## 2 Materials and Methods
### AI Capabilities  
An organization's AI capabilities, including the existing AI or machine learning initiatives, skills, tools, and infrastructure, need to be evaluated [26]. This step can provide insights into the organization's capacity to embark on AI-based digital transformation. This assessment is multifaceted and encompasses the following key components:  
Existing AI Initiatives:  
Understanding the existing AI or machine learning projects in an organization provides critical insights into the organization's experience with AI technologies and highlights recurring issues or challenges faced during these initiatives. Analyzing the scope, outcomes, and lessons learned from past and ongoing AI projects is invaluable for identifying potential pitfalls and adopting best practices in future projects [53]. When assessing existing AI initiatives in the context of digital transformation, it is important to evaluate the objectives of each project. Understanding the intended outcomes, such as improving operational efficiency, enhancing customer experience, or driving innovation, and aligning them with the overall AI strategy is crucial for assessing their relevance to the organization's digital transformation goals [82]. In the realm of data use and model development, it is essential to analyze how data are being used in current AI initiatives. Assessing the types of data, their quality, volume,  
Figure 7: Steps for data landscape assessment. and variety, and evaluating the effectiveness of data preprocessing, feature engineering, and data integration techniques used in these projects is crucial. In addition, reviewing the development process of AI models within these initiatives is key. This review should include an assessment of the algorithms, techniques, frameworks used, level of automation, model selection, and hyperparameter tuning techniques employed. Evaluating the model performance, accuracy, and generalization capability, as well as the integration of AI models with existing systems or processes, is also important. This includes considering the scalability, reliability, and availability of the deployed models, their level of integration with other IT systems, such as CRM, ERP, or IoT platforms, and the efficiency of model monitoring and feedback loops for continuous improvement. Evaluating the impact and value generated by existing AI initiatives is another critical step. This involves assessing measurable outcomes, such as cost savings, revenue growth, or improved customer satisfaction, and analyzing the effectiveness of AI solutions in achieving the desired objectives and driving business value. Feedback from stakeholders and end-users regarding the perceived benefits and limitations of the initiatives should also be considered [83]. Finally, analyzing mechanisms for the continuous improvement and learning from existing AI initiatives is essential. This includes evaluating feedback loops, monitoring processes, adaptation strategies, and using user feedback, data-driven insights, and emerging technologies to refine and enhance existing AI solutions. The organizational impact of AI initiatives should be assessed, including the level of change management required to integrate AI solutions into existing processes, workflows, or organizational structures, considering the cultural shift, skill development, and organizational readiness for embracing AI-driven changes. Identifying challenges related to change management and planning for mitigating resistance or barriers is also an important aspect of this assessment [84, 85]. Skills and Expertise:  
Assessing the technical skills and expertise within an organization is a fundamental aspect of preparing for AI-based digital transformation. This involves identifying staff members who possess skills in data science, machine learning, or related fields and pinpointing gaps in expertise that might require filling through recruitment, training, or external partnerships.