Lockheed Martin needed its PAC-3 MSE air and missile defense system to keep pace with evolving and emerging threats by increasing decision speed, improving decision quality, and adapting target engagement behavior in real time.
Reported impact
Lockheed Martin says the AI-enabled PAC-3 MSE improves precision and speed in detecting, tracking, and engaging targets, adapts to new threat behavior in real time, and increases overall mission capability and battlefield awareness for warfighters.
Lockheed Martin describes PAC-3 MSE as an in-house AI/ML-enabled interceptor capability rather than a cloud-based stack. The operational system is the PAC-3 MSE itself, which contains an embedded AI software component that Lockheed Martin says has existed in every fielded PAC-3 interceptor since the program’s inception. Within the engagement workflow, advanced fire control and battle management functions use AI to analyze battlespace data at high speed, improving decision speed and decision quality across detection, tracking, and target engagement. The implementation is positioned as spanning the kill chain so the interceptor can adapt to new threat behavior in real time while a threat is still airborne. The source does not identify a separate storage platform, external data source, or third-party integration layer; instead, it presents a tightly embedded, onboard AI decision stack inside the missile-defense system.
Threat data enters the PAC-3 MSE engagement stack, where the embedded AI software component and AI-enabled fire control and battle management functions analyze it across the kill chain. The system uses those results to detect, track, and engage targets, then adapts its response in real time as threat behavior changes while the threat is still in the air.
"As a result, PAC-3 MSE will be able to adapt to new threat behavior and tactics in real-time, even while the threat is still in the air."
"By continuing to invest in AI and advanced AI techniques, Lockheed Martin is improving overall mission capability and battlefield awareness for the warfighter to deliver a strategic advantage in every domain."
"By leveraging AI/ML techniques across the kill chain, Lockheed Martin will be able to improve the system's ability to detect, track and engage targets with unprecedented precision and speed."
"Lockheed Martin has used artificial intelligence tools and techniques in PAC-3 interceptors since the program’s inception. Every PAC-3 ever fielded has featured an embedded AI software component."
"This is made possible by advanced fire control and battle management functions that utilize AI to analyze data and make decisions at incredible speeds."
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Headquarters: Lockheed Martin Corporation 6801 Rockledge Drive Bethesda, MD 20817 U.S.A.
Related
In-house Sample Aug 27, 2026 Lenovo Lenovo needed to handle customer support at very large scale across its global device support operation, where routine issues created cost and complexity and human-led resolutions costing $30 to $50 could delay simple… $30 to $50 Lenovo Intelligent Voice Agent · Lenovo Machines Fix Machines Agent · Lenovo Smart Repair Agent In-house Sample Aug 13, 2026 Lockheed Martin Static flight-hour tables and usage metrics ignored real-time consumption, causing chronic under-resourcing, limited stock effectiveness, and a reactive sustainment supply chain that could not keep pace with modern… predict component failure up to 72 hours before it occurs LogC2 dashboard · deep-learning models · digital data fabric In-house Sample Apr 22, 2026 ABB ABB wanted to give employees a safe, responsible way to use generative AI in daily work so they could improve productivity, explore ideas more effectively, and support ongoing learning and career development. Abby supports more than 15 large language models (LLMs) Abby 