CAE, AI and ROI

Representative picture generated using Google Gemini

(Disclaimer: The views and opinions expressed in this article are strictly my own)

We are witnessing a Cambrian explosion of AI tools in the engineering space. Every week, a new startup pops up promising “real-time physics,” “generative design in seconds,” or “AI solvers that replace FEA.” The potential is undeniable. The demos are slick.

But for those of us responsible for delivering actual complex products — vehicles, powertrains, safety-critical systems — we have to park the hype and ask the hard questions.

The future of engineering is definitely AI, but the path to get there isn’t a switch we flip; it’s a “walk.” It is a long, deliberate journey to figure out the workflows, the governance, and the actual investment required to see tangible ROI.

Here is the reality of that walk.

1. The Scouting Phase: Don’t Use a Warhammer on a Thumb Tack

The first failure mode I see isn’t technical; it’s strategic. We often see a “solution looking for a problem.”

We need a rigorous Scouting Phase. If a linear static analysis takes 10 seconds in a traditional solver, applying AI to it is a waste of resources. AI belongs where the friction is highest:

• High Latency: Crash simulations or CFD runs that take overnight.
• High Iteration: DOE studies where you need to explore 1,000 variants.

We also need to distinguish between domains. The ROI of AI for software coding (like GitHub Copilot) is clearly identified. We are far more efficient at writing scripts and debugging because the cost of failure is low (syntax error -> fix it in seconds).

Engineering physics is different. The feedback loop is slow, and the cost of a “hallucination” in a structural part is catastrophic. We cannot budget them the same way.

2. The “Next Big Thing” Reality Check: Gen AI & The CAD Gap

We hear “Generative AI” thrown around constantly. In high-fidelity engineering, the ultimate goal isn’t just to generate a mesh for simulation; it is to generate a manufacturable CAD model.

Currently, we use simulation as a mandatory safety net because we don’t trust the AI yet. But the long-term vision is to reach a level of confidence where we can go straight from “Text-to-CAD” to “Production,” bypassing the heavy simulation loop entirely.

However, there are few hurdles :

The “Fuzzy” Geometry Trap Gen AI often generates “point clouds” or “implicit representations” that look great in a render but are mathematically useless for parametric CAD.

• The Trap: It doesn’t inherently understand B-Rep (Boundary Representation). It paints pixels or voxels, not precise mathematical surfaces.
• The Result: You get a design that requires massive manual reconstruction to become a solid model. If you spend 2 days remodeling the AI’s “suggestion” in NX or Catia, you haven’t saved time; you’ve just shifted the bottleneck. The Manufacturing “Validity” Tax This is the most critical unknown. Gen AI models optimize for form and topology (the “organic” bone-like look). But real parts need draft angles, minimum wall thicknesses, and tool access paths.
• The Conflict: As soon as you apply realistic manufacturing constraints to a Gen AI concept, you force the geometry to change.
• The Billion-Dollar Question: How much of the design’s performance is lost in that translation?
• If the AI predicts a 20% weight reduction, but making it castable adds 15% mass back, is the design still valid?
• Does the “manufacturing cleanup” inadvertently create stress risers that the AI didn’t predict?

Until we can guarantee that the Manufacturing-Constrained design performs as well as the Unconstrained AI concept, simulation will remain our necessary “confidence bridge.”

Agentic AI: Automation vs. Reasoning Agentic AI is sold as the ultimate assistant. But we must distinguish between Automation and Agency.

• Automation: A script that opens a file and meshes it. We’ve had this for 20 years.
• True Agency: An AI that acts like a Junior Engineer. If a mesh fails a quality check (e.g., Jacobian ratio < 0.7), an Agent shouldn’t just crash. It should reason, locally refine the mesh, and re-submit. The ROI here is in handling the messy, decision-heavy parts of pre-processing, not just pushing “Solve.”

3. The Feasibility Gate: The “Toy Model” Problem

Once you identify a use case, you hit the Scale Reality Check. Many vendors demonstrate impressive speed on “Toy Models” — simple brackets, idealized manifolds, or generic public datasets. But engineering reality is messy.

• The Topology Wall: Real-world OEM models have millions of elements, complex contacts, spot welds, and proprietary material files. A tool that predicts stress on a cantilever beam often chokes on a full vehicle assembly.
• The Data Moat: Software providers bring the powerful algorithms, but they typically lack the proprietary context. OEMs have the “Ground Truth” — petabytes of legacy data. The bottleneck isn’t software; it’s the gap between the software provider’s generic model and the OEM’s specific, complex reality.

