A practical guide to AI in manufacturing companies and the manufacturing industry
Artificial Intelligence (AI) is no longer an experiment reserved for tech companies or multinationals. Today, manufacturing companies and production firms are successfully deploying AI to work more efficiently, cut costs and reduce workload.
Yet for many entrepreneurs and directors, the same question keeps coming up:
how can I use AI in my manufacturing company – and where do I start?
In this blog we explain it step by step. No hype, no futuristic stories, just a practical guide to AI in the manufacturing industry.
What do we mean by AI in manufacturing companies?
AI in manufacturing companies is rarely about robots replacing people. In practice, it's mostly about the software-based automation of processes that currently require a lot of manual work.
Think of AI that:
- understands documents
- reads emails
- recognises and structures data
- links information to ERP, CRM or financial systems
In other words: AI as a digital colleague that takes over repetitive work.
Why AI is especially relevant right now for the manufacturing industry
Manufacturing companies are under pressure. This isn't caused by a single factor, but by a combination of them:
- rising wage costs
- staff shortages
- increasing administrative burdens
- margin pressure
- more complex customer requests
Where growth used to be solved by "hiring more people," that model is working less and less well. AI isn't a miracle cure here, but it does offer a structural way to work smarter.
AI helps manufacturing companies to:
- reclaim time
- deploy existing teams more effectively
- reduce errors
- make processes scalable
How can I use AI in my manufacturing company?
The biggest mistake companies make is wanting to start too big. AI actually works best when you start with one concrete process.
Step 1: Map out your bottlenecks
Ask yourself (or your management team):
- Where do we lose a lot of time every day?
- Which processes feel slow or cumbersome?
- Where do people mainly do administrative work?
Entrepreneurs often already know this intuitively.
Typical bottlenecks in manufacturing companies are:
- order processing
- invoice processing
- quotation requests
- internal sales and back office
- production planning
Step 2: Identify where there's a lot of repetitive work
AI is particularly strong in processes that:
- recur frequently
- are largely the same
- revolve around data, documents or emails
A simple rule of thumb:
If several people have the same job, there's usually repetitive work in the process.
These are ideal starting points for AI.
Concrete examples of AI for manufacturing companies
1. AI for order processing
In many production firms, orders come in via email, PDF or Excel. Employees read them manually and transfer everything into the ERP system.
With AI, this process can be largely automatic:
- AI reads the order
- recognises customer details and order lines
- matches this with product data
- prepares an order in the ERP
- (optionally) automatically sends an order confirmation
Result:
fewer errors, faster processing and more time for customer contact.
2. AI for invoice processing
Traditional OCR software is often limited. Layout, language or deviations lead to errors.
Modern AI models understand the content of invoices:
- regardless of format or language
- recognising amounts, suppliers, order numbers
- linking invoices to POs
- preparing everything for approval
Result:
up to 70–90% less manual work in finance.
3. AI for quotations and calculations
Work planners and engineers spend a lot of time analysing quotation requests.
AI can help by:
- structuring requests
- gathering relevant information
- comparing historical data
- preparing an initial proposal or calculation
People still make the decisions, but the preparation goes many times faster.
4. AI for production planning
Production planning often consists of repetitive decisions:
- which orders come first
- what capacity is available
- which schedule is feasible
AI can calculate scenarios and support planners with suggestions, so they can focus on exceptions rather than routine.
What AI (still) can't do well in the manufacturing industry
AI is no substitute for craftsmanship.
AI is (still) less suited to:
- complex technical assessments
- interpreting unique 3D drawings
- decisions that rely entirely on years of experience
That's why AI in manufacturing companies always works together with people. AI supports, people decide.
How important is data for AI?
Data is important, but it doesn't have to be perfect.
Many companies think they first have to clean up their entire data landscape. In practice, AI can also work with:
- unstructured data
- emails
- PDFs
- Excel files
- Word documents
That said:
The better your data, the smarter AI becomes.
But a lack of perfect data is no reason not to start.
Starting small is the key to success
The most successful AI projects in the manufacturing industry have three characteristics:
- They start with one process
- They deliver results quickly
- They build trust within the organisation
After the first success, a "lightbulb moment" often follows:
if this works, what else can we automate?
What does AI cost for manufacturing companies?
AI isn't a standard software package with a single price.
The costs depend on:
- the process
- the number of systems (ERP, CRM, finance)
- the desired level of automation
- the complexity of exceptions
But more important than costs is this question:
what does it cost you today to do nothing?
Lost hours, extra FTEs, errors and delays are often more expensive than an AI solution.
What can you do today to get started with AI?
You don't have to launch a major project straight away.
Start simple:
- analyse where you lose time
- test AI tools individually (e.g. for emails or document analysis)
- talk to your team about frustrations in processes
- explore one concrete use case
AI doesn't have to be perfect to add value. Above all, it has to give you time back.
Conclusion: deploying AI isn't a technology question, but a strategic choice
AI for manufacturing companies isn't about innovation for its own sake. It's about:
- working smarter
- deploying people where they add value
- making companies future-proof
The manufacturing industry won't be won by the companies with the most technology, but by the companies that dare to apply technology practically.
Those who start small with AI today build a lead tomorrow that's hard to catch up with.