## 4 Discussion
As technology continues to advance and high-level skills are needed, the issue of cross-functional collaboration arises to assess employees' ability to collaborate across different functions and domains to identify individuals who can effectively communicate and work with stakeholders from various backgrounds, such as business teams, domain experts, or data scientists, to bridge the gap between technical expertise and business requirements. To keep up with technological advancements, employees should have updated industry knowledge and awareness. Hence, it is essential to evaluate employees' knowledge of industry-specific AI applications, trends, and challenges to identify individuals who stay updated with the latest developments in AI within the organization's industry, follow industry-specific AI use cases, and understand the unique considerations and opportunities for AI-based digital transformation in the organization's sector. This takes employees to a higher level of continuous integration and deployment (CI/CD) [134] evaluation of their knowledge of CI/CD practices for AI model development and deployment. Thisindicates that they understand version control, automated testing, and continuous integration processes, which are crucial for maintaining code quality and ensuring the smooth deployment of AI models. Comprehensively covering AI skills among employees is vital, and this includes a variety of technological proficiencies. The expertise in natural language processing (NLP) techniques is crucial, particularly for organizations that handle textual data. Proficiency in deep learning techniques and neural networks, which form the backbone of many AI applications, should also be assessed among employees. For those dealing with image or video data, skills in computer vision are essential. Additionally, employee proficiency in big data technologies like Apache Hadoop or Apache Spark is important, as these technologies are commonly used for processing large volumes of data. Familiarity with cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure should be evaluated, considering their increasing importance in AI and data processing. Knowledge of DevOps practices and ML Ops (machine learning operations) principles is another critical area to assess, as these are increasingly relevant for efficient AI deployment and maintenance. Skills in data engineering, which include data integration, preprocessing, and pipeline development, are essential for managing the data lifecycle. Lastly, proficiency in data visualization tools and techniques should also be evaluated, as this skill is key in effectively communicating insights from data. However, AI-based digital transformation presents organizations with multifaceted challenges, inherent risks, and potential pitfalls that necessitate comprehensive analysis and strategic mitigation strategies. At the core of these challenges lies the intricate issue of data quality and availability. Organizations frequently encounter deficiencies and biases within datasets, which impede the accuracy and reliability of AI algorithms. In addition, a persistent scarcity of skilled professionals well versed in AI technologies amplifies the challenge, demanding extensive training initiatives and proactive talent acquisition efforts. The integration of AI systems with existing technologies poses a formidable challenge, requiring meticulous planning and implementation to ensure harmonious co-existence and minimal disruptions to ongoing operations. From a risk perspective, the vulnerability of AI systems to security breaches is a significant concern. Inadequately secured AI frameworks become susceptible targets for cyber-attacks, potentially leading to severe data breaches and the compromise of sensitive information. Furthermore, the ethical risk of algorithmic bias remains pronounced, with biased training data perpetuating societal prejudices and reinforcing discriminatory outcomes.