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JULY 2024 I PCB007 MAGAZINE 39 for opportunities to implement edge comput- ing to reduce data lag time. Develop processes, controls, and accountability. Ensure data secu- rity and responsible use of the data. Partner with trusted solutions and service providers. 4. Ensure good data. e data application and platform must be AI-ready. If the system's capability to support the data needs is questionable, upgrade the technology infrastructure. Identify the rele- vant data, perform the appropriate analysis, and then develop, fine-tune, and deploy the AI/ML model. All data is not necessarily appli- cable to the process. Identify the appropriate data. Put robust data governance processes in place. 5. Start with the intent to scale. Build the use cases from the beginning with an eye toward scaling. Focus on using best practices across all initiatives. Consider inte- grating them into existing workflows. nology adoption. e Technology Acceptance in a Manufacturing Environment (TAME) tool can help gauge the readiness of each work unit to accept and utilize advanced technologies (AI/ML technology). Be proactive in change management to reduce the impact on the workforce and invest in reskilling and upskill- ing your employees. 3. Understand the application and implement the technology. e most critical step is to make sure that the problem to be solved is completely understood. First, you must have the data. Connect the pro- cesses on the production floor. Successful com- panies have created connectivity for real-time access to data sources in production and mea- surement tools and sensors, auxiliaries, facili- ties, and others. With a general lack of system interoperability, achieving closed-loop status can be challenging. To overcome this, digita- lization must be implemented. Take advantage of cloud-based systems and capabilities. Look Figure 1: The phases and activities to create a strategic roadmap for an organization 3 .