## 2 Materials and Methods
### Existing Systems
The role of AI extends to pricing optimization, demand forecasting, customer  
Figure 6: Steps for existing systems assessment. behavior analysis, and recommendation engines for sales growth. In addition, in supply chain management, AI can streamline operations, enhance forecasting accuracy, optimize inventory management, and improve production planning, with applications in predictive maintenance and operational efficiency monitoring. For data and analytics and strategic decision making, it is essential to evaluate data management and analytics processes to establish a strong AI foundation, focusing on data governance, quality, integration, and infrastructure. The utility of AI in data discovery, cleansing, advanced analytics, and supporting data-driven decision making through predictive analytics, scenario modeling, and intelligent decision support systems is also significant. Lastly, in quality assurance and testing, identifying opportunities for AI integration is key, with applications in automated testing, anomaly detection, and quality control to enhance product or service quality, reduce defects, and boost testing efficiency, thereby facilitating continuous improvement and optimization efforts across all these functional areas. Technical Analysis:  
Performing a technical evaluation of the system is a critical step in understanding an organization's readiness for AI implementation. This evaluation encompasses an examination of the system architecture, compatibility, scalability, security, and performance. It also involves assessing the age of the systems and their ability to support newer technologies, including AI. To gain a comprehensive understanding, this evaluation can be further divided into two main areas: hardware capabilities and the software environment, and network infrastructure and integration capabilities. In terms of hardware capabilities and the software environment, it is essential to evaluate the computing power and hardware infrastructure available within the organization. This involves assessing whether the existing hardware can meet the computational demands of AI algorithms and models. Factors such as processing speed, memory capacity, and parallel processing capabilities are crucial in this regard. Along with hardware, reviewing the software environment and tools currently used is vital. This review should identify whether the organization possesses the necessary software and development frameworks to support AI initiatives. It is also important to consider whether the organization's software ecosystem is compatible with popular AI platforms, libraries, and frameworks. Evaluating the network infrastructure is another key aspect. This includes examining the bandwidth capacity and latency to determine whether the organization's network can manage the increased data traffic that AI applications typically bring. Assessing the need for network upgrades or optimizations is crucial to ensure a smooth data transfer and communication between AI systems and data sources. Furthermore, assessing how well the organization's existing technology infrastructure integrates with AI systems and tools is vital. This involves evaluating any limitations or challenges in integrating AI solutions with the organization's current systems, databases, and applications and ensuring compatibility with APIs, data formats, and protocols for seamless data exchange. Data Evaluation:  
Since AI heavily relies on data, take a close look at the type and quality of data that each system handles. Evaluate the data structure, quality, availability, and relevance for potential AI use cases. This can be elaborated by evaluating data processing capabilities and storage architecture. This can be achieved by assessing the processing capabilities of an organization's infrastructure in relation to AI workloads. Consider factors such as the ability to handle large-scale data processing, parallel processing, and distributed computing.