[ INSIGHT ] ARTIFICIALINTELLIGENCE
by Guy Beougher Senior Advisor to Seekr and VP of Logistics, Cypress International cypressintl.com | 703-549-5880
Enabling Smarter Government Logistics If I have learned one thing in my decades of navigating defense logistics, it’s that we can’t afford to solve tomorrow’s readiness problems with yesterday’s systems. Artificial intelligence (AI) is the first new technology with the viability to change how we forecast demand, manage Until then, AI can’t function as the force multiplier it promises to be.
The obstacles are political, structural, and technical. Government warehouses operate at 60% utilization, but are manned as if they’re full. Closing or consolidating them is nearly impossible due to union contracts and congressional interests. Sometimes, the challenges are completely avoidable. I’ve watched commands bid against each other for the same warehouse, each unaware that their competition was across the table. That kind of misalignment is systemic. AI won’t solve these problems alone. It needs human judgment from experienced leaders who understand the context behind the data and can guide its application. AI can identify inefficiencies, forecast demand, and even recommend inventory placements, but it takes people to interpret that information, question its assumptions, and implement decisions that balance efficiency with readiness. If we can demonstrate success in one silo, others will follow. But it starts with training our military and civilian leaders in data literacy, AI applications, and collaborative logistics strategies. You can’t build a modern logistics force with analog mindsets. When paired with experienced human insight, AI can deliver the outcomes our national security demands.
obsolescence, and optimize supply chains. But the path to realizing its potential is met with structural, cultural, and bureaucratic hurdles.
depots and shipyards, the Army and Marine Corps rely heavily on unit- level maintenance, where over 400 maintenance activities create 75% of the demand. This makes it nearly impossible to aggregate or forecast accurately, especially when diagnostic capabilities vary widely by unit. OBSOLESCENCE AND LONG LEAD TIMES To complicate things further, we’re still dealing with massive parts obsolescence. Many parts haven’t been manufactured in decades, yet they remain critical to legacy systems that could be pivotal in a high- intensity conflict. Long lead times force logistics planners to bet on future demand with limited historical data, especially since much of the pre-9/11 demand information was lost during transitions to new ERPs. This is where AI could play a transformative role. One solution could be a centralized Chief Data and AI Office overseeing policy across all services, independent of Title X silos.
The crux of the issue begins with the complexities of how we generate, store, and share data. For example, within the Department of Defense, supply chains are managed vertically by service branches under Title X authorities. Each branch operates independently, with its own working capital funds, logistics systems, and data protocols. Only at the strategic level do these systems attempt to align horizontally. The result? Siloed data pools, fractured communication, and an environment where something as basic as generating a shared operating picture requires months or years of negotiation. I’ve experienced this challenge firsthand. At DLA, it took over a year just to grant FFRDC access to just one segment of our energy data. Multiply that kind of delay across every service branch, and it becomes clear why forecasting models are often flawed. Maintenance is just as bifurcated. While the Navy and Air Force conduct most of their maintenance in centralized
68 Inbound Logistics • January 2026
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