Hiring an AI consultant for manufacturing

The common projects cluster around planning, quality, and maintenance: forecasting demand, catching defects, predicting equipment failure, and above all bridging the gap between what happens on the line and what the ERP and planning systems know. Among vetted providers, workflow automation leads by a wide margin, with AI agents second, reflecting how much of the value is in connecting systems rather than in a single clever model.

The stack leans heavily on custom work, because manufacturing data rarely arrives in a clean, hosted format. You will also see cloud infrastructure, n8n for connecting operational systems, and, in quality-inspection use cases, computer vision. A partner who only knows consumer no-code tools will struggle the first time they meet a legacy MES or a spreadsheet that has quietly run production for a decade.

Good manufacturing AI is judged on reliability under real conditions. It handles missing and noisy data instead of breaking on it, alerts a person when something is off, and integrates with the systems your team already lives in. Demos on clean sample data prove nothing here; ask what happened when real line data hit the system.

Favor partners who talk in terms of your operations: throughput, downtime, scrap, changeovers, and the specific systems on your floor. The ability to map a use case to a measurable operational metric, and to explain how they will get clean data out of an unfriendly source, is the strongest signal that they have done this before.

Ask directly about integration with your ERP, MES, or planning tools, and about who maintains the system once it is live. Manufacturing engagements often fail not at launch but three months later, when nobody owns the automation and it drifts. A candidate with a clear maintenance and monitoring plan, and a named prior engagement to point to, is worth a premium.

Manufacturing projects are quoted per project and vary enormously with complexity. Among providers who publish figures, simpler builds can start in the low five figures while multi-system or vision-based deployments run into the high tens of thousands and beyond. Some partners begin with a paid diagnostic to map the data and systems before committing to a build price.

Use those as a rough frame, not a quote. Data readiness, the number of systems involved, and whether hardware or vision is in scope are the real cost drivers. The healthiest arrangement is a scoped diagnostic first, then a fixed or clearly bounded build estimate, then a maintenance retainer so the system does not rot after launch.