From Interest to Expectation
In aerospace and defense (A&D), interest in AI has rapidly evolved into pressure to deliver measurable results. As programs shift from experimentation to implementation, digital teams face practical questions: where to begin, how to prioritize, and what it takes to scale.
AI depends on large volumes of accurate, high-quality data. But even the best data falls short when stripped of the structure, traceability, and context needed to interpret it. These elements turn raw inputs into actionable insight. They help AI understand not just what happened but how, when, and why.
The demands are greater than in other sectors because product lifecycles can last decades, regulatory standards are higher, and data moves through operational systems that are not designed to keep their provenance and context intact.
AI Learns from the Whole Picture
In A&D, process details—such as the suppliers for specific parts, the operator who ran a job, the type of tooling used, and the handling of parts—actively shape outcomes. Rollup metrics, like cycle time or defect rate, carry little meaning on their own. They need supporting context to be useful. Environmental conditions, material changes, or skipped steps are the details that determine whether variation reflects a real issue or a normal range.
When data is flattened for transfer or pulled without context, AI loses that clarity. Models may perform well in testing but break down in use. The signal gets buried in noise. That’s where the digital thread makes a difference.
The Digital Thread Preserves Meaning
The digital thread ties together systems across the lifecycle—design, production, quality, and sustainment—while maintaining the distinctive structures and histories of data streams. When that continuity stays intact, data retains its meaning. Teams don’t need to reconstruct the whole picture for every new application. Most importantly for the future of the business, AI can draw from a coherent set of inputs that reflect both the event and its context.
Most Context Already Exists
The data needed to provide this context often already exists. Core systems often track operator credentials, tool calibration, process settings, and environmental controls. The challenge isn’t capturing more data but keeping its structure and relationships as it moves. When metadata is removed or flattened, key distinctions are lost, reducing the potential to extract value from AI.
Take a predictive maintenance model. Trained solely on operating hours, it may provide basic forecasts. Add build records, test conditions, and install history, and the model improves. It doesn’t just react to wear—it helps anticipate failure based on how and where the part was made. This is the power of the digital thread.
How Digital Teams Can Prepare
To build AI that scales, digital teams should focus on preserving the integrity of data as it moves across systems. That begins with keeping data connected to its source. Metadata often holds the key to understanding how and why an event occurred. When that context is lost, AI models lose the ability to distinguish between meaningful variation and noise.
Structured data from core systems—such as CAD, MES, PLM, and MRO—already includes the checks and traceability needed to support AI. Rather than collecting new data from scratch, teams can start by aligning AI efforts with what these systems already track. This approach builds on existing process discipline and avoids redundant work.
Architecture also matters. Systems built to interoperate through shared standards allow teams to maintain continuity without relying on brittle integrations or manual extracts. When data flows cleanly, teams spend less time reconciling and more time applying insights.
Finally, models should be designed with the work in mind. The goal isn’t to produce more reports but to support real decisions. Whether guiding inspections, predicting failures, or adjusting schedules, AI should be closely tied to where the work occurs and what it requires.
Putting the Digital Thread to Work
The successful use of AI depends on how well teams carry context through the lifecycle. That means aligning across engineering, production, quality, and sustainment. Each team owns part of the data. To maintain structure, they need clear handoffs, shared formats, and agreements about what stays linked as data moves.
Progress usually begins with one area. Teams often start by mapping a process, such as inspection or rework. They examine how data flows, where the structure breaks down, and what existing systems already track. From there, they adjust how data is stored, shared, and reused without adding extra steps or collecting more than needed.
Of course, achieving the digital thread requires the right kinds of tools. Solumina by iBase-t supports this kind of structured, connected data flow. It keeps records tied to execution, captures process context as work happens, and ensures traceability across systems. For A&D teams working to scale AI, Solumina keeps process data intact during execution. By anchoring data in real operations and connecting it forward, it supports a digital thread that grows stronger with each use.
