Conceptual model of AI-transformation strategy

Kobelev Sergei Veniaminovich ORCID: 0009-0008-9359-0076 Lead Product Manager; MTS Intellectualization Center; MTS PJSC 121151, Russia, Moscow, Kutuzovsky ave., 24, sq. 23 serg@usefulpeople.ru

Introduction

Artificial intelligence (AI) is rapidly transforming the business landscape, opening up unprecedented opportunities to improve efficiency, create new products and services, and gain competitive advantages. The explosive growth in popularity of AI-based tools such as ChatGPT, which has reached 180,000,000 users ^[1], indicates a growing interest in AI technologies and their potential for business transformation. However, despite the reduction in the cost of implementing AI ^[2] and the widespread recognition of its advantages, many companies face serious difficulties along the way ^[3]. The lack of a systematic approach to AI transformation often leads to inefficient use of resources, project failures and failure to achieve expected results ^{[4, 5]}. Research shows that successful AI transformation requires an integrated approach covering three key management systems: human resources, automated and automatic ^[6]. At the same time, the digitalization of business processes should be carried out in stages, starting with a thorough analysis of the organization and the development of a strategy, through the introduction of technologies, to the analysis of the results obtained ^[7].

The subject of this research is the development of a conceptual model of an AI transformation strategy. The relevance of the study is due to the lack of a systematic approach to AI transformation in most organizations, which leads to inefficient use of resources and failure to achieve expected results. Existing digital transformation methodologies do not fully take into account the specifics of AI implementation, creating the need to develop a specialized model.

Among the most common problems are the lack of qualified specialists in the field of AI ^[8], the difficulties of integrating AI solutions into the existing IT infrastructure ^{[9, p. 261]}, the lack of a clear data management strategy ^[10], as well as ethical dilemmas associated with the use of AI ^[11]. The concept of artificial neural networks, which underlies many modern AI systems, was proposed back in 1958 by Frank Rosenblatt in his work on the perceptron ^{[12, C. 387]}, however, the practical application of these technologies in business requires the development of effective strategies. In addition, many existing digital transformation methodologies, such as those proposed by B. M. Garifullin and V. V. Zyabrikov ^[5] and the SAP methodology ^[10], do not fully take into account the specifics of AI implementation, which makes them unsuitable for solving AI transformation tasks. In this regard, the development of a specialized AI transformation strategy model becomes critically important for the successful implementation of AI in business processes.

This study aims to solve this problem by systematizing existing approaches, such as aiSTROM ^[13], and developing a comprehensive model that will allow companies to effectively plan, implement and control the process of AI transformation. The proposed model takes into account the specific requirements for data, technology, ethical aspects and competencies of personnel, and also provides practical recommendations for company executives. The implementation of this model will allow companies to minimize the risks associated with AI transformation, optimize the use of resources and maximize the return on investment in AI.

The purpose of the study: To develop a conceptual model of an AI business transformation strategy that takes into account the specifics of working with data, AI technologies, and ethical aspects and allows companies to effectively plan and implement the introduction of AI into business processes.

Research objectives:

· To conduct a comparative analysis of existing digital and AI transformation methodologies.

· Identify key success factors and barriers to the implementation of AI in business.

· Develop a 15-stage AI transformation strategy model.

· To substantiate the scientific novelty and practical significance of the developed model.

In this paper, we focus on developing a conceptual model of an AI transformation strategy, since existing approaches, despite their practical value, do not offer a holistic vision of the transformation process. The conceptual model allows us to create a unified methodological base for further research and practical developments in the field of business AI transformation. While most of the existing work focuses on individual aspects of AI implementation, the proposed conceptual model provides a systematic view of the transformation process.

1. Literature review

As part of the study, a systematic analysis of scientific and practical publications on the topic of AI transformation for the period 2018-2024 was carried out. The analyzed sources can be divided into two main categories:

As part of the study, a systematic analysis of scientific and practical publications on the topic of AI transformation for the period 2018-2024 was carried out. The analyzed sources were divided into two main categories.

