Using AI in Manufacturing: Start With Your Processes

Effectively using AI in manufacturing starts with understanding existing underlying business processes rather than selecting the technology first.

Manufacturers see opportunities to use AI across their operations, from improving production schedules to accelerating customer quotes. However, turning those opportunities into measurable business results requires more than technology. Manufacturers also need a clear understanding of their processes, business rules, and data.

Before investing in AI tools or building AI agents, manufacturers should examine how work gets done today. Five readiness questions can help them identify opportunities for automation, prioritize improvements, and determine where human judgment is essential.

Why process documentation matters for AI in manufacturing

AI needs more than access to data in an enterprise resource planning (ERP) system to work effectively. It also needs context about how employees use the information, the exceptions they encounter, and the business rules that guide their decisions.

Manufacturers often lack formal documentation of these processes. Instead, critical knowledge resides with experienced employees, in spreadsheets, or in established workflows.

Without sufficient context, AI might produce incomplete recommendations or automate inefficient workflows. Documenting each step of a process helps manufacturers identify where AI can add value and what information the technology needs to perform effectively.

Process documentation also can reveal opportunities that don’t require AI. Traditional automation or a straightforward process change might offer a better solution.

AI in manufacturing: Key readiness questions

Before launching an AI pilot, manufacturers can apply the following five questions to one business process:

• Are the process steps documented? Documentation should identify each task, its owner, the sequence of activities, and any recurring exceptions.
• Is the business context captured? Documentation should capture the rules, terminology, and employee knowledge needed to complete the work correctly.
• Do you know where the data resides? Manufacturers should identify relevant ERP systems, shared files, spreadsheets, and other data sources and assess whether the information is accurate and accessible.
• Can you measure the intended outcome? A baseline should establish current performance in terms of cycle time, cost, accuracy, or another relevant measure.
• Which decisions require human judgment? Manufacturers should distinguish repetitive tasks from activities that require greater experience, discretion, or accountability.

The value of preparation

Crowe consulting professionals worked with a manufacturer whose month-end financial close took approximately 10 to 15 days across multiple countries. The company wanted to accelerate the process but lacked documentation of the work involved.

Before introducing AI agents, the team mapped the close process and examined individual tasks. The exercise revealed opportunities to automate repetitive activities, including gathering data from multiple entities and performing calculations. Within approximately three to four months, AI agents were handling an estimated 30% to 40% of the work involved in the financial close.

The lesson extends beyond finance processes. Whether manufacturers want to improve quality inspections, order processing, or production planning, understanding the underlying workflow provides a starting point for identifying opportunities to apply AI.

A practical starting point for AI in manufacturing

Manufacturers don’t need to document every process to get started. Over the first 90 days, they can focus on one workflow with a measurable business problem, apply the AI readiness checklist, and identify opportunities for improvement.

The exercise can help manufacturers determine which tasks are suitable for AI, where traditional automation might be more effective, and which decisions require human judgment. With a clearer understanding of their processes, manufacturers can make informed technology decisions and establish a foundation for future AI applications.