Series: Military Readiness Reform – Part II: The Right to Predict
Predicting equipment failures before they happen has become a growing priority inside the Department of Defense, building on the momentum created by the military’s recent right-to-repair reforms. Those reforms expanded access to technical data and manuals, allowing maintainers to fix more gear at the field level.
Now, the next step is taking shape: using artificial intelligence to anticipate problems before they disrupt missions.
One company working directly with defense agencies on this shift is Virtualitics, a mission-AI and data analytics firm that supports U.S. defense and intelligence customers. Military.com reviewed written responses from Rob Bocek, Virtualitics’ Chief Revenue Officer and a former Navy SEAL, to understand how predictive analytics is changing sustainment.
Moving From Repair to Prediction
Right-to-repair reforms gave maintainers more freedom to repair equipment with fewer delays. Bocek said AI builds on that by helping troops and commanders look ahead instead of reacting after failure.
“AI is the tool that enables maintainers to anticipate problems before they appear and capture the entire readiness picture,” Bocek said.
He explained that predictive systems help users understand not only when a component may fail, but what actions matter most with the resources available.
“The real opportunity is not just predicting failure. It is understanding what to do about it, when, and with what resources,” he said.
This aligns with a broader DoD push to modernize sustainment and reduce maintenance delays, an initiative supported through programs such as the DoD’s Office of the Secretary of Defense Sustainment Strategy and service-level readiness data platforms
What Makes Defense AI Different
Many commercial analytics platforms cannot operate inside restricted defense networks or meet the security requirements for classified data. Bocek said mission-specific systems must support secure deployments and handle complex datasets across logistics, maintenance, personnel, and supply chains.
“Mission AI environments are complex and handle critical datasets. To be successful you must seek purpose-built defense solutions that are built enterprise ready interoperable, agile, cloud agnostic and maintain high security standards,” Bocek wrote.
Defense analytics platforms often integrate with systems such as ADVANA, the DoD’s central analytics environment, and service tools like the Air Force’s ODIN system for maintenance and supply chain visibility.
These platforms allow AI tools to connect with existing datasets and support decision making while keeping sensitive information protected.
Building Trust With Operators and Commanders
Bocek said that one of the biggest challenges in adopting AI inside mission environments is earning trust. He explained that trust grows when users can see clear results and understand why the system made a recommendation. According to him, commanders are more likely to rely on AI when they can view the reasoning behind a suggested maintenance action and then watch that decision play out correctly in the field. He added that explainability is essential because operators need to be able to examine the data and logic behind each recommendation.
This perspective aligns with Government Accountability Office findings, which emphasize the need for transparent and explainable AI systems across the Department of Defense.
Culture Change and Experimentation
Moving toward predictive readiness requires more than new technology. It also requires cultural and organizational shifts. Bocek said units tend to see the most benefit when they are allowed to run small, fast-paced experiments rather than waiting on lengthy multiyear efforts. During Virtualitics’ recent Frontiers of AI for Readiness Summit, he noted that several defense leaders encouraged a “learn fast” approach, where commands test tools in limited settings, gather lessons, and scale up what works.
This aligns with guidance from the DoD’s Chief Digital and AI Office, which has stressed the value of testing in short cycles and learning quickly as part of the department’s broader data modernization strategy.
How Close Is Predictive Sustainment?
Predictive sustainment is already becoming a reality across several defense organizations. Bocek said AI tools are capable of forecasting equipment issues at the asset level and can help leaders determine when to act and what resources they will need. He noted that early programs have shown measurable improvements in efficiency, including significant time savings, although detailed service-level numbers are not publicly released.
These efforts match the Pentagon’s push to modernize sustainment and reduce the impact of unplanned maintenance, as outlined in the Department of Defense’s Readiness and Sustainment Modernization Framework.
A New Stage in Readiness Reform
Right-to-repair gave troops more control over fixing their gear. Predictive analytics gives commanders more visibility into future readiness risks. Together, both reforms support a more agile and self-sufficient force.
As predictive systems mature, DoD leaders have signaled interest in shifting to preventive models that reduce failures altogether using modular design, digital twins, condition monitoring, and improved supply chain visibility.
Predictive analytics acts as the bridge between today’s repairs and tomorrow’s preventive sustainment.
Sources
- DoD ADVANA — https://www.ai.mil
- AFMC ODIN program overview — https://www.afmc.af.mil
- GAO Report on AI Risk Mitigation — https://www.gao.gov
- DoD Sustainment Strategy — https://www.defense.gov
- CDAO Data and AI Adoption Documents — https://www.ai.mil
- DoD Digital Engineering Strategy — https://www.cto.mil
Editor’s Note:
This story is part of Military.com’s ongoing “Military Readiness Reform” series exploring how the Department of War is modernizing sustainment — from field repair to predictive and preventive readiness.