The first category includes scientific and methodological research. Among them are theoretical works on the methodology of digital and AI transformation, presented in the works of B. M. Garifullin and V. V. Zyabrikov ^[5], as well as D. Herremans ^[13]. An important place is occupied by the research of principles and approaches to the introduction of AI into business processes conducted by E. Y. Belova and M. O. Shevchenko ^[6], as well as M. I. Maksimov and A. R. Shamilova ^[7]. In addition, academic publications on evaluating the effectiveness of AI transformation were studied, the authors of which are N. V. Gorodnova ^[14], E. Z. Glazunova and A.V. Chernaya ^[15].

The second category is represented by practice-oriented sources. It includes industry research by consulting companies such as McKinsey ^[16] and BCG ^[2], as well as methodological materials from technology companies, in particular SAP ^[10].

The analysis of scientific and methodological works has shown that research is mainly focused on the general aspects of digital transformation ^{[17, 5]}, while the specifics of AI transformation are not sufficiently disclosed. In the works of B. M. Garifullin and V. V. Zyabrikov ^{[5, C. 1355]}, a basic methodology of digital transformation is proposed, but it does not take into account the specifics of the introduction of artificial intelligence. D. Herremans ^{[13, C. 4]} offers the first attempt to structure the process of AI transformation, but its model does not include important stages of assessing the current state and analyzing the results.

In modern Russian research, various approaches are proposed to assess the effectiveness of AI transformation. E. Y. Belova and M. O. Shevchenko ^{[6, C. 23]} have developed an integral indicator of the level of digital intelligence of an enterprise, including an assessment of personnel competencies, the level of automation and the degree of implementation of AI technologies. M. I. Maksimov and A. R. Shamilova ^{[7, C. 140]} proposed a three-stage model of digital transformation, emphasizing the need for a preliminary analysis of the organization and the gradual implementation of changes.

Practice-oriented sources demonstrate the growing business interest in AI transformation. Documented cases of AI implementation ^{[15, 14]} show a positive impact on the efficiency of business processes, but also reveal the lack of a systematic approach to transformation. SAP methodological materials ^[10] offer a general structure of digital transformation, which requires significant adaptation for the tasks of AI transformation.

The analysis of the literature revealed the following gaps in existing research:

• The lack of a comprehensive AI transformation methodology that takes into account the specifics of working with data and ethical aspects
• Insufficient elaboration of the stages of assessing the organization's readiness for AI transformation
• Lack of clear criteria for prioritizing projects within the framework of AI transformation
• Incomplete description of the mechanisms for integrating AI solutions into existing business processes

The identified gaps determine the need to develop a holistic AI transformation strategy model that will take into account both theoretical developments in the field of digital transformation and practical experience in implementing AI in business processes.

Despite a significant amount of research in the field of digital transformation, the specifics of AI transformation remain insufficiently studied. Existing methodologies, such as the models of B. M. Garifullin and V. V. Zyabrikov ^[5] and SAP ^[10], are focused on general aspects of digital transformation and do not take into account the unique features of AI related to working with big data, the need for specialized teams and ethical issues.

Opponents may argue that the adaptation of existing models of digital transformation is sufficient to introduce AI into business processes. However, practical experience shows that this approach often leads to inefficient use of resources and failure to achieve expected results ^[4]. For example, the D. Herremans model ^[13], although aimed at AI transformation, does not cover the full range of necessary stages and does not provide a systematic approach.

Moreover, as N. V. Gorodnova notes [14 C. 1487], the introduction of AI is associated with a number of problems, such as the need for highly qualified specialists, difficulties with ensuring data quality and ethical dilemmas. These findings confirm our position on the need to develop a comprehensive AI transformation strategy model that takes into account these critical aspects.

In addition, C. Williams ^[17] proposes a matrix model for categorizing AI tasks, but it does not take into account organizational and technical aspects of implementation, and does not pay enough attention to data and infrastructure issues. Such limited approaches do not allow companies to fully integrate AI into their business processes.

