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Kobelev, S.V. (2025). A Framework for Implementing Generative AI in Business Processes. Finance and Management, 2, 1–21. . https://doi.org/10.25136/2409-7802.2025.2.73740
A Framework for Implementing Generative AI in Business Processes
DOI: 10.25136/2409-7802.2025.2.73740EDN: TFYZSJReceived: 18-03-2025Published: 07-04-2025Abstract: The subject of the research is the development of a comprehensive framework for the strategic implementation of generative artificial intelligence (GenAI) in the business processes of organizations of various sizes and industries. Existing approaches to the implementation of traditional artificial intelligence (AI) technologies, GenAI, and digital transformation are analyzed, and their limitations and shortcomings are identified in the context of the specific characteristics of GenAI models, such as the ability to create new content and the associated ethical and legal risks. The necessity of creating a specialized framework that takes into account the unique opportunities and challenges associated with GenAI, as well as the need for adaptation to diverse business contexts, including small and medium-sized businesses, is substantiated. The problem of the lack of structured methodologies that allow organizations to effectively integrate GenAI into their operational activities, maximizing return on investment and minimizing potential risks, is considered. The research is based on a systematic and comparative analysis of scientific literature and practical publications, as well as the synthesis of the conceptual basis of a new framework. A combined approach is used, including methods of qualitative and quantitative data analysis. The scientific novelty lies in the development of a nine-stage framework that, unlike existing approaches, integrates large language models (LLMs) already at the stage of business process diagnostics for semantic analysis of unstructured data (interviews, questionnaires, surveys). This makes it possible to identify hidden relationships and non-obvious optimization needs that are difficult to detect using traditional methods. The framework covers strategic, operational, and technological aspects of implementation, as well as change and risk management principles. The developed framework offers a universal, adaptive, and practically oriented approach to the strategic implementation of GenAI, contributing to improving the efficiency of business processes, minimizing risks, and maximizing the return on investment in GenAI technology. The practical significance is confirmed by approbation at the academy of a large consulting company and a pilot project at MTS PJSC. Keywords: generative artificial intelligence, implementation framework, large language models, business processes, strategic analysis, process optimization, risk management, change management, digital transformation, AI transformationThis article is automatically translated. You can find original text of the article here. Introduction Generative artificial intelligence (hereinafter referred to as AI) opens up opportunities for improving business efficiency, optimizing processes and creating new products [1, p. 494]. However, despite the significant potential [1, p. 494; 2, P. 158], many organizations face difficulties in implementing it [3, P. 73; 4, P. 35]. The lack of a clear framework leads to inefficient use of resources and project failures. The implementation of GII is fraught with a number of key challenges. Firstly, it is difficult for organizations to determine which business processes are most suitable for optimization using this technology, given its ability to generate diverse content. Secondly, there are ethical and legal issues related to copyright, confidentiality and the risk of spreading disinformation [4, p. 36; 5, P. 34]. The problems related to the limitations of international cooperation are also relevant for Russia [3, P. 74]. The existing concepts of digital transformation, developed for "traditional" artificial intelligence (hereinafter – AI), are not effective enough, because they do not fully take into account the specifics of generative models that require a special approach to management and control [3, p. 75; 7, P. 48]. This research is aimed at developing a framework for the strategic implementation of GII in business processes. The relevance is due to the need for a structured approach that is adaptable to various business contexts, including small businesses [1, p. 493]. The scientific novelty lies in the development of a comprehensive framework that, unlike existing approaches, integrates large language models (LLM) already at the stage of business process diagnostics for the semantic analysis of unstructured data. This allows you to identify hidden relationships and non-obvious optimization needs that are difficult to detect using traditional methods. The proposed framework will help companies implement GII by analyzing business processes based on strategic goals and using GII itself for data analysis. It provides a framework and recommendations to minimize risks and maximize the return on investment. The purpose of the research is to create a framework for implementing GII in business processes, taking into account the features of generative models and ensuring their effective integration. Research objectives: ● Identify relevant approaches to the implementation of GII. ● Identify the key characteristics of the GII for implementation. ● Formulate the principles of building the framework. ● Develop a framework structure with stages and optimization mechanisms. ● Define criteria for evaluating the effectiveness of the implementation. ● Formulate recommendations on change and risk management.
