How the Pentagon deploys AI to battlefields - and what enterprises can learn

The Department of Defence (DoD) invests billions in artificial intelligence research, yet a critical gap remains between laboratory breakthroughs and battlefield deployment. Although the Pentagon allocated $25.2 billion for AI and autonomous systems in 2025 alone, most cutting-edge algorithms have yet to reach the tactical edge where they're needed most. 

Near-peer adversaries are adapting daily, introducing new threats and countermeasures in weeks rather than years, and the U.S. is at an inflection point to either stay ahead, catch up, or fall short for decades to come. To maintain a decisive edge, the U.S. must adopt tools that enable rapid design, prototyping, and deployment across both national and allied defense networks. 

Closing that gap requires a secure hybrid cloud computing infrastructure, one capable of bridging DoD-owned supercomputers and forward-deployed systems without compromising security or performance. 

A secure hybrid cloud for the DoD involves computing platforms that seamlessly connect high-performance computers with commercial cloud services while maintaining the strict security controls required for sensitive defense data. These platforms must meet Federal Impact Level 5 (IL5) authorization standards, allowing them to handle mission-critical systems, Controlled Unclassified Information, and International Traffic in Arms Regulations (ITAR) workloads across multiple computing environments simultaneously. 

This infrastructure approach addresses the military's most pressing AI challenges: training complex models that require massive computational power, then deploying those same models to resource-constrained environments at the tactical edge. A secure hybrid cloud computing environment enables real-time data processing, dynamic model retraining, and rapid resource scaling based on mission requirements, all while maintaining a continuous security boundary from development through operational deployment. 

The DoD is already leveraging a secure hybrid computing cloud for several critical applications. Autonomous vehicle programs use these platforms to train navigation algorithms on DoD supercomputers, then deploy them to unmanned systems in the field. Intelligence, surveillance, and reconnaissance operations process sensor data through hybrid environments that can scale processing power dynamically while keeping classified information secure. Predictive maintenance programs analyze fleet-wide equipment data in the cloud while pushing diagnostic updates to individual systems deployed globally. 

Despite these successes, broader adoption that is critical to transforming military operations is not happening fast enough. Traditional DoD procurement processes, designed for hardware acquisitions, struggle to accommodate the rapid iteration cycles that AI development requires. Additionally, many defense contractors and military units remain hesitant to move sensitive workloads beyond existing DoD-owned computing centers, even when secure hybrid options offer superior capabilities and cost efficiency. 

Mission-critical objectives require the DoD to prioritize expanding secure hybrid cloud adoption across all major AI initiatives, particularly those supporting the Combined Joint All-Domain Command and Control program and the Replicator Initiative. This means streamlining authorization processes for hybrid platforms, providing clear guidance on acceptable use cases, and incentivizing contractors to leverage these environments for faster, more cost-effective AI development and deployment. 

Critics may argue that hybrid approaches introduce unnecessary complexity and potential security vulnerabilities compared to keeping everything within existing DoD computing centers. However, this perspective ignores the reality that purely DoD-owned infrastructure cannot provide the scale, flexibility, and rapid provisioning that modern AI workloads demand. When properly implemented with Impact Level 5 authorization, hybrid platforms can actually enhance security by providing standardized, continuously monitored environments that eliminate the security gaps often found in ad hoc computing arrangements. 

Obstacles to broader hybrid cloud adoption include budget constraints, security concerns, and organizational inertia. Cost concerns can be addressed through the resource efficiency that hybrid platforms provide including detailed billing and elastic scaling - all of which reduce overall computing expenses.  

Security apprehensions should diminish as more platforms achieve formal DoD authorization and demonstrate the ability to meet or exceed existing security standards. Overcoming organizational resistance requires leadership commitment to change management and clear demonstration of operational benefits through pilot programs and early adoptor success stories.