## 2 Materials and Methods
### AI Capabilities
This assessment is crucial for the effective planning and execution of AI initiatives because a lack of necessary skills can significantly hinder the success of these projects [86]. The first step is to identify the specific technical skills required for AI-based digital transformation initiatives. This includes skills in programing languages such as Python or R, understanding machine learning algorithms, statistical analysis, data manipulation, and data visualization [87, 88]. A comprehensive list of relevant skills that align with the organization's AI strategy should be created. Conducting an inventory of the current skills and expertise of the organization's employees is essential to assess their proficiency in the identified technical areas and their experience in AI-related projects. This assessment can be conducted through various methods such as self-assessments, surveys, interviews, or performance evaluations. The next step involves conducting a skill gap analysis. This entails comparing the current skills inventory with the skills required for AI initiatives. Identifying these skill gaps is crucial to understanding where an organization's capabilities fall short of its AI objectives [89]. Based on this analysis, developing training and upskilling programs is necessary to enhance the technical skills of employees. These could include workshops, online courses, or specialized training programs in areas relevant to AI, machine learning, and data science [90]. The third step is to evaluate the need for external expertise. If certain skills are lacking internally, it might be necessary to hire data scientists, AI specialists, or consultants who specialize in AI and data science. Collaborating with external partners, research institutions, or industry experts can provide access to additional technical skills and knowledge [91]. Finally, creating career development paths and growth opportunities for employees interested in AI and data science is essential. Providing mentorship programs, job rotations, or project assignments allows employees to apply and enhance their technical skills in AI initiatives. Supporting employees in gaining certifications or pursuing advanced degrees in relevant fields is also beneficial. Evaluating employees' practical experience with AI technologies and tools, including their involvement in AI projects such as data preprocessing, model development, and deployment, is also important. Identifying individuals with hands-on experience in implementing AI solutions and working with real-world datasets is key to building a robust AI-capable workforce [92]. Tools and Infrastructure:  
Evaluating the current AI infrastructure is critical in determining whether an organization has the necessary hardware and software to support AI projects. This includes assessing data storage capacity, computing power, and networking capabilities. Understanding the available and suitable AI tools and platforms, such as cloud-based AI services and AI development tools, is also essential for effective strategic planning [93]. For AI-based digital transformation, having the right tools and infrastructure is crucial. In terms of data preprocessing and cleaning, tools such as pandas, scikit-learn, and Apache Spark are popular choices. These assist in handling missing data, outlier detection, data normalization, and feature scaling [94; 95]. For machine learning and AI development, selecting appropriate tools and frameworks is vital. Python libraries such as TensorFlow, PyTorch, and scikit-learn offer a range of algorithms, models, and development frameworks for building, training, and deploying AI models [96]. For model training, deployment, and serving, tools that facilitate these processes, such as hyperparameter tuning, model evaluation, and comparison, are essential. Google Cloud AutoML, H2O.ai, or Microsoft Azure Machine Learning Studio are popular options [97; 98; 99].