How Manufacturers Can Use AI to Drive Operational Results
This article appeared in the October 2026 issue of MiMfg Magazine. Read the full issue and find past issues online.
Most manufacturers aren’t behind because they haven’t adopted AI. They’re behind because they haven’t landed on the problem they’re trying to solve and if their data is clean enough to let AI solve it.
Whether it’s an exhaustive five-day close process or a reactive sales and operations planning (S&OP) cycle, these are problems where AI can deliver measurable value.
Common starting points we see include:
Business intelligence frameworks
These frameworks pull data from ERP, CRM and operational systems into a centralized platform with tools like Power BI. It improves decision-making and promotes faster closes, better visibility and a single source of truth for the entire organization.
AP/AR automation
Accounts Payable (AP) and Accounts Receivable (AR) automation reduces manual processing time and improves cash flow management. Manual invoice processing leads to compounding errors, slow cycles and time away from strategic work. Automating these workflows also tightens your cash position.
Sales and operations planning
S&OP touches forecast and demand management, inventory planning, cash forecasting and capacity modeling, helping manufacturers get ahead of demand. When these functions are disconnected, the cost shows up as excess inventory, missed shipments, unplanned overtime and a lagging planning cycle. Connecting them with AI changes the equation.
Production scheduling
Production scheduling is one of the highest ROI opportunities for AI in manufacturing. Most manufacturers are still doing this on spreadsheets or whiteboards. Reactive and manual scheduling shows up as overtime, missed commitments and unrealized capacity. AI-assisted scheduling nets results fast. The goal is to identify a near-term, high-impact target and prove the value before scaling.
Why Data Discipline is Key to Successful AI Implementation
Data discipline is the groundwork that makes everything else possible. Manufacturers who build this foundation first can move quickly and confidently. This also means having clarity around who owns the data, who has access to it and how it’s being maintained. These crucial conversations determine whether your AI investment pays off. What we typically see when we walk into a manufacturer’s operation is siloed data and no one working from the same source of truth.
Change Management: The Missing Piece in Manufacturing AI Adoption
AI moves at the speed of electrons. Human systems move at the speed of trust. That means change management, communication and the ability to bring your team along on the journey are just as important as the technology itself. Leaders who build buy-in from the shop floor up tend to see the best results.
How Manufacturers Can Get Started with AI Today
First, identify your most pressing operational challenge, something with a clear cost or inefficiency attached to it. Second, assess whether your data is clean enough to support a solution. Third, start small with a pilot that has defined goals and measurable outcomes and build from there.
You don’t need a massive technology budget to make progress. What you need is the right partner who understands your industry, knows your opportunities, and can help you avoid common mistakes.
Five years from now, the most successful manufacturers won’t be those who moved first, but those who moved smart.
About the Authors
Clark Schaefer Hackett is an MMA Premium Associate Member and has been an MMA member company since September 2025. Visit online: cshco.com.