AI-Enabled Microservices Speed Air Force Battle Decisions in DASH Wargame

AI Tools Accelerate Battle Management Decisions in Latest Air Force DASH Wargame

The Air Force recently tested AI-enabled microservices that speed up decision-making for battle managers while offering a clearer view of the battlespace. The 805th Combat Training Squadron hosted the second Decision Advantage Sprint for Human-Machine Teaming (DASH) wargame in Las Vegas, bringing together Air Force personnel and industry developers to explore AI applications in command-and-control operations.

This experiment focused on a key subfunction called “match effectors,” where AI helps decide the best weapons system to destroy a target. Several companies, alongside Air Force software engineers, created AI microservices that analyze battlefield data and generate ranked lists of effectors for operators to select from.

Accelerating Decision Advantage for Battle Managers

Battle management involves complex variables: target specifics, available forces, environmental conditions, and risks to civilians. Traditionally, matching the right weapon systems takes time and mental effort. The AI tools developed at DASH 2 reduced this cognitive load by quickly processing data and suggesting optimal options.

During 45-minute simulations called “vulnerabilities,” AI microservices were stress-tested with real-time feedback from battle managers. This iterative process helped vendors improve their software to better fit the operators' workflow. One battle manager noted that what normally took 10 minutes could be done much faster with AI assistance.

Human-Machine Teaming in Action

Operators appreciated how the AI offered reasoning behind its rankings, which increased trust in the technology. Industry teams worked closely with battle managers, sometimes running simulations while receiving direct instructions to provide context, ensuring the AI aligned with real operational needs.

Challenges surfaced, such as occasional AI errors in interpreting messages and difficulties integrating diverse data sources. These highlighted a need for better data organization and a unified data layer, common issues in developing integrated AI systems.

Incorporating Multi-Domain Capabilities

The wargame included weapons systems from other military branches, such as Army THAAD batteries, Navy destroyers, space assets, and cyber capabilities. This multi-domain approach expanded operators’ options beyond Air Force resources and emphasized the importance of joint force collaboration.

Battle managers discovered they lack full training on non-Air Force systems, underscoring a gap that programs like DASH aim to address. Being able to consider space, naval, and cyber assets alongside traditional airpower is crucial for future conflict scenarios.

Building the DAF Battle Network

The DASH experiments feed into the larger Department of the Air Force (DAF) Battle Network initiative. This integrated system aims to connect sensors and weapons across all military services and partners, enabling rapid data sharing and coordinated responses.

As Col. Jonathan Zall, capability integration chief, put it: battle managers must solve problems that cross service boundaries. Effective solutions require seamless collaboration across air, naval, cyber, and space domains.

Future Steps and Modular AI

The 805th Combat Training Squadron plans more DASH sprints, with four scheduled in 2026. These exercises will help define precise requirements for AI microservices aligned with specific command-and-control tasks.

The goal is to develop modular, replaceable software components. This “plug and play” approach allows the Air Force to adopt better AI solutions as they emerge, avoiding lock-in to a single vendor or technology version.

Such flexibility supports ongoing innovation and ensures AI tools stay relevant to operational needs.

What This Means for Management

• AI can reduce decision time and cognitive overload in complex environments by offering ranked options and transparent reasoning.
• Close collaboration between end-users and developers is vital to create tools that fit naturally into workflows.
• Integrating diverse data sources remains a challenge but is essential for comprehensive situational awareness.
• Modular, replaceable AI software enables continuous improvement and adaptability.
• Cross-domain coordination requires broad knowledge and training beyond one’s primary field or service.

For managers overseeing teams working with emerging AI technologies, the DASH model highlights the value of iterative testing, real-time user feedback, and modular software design. These principles can apply to any fast-moving, data-intensive operational environment.

To deepen your understanding of AI tools and their impact on decision-making, explore relevant training opportunities such as those offered at Complete AI Training.