4. The Hidden Infrastructure Bill

Let’s say the tool works on a big model. Now you face the Security Moat. Most modern AI tools are SaaS (Cloud-based). For an automotive OEM, this is often a non-starter. We operate with a “Fortress” mentality regarding IP. Uploading future platform data to a startup’s cloud is a security nightmare.

To use these tools, we often need Air-Gapped or On-Premise deployments. This flips the ROI calculation upside down.

• The Cost: It’s no longer just the software license. It is the cost of internal GPU clusters (H100s/A100s), MLOps teams, and IT maintenance.
• The Calculation: ROI = (Time Saved) — (License + Hardware + Team). The “Hardware” variable is often much larger than expected.

5. The Integration Abyss: The “So What?”

This is the question nobody wants to answer. Suppose you have a secure AI tool that predicts a crash result in 5 seconds. So what?

If that result sits in a standalone Python window or a web dashboard, its value is limited.

• The Broken Loop: If the engineer has to manually interpret the result, export data, and rebuild the CAD, the system-level efficiency gain is zero.
• The Missing API: We haven’t fully figured out the “API” to turn fuzzy AI predictions back into exact, parametric CAD features (in NX or Catia). Until the AI is embedded seamlessly into the design environment — where the engineer actually lives — it remains an “alt-tab” curiosity.

6. The Benchmarking Vacuum: The Walk to Trust

Finally, how do we trust the answer? For 30 years, we have benchmarked CAE against Physical Testing. We know the “delta.”

• The Approximation of an Approximation: If we train AI on CAE data, and CAE is already an approximation of reality, are we compounding our errors?
• The Investment Walk: We need to establish a new “Ground Truth.” Do we benchmark AI against the solver (fast but biased) or against physical tests (true but expensive)?

7. The “Confidence” Tax: Uncertainty Quantification (UQ)

This is a topic near and dear to me (it was the focus of my PhD dissertation), and it is the missing link in most ROI calculations.

In traditional CAE, we are used to deterministic answers: “The Max Stress is 300MPa.” In AI, the answer is probabilistic: “The Stress is likely 300MPa ± 30MPa.”

To make AI usable for engineering, we have to invest in Uncertainty Quantification (UQ). We need the model to tell us not just the answer, but its confidence in that answer.

• The Investment: UQ isn’t free. It requires training Probabilistic Neural Networks or running Ensemble models. This multiplies your compute cost.
• The Value: It prevents the “Silent Fail.” If the AI encounters a design shape it has never seen before, a UQ-enabled model will say, “I don’t know.”
• The Reality: We need to budget for this “Confidence Tax.” Building a model that knows what it doesn’t know is significantly more expensive than building a model that just guesses.

8. The “Living System”: Continuous Retraining

Finally, a static AI model is a dying model. As our designs evolve — new materials, new architectures, new load cases — a model trained on 2024 data becomes obsolete by 2026.

We need to build a system that creates a Continuous Learning Loop.

• The Vision: Every time a traditional solver runs overnight, that “ground truth” data should automatically feed back into the training set. The AI model should be retrained (or fine-tuned) at regular intervals — weekly or monthly — without human intervention.
• The Reality: This requires an immense level of automation and data hygiene that most organizations don’t have yet. It will take a few years to build this pipeline, but it has to happen. Without it, your AI tool is just a snapshot in time.

Conclusion: The Implementation Puzzle

The goal shouldn’t be “perfect” accuracy everywhere. The goal is Contextual ROI.

AI might only be 80% accurate, but if it allows us to explore 1,000 design variants in the concept phase, that is infinitely more valuable than a 99% accurate solver that takes a week.

Do I get excited by a new tool or technology?

Absolutely. I’m an engineer; I love seeing what’s possible. But what keeps me awake at night is the puzzle of implementation. It is figuring out how to weave that technology into the messy, complex reality of our daily work to drive a measurable impact. That is the problem I am focused on solving. The winners won’t be the ones with the best demos; they will be the ones who figure out the governance, the workflow, and the integration to make those demos real.

Acknowledgements Yes, I used Google’s Gemini to help articulate my thoughts for this article. I have no problem accepting that I am an engineer, not a writer. The ideas, the skepticism, and the scars from the field are mine; the structure and polish are thanks to my AI thought partner.