Multi-Million Dollar Waste Avoided: Preventing AI Failure in Manufacturing

Client Overview

An international specialty manufacturer in the electromagnetics space, approximately $750M in annual revenue and part of a larger conglomerate. Their growth depends on engineering throughput, and their engineering throughput depends on how quickly new electrical engineers can be brought up to speed and trusted with complex design work.

Challenge

The client wanted to build a custom AI model that would help engineers find optimal electromagnetics designs based on past work, performance constraints, and cost targets. The goal was to compress design cycles from weeks to hours and accelerate the onboarding of new engineers.

They had issued an RFP that would have generated multi-million dollar bids and committed them to a full custom AI build with risk across every dimension. Technical feasibility was uncertain as large language models are the wrong tool for 3D engineering topology. Data readiness was uncertain. Compute requirements were uncertain. And without first understanding how their engineers actually worked or what their data actually looked like, no honest vendor could confidently estimate the project.

Solution

Before the first contract was signed, we challenged the RFP. Rather than bid on the full build, we recommended a Discovery and AI Readiness engagement first, at a fraction of the original RFP estimate, to surface what was actually true before committing to a multi-million dollar build.

Working alongside specialist AI/ML partners, we spent several months inside their engineering organization: deep-dive workshops with the team, a full inventory of every data source (CAD and mesh files, ERP data, test reports, Excel sheets, external sources), end-to-end mapping of their manual planar topology workflow, and architecture evaluation grounded in their actual engineering reality.

The Finding

Despite massive troves of historical design data, the data was largely unstructured. Cleansing and standardizing it would be a significant undertaking, and no custom AI model could be trained meaningfully until that work was done.

This is one of the most common blockers to AI adoption in established companies, and it is invisible from the outside. The client's original RFP would have spent its budget building a model on top of data that wasn't ready to support one.

Outcome

The client avoided committing to a doomed multi-million dollar project. They are now investing first in a data collection and standardization platform that will create the foundation a custom AI model actually needs. The custom AI model is deferred until the data foundation is in place, which is the order in which the work has to happen for it to succeed.

Why This Matters

Most AI engagements fail at the data layer, not the model layer. The discipline of validating data readiness before committing to a model is what separates AI that ships from AI that gets quietly shelved. Discovery cost a fraction of the original RFP and produced something more valuable than a faster path forward: an honest, evidence-based answer about what was actually possible to build.