AI for Engineering Productivity
Most AI programs cannot prove their return. Here is how mechanical engineering teams set a baseline, pick four honest metrics, and build a business case finance accepts.
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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 return in mechanical engineering is measurable, but only if you decide to measure it before you deploy. Capture five baseline numbers first: time to find prior art, new part numbers per quarter, change order volume and cycle time, repeat defect rate, and new hire ramp. Then track four things against them, keep adoption metrics clearly separate from impact metrics, and discount your own figures before finance does it for you. The reason most programs cannot prove a return is not that the return is absent. It is that nobody wrote down the starting point, so every improvement became a matter of opinion. One page of baseline measurement, captured in the two weeks before rollout, is the highest-value work in the entire program.
Every engineering leader who signed an AI budget in the last two years is now being asked the same question by someone in finance: what did it return? Most cannot answer it, and the reason is rarely that the tool did nothing.
MIT Project NANDA studied more than 300 enterprise deployments in 2025 and found that roughly 95 percent of organizations saw no measurable impact on the bottom line, despite tens of billions of dollars of spend. The report is blunt about the cause. The bottleneck was not model quality or talent. It was that the systems did not learn the organization, did not integrate with real workflows, and were never measured against a documented starting point.
For mechanical engineering teams the measurement problem is sharper than average. This is a practical guide to setting a baseline, choosing metrics that survive scrutiny, and presenting a case that a finance partner will sign.
Why Engineering AI ROI Resists the Usual Measurement
A support team measuring an AI assistant has an easy job. Tickets are countable, handling time is logged, and the before and after sit in the same system. Mechanical engineering has none of that structure, for four specific reasons.
The work is heterogeneous. An engineer might spend Monday on a tolerance study, Tuesday hunting for a fastener that was already qualified three programs ago, and Wednesday in a design review. There is no unit of output to divide cost by.
The cycles are long. A design decision made in August shows up as a tooling cost in February and as a warranty number two years later. Quarterly reporting cannot see that arc.
The savings land in a different budget. When an engineer reuses a qualified part instead of drawing a new one, the money is saved in procurement, quality, and inventory. The engineering department that paid for the software gets no credit in its own cost center.
The counterfactual is invisible. You never see the part that was not redesigned, the change order that was not raised, or the review comment that was not needed. The wins are absences, and absences do not appear in a report unless you decide in advance to count them.
None of these make measurement impossible. They make measurement something you have to design before deployment rather than reconstruct afterwards.
IN PRACTICE
We're less dependent on outsourced engineers. We do it all in-house. We get answers in a few minutes instead of a few days.
- Harel Oberman, CEO, Oberman Industrial Designs
Capture the Baseline Before You Deploy Anything
The single most common failure in engineering AI programs is that nobody wrote down what the work cost beforehand. Once the tool is in use, the honest baseline is gone and every number becomes an argument. Two weeks of deliberate measurement before rollout is worth more than six months of dashboards afterwards.
Five baseline measures are enough, and each can be pulled from systems your team already runs.
Time to find prior art. Ask a sample of engineers to log, for two weeks, how long it takes them to locate an existing part, calculation, or decision record. Published estimates put the share of engineering time lost to searching and redesigning existing work high enough that this is usually the largest single line, as covered in our breakdown of engineering part reuse.
New part numbers created per quarter. Export this from your PDM or PLM system. It is the cleanest proxy for whether reuse is happening, and it needs no new instrumentation.
Change order volume and cycle time. Pull the last four quarters of engineering change orders, split by root cause where your system records it. Our guide to engineering change orders and rework covers how to categorize these without a lengthy audit.
Repeat defect rate. Count how many non-conformance reports in the last year describe a problem the organization had already seen and solved.
New hire ramp time. Weeks from start date to first independent release, averaged over your last several hires.
Write these five numbers down with the date and the method used to get them. That single page is the difference between a defensible result and a story.
Four Metrics That Track Real Engineering Impact
Once a baseline exists, resist the temptation to track everything. Four metrics carry almost all of the signal, and each maps to a cost that someone outside engineering already cares about.
