Helping a manufacturer move AI out of pilots and into the plant

The company wanted to use AI for predictive maintenance, automated quality checks, production scheduling and process tuning. It had finished several proofs of concept. Almost none of them had made it into how the plants actually worked.

Today's industrial AI roles mix model building with cloud architecture, MLOps, real time plant data, scheduling, forecasting and real manufacturing knowledge.

The company asked for data scientists. But the problem was not a shortage of modeling ideas. It was the gap between a demo and something running on the floor.

They needed leadership to pick the right use cases, data scientists to build models, ML engineers to deploy them, data engineers to feed them clean inputs, and plant champions to redesign the work around what the models produced.

We started with the list of use cases, not a list of job titles. For each one we looked at data readiness, operational value, how it fit the workflow, where it would run and who would own it.

That made the answer clear. Data scientists alone would not solve data quality, MLOps, edge deployment, app integration or getting people on the floor to trust it. The job needed a mixed team.

Leadership:

• Director of Manufacturing AI
• Owns the use case portfolio, value delivered, team design, responsible AI and adoption

Architects:

• AI/ML Architect
• Manufacturing and OT Data Architect
• MLOps Architect

Builders:

• Data Scientists
• ML Engineers
• AI Application Engineers
• Data Engineers
• Computer Vision Engineers
• MLOps Engineers

Domain and adoption:

• Reliability Engineers
• Quality and Process Engineers
• Maintenance SMEs
• Plant Product Owners
• Change and Training Leads

We assessed people on:

• Applied work in maintenance, quality, scheduling, forecasting or process optimization
• Python, statistics, machine learning and experiment design
• Time series, anomaly detection, optimization or computer vision
• Model deployment, APIs, CI/CD, monitoring and retraining
• MLflow, Azure ML, Databricks, Azure AI or similar
• ERP, MES, historian, sensor and machine data integration
• Proof that a model reached production and changed a real decision
• The ability to work with operators, engineers and plant leaders

We told model developers, production ML engineers and manufacturing domain leaders apart. Being able to talk about AI was not enough. People had to walk us through the data, the model, the deployment, the workflow, the adoption and the result.

We also looked for people who respected plant reality: safety, uptime, latency, explainability, reliability and the trust of the people on the line.

Our advisors here had put AI into real plants, not just labs. They vetted the leaders on use case prioritization and responsible AI, and the builders on modeling, MLOps and deployment. We found talent through our applied AI and manufacturing data communities, where production work is visible and easy to verify.

The company could build around one or two priority use cases instead of hiring a big AI team with no way to deliver. Every role had a clear job in getting something into production and showing value.

The company built a repeatable path from idea to validation to deployment to monitoring to adoption. Its own leaders could then rank future use cases by what was feasible and valuable, not by what sounded new.