AI for Engineering Productivity
Where AI has actually landed in mechanical engineering, where it has not, and how to tell the difference before you rely on it.
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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
AI works best in mechanical engineering where answers can be verified: retrieving company knowledge and parts, supporting routine calculations with cited sources, guiding CAD work, and running rule-based design checks. It is weakest where judgment and accountability dominate, such as simulation sign-off, novel design and anything that needs physical testing. Evaluate any tool by asking for sources, testing it on your own assemblies and keeping an engineer responsible for the result.
Ask ten people what AI does for a mechanical engineer and you will get ten different answers, ranging from "it designs the whole part" to "it is a chatbot that gets the physics wrong." Neither is a useful picture. The more accurate one is uneven: AI has landed firmly in a few parts of the job, shows up unevenly in others, and has not arrived at all in a handful of places that matter a great deal.
This post is an explainer, not a tool list. It walks through where AI is doing real work in mechanical engineering today, what makes those areas suitable, and where the limits still sit. If you want a wider view across the profession, our overview of what works in AI for engineering in 2026 covers the neighbouring ground.
Finding what your company already knows
The most reliable gains so far come from retrieval: finding an answer or a part inside the information an engineering organisation has already produced. This is not glamorous, but it matches what language models do well. They read a lot of text, connect a question to the relevant passage, and say where it came from.
Most engineering teams are sitting on years of design decisions, test reports, supplier notes, drawings and standards. The knowledge exists, but it is spread across file shares, product data systems and individual inboxes. A new engineer asking why a flange was changed three years ago usually has to find the person who remembers. That gap grows quickly as experienced engineers retire or change roles.
Part reuse is the geometry version of the same problem. Searching by shape and function, not by part number or a guessed description, helps an engineer find a standard part instead of drawing a new one. We looked at this in why engineers spend so much time redesigning parts that already exist.
Leo AI works in this area as one capability among several. It is an AI assistant for mechanical engineers, trained on more than a million pages of standards, books and articles, and it can connect to an organisation's own knowledge base, including product data and lifecycle systems and local or network directories. It acts as an intelligence layer on top of existing PDM and PLM systems, not a replacement for them, and offers integrations with leading platforms such as SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, Arena PLM and others. The value driver is time: answers with a cited source, found in minutes instead of by interrupting a colleague.
The reason retrieval works is that the answer already exists and can be checked. The engineer clicks the citation, reads the original and decides. That keeps a person in the loop at the point where the risk sits.
IN PRACTICE
Leo uses a Large Mechanical Model trained on 1M+ technical sources. It also provides citations, so we don't have to guess whether a material property or tolerance is correct. We see 96% accuracy on technical queries.
- Dorian G., AI Engineer
Calculations and standards lookups
The second area where AI has found a footing is routine calculation support and standards lookup. Engineers spend a surprising amount of time on questions that have a known method: the allowable stress for a material at temperature, the minimum edge distance for a fastener, the bend allowance for a given sheet, or what a clause in a standard actually requires.
A general chatbot can produce a number for each of these, and that is exactly the danger. The number looks confident whether or not it is right. What changes the picture is grounding. An assistant that works from named standards, shows the formula it used and cites the source gives the engineer something to verify. One that returns a bare figure does not.
Three habits separate safe use from risky use:
Ask for the method and the source along with the answer, then check the source.
Keep units visible at every step, since unit slips are one of the most common errors in generated calculations.
Treat the result as a first pass that a responsible engineer still signs off.
We cover the accuracy question in more depth in how AI handles engineering calculations in 2026. The short version is that AI is a fast and useful second pair of eyes on routine maths, and it is a poor replacement for the engineer who owns the result.
One of our users, an AI engineer, described the appeal in practical terms: citations mean there is no guessing about whether a material property or tolerance is right. That is the pattern to look for in any tool.
CAD assistance is narrower than the demos suggest
Generating geometry from a description is the most visible AI capability and the one with the widest gap between a demo and daily work. Simple parts such as brackets, plates and housings with a handful of features can come out usable. Real products add constraints that a prompt rarely captures: tolerance schemes, manufacturing process, mating parts, material, finish and the many small decisions that live in an experienced designer's head.
Where assistance helps more reliably is around the model, not in place of it. Examples include guidance on how to perform a feature in a particular CAD system, finding the right command, explaining why a rebuild failed, or suggesting an approach to an assembly problem. These tasks have a clear right answer that the engineer can confirm on screen in seconds.
Assemblies are a known weak point. A single part can be generated in isolation, but keeping dozens of parts consistent with each other, with correct mates and fit, is a different level of difficulty. If a vendor shows you a single clean bracket, ask to see an assembly with moving parts and a bill of materials that has to add up.
Our view is that generated geometry should be judged the way you would judge a junior colleague's first draft. It saves a blank-page start, and it needs review before it goes anywhere near a drawing.
Checking and review is a natural fit
Review work suits AI because it is repetitive, rule-based and tedious for people. Checking a drawing against company standards, flagging a missing tolerance, comparing a bill of materials against a drawing, or screening a model for manufacturability issues are all tasks where the rules can be written down and a missed item is expensive.
The practical benefit is catching mistakes earlier, when a fix costs a few minutes in CAD instead of a scrapped batch or an engineering change order. Typical checks include wall thickness against a process limit, hole-to-edge distance, undercuts that need a side action, and inconsistent callouts between the model and the drawing. We walk through the approach in how AI design review catches errors before manufacturing.
These checks work best as a safety net, not as a gate. A flagged item is a prompt for an engineer to look, and an unflagged item is not proof that everything is fine. A tool that finds ninety percent of the usual problems still leaves the remainder to the person who understands the part.
Teams get the most from these checks when they write down their own rules first. A check that encodes your company's minimum wall thickness, preferred hole sizes and drawing conventions is far more useful than a generic list, because it reflects how your parts are actually made and which suppliers you actually use.
The same logic applies to consistency work such as naming, revision control and document completeness. These are areas where a quiet, automatic check keeps a team honest without adding meetings.
Where AI has not arrived yet
A fair account has to include the gaps. Several parts of mechanical engineering remain firmly human, and it is worth being clear about them so that expectations stay realistic.
Simulation sign-off. AI can help set up an analysis or explain a result, but deciding whether boundary conditions, mesh quality and load cases represent reality is a judgment call that depends on physical understanding.
Novel design under uncertainty. Language models are strongest where existing knowledge applies. Truly new architectures, unusual failure modes and trade-offs between conflicting requirements still need an engineer who can reason from first principles.
Accountability. A signed drawing carries professional responsibility. No tool changes who is answerable when a part fails in service.
Messy real-world data. Scanned drawings, inconsistent part numbers and years of unlabelled files limit what any assistant can retrieve, however capable it is.
Physical verification. Testing, inspection and the feel of a prototype in the hand are outside the reach of software.
The pattern across these gaps is the same. Where an answer can be checked against a source, a standard or a screen, AI is useful today. Where the answer depends on judgment, physical evidence or accountability, it assists at best.
This is also the honest answer to the common worry about replacement. Our discussion of whether AI will replace mechanical engineers reaches a similar conclusion: the tasks are changing, and the responsibility is not moving.
FAQ
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