AI for Engineering Productivity
How engineering and design consultancies evaluate, deploy, and measure AI tooling, from data isolation across clients to the ROI math that actually matters for billable teams.
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10 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
An engineering firm's AI deployment succeeds or fails on a different scoreboard than a product company's. The questions worth asking upfront are data isolation across clients, where design data actually lives during use, and vendor security certifications, since a client's own review will ask the same questions. The most valuable use case is retrieval across a firm's own backlog of past projects, turning years of prior client work into searchable prior art instead of tribal knowledge locked in a few senior engineers' memory. Roll out account by account, starting with slack in the schedule rather than a deadline, and measure payback through utilization rate, time-to-quote, and capacity rather than hours saved in the abstract.
A design and engineering consultancy sells hours, not headcount. When a product company adopts an AI tool, the win shows up as a shorter internal cycle time. When a consultancy adopts the same tool, the win has to show up somewhere a client invoice can see it: a faster quote, a tighter turnaround, or a project taken on without adding a seat. That difference changes almost everything about how an engineering firm should evaluate, roll out, and measure AI, and it is why the deployment playbook that works for an in-house design team does not transfer cleanly to a firm working across a dozen clients' projects at once. A related look at how design agencies are already using AI day to day covers the practice side of this; this piece covers the deployment and selection side.
Why an Engineering Firm's ROI Math Is Different
An in-house engineering team measures a tool by whether it shortens the path from concept to release. A consultancy has to ask a second question first: does this hour convert to revenue. Time spent searching for a similar part, re-deriving a calculation a senior engineer already solved for a different client, or reformatting a report so it matches a new client's template is non-billable by definition. It does not show up as a missed deadline. It shows up as a thinner margin on an otherwise healthy project.
That reframes the ROI question. The relevant number is not hours saved per engineer per week in the abstract. It is utilization rate: the share of paid hours that go to billable engineering work rather than internal overhead. A firm evaluating AI tooling should ask what fraction of a typical engagement is spent on lookup, reformatting, and re-derivation rather than judgment calls, because that fraction is exactly what an AI intelligence layer is positioned to absorb. A closer breakdown of how mechanical engineering teams should measure AI ROI walks through the underlying math in more detail.
There is also a capacity effect that an in-house team never has to think about the same way. A product team that gets faster still has one roadmap to work through. A consultancy that gets faster has a sales pipeline to fill instead, and the question becomes whether the firm can take on the next engagement without adding a seat, or whether it has to turn work away because every engineer is already at capacity. That makes the ROI conversation as much about business development as it is about engineering, and it is why the tooling decision at a consultancy usually involves a principal or a practice lead, not only an engineering manager.
The margin effect compounds across a portfolio of engagements rather than showing up on any single project. A few reclaimed hours on one job barely register. The same few hours, reclaimed consistently across every job a firm runs in a quarter, is the difference between a practice that grows headcount every year to keep up with demand and one that grows revenue per employee instead.
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
What to Evaluate Before Deploying Across Client Projects
A product company only has to protect its own IP. A consultancy holds design data, drawings, and sometimes entire part libraries belonging to several clients at once, often under separate non-disclosure terms. Before any AI tool touches that data, a firm needs clear answers on three points.
Data isolation between clients. Nothing learned or retrieved on one engagement should leak into another, even accidentally through a shared model or a shared index.
Where the data lives during use. A tool that requires uploading drawings to a general-purpose model is a different risk profile than one that queries an organization's own PDM, PLM, or network drive in place.
What the vendor can say about certification. SOC 2 certification and GDPR compliance are the baseline questions a client's own security review will ask, so the firm should be able to answer them before the client does.
Leo AI, for example, is built as an intelligence layer that sits on top of an organization's existing PDM, PLM, ERP, and file systems rather than replacing them, is SOC 2 certified and GDPR compliant, and is not trained on customer data, which keeps each client's information separated from every other client's. A fuller look at what makes mechanical engineers trust an AI copilot in the first place covers this evaluation from the engineer's side of the table, not just the buyer's.
Retrieval Across Client Part Libraries and Historical Designs
The single most valuable use case for a consultancy is different from the most valuable use case for a product company, and it comes down to volume of prior art. An in-house team works against its own history. A firm that has served fifty clients over a decade is sitting on fifty histories, most of which are only searchable if someone remembers which project it was on.
An AI layer that can search across CAD geometry, not just file names and folder structures, turns that backlog into a working asset instead of a liability. A design engineer starting a new bracket, fixture, or enclosure can search for a similar part by shape rather than by guessing what a past engineer might have called it, and pull up a validated solution instead of starting from a blank sketch. That is retrieval doing the job a senior engineer's memory used to do, minus the dependency on that senior engineer still being at the firm.
The same retrieval layer also answers technical questions against a firm's own standards, past calculations, and design decisions, with a citation back to the source document, which matters more at a consultancy than anywhere else: a junior engineer on a new account has no tribal knowledge of that account to draw on, and a citation is what lets them trust an answer they cannot yet verify from experience.
There is a staffing consequence to this that firms notice quickly once retrieval is in place. Rotating engineers between accounts becomes less costly, because the engineer arriving on a new account is not starting from zero. Historically, a firm kept one engineer permanently tied to a given client simply because that engineer was the only one who remembered how past projects on that account were solved. A searchable backlog loosens that constraint, which gives a practice lead more flexibility in how engagements get staffed during a busy quarter.
Rolling Out Across Multiple Teams and Client Engagements
A phased rollout works better than a firm-wide switch-on, for a reason specific to consultancies: every engagement has its own deadline, its own client contact, and its own tolerance for disruption. A rollout that is felt on a live client project reads as risk, not innovation.
Start with one project team on an engagement that has schedule slack, not the one closest to a deadline.
Measure turnaround time and utilization rate on that engagement before and after, using the firm's own timesheets rather than a vendor's estimate.
Fold the tool into onboarding for the next new hire before asking existing senior engineers to change habits, since a new hire has no old workflow to unlearn.
Expand account by account, prioritizing clients with the deepest design history, since that is where retrieval has the most prior art to work with.
This is also where a firm decides how much to expose to a client directly. Some consultancies present AI-assisted turnaround as a selling point in the proposal; others treat it as an internal efficiency they do not surface at all. Either is defensible, but the decision should be made deliberately rather than by default. Firms still weighing which tool to standardize on can start from a broader checklist of what to look for in an AI copilot for mechanical engineers before committing to a firm-wide rollout.
Measuring the Payback
Three numbers tell a consultancy whether a deployment is working, and none of them is "hours saved."
Utilization rate, before and after, on comparable engagements.
Time-to-quote, since a faster, more confident estimate wins more of the proposals a firm bids on.
Capacity, measured as the number of concurrent engagements a team can carry without adding headcount.
A firm that sees utilization rise and time-to-quote fall has a deployment that is paying for itself in the only currency a consultancy actually keeps score in: billable margin. A firm that only sees anecdotal enthusiasm from engineers, with no movement in those three numbers, has not yet found where the tool actually earns its keep, and should revisit which workflows it was rolled out against before expanding further.
It is worth tracking these numbers per account, not only firm-wide, since the payback is rarely uniform. An account with ten years of design history behind it will show a bigger retrieval win than a brand-new client with no prior engagements to search against. That account-level view is also what tells a principal which client relationships benefit most from being staffed with the tool first, rather than rolling it out in whatever order engagements happen to come up.
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