As recent events have shown, military decision-making is one of the highest-stakes challenges in the world: Diplomatic relations are at stake; billions of dollars of tax-funded budgets are in the balance; the safety and well-being of thousands of military and civilian personnel around the globe are on the line; and above all, the freedom and liberty of the United States and its more than 330 million citizens must be protected. But with such immense stakes comes an almost unfathomably large amount of related data that must be taken into account. Whether it is managing population health in an increasingly complex and connected world, or managing decisions on the network-centric battlefield, standalone humans are proving insufficient to harness the data, analyze it, and make timely and correct decisions.

Spanning six branches and upward of 1.3 million active duty military personnel on all seven continents, how can all of the data points — from dictates from the commander-in-chief to handwritten notes on the deck of an aircraft carrier — be taken into account? In matters of national security, speed and reliability in decision-making and avoiding technological surprises or being caught off guard by the nation’s political rivals require massive real-time analysis and first and second order thinking that includes the complexities of human behavior.

Consider all of the stakes and moving parts facing the leadership at a large domestic military base during the recent COVID-19 pandemic. Concerns of COVID-19 did not just need to consider the base personnel, but also the behavior of the civilians in the surrounding counties, as people from throughout the region, military and civilian contractors alike, were coming and going daily. The information necessary to consider starts with infection and hospitalization rates, but also includes behavior monitoring (and influencing) as well as staying up to date with steps being taken by local, regional and state officials to monitor the virus and limit its spread. With so many moving parts, it is very difficult to stay up to the minute on everything and to determine the right decision with any degree of certainty.

The answer to this guesswork and analysis paralysis lies in the capabilities of artificial intelligence and machine learning. If the military continues to waste too much time with human hours of effort and analysis that could be handled by machines, that could lead to danger and even death of military personnel or civilians. At the heart of complex systems, such as the U.S. military, there is a critical tipping point where the systems are so complex that humans can no longer track them. But AI solutions are capable of delivering up-to-the-minute data modeling, considering all factors at play and second and third order consequences, that can present tangible, data-driven intelligence that takes actions far beyond the limitations of linear human minds. Perhaps the biggest benefit is the confidence to avoid the negative publicity from the “podium moment,” when asked to justify decisions. Decision-makers can confidently move beyond relying on hunches and instead identify data based on sub-indexes, models from experts, and simulations specific to that day and the circumstances specific to each facility.

When President Biden was recently called onto the carpet to explain the rapid fall of Afghanistan in nine days, he should have had an AI that could at least explain the data, the models and weights that fed the analysis, conclusions and decisions based on the belief that the 300,000 strong Afghan army would be able to hold off the 60,000 Taliban fighters long enough for an orderly withdrawal. Journalists would then be free to question the data sources, the models or the weightings, but not the president, who would be relying on these systems for his judgment. But more importantly, such a system would have certainly predicted this rapid fall in its Monte Carlo distribution of potential outcomes, and would have generated counter measures and cautions.

Without a deeper commitment to AI, the military risks missing out on intelligence that transcends classified, siloed and otherwise restricted information without compromising security. One of the biggest challenges to high-stakes decision-making in the military is silos of classified information, making it difficult or impossible for every party to know every factor that is shaping the situation.

Using AI and machine learning solves this challenge safely. Rather than dumping disparate data from various branches of the military and clearance level into one gigantic data lake, it is possible to leave all the data safely and securely where it is, and train a machine to know and inform the human decision-makers that the data exists. AI is capable of processing not only all of the information in the corpus, but it is also able to know which parties do and do not have clearance to each individual piece of data. In matters of classified information, it can tell different personnel that the information exists, and direct these individuals to the authority qualified to disclose it.

Capabilities like these can be readily applied to large, complex military undertakings, featuring processes, decisions and volumes of information. For instance, when a new aircraft carrier is being built, management requires information in hand-written reports. It is difficult for the naked eye to tell if the project is on time or on budget because of the heavy reliance on human judgment. If any human assessment is just a fraction off, it can massively impact the whole project.

Recent challenges that factor in the vagaries of human behavior illustrated starkly by COVID-19 and the withdrawal from Afghanistan, beg for the rapid analysis and creative input of machine learning systems. From digestion and quantification of countless data points to absorbing and cataloging knowledge of experts who will not always be around to help with predictive modeling of circumstances with dozens of variables, this amplified intelligence is the key to better outcomes.

Richard Boyd is CEO at Tanjo, a machine learning company.

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