Thus, the existing methodologies are insufficient for successful AI transformation, which justifies the need to develop a new integrated model that takes into account the specific aspects of the introduction of artificial intelligence into business.

The gaps identified during the literature review determined the choice of the research methodology presented in the next section.

2. Research methods

To achieve the goal of developing a comprehensive model of the AI transformation strategy, a methodology based on comparative analysis and synthesis of existing approaches was applied.

2.1. Comparative analysis.

The main research method was a comparative analysis of existing digital and AI transformation methodologies. The following sources were studied in detail and compared:

· B. M. Garifullin and V. V. Zyabrikov ^[5]: A model of digital business transformation based on a systematic approach and iterative implementation cycles.

· SAP methodology ^[10]: A digital transformation strategy with an emphasis on data management and customer centricity.

· N. White's approach ^[18]: Applying Lean Six Sigma principles to accelerate digital transformation and achieve "quick wins".

· The D. Herremans model ^[13]: aiSTROM is a roadmap for developing a successful AI strategy that takes into account specific aspects of AI transformation.

· C. Williams task classification ^[17]: A matrix model for categorizing AI tasks based on the degree of human participation and the nature of information processing.

The criteria of the comparative analysis included:

· Focus on AI transformation: To what extent the methodology is adapted to the specific requirements of the introduction of artificial intelligence.

· Having a step-by-step action plan: Does the methodology provide a clear sequence of steps to implement the transformation.

· Inclusion of the current status assessment stage: Is there an analysis of the initial conditions and the organization's readiness for transformation.

· Consideration of ethical and social aspects: Whether the possible risks and consequences of AI implementation are considered from the point of view of ethics and society.

· Mechanisms for integrating AI into business processes: Does the methodology describe how to implement AI solutions into existing company processes.

· Tools for evaluating and adjusting the strategy: Are there methods for monitoring, evaluating the effectiveness and adjusting the chosen strategy.

Comparative analysis results:

· Strengths of existing methodologies: Detailed elaboration of the stages of digital transformation, emphasis on data management, attention to organizational changes.

Identified gaps:

· The lack of a comprehensive methodology specifically adapted for AI transformation.

· Insufficient elaboration of the ethical and social aspects of AI implementation.

· Not taking into account the specifics of working with data and forming a specialized AI team.

· Lack of stages for assessing the current state and mechanisms for adjusting the strategy.

2.2. Synthesis and development of the model.

Based on the results of the comparative analysis, the synthesis method was applied to develop a new integrated model of the AI transformation strategy. The process included:

· Identification of key elements from existing methodologies that have shown their effectiveness.

· Adding missing steps necessary for a full-fledged AI transformation, such as a data strategy and the formation of an AI team.

· Integration of ethical and social aspects at all stages of the strategy.

· Develop a step-by-step plan that includes mechanisms for evaluating and adjusting the strategy.

2.3. Justification of the choice of methods.

Comparative analysis was chosen as the main research method because it allows you to systematically compare different approaches and identify their strengths and weaknesses. The synthesis method ensured the integration of best practices and the addition of new elements into a single, holistic model.

2.4. The impact of the analysis results on the development of the model.

The results of the comparative analysis directly influenced the structure and content of the developed model:

· Steps missing from other methodologies are included, but critical for AI transformation (for example, data strategy, formation of an AI team).

· Increased attention to ethical and social aspects, given their importance in the context of the use of AI.

· Mechanisms for evaluating and adjusting the strategy have been developed to ensure the flexibility and adaptability of the model.

· A stage of cultural change has been added, reflecting the need to transform the corporate culture for the successful implementation of AI.

The application of the described methodology allowed for a detailed comparative analysis of existing approaches to AI transformation.

3. Comparative analysis of digital and AI transformation strategies

3.1. Analysis of the methodology of digital transformation.

For a systematic analysis of existing approaches to digital transformation, we will consider key methodologies that have been widely recognized in the business environment. The analysis of each methodology will be carried out in the following aspects: the basic principles and structure of the approach, the strengths of the methodology, limitations when applied to AI transformation, as well as the possibilities of adaptation for the tasks of implementing artificial intelligence. Such an analysis structure will make it possible to identify both useful elements of existing approaches and areas that require additional study in the context of AI transformation.

