Introduction
Digital banking and smart banking represent a paradigm shift in how banking services are delivered. Digital banking allows for access to banking services anytime and anywhere, removing temporal and spatial constraints. Smart banking, by leveraging advanced technologies, enhances customer experience and improves the speed and efficiency of service delivery. In this context, artificial intelligence (AI) serves as the main driver and value-creating tool, laying the foundation for the development of AI-based or Intelligent Banking. In this form of banking, the focus is on utilizing AI to learn more from data, develop cognitive abilities, improve analytical thinking, and enhance deep intelligence to improve banking operations and adopt precise approaches for customers. With the continued evolution of digital banking, AI is poised to create intelligent values and deliver a more connected, efficient, and customer-centric banking experience. AI-based banking is still in the early stages of design and implementation, and given its advantages and the costs of implementation, its establishment requires a strategic and effective approach. This approach must be tailored to the type of bank and its customers, including capability-driven internal analyses, identification of key desirables, designing intelligent value packages, and developing an implementation roadmap. Additionally, beyond the necessity of utilizing this opportunity, a precise understanding of the challenges of implementation and barriers to the development of AI banking is required to optimize the selection and successful execution of change management. The goal of this research is the efficient strategic change management in the establishment path of AI-based banking. Efficient change management involves identifying challenges, prioritizing them, recognizing solutions, evaluating their effectiveness, and ultimately making optimal choices to create necessary preparedness and strategically face upcoming challenges.
Methodology
To achieve the research goals, a hybrid methodology consisting of six phases—identification, measurement, reduction, selection, evaluation, and readiness—has been developed. In the first phase, the challenges in the implementation of AI-based banking, along with proposed solutions, were identified based on systematic literature review and expert opinions. In the second phase, expert opinions were gathered, and a multiple criteria decision making approach (MCDM: best-worst method) was used to calculate the weight of each challenge and subsequently rank them. In the measurement phase, the efficiency of the proposed solutions was evaluated and ranked using data envelopment analysis (DEA). In the fourth phase, multi-objective mathematical modeling was conducted, and the problem was solved using the epsilon constraint method to select priority challenges. Finally, in the evaluation phase, the effectiveness of the proposed solutions was assessed using DEA, and the most effective solution for each prioritized challenge was identified. In the last phase, after determining the effective solutions to the prioritized challenges, a readiness plan and roadmap for addressing implementation challenges were developed.
Results and Discussion
 The process of implementing AI-based banking includes several key steps that must be managed by balancing risks and benefits. Along with technological changes and the development of new models, there is a clear need to revise data security policies, establish new regulations, and build stronger infrastructures for data processing. Additionally, issues such as regulatory compliance, ethical considerations, and building trust among customers are other main challenges that must be systematically addressed. One of the fundamental challenges in implementing AI banking is organizational resistance and the lack of readiness among employees to adopt changes. Without the implementation of a change management strategy, even the most advanced technologies cannot lead to desirable outcomes. In some Asian banks, when AI processes were used to assess employee performance, the lack of transparency regarding how the system operated resulted in reduced employee motivation and increased concerns. In contrast, other successful banks used a hybrid model and, alongside deploying the technology, provided extensive training for employees to help them understand their roles in the new environment. These experiences suggest that banks must design appropriate communication models and align their workforce with these changes before implementing AI systems. Another key challenge is data security and customer privacy. Intelligent banking relies on analyzing vast amounts of customer data, but misuse of this data can have irreversible consequences for both banks and customers. Banks need to establish clear frameworks for storing, processing, and sharing customer data and use advanced encryption models to protect information. Furthermore, clear regulations regarding customer data processing must be put in place to prevent any potential misuse. From a legal perspective, many banks, before investing in AI, must ensure that their approaches align with national and international regulations. In the European Union, the General Data Protection Regulation (GDPR) imposes stringent restrictions on how customer data can be used, and non-compliance may result in heavy fines. Therefore, close cooperation with regulatory bodies and the design of adaptable models can help mitigate legal risks.
Conclusion
Effective innovation, improved accuracy, and enhanced productivity are among the key expected outcomes of successfully implementing AI-based banking. AI-based banking is not only an advanced technology but also a fundamental transformation in the business model of banks, which can improve financial performance, operational efficiency, and customer experience. However, its successful implementation requires a precise understanding of challenges, the formulation of change management actions, and the development of effective strategies. In this research, the most significant obstacles and challenges of this transformation were identified, and practical solutions were proposed. The findings indicate that challenges such as weak technological infrastructure, banks' reliance on traditional systems, legal and security issues, and ethical concerns are among the primary obstacles facing AI-based banking. Moreover, the studies show that many banks still adopt a non-strategic approach to implementing AI and are mostly focusing on short-term solutions like personalized customer services. In contrast, the successful implementation of this technology requires a more comprehensive approach that considers all competitive, operational, and managerial aspects. Large banks worldwide use AI primarily for credit risk management and fraud detection. These banks, by leveraging machine learning algorithms, can analyze customer behavior and accurately predict the likelihood of loan defaults. Meanwhile, smaller banks, which focus solely on developing chatbots and recommendation systems, have not been able to achieve the expected efficiency from this technology due to the lack of a well-developed strategy. Therefore, before the development and deployment of AI technologies in banking, it is essential to first define the smart value creation model for the bank’s target customers. The results suggest that if bank customers are non-price sensitive, focusing on personalized services and offering a unique experience would be an effective strategy for AI-based banking. However, for price-sensitive customers, optimizing processes and reducing operational costs through AI to increase efficiency will be the effective strategy for the development of this type of banking. Effective strategies should be designed in the form of a roadmap that is both flexible and adaptable to environmental changes.