## 4 Discussion
This allows data handling to be taken to a further step related to data integration and data fusion from multiple sources to create a comprehensive and holistic view to combine data from disparate systems or departments to gain insights that may not be apparent when analyzing individual data sources in iso lation as a result of data integration techniques to ensure seamless data connectivity and interoperability. There are tools that facilitate data integration and extract, transform, load (ETL) processes [129]. These tools enable the seamless extraction of data from various sources, transformation into a usable format, and loading into the target systems. The organization may consider tools such as Apache Kafka [130], Talend [131], or Informatica [132] for efficient data integration. Implement alerts or triggers to notify stakeholders of any significant changes or deviations in data patterns. Humans cannot deal with data in any format by requiring data exploration and visualization techniques to gain insights from the data by using exploratory data analysis (EDA) [133] to understand the characteristics, patterns, and relationships within the data. It is practical and time saving to visualize data through interactive dashboards, charts, and graphs to effectively communicate insights to stakeholders. It provides deep insight into the use of data visualization and storytelling techniques to effectively communicate insights derived from data. Such a project requires an analysis of the compliance process and risk management activities to identify areas where AI can improve efficiency, accuracy, and risk assessment, and where AI applications for regulatory compliance monitoring, fraud detection, anomaly detection, and automated compliance reporting can be used across different functional areas. The assessment of the current state includes the evaluation of risk assessment and fraud detection processes to identify opportunities for AI applications to automate risk analysis, identify patterns of fraudulent activities, and enhance fraud detection capabilities. Furthermore, it must assess compliance and ethics processes to identify areas where AI can ensure adherence to regulatory requirements and ethical standards. This promotes AI applications for automated compliance monitoring, audit trail analysis, and ethical decision support systems. Therefore, the other dimension of AI-based digital transformation is related to expertise, knowledge transfer, and upskilling. The adoption of such technology requires promoting knowledge transfer and upskilling initiatives to empower employees with the skills and knowledge to effectively utilize data. Success can be maintained by providing training programs on data analysis, AI techniques, and data visualization tools and by fostering a data-literate workforce that can leverage data insights for informed decision making. This encourages risk taking and innovation in AI initiatives as it creates an environment where employees feel empowered to experiment, learn from failures, and propose new ideas to recognize and reward innovation and creative problem solving. This leads to the development of an agile and adaptive mindset to respond to the rapidly changing AI landscape, to encourage flexibility, agility, and adaptability in AI projects, and to foster an environment that embraces iteration, feedback, and continuous improvement. AI-based digital transformation requires continuous learning and adaptation to recognize that technical skills in AI and data science are continuously evolving. This requires encouraging employees to stay updated with the latest advancements, trends, and technologies in AI through participation in conferences, webinars, or industry events. Foster a culture of continuous learning and adaptation to keep pace with the fast-changing AI landscape.