development according to the selected criteria, a comparison of the alternatives is carried out according to each of the sustainability criteria. It is advisable to use the scale of relative importance proposed by T. Saaty and K. Kerns [148]. The results of the comparison are brought to a matrix of pair comparisons for each of the sustainability criteria, after which the matrix of pair comparisons is normalized by the sum of each column of the matrix and the determination of the weight of each project in achieving the selected criterion. For example, the sequence in which a priority project is identified by environmental criteria is presented in

Table 4. Result of the environmental comparison of alternatives.

Environmental Criterion	P1	P2	…	Pn
P1	1	EC21	…	ECn1
P2	EC12	1	…	ECn2
…	…	…	…	…
Pn	EC1n	EC2n	…	1
Σ by columns	${\displaystyle \sum _{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{1\mathrm{n}}}$	${\displaystyle \sum _{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{2\mathrm{n}}}$		${\displaystyle \sum _{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}}$

and

Table 5. Normalization of the matrix of pair comparisons of alternatives according to environmental criteria.

Environmental Criterion	P1	P2	…	Pn	AVERAGE
P1	$1/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{1\mathrm{n}}$	$\mathrm{EC}21/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{2\mathrm{n}}$	…	$\mathrm{ECn}1/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$	$\mathrm{AVERAGE} (\mathrm{ECn}1/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}})$
P2	$\mathrm{EC}12/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{1\mathrm{n}}$	$1/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{2\mathrm{n}}$		$\mathrm{ECn}2/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$	$\mathrm{AVERAGE} (\mathrm{ECn}2/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}})$
…	…	…	…	…
Pn	$\mathrm{EC}1\mathrm{n}/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{1\mathrm{n}}$	$\mathrm{EC}2\mathrm{n}/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{2\mathrm{n}}$	…	$1/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$	$\mathrm{AVERAGE} (\mathrm{ECnn}/{\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}})$

.

The priority project is the project that has the highest weighting factor in the average value calculation column.

In a similar manner, it determines the weighting coefficients of projects based on the criteria of social responsibility and corporate governance. The results are then entered into the matrix of project alternatives for each sustainability criterion.

After determining the weight of each project about the sustainability criteria, the projects are ranked about the selected criteria in the form presented in

Table 6. Ranking of Sustainable Development Projects.

Alternatives	EC	SC	GC
P1	AVERAGE (ECn1/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)	AVERAGE (SCn1/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{S}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)	AVERAGE (GCn1/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{G}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)
P2	AVERAGE (ECn2/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)	AVERAGE (SCn2/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{S}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)	AVERAGE (GCn2/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{G}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)
…
Pn	AVERAGE (ECnn/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{E}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)	AVERAGE (SCnn/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{S}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)	AVERAGE (GCnn/${\sum }_{\mathrm{n}=1}^{\mathrm{j}}{\mathrm{G}\mathrm{C}}_{\mathrm{n}\mathrm{n}}$)

.

Thus, for each project, the weight of importance in terms of achieving sustainable development goals is determined according to environmental, social, and corporate governance criteria. The company’s portfolio of sustainable development strategy projects will include projects that will ensure the achievement of the set goals to a greater extent.

The proposed method also involves certain assumptions and limitations. For instance, it assumes that complex problems are broken down into simpler ones, resulting in the formation of a hierarchical structure of criteria. The evaluation of alternatives or criteria is performed through pairwise comparisons, and decisions are made based on relative preferences, which significantly simplifies the decision-making process. Furthermore, the method presupposes that if one alternative is preferred over another, and the latter is preferred over a third, then the first is also preferred over the third. When using this method, it is important to ensure consistency in experts’ opinions as well as to take uncertainty and variability in assessments into account. Notably, as the number of criteria and alternatives increases, the comparison process generates larger matrices, potentially complicating the task and increasing the likelihood of errors. On the other hand, given our proposal to use this method with the support of artificial intelligence, the aforementioned assumptions and limitations are largely offset by the capabilities inherent in that technology. Moreover, since only three criteria are employed in selecting projects, the process is relatively straightforward while still yielding well-founded results.

6. Discussion and Conclusions

In our opinion, the presented algorithm for selecting sustainable development projects will ensure the achievement of the company’s sustainable development goals, which is due to the complementary interaction of the advantages of artificial intelligence technologies and design thinking.

This conclusion can be confirmed by modeling the process of strategic sustainable development of the company, based on the implementation of the proposed integration of artificial intelligence technology.