1. Literature review An analysis of modern scientific literature and practical publications has revealed the main trends and gaps in the implementation of AI, and especially GII, in business processes. A significant part of the research is devoted to general methodological approaches to digital transformation and the introduction of AI [1, 3, 7]. However, these works, while offering valuable models, are limited in taking into account the specifics of the GII, in particular, its ability to create new content. Another group of studies focuses on the technological aspects of the development and application of GII [2, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16], However, it often does not pay due attention to the organizational and strategic issues of integrating these technologies into business processes. The third category of works analyzes the impact of GII on individual business functions and industries [17, 18, 19], demonstrating the potential of GII, but not offering a comprehensive framework for implementation at the level of the entire organization, especially in the context of small business [1, p. 493]. The analysis revealed a number of controversial issues. In particular, the compromise approaches between highly specialized and universal GII solutions have not been sufficiently studied. The issue of the impact of AI on the labor market is acute [14, P. 49], but there is no clear picture of the balance between automation and the creation of new jobs. A critical gap is the insufficient elaboration of ethical aspects and risk management issues related to GII [4, 5, 6]. Thus, existing research does not provide an integrated approach to the implementation of GII. The main disadvantages are the lack of a framework that takes into account the specifics of generative models; insufficient elaboration of organizational aspects and ethics; lack of universal recommendations, especially for small businesses; weak integration with the company's strategy. In the Russian context, these problems are getting worse [3, P. 74]. At the same time, a number of works [1, 4, 5] indicate the importance of a systematic consideration of not only technological, but also organizational and legal issues when working with GII. However, in most cases, the specifics of generative models and the need to integrate them into strategic goals remain insufficiently detailed, which underscores the urgency of developing a new framework. The identified gaps justify the need to develop a new comprehensive framework offering a universal and adaptive approach to the implementation of GII. 2. Research methods The development of the framework for the strategic implementation of GII in business processes was based on a combined methodology, including systematic and comparative literature analysis, as well as synthesis. A systematic and comparative analysis was necessary to identify existing knowledge, gaps and best practices in the field of AI implementation, as well as to identify specific requirements for the implementation of AI. The synthesis allowed us to create a new conceptual framework that integrates this knowledge and takes into account the specifics of the GII (in particular, the need to manage risks associated with content generation). The choice of a methodology based on literature analysis corresponds to the purpose of the article – to develop a conceptual, rather than an empirical framework. The main source of data was a systematic analysis of scientific literature and practical publications. The described framework has been practically tested as part of the educational programs of the academy of a large consulting company and during a pilot project to optimize business processes at MTS PJSC, confirming its applicability to solving problems related to the implementation of GII. To develop the framework, a systematic and comparative analysis of existing methodologies for the implementation of AI, GII and digital transformation was carried out. The approaches of digital transformation, consulting companies, technologically and functionally oriented approaches are considered. The comparison was based on the following criteria: focus on GII, versatility, complexity, practical applicability, consideration of strategic goals, and the potential for using GII for optimization. The results of the analysis revealed the gaps in existing approaches and identified the directions for the development of a new framework. Based on the results of a systematic and comparative analysis, the synthesis method was applied to form the conceptual basis of the new framework. The synthesis process included the integration of the most effective elements from the analyzed approaches, the adaptation of these elements to the specific requirements of the GII implementation and the addition of original components aimed at eliminating the identified gaps. The developed framework is a structured sequence of steps, principles and recommendations that provide an integrated and adaptive approach to the strategic implementation of GII in various organizational contexts. The following tools were used in the preparation of this article. In particular, the following models were used: OpenAI ChatGPT (version o1, o3-mini-high), Google Gemini (version 2.0 Flash Thinking Experimental, 2.0 Pro Experimental) and OpenAI DeepResearch. These tools were used to analyze approaches to the implementation of GII presented on websites, to formulate the preliminary structure of the article. In addition, GII was used to select keywords. All the generated text fragments have been subjected to significant editorial revision and verification for correctness and consistency with scientific sources. The author is solely responsible for the content of the article.
