By Lucas Assimos
AI is moving deeper into engineering, simulation and product design. That can shorten iteration cycles and help teams evaluate more possibilities before committing to a design. But there is a basic lifecycle problem that can limit how useful those systems become: the evidence created after a product is deployed often never makes it back to the people and models influencing the next design.
Field service generates some of the most valuable information in the product lifecycle. It shows what failed, under what conditions, which diagnostic path was taken, what was changed, what actually restored the asset and whether the repair held under normal use. Yet much of that evidence remains trapped in work orders, technician notes, warranty systems, disconnected diagnostic logs and free-text comments.
If AI-assisted engineering is going to improve from real-world performance, that gap has to close. The next step is not simply collecting more data. It is building a disciplined feedback path from service back into design.
The digital thread often becomes thin after deployment
Many organisations have invested heavily in connecting design, manufacturing and quality data. Digital-thread initiatives are meant to keep information linked across the product lifecycle. NIST, for example, describes the digital thread as a way to integrate information across design, manufacturing and product-support processes, including feedback to design.
In practice, however, the field side is often the least structured part of that thread. A design model may know the intended specification. Manufacturing systems may know how the product was built. Quality systems may know whether it passed inspection. But once the product is operating in the real world, the data often changes shape.
Instead of a clean engineering record, the organisation may receive a complaint, a fault code, a service ticket, a replaced component and a note that says the unit is fixed. That is useful for closing the job. It is not necessarily enough for learning why the design behaved the way it did.
A failure code is not a failure story
The first mistake is treating a failure code or replaced part as a complete explanation. In technical service, a symptom is usually the start of the investigation, not the end of it.
A useful field record should preserve context. What configuration was the asset running? What software or firmware version was installed? What load, temperature, duty cycle or operating condition was present when the symptom appeared? Was the failure constant or intermittent? What evidence supported the diagnosis? Which corrective steps were attempted, in what order, and which one changed the outcome?
Just as important, what happened after the intervention? Did the system pass a defined verification test? Did the symptom return after a normal operating cycle? Was the asset merely able to restart, or was the underlying condition actually gone?
Without that context, an AI system can easily learn the wrong relationship. It may see that component X is often replaced after symptom Y and conclude that replacement is the correct response. In reality, component X may simply be the most common first attempt, while the true cause sits elsewhere. Poorly structured service history can turn repeated workarounds into apparent best practice.
Build a field-failure packet, not just a service ticket
A practical way to improve the loop is to define a minimum field-failure packet for engineering use. It does not need to capture every possible data point. It needs to capture the information that allows a downstream team to reconstruct what happened.
At a minimum, I would include the asset or product configuration, software version where relevant, operating context, initial symptom, diagnostic evidence, actions taken, parts or settings changed, verification result and recurrence status. The record should also distinguish between a confirmed root cause, a probable cause and a workaround that restored operation without proving the cause.
That distinction matters. Engineering teams need to know the confidence level behind the field conclusion. Otherwise, a tentative service decision can become a permanent label in the dataset.
The same approach can work across industries. In industrial equipment, the packet may include vibration, temperature and load data. In enterprise hardware, it may include firmware state and event logs. In software, it may include version, environment, reproduction steps, telemetry and rollback outcome. The fields change, but the principle does not: preserve enough context to make the failure useful beyond the immediate repair.
Separate closure from verification
Another important change is to stop treating “work complete” and “system healthy” as the same state.
A technician may complete the assigned repair correctly, yet the organisation may still need evidence that the asset behaves normally after the intervention. That can require a verification window, a normal-load test, a post-repair scan, a repeated operating cycle or simply confirmation that the original symptom does not return.
This creates a cleaner label for future analytics. Instead of a binary record that says repaired or not repaired, the dataset can distinguish between action completed, verification passed, verification failed and recurrence detected.
For AI-assisted engineering, those labels are far more valuable. They make it possible to compare which interventions consistently produce durable outcomes instead of merely closing tickets.
Create two feedback loops
I would also separate the process into two loops: a fast operational loop and a slower engineering-learning loop.
The fast loop exists to restore the asset safely and efficiently. It should help service teams diagnose, repair, verify and return the system to operation. The engineering loop has a different purpose. It looks across repeated events and asks whether there is a pattern that should influence design, software, documentation, supplier choice, test coverage or service procedure.
Not every field event deserves a design change. But repeated events with similar conditions should not remain isolated service tickets either. A good system should make it possible to aggregate them, identify common context and route the pattern to the team that can act on it.
That is where AI can be useful: clustering similar cases, finding relationships across configurations, surfacing unusual recurrence patterns and helping engineers search large volumes of service history. But the model still depends on the quality and meaning of the records it receives.
Human review remains part of the loop
A closed feedback loop does not mean automatically feeding every technician note into a model and allowing the model to redesign the product.
Field data can be incomplete, inconsistent or wrong. Service teams work under time pressure. Different people may describe the same symptom in different ways. A replaced part does not prove that the part was defective, and a cleared fault does not prove that the underlying cause disappeared.
The governance principle is similar to broader AI risk-management guidance: context, measurement, documentation and continuous monitoring matter. NIST’s AI Risk Management Framework emphasises managing AI across its lifecycle rather than treating deployment as the finish line.
For engineering organisations, that means human domain experts should still review high-impact patterns, validate causal assumptions and decide whether the evidence is strong enough to change a design or process. AI should help organise the evidence, not erase the need for engineering judgement.
The field should become part of the design dataset
The strongest AI-assisted engineering systems will not learn only from simulations, specifications and laboratory tests. They will also learn from what products do after they leave controlled environments.
That does not require turning every service department into a data-science team. It requires a small number of disciplined decisions: define the field context worth preserving, separate symptoms from confirmed causes, record what was changed, verify the outcome and make the resulting information accessible to engineering.
Once that happens, the service organisation stops being only the place where problems are repaired. It becomes one of the most important sensors in the product-development system.
AI can help teams design the next version faster. But if the richest evidence from deployed products never returns to the design process, the organisation is still learning with one eye closed. The real opportunity is a closed loop in which field failures do more than generate warranty cost or service work. They become structured evidence for better engineering decisions.
About the author: Lucas Assimos is an automotive specialist and entrepreneur in Florida and CEO of RA Parts and Service. His work focuses on diagnostics, electronics, service operations and the operational side of complex technical systems. Website: https://lucasassimos.com