Thus, the company’s sustainable development strategy is based on a set of “sustainable” projects implemented in a certain sequence according to a selected criterion during the period T. Then the dynamics of the implementation of such a strategy can be represented in the form of the following equation:

$$S({\displaystyle \sum _{i=1}^n{x}_i}(t))=\left\{(U_i(x_{i-1}(t),r_i(t)) x_i(t) \left[Q_i-x_i(t)\right]\right\} F(t\ge t_i)\to G$$

(4)

where $F(t)$ is the indicator-function, reflecting the movement of sustainable strategic development over the time period [0;T] to the set strategic goals G (G1, G2, …, Gn); xi is the level of ESG transformation of the company, characterized by the dynamics of implementation of projects εi on the environmental component, μI is social and ${\mathsf{\varphi }}_i$ governance components of the strategy; $U_i(x_i(t),u_i(t))$ is a function describing the dynamics of achieving sustainable strategic objectives at each stage of the strategic development process (xi), taking into account the use of resources; $Q_i$ is the levels of sustainability achieved by the company during the strategy implementation process.

The trajectory of change $S({\displaystyle \sum _{i=1}^n{x}_i}(t))$ as a function of xi over the period $t\subset [0;T]$ reflects the dynamics of sustainable development of the company, expressed in the achievement of defined strategic goals.

In general terms, the components of Ui are the income function Di(xi(t)), the costs function Ci(xi(t)), and the sustainability function Yi(xi(t)).

While the definition $D(x)$ and $C(x)$ can be derived on the basis of calculations of the value of sustainable development projects, it is possible to define $Y(x)$ based on expert assessment from a sustainability perspective, which can be achieved through a balanced scorecard system and the definition as an indicator function F(BSC), reflecting the dynamics of achieving the targets of the sustainable development strategy:

$$U(D(x),C(x),Y(x))=F(BCS)$$

(5)

The performance criteria of the Ui function will be so-called KPIs in terms of basic prospects (profit, market, business processes, personnel), reflecting the process of sustainable strategic development through a set of indicators, corresponding to each of the stages of the company’s development, and the integrated dynamics of these indicators over the period of the strategy will reflect the achievement of sustainable development goals.

The effectiveness of sustainable development strategy implementation will be determined by appropriate changes in ESG aspects that make up it.

Certainly, the presented model is rather “simplified”, but it reflects the main idea, showcasing the dynamics of achieving the stated sustainability goals. It is also important to bear in mind some assumptions and limitations inherent in such models. For instance, one must have up-to-date and high-quality data to draw “correct” conclusions and account for external factors. There may be nonlinear interdependencies among the mentioned resources, which can also lead to inaccurate conclusions. In addition, having current values for the parameters used is crucial, as they may change over the period in question. In the proposed model, the primary constraints concern the interrelationship between the projects that lie along the trajectory of implementing a sustainable strategy and how they influence the speed at which the stated goals are achieved.

Considering the proposed integration of artificial intelligence in the development (design thinking) and project selection stages, ensuring the formation of a project pool according to the best compliance with ESG criteria, It is possible to affirm the achievement of a synergistic effect expressed by the sum of the effects achieved as a result of the implementation of an environmental, social and management criterion:

$${\mathrm{E}}_{\mathrm{s}\mathrm{s}}={\mathrm{E}}_{\mathrm{E}\mathrm{C}}+{\mathrm{E}}_{\mathrm{S}\mathrm{C}}+{\mathrm{E}}_{\mathrm{G}\mathrm{C}},$$

(6)

where E_SS is the effects of sustainable strategy; E_EC is the effects of environmental project implementation; E_SC is the effects of social project implementation; E_GC is the effects of corporate governance.

Accordingly, the implementation of projects that simultaneously achieve the objectives of the Core Criteria for Sustainable Development will result in additional benefits, expressed through increased efficiency of internal resources and meeting the requirements of stakeholders:

$${∆\mathrm{E}}_{\mathrm{s}\mathrm{s}}={∆\mathrm{S}\mathrm{S}}_{\mathrm{E}\mathrm{C}/\mathrm{S}\mathrm{C}/\mathrm{G}\mathrm{C}}-({\mathrm{E}}_{\mathrm{E}\mathrm{C}}+{\mathrm{E}}_{\mathrm{S}\mathrm{C}}+{\mathrm{E}}_{\mathrm{G}\mathrm{C}}),$$

(7)

where ${∆\mathrm{S}\mathrm{S}}_{\mathrm{E}\mathrm{C}/\mathrm{S}\mathrm{C}/\mathrm{G}\mathrm{C}}$ is the synergistic effect of a sustainable strategy; ${∆\mathrm{S}\mathrm{S}}_{\mathrm{E}\mathrm{C}/\mathrm{S}\mathrm{C}/\mathrm{G}\mathrm{C}}$ is the effect of implementing projects on environmental, social, and corporate criteria.

