The US Military & I.T. Equipment
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The Evolution of IT and AI in Military Warfare
It’s no secret that emergence of robotics and drones have already made an impact on today’s military warfare strategy and tactics. It won’t be much longer until artificial intelligence (AI) and machine learning (ML) control the landscape of decision making for the military.
Until recently, military strategy involved humans trying to discourage other humans from taking certain courses of action, some potentially deadly. What happens when the thinking and decision processes involved are no longer purely human? Some military experts believe that machine decision making can result in unintentional escalation, due to the speed of machine decision making and the ways in which it differs from human understanding.
The evolution of IT and AI in the military has dramatically changed the way we perceive conflict with other nations. Advancements in technology allow us to spare the lives of troops on the ground, and collect data to make more calculated decisions.
The New Role of IT in the Military
The Department of Defense‘s Joint Artificial Intelligence Center (JAIC) aims to help the military become a data-driven entity, migrating away from its focus on fielding tools. JAIC leaders desire is to have both uniformed and civilian leaders understand how AI works with data and how they can use it to improve their decision making on and off the battle field.
The JAIC is embracing a new role as a data advocate, trying to change the nature of warfighting decision making and getting non-technologists to work with its tech. However, a shift of that magnitude requires buy-in from the whole department at all ranks of leadership.
The end goal of the JAIC is to give military leaders more support in the field. Senior-level commanders need to make decisions without accurate and honest data to help guide them. That’s a situation that they intend to end by working directly with leaders to figure out what data they need and help them process it to make better decisions faster.
The Costly Repercussions of Obsolete Equipment
Federal and military agencies handle a vast number of investigations and data inquiries that are critical to national security and agency lines of business. However, with the explosion of both personal and corporate electronic records, the discovery process in these instances has grown increasingly complex.
New innovations in data discovery tools are helping leaders tackle this increased difficulty of real time decision making. Electronic data discovery, also known as e-discovery, and IT teams will benefit from developing tools used to assemble and review information from archived sources.
The current process used to analyze investigations and data inquiries relies on a patchwork of individual data discovery tools which is time-consuming and costly to agencies to manage and maintain. One of the main challenges stems from the broad list of file types that require agencies to maintain obsolete systems capable of searching and retrieving audio, video, image, documents and files.
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Even more so, depending solely on on-site legacy equipment, requires ongoing investments in infrastructure, storage and maintenance. As data requirements continue to grow, so do the costs associated with storing it. Data storage processes often go unnoticed by military IT leaders who are typically focused on other modernization projects. IT and other internal departments often have their own data siloes and may not understand the cost of time, maintenance, storage and equipment duplication needed with the current system.
A move to more advanced, cloud-based solutions, specifically designed to help simplify the assortment of e-discovery tools will significantly improve the speed and ability for leaders to search, share and collaborate around critical information on a single platform. Integrated with the cloud’s ability to scale up and down as data needs change, this means military organizations can simplify the collection and processing of their data, search and review of file types, and reduce the number of hours needed for data discovery as a whole.