Time to a sourced answer. Not time to an answer, time to an answer an engineer is willing to act on. An unsourced response that has to be verified by hand has consumed time rather than saved it.
Reuse rate against new part creation. If new part numbers per quarter fall while output holds steady, the downstream saving in tooling, qualification, and inventory is real and quantifiable by procurement.
Change order and repeat defect volume. Rework avoided is the most credible saving an engineering team can claim, because the cost of each event is already recorded. The same logic applies to non-conformance reports, where recurrence is the metric that matters.
Onboarding ramp. Weeks to first independent release is a direct labor number, and it is the metric that moves fastest, as we discuss in our guide to accelerating engineering onboarding.
This is where the choice of platform stops being a procurement question and becomes a measurement question. Leo is built as an intelligence layer on top of the systems an engineering organization already runs, offering integrations with leading PDM and PLM platforms including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM, alongside network directories and ERP. Because every query and every cited source is logged, the first metric stops being an estimate and becomes a record. A platform that cannot tell you what it was asked, what it answered, and what it cited cannot participate in its own ROI case.
Adoption Metrics Are Not Impact Metrics
Seats activated, weekly active users, and total queries are adoption metrics. They tell you whether people opened the tool. They say nothing about whether the organization is better off, and presenting them as return is the fastest way to lose credibility with a finance partner.
The confidence gap is well documented. KPMG's Global AI Pulse for the first quarter of 2026, which surveyed more than two thousand senior leaders at companies with at least 100 million dollars in revenue, found high confidence in measuring returns on productivity at 76 percent and on quality of work at 71 percent. Confidence collapsed to 14 percent for indirect and strategic benefits. Respondents named difficulty quantifying indirect or long-term benefits as a leading barrier, cited by 40 percent.
Read alongside the MIT finding, the pattern is consistent. Organizations can measure the direct, near-term effects if they choose to, and they mostly do not choose to in advance. The MIT work adds a second warning that matters for engineering specifically: tools that do not retain organizational context stall at the pilot stage regardless of how capable the underlying model is. An assistant that cannot see your vault, your standards, and your past decisions will produce plausible answers that an engineer has to check, which is a cost rather than a return. Our overview of what actually works for mechanical teams goes further into that distinction.
Track adoption, because a tool nobody opens cannot return anything. Just never present it as the return.
Building a Business Case Finance Will Accept
A credible engineering AI case is short, conservative, and explicit about what it cannot prove. Five things make the difference.
Use a fully loaded engineering hour, not salary divided by hours. Finance will substitute their own figure if you do not, and the conversation restarts.
Discount your own numbers deliberately. If the logs show four hours saved per engineer per week, present two and say why. A case that survives a skeptical read is worth more than a larger one that does not.
Name the counterfactual out loud. State plainly which savings are measured, which are attributed, and which are estimated. Auditors reward that distinction, and reviewers assume the worst when it is missing.
Size the pilot so the result is readable. A team of five to twenty engineers on one product line, running one quarter, produces a signal you can attribute. A thin deployment across five departments produces noise.
Answer the security question before it is asked. Leo is SOC-2 certified and GDPR compliant, no models are trained on customer data, and customer intellectual property stays protected. In regulated and defense-adjacent programs this determines whether the case gets read at all.
Then present it once a quarter against the same baseline page, using the same method. Consistency is what turns a claim into a track record, and it is also what tells you early when something is not working. A program reviewed the same way four times running is far harder to dismiss than one that produces a new headline number every quarter.
FAQ
Challapally, A., Pease, C., Raskar, R., and Chari, P. The GenAI Divide: State of AI in Business 2025. MIT Project NANDA, July 2025. Based on 52 executive interviews, 153 survey responses, and reviews of more than 300 enterprise deployments.
KPMG Global AI Pulse Survey, first quarter 2026. Fieldwork 17 February to 17 March 2026, 2,110 C-suite and senior business leaders at companies with annual revenues of at least 100 million US dollars.
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