In the coming years, the federal government plans to ramp up its investments in artificial intelligence (AI) to keep pace with competitors. In fact, the National Security on Artificial Intelligence (NSCAI) predicts that China will surpass the U.S. as the world’s leader in AI by 2031, which is a driver for increased investments. Yet to see the true advantages of AI – from automation of agency operations and services to improved decision making and real-time responses – an agency’s AI strategy must move beyond the data center to optimize infrastructure at the edge.
To prepare agencies for incorporating AI into their data strategies, Craig Heartwell, CTO of Presidio Federal, said, “Federal AI strategy needs to focus on optimizing data operations and IT infrastructure to take AI to the edge. With much of agency data stored in data centers, a key component of this strategy is to extend AI capabilities into the field.”
Extending AI to edge computing will make it possible to analyze and use data where it is being collected. In a time of growing remote work in the federal space and a subsequent reliance on digital services, the ability to automate manual and on-site processes is more important than ever. It is predicted that by 2023, over 50 percent of primary data responsibilities will feature data that is created, managed, and analyzed in edge environments. There are four key requirements that will enable agencies to optimize edge computing for AI – solid data collection, servers and platforms for managing real-time data in the field, strong network connectivity, and cloud-based analytics.
To develop this core foundation, agencies should first focus on getting the proper hardware and software with AI in mind. The goal is for these solutions to eventually keep AI firing on all cylinders once agencies deploy it at the edge. Next, agencies need to look towards flexible cloud-based IT solutions to maintain agency-wide management and scalability of their data. For example, the Department of Veterans Affairs recently invested in edge computing capabilities to conduct medical procedures remotely through virtual and augmented reality. But for this data to drive AI, it’s necessary to invest beyond mobile connectivity and towards networks that seamlessly integrate that data in real-time.
Modern IT networks integrate edge devices such as tablets into agencies’ existing enterprise networks. Investing in such network solutions means that agencies’ edge computing data also flows smoothly into AI tools.
AI stands to reduce manual workload and regulatory burden for federal employees – but in order to achieve this, agencies need to look beyond data collection and towards ensuring their networks are prepared for AI. The NSCAI recommends that the federal government should spend $32 billion annually on AI research and development by 2026. Before adding AI to the mix, agencies should determine how their employees will collect, process, analyze and store data everywhere on their IT networks. To take full advantage of AI, agencies need to think in terms of an extended enterprise.
To learn more about how federal agencies can extend AI to the edge, download the infographic “How to Optimize AI for the Extended Enterprise” from Presidio Federal and Dell Technologies.