AI for Engineering Productivity
Engineering change management is slow because impact discovery is slow. See how an AI agent finds every affected assembly and document while engineers decide.
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8 min read
Dr. Maor Farid
Maor Farid is the Co-Founder and CEO of Leo AI, the first AI platform purpose-built for mechanical engineers. He holds a PhD in Mechanical Engineering and completed postdoctoral research at MIT as a Fulbright fellow. A Forbes 30 Under 30 honoree and former AI researcher and Mechanical Engineer in an elite military intelligence, Maor leads Leo AI's mission to transform how engineering teams design better products faster.
BOTTOM LINE
Engineering change management is slow because discovery is slow, not because approval is. Finding everything a change touches means five kinds of lookup across systems that do not share an index. An AI agent can run those lookups, cite its sources and flag what it could not search, while engineers keep every decision. Start by replaying five closed changes, measure the dependencies it would have caught, and expand from there.
Engineering change management looks tidy on a process diagram. Someone raises a request, a review board weighs it, a change notice goes out, and the revised drawings release. What the diagram hides is that the slowest step is rarely the approval. It is the discovery that comes before it. Before anyone can judge a proposed change, somebody has to find every assembly, drawing, supplier specification, work instruction and open order that the changed part touches.
That search is usually done by hand, partly from memory, across systems that were never designed to answer the question together. It also decides how good the whole change turns out to be, because a change approved on an incomplete picture comes back as rework, scrap or a late field fix. This article walks through the change loop stage by stage, shows where impact discovery consumes the calendar, and describes what changes when an AI agent takes over the searching while engineers keep every decision.
Where Engineering Change Management Actually Slows Down
A typical change moves through five stages: a request is raised, the impact is assessed, the change is approved, the documents are updated and released, and the change is implemented on the floor and in the field. Teams tend to measure the approval stage because it has a clear owner and a clear date. The assessment stage has neither, and that is where days disappear.
Consider a request to move a four-hole bolt pattern on a mounting bracket by six millimetres. The engineer who raised it knows the bracket. They may not know that the same bracket is used in a second product line, that a supplier fixture was cut to the old pattern, or that a service kit still ships the previous revision. Each of those facts lives somewhere different: the parts list in one system, the supplier drawing in a shared folder, the service kit in a spreadsheet owned by another team.
The result is a pattern most engineering managers recognise. The assessment is done by the most experienced person available, because only they know where to look. It takes longer than planned, it is rarely complete, and the gaps surface after release. The cost shows up as a second change request that corrects the first. For a deeper look at what a change record needs to carry so that questions like these can be answered at all, see what an ECO record has to carry before an agent can answer across changes.
Approval boards compound the problem. A board can only approve what it has been shown, so a thin impact summary produces a confident decision on thin evidence. Speeding up the meeting does nothing for that. Improving the evidence does.
IN PRACTICE
It integrates directly with PLM and existing workflows, making past designs, standards, and calculations instantly available. The result is fewer errors, faster decision-making, and a more consistent process across teams.
- Sergey G., Board Member
What Impact Discovery Really Involves
Impact discovery is a retrieval problem wearing a process costume. The question is simple to state: given this part, this feature or this specification, what else depends on it? Answering it takes several kinds of lookup, and each kind fails in its own way.
Where-used lookup. Walk up the bill of materials from the changed part through every parent assembly, including parents in other products. Native where-used reports stop at the boundary of one structure or one revision state, so cross-product reuse is easy to miss.
Drawing and model references. Find drawings, models and calculations that cite the part by number, by name or by a dimension that is about to change. Many references are written in notes or titles rather than structured fields.
Supplier and manufacturing artefacts. Find purchase specifications, inspection plans, fixtures, programs and work instructions built to the current revision.
Past decisions. Find earlier change records, nonconformance reports and design reviews that explain why the part looks the way it does, so nobody reverses a deliberate choice by accident.
