Can enterprises design internal AI factories the way Amazon builds fulfillment centers – as systems that convert raw data into scalable, intelligent outcomes?
For most organizations, the answer today is no. While investment in AI is surging, impact often stalls at the pilot stage. Teams experiment with models, but fragmented tools, unclear governance, and resource bottlenecks prevent scaling. The result: executives see isolated demos instead of enterprise transformation.
Lockheed Martin confronted this same reality. Years ago, it recognized that scaling AI required a new operating model, not just better tools. In response, the company built both: an AI Factory operating model and a suite of purpose-built platforms designed to meet the most demanding requirements for security, governance, and orchestration. These tools weren’t created in a vacuum-they were engineered to drive Lockheed Martin’s own internal transformation and to support Department of Defense mission programs where failure is not an option.
The results are instructive for any R&D, product development, or innovation leader:
The AI Factory Model
At its core, the AI Factory treats AI not as a project, but as a production line. Just as a manufacturing plant transforms raw input into finished goods, the AI Factory converts data, models, and human expertise into trusted, repeatable, and measurable outputs.
A modern AI Factory has four pillars:
| Pillar | Description |
| Build | Tools to create domain-specific AI. For example, no-code/low-code environments where engineers and business users can design custom models or agents quickly. |
| Operate | Secure, user-friendly interfaces to interact with AI daily. This is where adoption turns from theory into productivity. |
| Infrastructure | Integrated MLOps and DataOps capabilities that handle training, deployment, monitoring, and versioning at scale. |
| Governance & Talent | Security, compliance, provenance, and expert consulting baked into the operating model-ensuring responsible innovation. |
This factory model creates a feedback loop where innovation compounds. Instead of reinventing the wheel with every project, organizations industrialize the process of discovery, deployment, and improvement.
What Success Looks Like
The Lockheed Martin story illustrates the impact of this approach. By applying the AI Factory model and equipping it with in-house tools designed for mission assurance, the company has moved far beyond pilots:
- 70,000+ users across the enterprise have access to AI Agents.
- 10,000+ daily active users rely on them for core tasks and decision-making.
- These agents process billions of tokens each week, representing accelerated analysis, streamlined workflows, and faster innovation cycles.
- In Q1 of 2025 alone, employees created more than 12,500 personal AI Assistants-tailored to specific roles, missions, and functions.
This is not hypothetical. It is production-grade AI at enterprise scale-built not on proprietary lock-in, but on a flexible foundation of open-source models hardened with defense-grade security. This approach gives Lockheed Martin the best of both worlds: the adaptability and speed of open ecosystems with the governance and assurance demanded in mission-critical environments.
From Defense to Enterprise
The story doesn’t end inside Lockheed Martin. The same architecture, tools, and expertise that powered internal transformation and DoD mission programs are now available to other enterprises through Astris AI, a Lockheed Martin company.
- Genesis enables rapid creation of domain-specific agents, accessible to engineers and business users.
- Navigator provides a secure, RAG-enabled conversational interface that makes AI accessible to every employee.
- Panel delivers full-spectrum MLOps/DataOps for lifecycle management, monitoring, and governance-critical for scaling securely.
These platforms are direct descendants of what Lockheed Martin built for itself. And they come with something equally important: domain expertise on how to deploy and scale secure AI responsibly, learned through years of high-assurance use cases.
For innovation leaders, the takeaway is not about buying a product. It’s about recognizing that a proven framework now exists-tested at scale in one of the most demanding environments in the world-and that it can be adapted to accelerate transformation in their own organizations.
A Useful Analogy
Think about Amazon’s fulfillment centers. They are not just warehouses-they are finely tuned systems designed for throughput, speed, and quality. Every input is tracked, every output measured.
The same principle applies to an AI Factory. Instead of packages shipped, the output is tokens processed and agents created. Each token represents a decision accelerated. Each agent represents human expertise codified and scaled. Open source ensures the system never becomes a closed warehouse with a single supplier; it’s more like a global logistics network where the best tools can be plugged in at will, without sacrificing security or provenance.
What Executives Can Do Now
Innovation leaders can begin applying the AI Factory model in their own organizations by taking a few practical steps:
- Audit the chain: Map your AI value chain from data ingestion to deployment. Identify siloes that slow adoption.
- Define the factory: Establish an internal architecture that treats AI creation, deployment, and monitoring as a production system.
- Start modular: Adopt tools that can integrate with existing infrastructure rather than creating new siloes.
- Bake in security: Compliance, provenance, and governance must be embedded from the start-not added later.
- Invest in adoption: Success is not just technical; empower employees to build, use, and adapt AI agents as part of their daily work.
Looking Ahead
For executives in R&D, product development, and innovation, the challenge is no longer proving AI’s potential. It is industrializing the process of delivering value. The AI Factory model offers a path forward: measurable, secure, and scalable innovation.
Lockheed Martin’s success shows what’s possible-not only by designing a new operating model, but by building the secure, orchestrated tools required to deliver it at mission scale. Through Astris AI, those same tools and expertise are now available to commercial enterprises.
The question for leaders is simple: are you still experimenting with AI, or are you building your factory?
James brings over 25 years of experience guiding enterprises through transformative AI journeys, including extensive hands-on expertise in successfully scaling AI initiatives from early experimentation to strategic deployment. With deep insight into operationalizing AI securely and effectively, he shares practical, results-focused strategies grounded in real-world execution.
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