## 2 Materials and Methods
### Current Processes
Once the process is designed, the next step is to build and test the automated process. This stage often involves IT professionals or consultants who have the skills to set up the automation and ensure that it works as expected [44]. **Step 5: Training:**  
Before fully implementing the automated process, ensure that all involved parties understand how it works and their role in it. They should know how to interact with the automation tool, how to manage exceptions, and who to contact if something goes wrong [45]. **Step 6: Implementation, Monitoring, and Continuous Improvement**  
After successful testing, roll out the automated process. It is often a good idea to do this gradually, starting with a pilot phase before full implementation [46]. After the process has been automated, it is important to monitor its performance to ensure that it meets its intended goals. Use the data from the automated process to identify areas for improvement and continually refine the process as needed [47]. The assessment of process automation opportunities needs to be carried out carefully, considering several factors. The complexity, frequency, and volume of the process are some of the aspects to be evaluated. It is also important to consider the cost, potential return on investment, and impact on customer service or other business functions [48]. Additionally, organizations should bear in mind that not all processes are suitable for automation. Processes that require human judgment, creativity, or complex decision making may not be ideal candidates for automation. Therefore, the assessment of automation opportunities should not only be based on the potential for efficiency gains but also on the strategic fit with organizational goals and values [49]. In recent years, AI-driven process mining tools have emerged as powerful aids for identifying automation opportunities. They can automatically analyze event logs from  
Figure 4: Steps to automate business. different IT systems to discover, monitor, and improve real-world business processes [50]. AI-powered technologies such as robotic process automation (RPA) [51] and intelligent process automation (IPA) [52] are increasingly being used to automate business processes. RPA involves the use of software robots or 'bots' to mimic human actions and perform repetitive tasks, whereas IPA incorporates machine learning and cognitive technology to automate and optimize more complex processes [53]. This assessment requires a multi-disciplinary approach that incorporates technical expertise, business acumen, and strategic thinking. Moreover, as AI technologies evolve and become more capable, the scope for automation is likely to increase, making the continuous re-evaluation of automation opportunities a necessity in the digital transformation journey. AI Alignment:  
Finally, current processes should be assessed for their alignment with potential AI capabilities. For instance, tasks involving large volumes of data or those requiring real-time decision making can particularly benefit from AI. This step requires a good understanding of both the business processes and the possibilities offered by AI [54]. AI alignment is a critical aspect of AI-based digital transformation that warrants careful attention and planning. It involves aligning AI applications and initiatives with the strategic objectives and values of the organization [55]. A successful digital transformation is not merely about implementing advanced technologies but also about leveraging these technologies to achieve business goals and create value [56]. Thus, assessing the alignment of AI initiatives with business strategy is essential to ensure that AI adoption is purposeful and effective. AI alignment involves several key dimensions that are essential for AI integration within an organization. Strategic alignment is crucial, as AI initiatives need to align with the organization's strategic objectives to effectively contribute to their achievement.