Analyzing the approach to digital transformation presented in the work of B. M. Garifullin and V. V. Zyabrikov ^{[5, C. 1355]}, the following key features of their methodology can be identified. The authors propose a cyclic transformation model, where each cycle begins with an analysis of the current state and ends with an assessment of the implementation results.

The methodology is based on three main principles:

1. A systematic approach to the analysis of the business model, involving the identification of "bottlenecks " based on evidence

2. Iterative implementation of changes through piloting and subsequent scaling

3. Parallel consideration of two areas of optimization: the introduction of new technologies and simplification of existing processes

The authors' emphasis on the need for an economic justification of each stage of transformation and constant adjustment of the strategy based on the results obtained is particularly important. This approach minimizes risks when implementing changes and provides flexibility in a changing market environment.

A critical analysis of this methodology shows that, despite its consistency, it does not pay enough attention to data management and ethical aspects of AI implementation, which are critically important in modern conditions of digital transformation.

Of particular interest is the methodology of digital transformation developed by SAP ^[10], which focuses not only on technological aspects, but also on organizational changes. An analysis of their approach shows three key differences from the previously considered methodology:

1. Unlike the approach of B. M. Garifullin and V. V. Zyabrikov, the SAP methodology begins with a broader assessment not only of the current state of business processes, but also of all existing digital transformation initiatives. This avoids duplication of efforts and ensures synergy between different projects.

2. A fundamentally important element of the SAP methodology is the separation of work with data into a separate strategic direction. The company emphasizes the need to use data analytics as a basis for decision-making at all stages of transformation. This aspect is especially relevant in the context of AI transformation, where the quality and availability of data become critical success factors.

3. The SAP methodology identifies customers as a key driver of transformation, which reflects current trends in building business strategies. This approach ensures that transformational initiatives focus more clearly on creating value for end users.

A comparative analysis of these approaches allows us to conclude that the SAP methodology offers a more structured and modern view of the digital transformation process, especially in the context of subsequent AI transformation. However, it is worth noting that this methodology requires significant resources and may be redundant for small and medium-sized enterprises.

The analysis of N. White's methodology ^[18] reveals several significant features:

1. Unlike the previously discussed methodologies, N. White suggests using the principles of Lean Six Sigma to accelerate the transformation process. Especially important is the concept of "quick wins", which must be implemented within 30 days ^[19]. This approach allows you to:

· Quickly demonstrate the value of transformation

· Increase employee engagement

· Create a positive dynamic of change

2. The methodology pays considerable attention to the cultural and organizational aspects of transformation, putting them even before technological solutions. This is reflected in the sequence of stages, where preparation for a culture change precedes planning for the introduction of technologies.

3. Of particular value is the inclusion of the feedback collection and strategy adjustment stage. This allows you to:

· Identify problems in a timely manner

· Adapt the approach to real conditions

· Minimize risks when scaling

The main advantage of this approach is its practical applicability and focus on quick results. However, it is worth noting that the methodology may underestimate the complexity of the technical aspects of transformation, especially in the context of AI implementation.

3.2. Analysis of specialized approaches to AI transformation.

The analysis of the aiSTROM methodology shows a number of significant features and limitations:

1. Structural advantages

· Highlighting data as a separate strategic direction, which is critically important for AI projects

· Integration of technical and organizational aspects (from technology to cultural change)

· Emphasis on an interdisciplinary approach in team building

2. Significant omissions

· The absence of a stage for assessing the current state of the organization, which makes it difficult to determine the starting point of transformation

· There is no mechanism for evaluating the results and adjusting the strategy

· The absence of a stage for the formation of a specific project portfolio

· The issue of creating a roadmap for implementation has not been worked out

3. Comparative features In contrast to approaches to digital transformation, the aiSTROM methodology:

· Pays significant attention to the specifics of working with data and AI technologies

