As demand for flexible electronics increase; with more complex research and integrated supply networks, labor and resources are wasted due to the siloed nature to re-invent test assessments and to reinterpret its data. Currently, only a few global companies with the largest resources deploy custom, single purpose shared data environments; for there are no common testing platforms for data analytics, meaning there is no way to share testing condition data between product developers, their suppliers and manufacturers. As we have recently witnessed in new materials development, scaling reliability with Artificial Intelligence capabilities can improve efficiency when deployed in both R&D and manufacturing environments. Similarly, with specific purpose tools which compliments a laboratory or line technician’s effectiveness, whose software tools are versatile and scalable; remote monitoring of failure analysis is possible, leading to exponential collaboration to diagnose test assessments, enabling faster scale of deployment as each flexible market grows, and iportantly, disseminate best practice reliability knowledge and education of our workforce. The first step towards an independent data platform is lab automation consists of; integrated commonly used mechanical testers and various devices such as multimeters, sensors, imaging systems and unlimited storage capabilities. Mindful of cyber security concerns, these elements combined, can monitor, predict and analyze various most significant testing assessment failures; such as to ensure the integrity of new materials and microelectronics while in continuous use (cracks and contact failures), detection of single or multi-layer delamination, preventing material deformation in ambient and hostile conditions and, identifying improvements in inline printing systems. In today’s environment when one must do more with less resources, only with common data protocols, can we develop software core engines that enable next-generation of data parsing of relevant data and comparing image streams to develop the early beginnings of machine learning modules to create data simulation models which are the foundations to accelerating product lead-times and reliability with future generation artificial intelligence tools. During this presentation, we will provide insights of several leading testing practices and performance criteria of leading organizations into harnessing these practical capabilities and to create an opportunity for all to thrive in these markets.