Comparative analysis of approaches to the implementation of GII in business processes Despite the growing interest in GII, comprehensive frameworks for its implementation are limited in the scientific literature. This section analyzes the existing approaches offered by consulting companies, technology vendors, and business analysts, identifying their strengths and weaknesses. The existing approaches are divided into the following categories: ● Grammarly Business Models: Describe the stages of development of an organization in the field of AI application (for example, the Grammarly Business model (The 5 Stages of Enterprise-Wide Gen AI Adoption // Grammarly Business. URL: https://www.grammarly.com/business/learn/generative-ai-adoption-framework / (date of access: 02/14/2025)) and similar (Goyal V. Generative AI Adoption Maturity Model. URL: https://vikasgoyal.github.io/genai/AdoptionMaturityModel.html (date of request: 02/14/2025))). They help to assess the current level, but they do not give instructions on how to switch between stages. ● Step-by-step strategies: Suggest a sequence of steps for implementing GII (for example, IBM (Villanueva J., Moncau G. Step-by-step guide: Generative AI for your business // IBM. URL: https://www.ibm.com/think/insights/step-by-step-guide-generative-ai-for-your-business (accessed: 02/14/2025)), Vistage (Tsipursky G. 7 steps to adopting a comprehensive Gen AI strategy // Vistage. URL: https://www.vistage.com/research-center/business-operations/business-technology/20250205-gen-ai-strategy / (accessed: 02/14/2025)), AWS (Sperry D., Sullivan W. Working backwards from generative AI business value in the public sector // AWS. URL: https://aws.amazon.com/blogs/publicsector/working-backwards-from-generative-ai-business-value-in-the-public-sector / (date of access: 02/14/2025))). They provide more specific recommendations, but may not be flexible enough. ● Approaches focused on data and skills: Emphasize the importance of data preparation and staff training (for example, Orange Business (Generative AI implementation: A roadmap from concept to reality // Orange Business. URL: https://digital.orange-business.com/en-en/insights/digital-newsroom/generative-ai-implementation-roadmap-concept-reality (date of request: 02/14/2025))). Recognize the importance of organizational readiness, but may be overly focused on preparation. ● Responsible AI frameworks: Focus on ethics and security (for example, OpenText (Generative AI governance essentials // OpenText. URL: https://www.opentext.com/media/white-paper/generative-ai-governance-essentials-wp-en.pdf (accessed: 02/14/2025)), California Management Review (Morton J. Generative AI Adoption and Three Traps for Organizational Agility // California Management Review (CMR). URL: https://cmr.berkeley.edu/2024/03/generative-ai-adoption-and-three-traps-for-organizational-agility / (date of access: 02/14/2025))). They are important for sustainability, but they can slow down innovation. Table 1 shows a comparison of the considered approaches on key aspects of GII implementation.
Table 1. Comparative analysis of approaches to the implementation of GII
Designations: ● "+": the aspect is clearly and fully covered. ● "+/-": the aspect is partially covered or insufficiently detailed. ● "-": the aspect is not covered or mentioned in passing. The analysis shows that, despite the general principles, the existing approaches have limitations: insufficient flexibility, fragmentation, insufficient attention to continuous learning, limited risk management and limited use of the potential of the GII itself. Existing approaches do not fully take into account the specifics of a particular organization, its business processes and strategic goals. They are often fragmented, do not integrate risk management sufficiently, and do not use the capabilities of the GII itself to optimize the implementation process. A more comprehensive, flexible and adaptive framework is needed, actively using the capabilities of the GII itself, and ensuring iterativity and continuous improvement.
4. GII implementation framework in business processes The proposed framework for the strategic implementation of GII in the business processes of organizations is an integrated approach designed to provide a universal and adaptive approach to the integration of GII technologies. The framework is based on a number of key principles and includes a sequence of interrelated stages covering strategic, operational and technological aspects of implementation. The GII strategic implementation framework is based on the following key principles: ● Versatility and adaptability. The framework is designed for use in organizations of various scales, industries, and forms of ownership. Versatility is ensured by focusing on the analysis of business processes and the ability to adapt to the specific needs of each organization. ● Strategic goal orientation. The implementation of GII is considered as a strategic tool for achieving measurable business goals, rather than as an end in itself. The framework involves defining the company's strategic priorities as the starting point of the implementation process. ● A combined approach to data collection (Top-down & Bottom-up). To ensure a comprehensive understanding of business processes, a combination of top-down (interviews with management) and bottom-up (employee surveys) data collection methods is used, as well as active employee involvement not only at the data collection stage, but also at the process detail stage. This allows you to integrate the strategic vision of management with the operational experience of employees. ● Integrated use of GII. The framework assumes the use of GII not only as an object of implementation, but also as a tool for analyzing data collected at the stages of information collection and process evaluation. ● Iterative and phased implementation. The framework is implemented in stages, using an iterative approach, starting with pilot projects and MVPs to test hypotheses and evaluate effectiveness, followed by scaling successful solutions. ● Human-centricity. The framework involves the active involvement of employees at all stages of implementation, from data collection to testing and using GII-based solutions, ensuring change management and staff adaptation. 