· Focuses on the formation of a specialized AI team

· Includes ethical aspects and risks associated with AI

4. Practical limitations

· Insufficient elaboration of mechanisms for integrating AI initiatives into existing business processes

· Lack of clear criteria for prioritizing projects

· Insufficient attention to scaling up successful initiatives

Note that the structure is quite different from the structure of the digital transformation strategy. That is why we believe that AI transformation should have its own strategy, different from digital transformation. Data appears in the model, as they are the basis of AI transformation, as well as the AI team and technologies, without which the implementation of the strategy becomes impossible ^[20]. But in this structure, important stages are missing: the analysis of the current state and the results of the transformation launch. We also missed an important stage, in our opinion, at which a list of projects that will be implemented by the AI team is being formed. Also, the strategy becomes completed at the moment when a plan for its implementation or a roadmap appears ^[21].

The approach to the classification of AI transformation tasks proposed by C. Williams ^[17] deserves special attention. It provides a very simple approach to determining the points of application of AI, depending on the type of task and the level of involvement of people in the task (Fig. 1):

Figure 1. Matrix of AI transformation tasks by C. Williams.

The author presents a matrix model of task categorization based on two key dimensions: the degree of human participation (from fully automated to requiring active human participation) and the nature of information processing (from analysis to action).

The analysis of this methodology reveals the following features:

1. Structural advantages

· Simplicity and clarity of classification

· Clear differentiation of task types based on objective criteria

· The possibility of a quick initial assessment of potential projects

2. Practical applicability

· The matrix allows you to categorize existing business processes

· Helps in prioritizing projects based on their characteristics

· Facilitates the assessment of the complexity and potential impact of each initiative

3. Limitations of the approach

· Lack of detailed recommendations for the implementation of the transformation

· Organizational and technical aspects of implementation are not taken into account

· Insufficient attention to data and infrastructure issues

4. Integration potential

5. Despite its limitations as an independent methodology, this approach can effectively complement more comprehensive AI transformation strategies at the stage of initial evaluation and categorization of projects.

This approach helps to quickly distribute the company's current business processes into appropriate groups. After that, it becomes easier to find solutions to problems and evaluate the complexity and impact of each project individually. At the same time, in addition to this logic, the source does not provide any clear way to form an AI transformation strategy.

3.3. Comparative analysis and identification of gaps.

To systematize the results of a comparative analysis of existing approaches to digital and AI transformation, a criteria analysis of the main methodologies was carried out. Table 1 presents a comparison of the key characteristics of the strategies considered, which allows us to identify their strengths and limitations.

Table 1. Comparative analysis of methodological approaches to digital and AI transformation strategies.

As can be seen from the presented analysis, the existing methodologies have a number of significant limitations. In particular, only the strategies of D. Herremans and C. Williams are directly focused on AI transformation, while they do not provide full coverage of the necessary implementation stages. The strategies of B. M. Garifullin and V. V. Zyabrikov and SAP, despite the detailed study of the stages, do not take into account the specifics of the implementation of artificial intelligence. The identified limitations of existing approaches justify the need to develop a comprehensive model of the AI transformation strategy.

Before moving on to the new structure of the AI transformation strategy, I would like to discuss a few more theses:

1. Before forming AI transformation goals, it is necessary to understand how these goals will be related to the company's business goals or its strategy.

2. After the stage of forming projects in which AI will be implemented, a stage of prioritization of these projects should be added. Since the number of processes in a company can reach tens, or hundreds and thousands, depending on the size. The company will always be limited in resources (both financial and human), therefore, maximum efforts should be applied to those projects that give the maximum increase in achieving the goal, while having less burden on the team (lower complexity of implementation).

3. Resources should be invested in the development and training of personnel, both among the AI team and among the rest of the company's team.

4. The "Risk level" stage in the structure from D. Herremans should be moved beyond a specific stage. Risks must be taken into account at each stage of the strategy development: from the data strategy to the choice of technologies. At the same time, there is a rather important stage about ethical and social issues, since in the case of AI transformation, we most often work with user data.

