In defense, predictive maintenance uses equipment data to inform when maintenance is needed; autonomy concerns what a system can do with less direct human control; and decision-support AI helps people interpret information and decide what to do. They address different problems, so public U.S. government sources do not support ranking them as more or less effective overall. They also offer different kinds of evidence: detailed sustainment findings for maintenance, policy requirements for weapon-system autonomy, and strategic goals and responsible-use guidance for decision support.
What each application of AI does
| Application | Primary function | Typical decision owner | What the evidence establishes |
|---|---|---|---|
| Predictive maintenance | Uses equipment condition data and analytics to inform maintenance timing and anticipate failures. | Maintainers, logisticians, and program or readiness leaders. | The U.S. Government Accountability Office (GAO) describes service pilots, sustainment measures, uneven adoption, and later Marine Corps progress. |
| Autonomy | Allows a system to perform functions with reduced direct human control. Weapon-system autonomy is subject to specific Department of Defense (DoD) policy. | Authorized commanders, operators, and personnel responsible for the system’s use. | DoD policy sets responsibilities for people who authorize, direct, or operate autonomous and semi-autonomous weapon systems; that policy is not evidence of every system’s field performance. |
| Decision support | Processes, prioritizes, or presents information to help people understand a situation and make decisions. | Commanders, staff, analysts, and other designated decision-makers. | DoD describes decision support as part of broader AI use and joint-force concepts; these strategic descriptions do not by themselves demonstrate operational impact. |
The categories can overlap in a larger operation, but their purposes are distinct. A maintenance forecast informs a sustainment choice; an autonomous function changes how a system behaves; a decision-support tool informs a human’s judgment. A single technology label does not tell you which of those jobs a particular system performs.
Predictive maintenance: using condition data to plan sustainment
Predictive maintenance uses condition-monitoring technology and data analytics to schedule maintenance based on evidence of need rather than relying only on fixed schedules or waiting for equipment to fail. The intended value is practical: spot a problem early enough to plan labor and parts, avoid some unplanned work, and keep equipment available when needed.
What GAO found—and what changed later
In its December 2022 report, GAO found that military services had piloted predictive-maintenance programs on some weapon systems but were not regularly replacing components based on forecasts. The services generally lacked metrics for assessing results. Service officials described potential benefits, including reduced unplanned maintenance and possibly avoided aircraft accidents, but GAO stressed that experience behind such examples was limited.
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The report framed the scale of the maintenance challenge as nearly $90 billion spent annually on maintenance of ground systems, ships and submarines, and aircraft (GAO, 2022). That figure describes the broader maintenance burden; it is not an estimate of savings predictive maintenance would produce.
Subsequent updates on GAO’s report page show progress in one area, not uniform adoption across DoD. In May 2026, GAO reported that the Marine Corps had expanded predictive sustainment monitoring to more than 1,000 medium- and heavy-tactical vehicles. The Corps’ Condition-Based Maintenance Plus (CBM+) dashboard uses sensor-derived telemetry and displays active faults, warnings, and vehicle service status. GAO marked a Marine Corps metrics recommendation implemented in August 2026. GAO’s January 2026 updates still listed open Army and Air Force recommendations.
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How to judge whether it is working
A forecast is not an outcome. Evaluation should connect the model’s warning to a maintenance decision and then measure the result. GAO identifies measures including scheduled and unscheduled maintenance man-hours per flight hour and mean time between failure. Those measures answer different questions: fewer unscheduled hours may reduce workload, while longer intervals between failures may indicate improved reliability. Neither alone establishes whether readiness, safety, parts use, and labor burden all improved.
- Specify the outcome being measured: failure anticipation, unplanned work, readiness, labor, parts use, or safety.
- Record whether forecasts actually changed maintenance timing or component replacement.
- Compare results across a defined system and time period; do not treat a pilot or one service’s expansion as evidence of department-wide performance.
Autonomy: system behavior under human responsibility
Autonomy is about how a system performs functions and how much direct human control is involved. In weapon systems, that question carries explicit legal, operational, and safety responsibilities. DoD’s January 25, 2023 announcement of an update to Directive 3000.09 says people who authorize, direct, or operate autonomous and semi-autonomous weapon systems must use appropriate care and act consistently with the law of war, applicable treaties, weapon-system safety rules, and rules of engagement.
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Deputy Secretary of Defense Dr. Kathleen Hicks stated: “DoD is committed to developing and employing all weapon systems, including those with autonomous features and functions, in a responsible and lawful manner.” The directive is a policy anchor for weapon-system use; it should not be treated as a performance assessment of every autonomous system or generalized to autonomy in non-weapon platforms and administrative functions.
Questions to ask about an autonomous function
- Which functions are autonomous, and which remain under direct human control?
- Who is authorized to approve, direct, or operate the system in the relevant context?
- How are capabilities and limitations understood, and how is performance validated for the intended use?
- What safeguards and applicable operational rules govern its use?
DoD’s account of the Political Declaration on Responsible Military Use of AI and Autonomy also emphasizes that military AI includes decision-support systems. It calls for users and approvers to understand a system’s capabilities and limitations so they can make context-informed judgments and mitigate automation-bias risk. The declaration provides responsible-use context; it is distinct from the weapon-system requirements in Directive 3000.09.
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Decision support: helping people make sense of information
Decision-support AI is intended to help people interpret, prioritize, or act on information—not to make the human decision disappear. DoD’s Joint All-Domain Command and Control (JADC2) implementation announcement describes a joint force using automation, AI, predictive analytics, and machine learning to “sense,” “make sense,” and “act” on information across the battlespace through resilient networks. This is a strategic framework and design intent, not proof that every envisioned capability has been fielded or shown to improve outcomes.
Human judgment and automation bias
A decision aid can influence a choice even when a person formally retains authority. Users and approvers therefore need to understand the system’s limits, uncertainty, and capabilities well enough to judge its output in context. Automation bias—placing too much weight on a system’s recommendation—is a risk identified in DoD’s account of the Political Declaration on Responsible Military Use of AI and Autonomy. The practical evaluation question is not simply whether a tool produces an answer, but whether it improves decision quality, timeliness, or understanding without obscuring uncertainty.
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Strategy is not the same as demonstrated results
DoD announced its AI Adoption Strategy in 2023 with the aim of accelerating adoption of advanced AI capabilities. GAO later recorded that it superseded the 2018 AI Strategy and 2020 Data Strategy, and noted principles concerning data, governance, performance, and monitoring. GAO’s August 2026 update said DoD officials were still coordinating charter and directive updates and estimated completion by April 2027. These dates describe evolving strategy and governance work, not measured operational gains from decision-support systems.
How to compare the three fairly
There is no defensible cross-domain effectiveness score in the cited public evidence. GAO provides implementation detail and sustainment measures for predictive maintenance; the autonomy material is primarily policy; and the decision-support material is strategic and responsible-use guidance. Comparing them as though they had been evaluated in the same way would conflate unlike evidence.
A fair comparison starts by defining the mission and consequence, then asks what data and infrastructure the application needs, what role people retain, how mature the system is, and what outcome can be measured. The right outcome depends on the application:
- For maintenance: examine forecast accuracy alongside maintenance workload, failures, readiness, parts, and safety.
- For autonomy: identify the specific autonomous functions, human responsibilities, safeguards, and validation relevant to the mission.
- For decision support: examine whether people make better, faster, or more informed decisions, and how uncertainty and automation bias are handled.
The central distinction is the decision each application affects: when to maintain equipment, how a system behaves, or how a person understands and acts on information. Evidence about one does not establish the effectiveness of the others.
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