4.1. The stages of the GII implementation framework The framework includes nine consecutive stages that provide a structured approach to the strategic implementation of GII: Stage 1: Strategic analysis and goal setting. The initial stage includes an analysis of the company's strategic documents to determine the priority areas in which the implementation of GII can bring maximum benefit in achieving strategic goals. An interdisciplinary implementation working group is being formed. A key aspect of this stage is its periodic review (every 1-3 years) to make adjustments based on changes in the external environment and business goals. Stage 2: Comprehensive collection of business process data. A combined approach to data collection is being implemented: ● Interviews with managers. Semi-structured interviews are conducted to identify key business processes, problem areas, and potential optimization opportunities using GII. ● Mass employee survey. Anonymous questionnaires are conducted to collect detailed information about routine, time-consuming operations, time costs, and data used at the operational level. Stage 3: Detailing business process data (in-depth interviews/focus groups). In-depth interviews and / or focus groups are conducted with employees performing the most priority processes identified in Stage 2 in order to understand in more detail the steps of the process, the data used, the tools, to identify "pain points", difficulties and opportunities for automation using GII. A structured interview scenario is used. All the details of the process are recorded, including time costs, data used, problems and suggestions from employees. Both individual interviews and focus groups are conducted (depending on the situation and the specifics of the process). Stage 4: Data analysis using GII. The collected data is processed and analyzed using GII for: ● Clustering and semantic task systematization: LLMs are used to automatically cluster tasks described in interviews and questionnaires according to various criteria (task type, data used, division, etc.). This allows you to identify groups of similar tasks and determine the main activities of the organization. ○ Examples of LLM promptings: ■ "Group the following task descriptions into clusters by meaning. Specify a short name for each cluster and list the tasks included in it: [list of task descriptions]" "Identify the main topics that are discussed in the following answers to the questionnaire questions. For each topic, provide a brief description and examples of quotations: [list of answers to questions]" "Break the following list of tasks into categories depending on the data used. Display a list of categories and their tasks: [task list]" ● Identification of functional intersections and anomalies: LLMs analyze texts to identify duplicate functions, intersections in the functionality of different departments, as well as anomalies in business processes (for example, unusually long operation deadlines, non-standard sequences of actions). ○ Examples of LLM promptings: "Find duplicate or overlapping functions in the following task descriptions. Specify which tasks are duplicated and what the intersection is: [list of task descriptions]" "Determine which of the following tasks are performed by different employees or departments, but have similar goals or use the same data. Describe the similarities and differences: [list of task descriptions]" "Identify unusual, illogical, or potentially problematic sequences of actions in the following task descriptions. Explain the problem: [list of task descriptions]" "Find references to problems, failures, delays, or bottlenecks in business processes in employee responses. Group the problems into categories and give examples: [list of answers]" ● Creating a process and competence map: Based on the analysis of LLM texts, they help to create a comprehensive business process map that displays key operations, their sequence, areas of responsibility, execution time, and relationships between processes. LLMs can also be used to create a competence map that describes what skills and knowledge employees need to perform various tasks. ○ Examples of LLM promptings: "Based on the following task descriptions, draw up a business process diagram, specifying the sequence of actions, responsible persons (or departments), and the data/information systems used: [list of task descriptions]" ■ "Extract information from the following texts about which data and information systems are used at each stage of the process: [list of texts describing the process]" "Determine what skills and knowledge are needed to complete the following tasks. Group skills into categories (for example, technical skills, communication skills, analytical skills): [task list]" ■ "Based on the job description, make a list of key competencies for this position: [job description]" ● Sentiment Analysis: In addition to identifying problems, it allows you to determine the tone, to assess the attitude to the processes, to identify hidden discontent, which may not be expressed explicitly. ○ Industrial example: ■ "Determine the tone of the following statements: [list of statements]. Give the answer in the format: utterance - tonality (positive, negative, neutral)” Stage 5: Validation and peer review of the data. The data obtained, including process and competence maps, is validated with department heads and experts to ensure reliability and consistency with the actual functioning of business processes. Stage 6: Assessment and prioritization of processes for the implementation of GII. A detailed assessment of the identified business processes is carried out to determine the priority areas for the implementation of GII. ● Development of criteria of suitability. Criteria for assessing the feasibility and potential effectiveness of implementing GII are formulated (formalizability, data structure, data availability, labor intensity, automation potential, impact on KPIs). ● Prioritization of processes and ROI assessment. Processes are evaluated and ranked based on criteria of suitability and potential return on investment (ROI), as well as indirect effects. The use of the "Effect – Complexity of implementation" prioritization matrix is recommended. Stage 7: Development and design of solutions based on GII. A solution is being developed for each priority business process, including: ● Employee training in the use of GII tools. Integration of GII tools into employee workflows to increase productivity. ● Full automation of the business process. Development and implementation of automated systems with GII integration into the company's IT systems for full automation of the cycle of operations. Stage 8: Pilot implementation, MVP and scaling. The implementation of solutions is carried out in stages, starting with pilot projects and MVPs in limited areas for testing, collecting feedback and evaluating effectiveness, followed by scaling successful solutions. Stage 9: Monitoring, performance evaluation and adjustment. The functioning of the implemented solutions is monitored on an ongoing basis and regular effectiveness assessment is carried out based on actual indicators and user feedback. The necessary adjustments are being made to maximize the effect of implementing GII in the long term. The presented framework (Fig. 1) provides an integrated and structured approach to the strategic implementation of GII, taking into account the technological, organizational and human aspects of the transformation of business processes. Further sections of the article will be devoted to a more detailed consideration of the specifics of using the framework, as well as issues of change and risk management.
