AI Consulting for Manufacturers

The AI readiness assessment

Every engagement opens with an AI readiness assessment, usually three to four weeks. We audit your ERP structure, master-data quality, the systems that don't talk to each other, and the KPIs nobody fully trusts. The output is a short, honest map: which operational question is worth answering first, what it takes to answer it, and whether you're ready to act on the answer. Some manufacturers learn their data needs work before any AI makes sense. That's a far cheaper thing to learn in week three than in month six.

A unified data foundation

With a target in hand, we build the foundation. That means pulling your ERP, CRM, ecommerce, carrier, and shop-floor data into one governed warehouse, with real-time dashboards so the numbers agree before any model touches them. This is the unglamorous part, and it's most of the value. On a typical engagement, the first real win isn't AI at all. It's three teams finally agreeing on one revenue number because the data comes from one place instead of four. That agreement is what makes everything we add later worth trusting. An AI answer is only as good as the data underneath it, which is why our data and AI consulting spends more time here than anywhere else.

A governed AI analyst

Once the data is solid, we add the AI analyst, the part people picture when they think "AI consulting." Your team asks a question in plain language and gets an answer traceable to the source record. It runs read-only, logs every query, and recommends instead of executing. It proposes the change, and a human approves it before anything writes back to your ERP. For ERP AI consulting specifically, that governance is the line between a tool your controller trusts and one your auditor flags. In practice, a plant manager can ask why a line ran behind last week and get an answer with the underlying records attached, instead of exporting a spreadsheet or trusting a black box.

Data is the first. Duplicated item masters, inconsistent vendor records, three systems that each report a different number for the same order. No model fixes that on its own. Point one at messy data and you get confident wrong answers, which are worse than no answers because people act on them. A planner who trusts a forecast built on a duplicated item master orders against numbers that were never real, and the error compounds down the line.

Governance is the second. A model with write access to your ERP, no logging, and no approval step becomes a liability the moment it acts on a bad inference. Most pilots skip this because it isn't the fun part, then stall when the risk gets obvious to someone in finance.

Scope is the third. "Add AI" isn't a project by itself. Open-ended programs burn budget and ship nothing anyone can point to. The work that survives executive review targets one measurable operational problem, with a deadline and a number attached. We design around all three of these from day one, which is the whole point of the fixed-scope, phased model.

We know your ERP by name. We've gone deep on NetSuite AI and Acumatica AI, what each does natively, where it stops, and how to add real intelligence on top without ripping anything out. A consultant who can't tell you what your ERP already ships is starting two steps behind.

We focus on commercial and operational data, not the shop floor's sensors. Vision systems and predictive maintenance are real work, but they're a different discipline with different economics. Our lane is the ERP, the order book, inventory, margin, and forecasting, the data that runs the business. For most mid-market manufacturers, that's where AI pays back first.

Governance leads the design instead of bolting on at the end. The read-only access, audit logging, and recommend-versus-execute pattern are how we get AI into production at a manufacturer that can't afford a bad automated decision. That posture is the AI strategy for manufacturers we'd defend in front of any auditor.

And we scope fixed. You get phases with deliverables and a timeline, not a meter running while the goalposts move.

On systems, we build on top of what you run instead of replacing it. That covers NetSuite, Acumatica, Epicor, SAP Business One, and Microsoft Dynamics, plus the CRM, ecommerce, and carrier tools around them. If you're weighing whether AI belongs inside your ERP or in a layer above it, our guide to the AI manufacturing vendor landscape maps the options by buyer profile so you can place yourself before you spend a dollar.