5. It should also be noted that in the case of AI transformation, it is considered a common practice to involve external partnerships (both technological and informational).

3.4. Justification of the need for a new model. Based on the comparative analysis of existing methodologies and the identified limitations, it becomes obvious that it is necessary to develop a new integrated AI transformation model. Such a model should take into account both the strengths of existing approaches and fill identified gaps in the field of working with data, ethical aspects and the formation of specialized teams. The next section presents a 15-step model that we have developed, which integrates the best practices of existing approaches and complements them with new elements. In addition, the rapid growth of the global AI technology market, noted by Glazunova and Chernaya ^{[15, C. 8]}, emphasizes the need for a more structured and systematic approach to the implementation of AI in business processes. Based on the identified features and limitations of existing approaches, a comprehensive AI transformation model has been developed.

4. The AI Transformation Strategy Model

The proposed 15-stage model of the AI transformation strategy has been developed based on the synthesis of successful practices of existing approaches and filling identified gaps. The structure of the model is based on the principle of sequential development from the assessment of the current state to practical implementation, while each subsequent stage logically follows from the previous one. The choice of 15 stages is due to the need to cover all critical aspects of AI transformation: from strategic planning and working with data to ethical issues and cultural changes. At the same time, the stages are grouped into logical blocks: preparatory (stages 1-3), technological (stages 4-8), implementation (stages 9-13) and transformational (stages 14-15), which provides a systematic approach to the implementation of AI.

Taking into account all the above, the final structure of the AI transformation strategy, in our opinion, includes the following interrelated stages (Fig. 2).

Figure 2. Stages of the AI transformation strategy

Below is an extended description of each of the stages

1. Assessment of the current state. The research of existing methodologies, systematic structures and information bases is carried out. This allows you to determine the starting position for the introduction of AI and unlock the potential for its use.

2. Connection with business goals. It is guaranteed that the proposed AI initiatives are within the framework of the overall strategic directive of the organization. This ensures that AI implementation initiatives are focused and consistent. For example, improving production efficiency, reducing costs, or improving customer service.

3. Formulation of AI goals. From 3 to 5 clear AI-based transformation tasks are designed, developed jointly with representatives of various functional areas of the company. For example, the goal is to create a machine learning model to optimize the decision-making process or to develop a chatbot for customer support.

4. Data strategy. An integrated approach to data management is defined, including the selection of sources, consideration of legislative norms and information storage mechanisms. Sources can be internal databases or external partners and cloud services, and requirements for geo-dependent data storage.

5. Forming an AI team. The complex of AI specialists is formed on the basis of an interdisciplinary approach and in accordance with the company's strategy for attracting staff. These can be internal teams, or external partners.

6. Positioning of AI in the company. A strategic plan for integrating AI into the organization's structure is being developed, considering both centralized and decentralized models.

7. The strategy of technological support. Various AI technologies are being explored, with particular attention to the principles of interpreted AI and the balance between accuracy and the use of "black boxes". For example, a company may choose to use Python and TensorFlow to develop machine learning algorithms, and then evaluate the accuracy and transparency of these models.

8. Ethical and social issues. Potential ethical and social consequences of AI implementation are identified and analyzed in order to minimize possible risks. It is necessary to take into account issues of privacy and fairness in AI, for example, avoiding bias in AI algorithms or protecting personal data.

9. Projects and implementation program (taking into account priorities). Projects or programs are being formed and prioritized to achieve AI objectives, which ensures consistent and focused development. For example, one of the projects may be the development of a system of recommendations to increase sales, and within the framework of the program — a whole set of initiatives to improve the efficiency of staff.

10. Implementation roadmap. A detailed roadmap for the implementation of projects is being developed, which includes all key stages, time frames and international dependencies.

11. Learning and development strategy. A training and development plan is being created to increase the level of readiness of employees to work with AI.