Figure 1. The stages of the GII implementation framework in business processes
5. Criteria for evaluating the effectiveness of the GII implementation To evaluate the effectiveness of the framework implementation, it is recommended to use the following groups of criteria: · Financial criteria: o ROI (Return on Investment): the ratio of the benefit received to the amount of investment; o TCO (Total Cost of Ownership): total cost of ownership (costs of licenses, infrastructure, support); o Reduction of operating costs: direct savings due to automation or optimization of processes (for example, reduction of costs for IT, IT support, etc.). · Operational criteria: o Process execution speed: reduction of cycle time (lead time) or number of iterations; o Accuracy of results: reduction of the percentage of errors when performing routine tasks; o Automation level: the proportion of tasks transferred to GII. · Service quality and satisfaction: o Satisfaction of internal users (employees) and external clients (for example, NPS, CSI level); o Number of complaints or complaints (if applicable); o The speed of processing customer requests. · Innovative activity: o The number of new ideas/products created with the help of GII; o Reducing the time to bring a new product to market (time-to-market). The presented criteria make it possible not only to obtain a generalized efficiency assessment, but also to flexibly adapt it to the specifics of a particular organization. Special attention should be paid to the economic justification during the assessment, which is calculated as follows: · Determine the amount of initial investment: o Expenses for the purchase/licensing of model services; o IT infrastructure costs (servers, cloud resources, GPU); o The cost of training and consulting for employees; o Integration and support costs. · Evaluate potential benefits: o Direct (for example, reducing payroll costs by automating routine operations); o Indirect (revenue increase through improved service quality, faster customer service, increased sales); o Reputational effects (attracting new partners, shaping the image of an innovative company). · Calculate a key financial indicator: o Return on Investment (ROI) – the indicator characterizes the level of return on investment costs. It is defined as the ratio of the net financial result of an investment to its cost (volume of costs) and expressed as a percentage. · Create an implementation budgeting plan: o Divide the project into stages (MVP, pilot, scaling); o Specify the costs and benefits for each stage; o Adjust calculations as the pilot's results become more accurate. This methodology can be supplemented with industry and organizational specifics for a more accurate assessment.