Therefore, the implementation of a sustainable development strategy based on the implementation of projects selected using artificial intelligence technology at the stages of design thinking will allow the company not only to achieve the set goals, but also to achieve synergistic effects, which will obviously have a positive impact not only on its financial performance but will also contribute to the strengthening of market positions and the formation of a positive business reputation.

The study thus demonstrates the increasing role of digital technologies in the activities of modern companies to address a wide range of issues, one of the most important at present is the achievement of sustainable development. As the practice shows, an especially sought-after digital technology in the aspect of ESG transformation of enterprises is artificial intelligence, the application of which allows not only to accelerate and simplify processes but also lies at the core of generating solutions in various aspects of sustainable development. However, there is a real opportunity to expand the range of applications of this technology to improve the efficiency of ESG transformation processes of enterprises. In particular, due to its introduction into the processes of development and implementation of management decisions in the strategy of sustainable development based on integration into the key stages of design-thinking, it makes it possible to achieve the “best” result in line with the stated strategic goals.

As revealed by our analysis of the current state of theory and practice in applying artificial intelligence for sustainable development, the main focus currently generally lies in integrating digital technologies into data collection, data analysis, and the generation of up-to-date reports. There is also an interest in implementing “resource-saving” technologies based on AI. However, AI’s capabilities in managerial decision-making, particularly in the context of devising and implementing a “sustainable” strategy, remain confined to aggregating and processing information. This, in turn, negatively affects the effectiveness of strategic planning for the reasons highlighted in this paper.

As demonstrated by the solutions presented here, the existing arsenal of AI capabilities at every stage, whether strategy development in general or the methodology of design-thinking specifically, provides a wide range of benefits, from shortening the duration of the development process to improving quality and ensuring a high degree of alignment with existing needs. Moreover, the research has shown that the use of artificial intelligence technologies in the design stages of sustainable development projects and the creation of a pool for the implementation of sustainable development strategies can not only ensure the effective achievement of goals but also promote obtain additional synergistic effect, as shown by the presented mathematical models, whose validation is planned by the authors in subsequent studies.

Moreover, in our view, the results obtained in this study may form the foundation for examining other, equally pressing current questions and issues. These may include, for instance, further developing the integration of AI technologies and design-thinking methods by merging the principles of design-thinking with machine learning algorithms to create innovative solutions in the sphere of sustainable development, as well as analyzing the role of empathy and the human factor in the process of developing AI-based solutions. Substantiating the synergistic effect could be advanced through research on evaluating the effectiveness of ESG projects, including their social aspects (e.g., identifying ethical and socioeconomic consequences). Given the profound “analytical” capabilities of artificial intelligence identified here, studies could explore how AI might be applied to big data analysis related to sustainable development and to generating strategic scenarios. The resulting “sustainable” strategic models can be further refined into specific business models for enterprises focused on implementing “green” technologies. It is important to note that the directions mentioned above are merely examples of possible applications of the findings, especially given the rapid pace of development in AI, design thinking, and sustainable, strategic development under modern conditions.

At the same time, these avenues can also be tailored according to the specific industry and operational context of the enterprise. For example, in the energy sector, such collaboration should primarily be viewed through the lens of sustainable energy consumption, whereby the integration of AI with design-thinking should emphasize creating solutions oriented toward optimizing energy consumption, forecasting demand, and managing resource allocation. In agriculture, strategic sustainable development is based on increasing yields and productivity through environmentally sound practices that preserve soil fertility and biodiversity while reducing environmental pollution. In the transportation sector, one key success factor in the context of sustainable development is the creation of accessible and convenient transport systems, enabled by optimizing routes, managing traffic, and forecasting peak loads.

Nevertheless, it is possible to affirm the importance of the study findings that can be used in the development of a sustainable development strategy for modern companies with the aim of ESG transformation.