Open work. Find orders, prototypes and field units still built to the old definition, because the change has to say what happens to them.
Only the first lookup is well supported by tooling, and even there the handoff between engineering and manufacturing structures can break the chain. The other four depend on search across unstructured files, and on knowing which files exist in the first place. That is the slow, human part of the loop.
It is also the most repetitive part. Every change repeats the same five lookups with different part numbers. Repetitive, read-heavy work with a clear checklist is the kind of work software agents handle well, provided they are limited to reading and reporting rather than deciding.
Handing Discovery to an Agent Without Losing Control
The sensible division of labour is narrow. The agent gathers and organises evidence. Engineers judge it. The agent never approves, releases or edits a record on its own authority. Drawing that line early is what makes the arrangement safe enough to adopt.
In practice, a request arrives with a part number and a description of the intended change. The agent runs the five lookups above across the connected systems and returns a structured impact summary: affected parents, affected documents, suppliers and fixtures, relevant history, and open work. Every item carries a link to its source, so a reviewer can open the original and check it rather than trusting a paraphrase.
Three properties matter more than raw speed. First, citations: an impact summary that cannot be traced to source files is just another opinion. Second, permissions: the agent should see only what the requesting engineer is already allowed to see, and a security review should settle that before rollout. Third, an honest statement of what was not searched. A summary that says "no supplier documents found in the connected folders" is far more useful than one that silently skips them.
Teams comparing tools for this job will find a practical checklist in the five tests that matter when choosing AI for engineering change orders. The short version is that a good agent reduces the chance of a missed dependency, and its value is measured by the changes that do not need a second round.
Where Leo Fits in the Change Loop
Leo is an AI assistant for mechanical engineers, trained on more than one million pages of standards, books and articles, and connected to an organisation's own knowledge base. Leo offers integrations with leading PDM and PLM platforms (SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, Arena PLM, and others), as well as local and network directories. It sits as an intelligence layer on top of those systems rather than replacing them.
For change management, that position matters because the five lookups cross system boundaries. An engineer can ask in plain language which assemblies, drawings and earlier decisions relate to a part, and receive an answer with cited sources they can click and verify. The answer comes from the organisation's own data, so it reflects how that company actually names parts and records decisions, not a generic template.
Leo does not change who owns the decision. The change request, the review board and the release stay in the existing PLM workflow. What shifts is how much of the assessment stage is spent searching. Teams that have tried this describe the benefit in terms of fewer surprises after release, which is the number that matters. Security is handled to a standard enterprise buyers expect: Leo is SOC-2 certified, GDPR compliant, trains no AI on customer data, and protects intellectual property.
For background on how automation fits around the change record itself, engineering change order automation with AI covers the workflow side, and this article concentrates on the discovery step that feeds it.
A Rollout That Starts Small
Teams do not need a programme to try this. A sound first step is to take five recently closed changes and replay the impact assessment with an agent, then compare its summary with what was found at the time and what was found afterwards. The comparison answers the two questions that matter: would the agent have caught the dependency that caused the follow-up change, and did it surface anything irrelevant enough to waste reviewers' time?
From there, a staged approach works well.
Connect one product line and its main document sources, and test retrieval on real part numbers.
Agree on the format of the impact summary with the people who sit on the review board, so it answers their questions in their order.
Run the agent in parallel with the manual assessment for a month, and log every miss on either side.
Move the agent into the normal assessment step once misses are rare, keeping the human review in place.
Two cautions apply. Do not judge the pilot on speed alone, because a fast summary that misses a supplier fixture is worse than a slow one that finds it. And keep a record of what the agent was asked and what it returned, attached to the change, so the history stays useful. This builds on the idea of product memory in PLM: the more complete the record of why changes were made, the better every later assessment becomes.
FAQ
See change impact before you approve
Ask Leo which assemblies and documents a part touches.
Leo searches your PDM, PLM and shared drives and answers with cited sources, so reviews start from the full picture.
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