## 2 Materials and Methods
### Existing Systems  
Examining the existing technology infrastructure is critical to gauge an organization's technological readiness for AI deployment [60]. This understanding can help identify the required changes in the IT infrastructure and guide the strategic selection of AI tools and technologies. It serves as the foundation for understanding the current technology landscape, which includes hardware, software, data storage, and processing systems. Figure 6 shows the Steps for existing systems assessment. System Identification:  
Identifying the existing systems within an organization is the first step toward AI-based digital transformation. An exhaustive list should include everything from customer relationship management (CRM), and enterprise resource planning (ERP) systems to specialized tools for inventory management, payroll, or content management. It is also crucial to account for informal and legacy systems that are still in use. Once all systems have been identified, the next step is categorization. Organize the systems and processes into functional categories such as customer management, finance, supply chain, operations, HR, marketing, and sales. Such categorization is pivotal for pinpointing areas where AI can deliver the most impact. Following categorization, evaluate each system's suitability for AI integration. Factors to consider include data availability, system architecture, scalability, flexibility, and compatibility with AI technologies. An essential part of this process is engaging with users and stakeholders to identify the pain points and inefficiencies. Their insights are valuable for understanding how AI can resolve existing issues and improve the system performance. Concurrently, assessing the technical readiness for AI integration is necessary. This assessment should look at  
Figure 5: The steps for current processes assessment. the system architecture, data format compatibility, and integration capabilities with AI frameworks and tools to determine whether system modifications or upgrades are needed. The scalability and interoperability of the systems must also be evaluated. Check whether the systems can handle increased demands from AI integration and scale accordingly to meet the AI's evolving needs. Assess the ability of systems to communicate and exchange data with one another by checking for existing APIs, connectors, or frameworks that enable this integration. Finally, develop an implementation roadmap that details the sequence and timeline for AI integration, including the necessary steps, required resources, and milestones. Account for dependencies between systems and prioritize initiatives based on these factors. Remember that system identification is not a one-off task but a continuous part of the AI transformation journey. It is important to constantly evaluate and refine the system identification process, adapting the roadmap based on insights gained from ongoing implementation and feedback. Functional Analysis:  
For each system, conduct a detailed functional analysis. Understand the purpose each system serves, who uses it, what data it handles, and how it interacts with other systems. Document any known issues, limitations, or inefficiencies in the system. This analysis involves evaluating different functional areas within an organization to identify opportunities for AI integration and transformation. In enhancing various functional areas through AI, it is important to consider several key aspects: for customer experience, it is crucial to analyze customer-facing processes and touchpoints, identifying opportunities for AI-driven enhancements such as personalized recommendations, chatbots for customer support, sentiment analysis, and predictive modeling to improve satisfaction and engagement. In the realms of sales, marketing, and supply chain, evaluating processes to determine where AI can boost lead generation, customer segmentation, targeting, and campaign optimization is vital.