Scaling AI for operational advantage across the DoW
Why it matters
- AI success depends on trusted data foundations
- Faster decisions deliver stronger mission outcomes
- Governance can accelerate adoption, not slow it
As the Department of War (DoW) moves from experimenting with AI to fielding capabilities, four priorities are emerging: trusted data, mission-ready solutions, coalition interoperability and governance that keeps pace with deployment.
Together, they point to a larger shift: Success will depend less on individual algorithms and more on enterprise-level ability to turn data into decisions and decisions into action.
Discussions among defense and industry leaders during a Billington Cyber event highlighted a broader shift in how the Department of War is approaching AI adoption.
Recent guidance, including the Department's AI Strategy, new Executive Order on AI security and innovation, and the continued expansion of programs such as the War Data Platform and Maven Smart System, reflects growing emphasis on operationalizing AI capabilities that deliver measurable mission outcomes.
For Leidos, that challenge sits at the center of AI trust, mission software, and digital modernization efforts across defense and national security programs.
From experimentation to operational capability
Since the creation of the Chief Digital and Artificial Intelligence Office (CDAO) in 2022, the Department of War's AI enterprise has continued to evolve, most recently with the organization's transition into the Office of the Under Secretary of Defense for Research and Engineering.
Across these changes, the emphasis has shifted from AI experimentation to operational deployment.
The DoW's AI strategy places increasing emphasis on measurable mission outcomes, user adoption, and operational impact. Achieving those goals requires integrating data, analytics, and AI directly into mission workflows.
Leidos supports customers across defense programs by integrating infrastructure, applications, data, AI, and cyber capabilities to address some of their most complex mission challenges. Organizations achieve results when these capabilities work together to support a specific operational outcome.
Technology is not the objective. Faster decisions, greater resilience, mission effectiveness, and decision advantage are.
One lesson continues to emerge: AI scale requires data scale. Organizations making the most progress are building architectures that separate data from legacy systems, improve accessibility and support multiple AI applications from a common foundation.
Foundation models: Build, buy or adapt?
One of the DoW’s most important decisions is how to balance commercial AI advances with defense-specific requirements.
Commercial models continue to improve at a rapid pace. At the same time, defense missions require security, transparency, resilience and operational assurance that extend beyond most commercial use cases.
The challenge is not choosing a model. It is creating an architecture that allows organizations to adopt new capabilities while maintaining control of mission risk.
Leidos approaches this challenge through its Framework for AI Resilience and Security (FAIRS). The focus is on reliability, auditability, security and performance across mission environments. That includes addressing adversarial threats, reducing bias and maintaining governance throughout the AI lifecycle.
The discussion is also expanding beyond models. Sovereign compute, domestic talent and secure deployment environments are becoming increasingly important as AI becomes a strategic national capability. Organizations will need the flexibility to incorporate commercial advances while preserving operational independence and reducing supply chain risk.
Data infrastructure is mission infrastructure
The evolution of Advana and the expansion of the War Data Platform reflect a growing recognition across the department: data infrastructure now plays a direct role in mission success.
Historically, analytics, financial accountability and operational decision-making often operated in separate environments. Today, organizations are requiring those functions to be more connected as they move forward with AI.
The goal is a common data foundation that supports enterprise management, audit requirements and operational decision-making from a single source of truth.
Industry should view these efforts as complementary capabilities within a larger ecosystem. The objective is straightforward: move trusted data from collection to analysis to action faster and with greater confidence.
This aligns with the Leidos’ approach to modern mission architectures. Through capabilities such as HeadWay™, Leidos combines enterprise data, analytics and AI trust to help leaders act on information they can trust and audit.
Moving beyond pilots
Across the department, the conversation is shifting from experimentation to operational impact.
The most successful AI programs are helping users reduce cognitive burden, accelerate decision cycles, improve intelligence analysis and provide commanders with relevant information faster.
Leidos is seeing that AI creates the greatest value when it’s embedded directly into mission workflows, whether it is maritime autonomy, battle management, intelligence analysis or emergency response.
Human-machine teaming, transparency and trust often determine whether a capability progresses from prototype to program of record.
Humans remain accountable for decisions, AI-driven decisions remain auditable and AI serves as a force multiplier for human expertise.
Governance and mission speed go together
The department continues to balance rapid deployment with responsible AI governance.
Leading organizations increasingly recognize these objectives reinforce each other.
When governance is built into development and deployment processes, organizations can reduce risk, build user trust and accelerate adoption.
Leidos applies this philosophy of AI trust through FAIRS. Security, resilience and governance are integrated throughout the AI lifecycle, from development and testing through deployment and sustainment.
This approach becomes even more important as AI reshapes the cyber threat landscape. Organizations must use AI to gain decision advantage while defending against AI-enabled adversaries. Combining AI with Zero Trust principles, automated cyber defense and continuous monitoring will be critical to protecting future defense systems.
How industry can accelerate defense AI adoption
As the department scales AI adoption, industry's role is evolving.
The need is no longer limited to building algorithms. The challenge is integrating data, infrastructure, security, software and operational workflows into deployable mission capability.
Organizations that can connect commercial advances to mission execution will be best positioned to support DoW’s priorities.
Leidos continues to focus on that intersection. Success requires mission understanding, secure integration, human-centered design and deployment models that can scale. It also requires partnerships that bring together government, industry and technology providers around a shared mission outcome.
The future of operational AI in defense
Over the next two years, department priorities are expected to focus on operationalizing AI, strengthening enterprise data architectures, enabling agentic mission workflows, improving coalition interoperability and deploying advanced capabilities securely.
The organizations that succeed will be those that combine trusted data, mission-ready AI and resilient digital infrastructure.
For industry, the path forward is clear: invest in trustworthy AI, secure-by-design architectures, open ecosystems and capabilities that produce measurable mission outcomes.
Key takeaways
- The Department of War is shifting AI focus from experimentation to operational mission outcomes
- Trusted data, coalition interoperability and governance are critical to scaling defense AI
Leidos helps integrate data, AI, cyber and mission software into deployable capabilities
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