6. Change and risk management The implementation of GII requires cultural and organizational changes. For their effective implementation, it is advisable to use a combination of classical models (Kotter, ADKAR, Lewin) and modern agile transformation techniques. Key recommendations: · Create a "coalition of supporters of change." Include highly respected employees and key managers in the working group who are able to convey the value of the implementation. · Ensure transparent communication. Regularly inform staff about the goals, progress and preliminary results of pilot projects. · Organize training and retraining. To develop competence development programs (both technical and "soft skills") for staff who will use GII tools. · Motivate employees to participate in the project. Use KPIs that stimulate automation and innovation, as well as non-material incentives (public recognition, career growth). · Apply an iterative approach (MVP). Start with the pilots and show "quick wins" that reduce resistance and increase confidence. Effective change management is directly related to an organization's ability to anticipate and mitigate potential difficulties, so risk management is an integral part of the implementation process. The main risks can be roughly divided into four categories: · Technological risks: o Unpredictable behavior of the model; o Insufficient/poor quality of training data; o The complexity of integrating GII with existing IT systems. o Management strategy: thorough testing of the model at the pilot stage, data quality control, reservation of budgets for revision. · Organizational risks: o Employees' resistance to change; o Insufficient staff training; o Conflict of interests between departments. o Management strategy: a change management plan (see subsection 6.1), training of key employees, and a clear allocation of responsibilities. · Legal and ethical risks: o Copyright violations during content generation; o Leakage and misuse of personal data; o The formation of "fake" materials that damage the company's image. o Management strategy: coordination with lawyers at each stage, setting up corporate ethics rules, and using tools to filter "undesirable" content. · Financial risks: o Exceeding the initial investment; o Delayed deadlines and delayed payback periods. o Management strategy: phased financing (MVP → pilot → scaling), tight control of budget and deadlines, regular monitoring of ROI. Thus, the integration of Change Management and systematic risk management helps to minimize the negative effects of implementation and increase the chances of project success. 7. Conclusions As a result of the research, a comprehensive framework for the strategic implementation of GII has been developed, providing organizations with a structured and adaptive approach to integrating this technology. The key features of the framework are: ● A combined (top-down and bottom-up) approach to collecting data on business processes, ensuring the completeness and objectivity of information. ● The technique of using LLM for semantic analysis of unstructured data at the diagnostic stage, which allows to identify hidden relationships and unobvious optimization needs. ● An iterative and step-by-step approach to implementation that minimizes risks and makes it possible to adapt the strategy. ● Focus on achieving measurable business goals and taking into account the organizational aspects of implementation. The results of the study confirm the prospects of using LLM to identify insights that are not available with traditional data processing methods. The developed framework allows organizations to: ● Identify priority areas for GII implementation more accurately and effectively. ● Reduce the time and cost of analyzing business processes. ● Minimize the risks associated with the introduction of new technologies. ● Increase the likelihood of successful transformation of business processes. The GII Strategic Implementation Framework is a comprehensive and adaptive tool that integrates LLM for business process analysis and ensures step-by-step implementation, taking into account the strategic goals of the organization. The effectiveness of the framework has been proven in practice: it has been successfully used to evaluate business processes in a consulting company, as well as in a pilot project to optimize business processes at MTS PJSC, demonstrating its ability to identify opportunities for improvement and reduce analysis time.
8. Limitations of the study The present study has a number of limitations that must be taken into account when interpreting the results and applying the framework in practice.: ● The developed framework is conceptual in nature and requires empirical validation in various organizational contexts. ● The literature analysis underlying the development of the framework is limited to available publications and may not cover all existing approaches and practices. ● The effectiveness of using LLM for data analysis depends on the quality and volume of training data, as well as on the choice of a specific model and its settings. The results of the analysis may be subject to biases inherent in the LLM itself. ● The framework is focused on the strategic level of GII implementation and does not detail the technical aspects of implementing specific solutions. ● The rapid development of GII technologies may lead to the obsolescence of some aspects of the framework. ● The framework does not consider in detail information security issues specific to the public sector, as well as economic aspects related to the high cost of infrastructure (in particular, GPU) for LLM deployment in the internal contour of the organization. This is a significant limitation, especially for the use of the framework in government organizations. Further research should be aimed at empirical verification of the framework, the development of detailed techniques and tools for its implementation, as well as the adaptation of the framework to various industry contexts. Conclusion In the context of the rapid development of GII technologies, the developed framework is becoming particularly relevant for organizations seeking to increase competitiveness. The key advantage of the framework is its versatility and adaptability, achieved through its modular structure and the ability to customize to specific business processes and industries. The addition of in-depth interviews/focus groups makes the framework even more practice-oriented and responsive to the real needs of employees. Successful AI transformation requires an integrated approach that takes into account technological, organizational, managerial and ethical aspects. The proposed framework provides exactly this approach, offering a step-by-step action structure and tools for continuous monitoring and strategy adjustments. Further development of research involves empirical validation of the framework in various industries, the development of industry modifications, the study of the impact of organizational culture and ethical aspects, as well as the creation of metrics to assess the effectiveness of the implementation of GII. When using the framework in practice, it is critically important to assess the organization's readiness, form a competent team, stage-by-stage implementation with pilot projects, involve employees, and continuously monitor strategy adaptation. The framework provides a methodological basis for a systematic approach to AI transformation, which is especially important in an environment where AI is becoming a key factor in competitiveness. The results of the study complement and develop the conclusions obtained in the works devoted to the practical application of GII [20, P. 107], the development of AI transformation strategies [21, P. 65] and the analysis of the impact of GII on industry productivity [22, P. 85]. The proposed framework offers a comprehensive and practically oriented approach to the strategic implementation of GII at the business process level, taking into account both technological and organizational aspects. References
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