12. Key Performance Indicators (KPIs). Metrics are defined to track the success of projects that reflect the importance of AI for the organization, such as improving forecast accuracy, increasing sales or reducing query processing time (or others, depending on the goals of the organization and the projects in which AI will be implemented)

13. The strategy of external partnerships. An approach to cooperation with external partners, including technology suppliers, consultants and scientific institutions, is being developed.

14. Re-checking and optimization. The AI implementation strategy is periodically reviewed and adjusted based on the feedback received and new data.

15. Cultural changes. The process of introducing AI into the corporate culture and stimulating the active adoption of new technologies by employees is underway. This is achieved through internal seminars and training sessions for employees to help them better understand and adopt new AI technologies.

It should be noted that the issues of practical implementation of the proposed conceptual model require a separate in-depth study and go beyond the scope of this work. The implementation process includes many specific aspects, depending on the specific industry, the size of the organization, the level of technological maturity and other factors. These issues deserve a separate study and will be considered in subsequent papers.

5. Conclusions and scientific novelty

As a result of the conducted research, a comprehensive 15-stage model of the AI transformation strategy was developed, which integrates the best practices of existing approaches with new elements. The scientific novelty of the work lies in the creation of a systematic approach to data management, ethical aspects and competence development in the context of AI transformation. For the first time, a holistic methodology for assessing an organization's readiness for AI transformation has been proposed, and a mechanism for prioritizing and evaluating the effectiveness of AI projects has been developed. The results obtained are consistent with the conclusions of N. V. Gorodnova ^[14] on the need for a systematic approach to the introduction of AI into business processes and confirm the trends identified by E. Z. Glazunov and A.V. Chernaya ^[15] in the development of AI in business. The practical significance of the study lies in the fact that the developed model provides company managers with structured tools for planning and implementing AI transformation. The proposed evaluation criteria and KPIs make it possible to effectively control the process of AI implementation, minimize risks and optimize the use of resources in the implementation of AI initiatives. A significant methodological contribution of the research is to systematize existing approaches to AI transformation and identify gaps in existing methodologies. The developed model not only fills in these gaps, but also creates a solid foundation for further research in the field of business AI transformation.

Conclusion

In the context of the rapid development of artificial intelligence and its increasing influence on business processes, the developed AI transformation strategy model is becoming particularly relevant. One of the key advantages of the model is its adaptability to various industries and business scales. The proposed approach allows us to take into account the specifics of a particular organization, and the step-by-step structure provides the possibility of phased implementation, which is especially important for companies starting their path in AI transformation. The experience of Russian enterprises shows that successful AI transformation requires a systematic approach that includes both technological and organizational changes. At the same time, a key success factor is the phased implementation of changes with constant monitoring of results and adjustment of the strategy based on the data obtained. The conducted research opens up several important areas for further study. Empirical validation of the model through pilot projects in various industries is of paramount importance. Of significant interest is the development of industry-specific modifications of the model, taking into account the specifics of different sectors of the economy. Special attention should be paid to the study of the influence of organizational culture on the success of AI transformation and an in-depth study of the ethical aspects of AI implementation. In the light of recent technological advances, the development of specialized approaches to the implementation of generative AI is becoming particularly relevant. At the same time, it is important to take into account existing limitations and challenges. The model requires empirical verification in real conditions and adaptation to the specifics of specific industries. The dynamic development of AI technologies can affect the relevance of individual elements of the model, which requires its regular updating and adjustment. Based on the conducted research, a number of practical recommendations can be formulated. When implementing a model, it is critically important to start with a thorough assessment of the current state and readiness of the organization for transformation. Special attention should be paid to the formation of a team and the development of the necessary competencies. The success of the implementation largely depends on regular monitoring and timely adjustment of the strategy based on the results obtained. In conclusion, it is worth noting that the proposed model creates a methodological basis for a systematic approach to business AI transformation. In the context of increasing competition and digitalization of the economy, this approach is becoming critically important for maintaining the competitiveness of organizations. Further development and adaptation of the model, taking into account the practical experience of its application, will create an even more effective tool for managing the processes of AI transformation.