A new era of AI takes off for Lockheed Martin

An aerospace defense company unifies its disparate data with the help of IBM.

Data fuels AI takeoff

Lockheed Martin—a global leader in aerospace and defense technology—teamed up with IBM to transform its business with AI, starting with its valuable but disconnected data. With the help of IBM, Lockheed Martin streamlined its data landscape and overcame the complexities of data integration.

The result was a unified, accessible database that led to experimentation with AI at scale and use of agentic AI for workflows and employee interactions.

Lockheed Martin logo

Outcomes

50%

reduction in data and AI tools.

46

data systems and tools replaced with one integrated platform.

216

data catalog definitions automated.

Abstract image of a blue wave of rectangles on a white background
Data is the foundational element by which we pull everything together across the enterprise.
Stephan Gerali Chief Architect and Senior Fellow Lockheed Martin
Unifying data, igniting innovation

Going from 46 systems to a single integrated platform.

It previously struggled with data silos due to several data lakes and 46 disparate systems for data management, data analytics and business intelligence.

With the help of IBM, Lockheed Martin replaced those systems with a single, connected and accessible environment. Once a unified, scalable data foundation was in place, the company was able to access its highest-quality data, then leverage that data to fuel the creation of a strong AI ecosystem.

Black minimalist spaceship on a white background with blue particles around it's path
We’re getting closer each day to a data-driven culture where folks can take the data they have and immediately make use of it.
Stephan Gerali Chief Architect and Senior Fellow Lockheed Martin
Atmosphere to build in

In the secure AI Factory, engineers build fast and at scale with open AI.

Accessible data fueled the Lockheed Martin AI Factory, a secure AI ecosystem where 10,000 engineers can build, iterate on and deploy large-scale AI solutions—fast. IBM® Granite®, a family of open AI models, accelerates application deployment within the AI Factory, so engineers can rapidly rollout intelligent solutions at scale.

Agentic frameworks and advanced virtual agents—which were developed in the AI Factory—helped the company better manage complex workflows, simplify interactions between people and technology and enhance efficiency through process automation and optimization. This technology led to an initiative to transform internal operations with AI.

Wireframe of a cube made of blue particles
AI is transforming our customer’s mission. They need AI tools that help them make better, faster decisions.
Stephan Gerali Chief Architect and Senior Fellow Lockheed Martin
Agentic AI from the ground up

Lockheed Martin is experiencing faster, more accurate responses when employees use agentic AI for research.

They’re building their own internal AI engine to boost efficiency, streamline operations and achieve cost-effectiveness across the enterprise. Generative AI (gen AI) models built with watsonx® have eliminated the need for specialized coding by letting employees inquire about source data in their natural language. Lockheed Martin has also been able to better manage employee inquiries and HR-related tasks using watsonx conversational AI chatbots. These AI assistants have helped simplify the company’s rapid response to employee questions and improve HR processes.

Early results for a gen AI question answering pilot program showed a substantial increase in the accuracy of responses to sample questions—rising from 45.45% to 56.8% with prompt engineering and to 66.0% with query optimization.

Outcomes

11%

increase in accuracy of responses with prompt engineering.

20%

boost in AI response accuracy through query optimization using watsonx.

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When problems are beyond human scale, AI can help illuminate solution opportunities.
Stephan Gerali Chief Architect and Senior Fellow Lockheed Martin
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Examples presented as illustrative only. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.