Guide: AI in business › AI in sales and business departments
Artificial intelligence in manufacturing delivers results fastest in four areas: image-based quality control using cameras, predicting machine failures, demand forecasting and planning, and document handling in the ERP system – provided the company has well-organised data from its machines and systems. In Poland this is still rare. According to Eurostat, in 2025 8.4% of companies with at least 10 employees used AI, compared with 20% across the EU. This guide shows where to start, what is already built into popular ERP systems and which regulations apply to machinery with AI.
In 60 seconds AI on the shop floor and in ERP in brief
- What it involves: models learn from machine and quality control data, sales data and the ERP in order to forecast, detect errors and automate decisions.
- Where it works: in manufacturing plants and distribution companies that have machine data or several years of ERP history and recurring problems such as shortages, downtime or complaints.
- Cost range: from features included in the ERP licence to projects with sensors and an integrator; in SAP and Microsoft some AI features are billed based on consumption.
- First project: one problem with a measurable cost (e.g. downtime on one line) and checking whether data exists that describes it.
- Date to remember: from 20 January 2027, the new EU Machinery Regulation covers machinery with self-learning systems, so machine manufacturers need to prepare now.
What is artificial intelligence in manufacturing and in ERP?
Artificial intelligence in manufacturing means software that learns from data from machine sensors, cameras, the quality system or order history, and on that basis predicts, detects or suggests a decision. Robots that “think” are not the subject here. It is about forecasts and classifications such as: this machine will probably stop within a week, this part is defective, orders for this product will rise in November.
ERP (Enterprise Resource Planning) is the system in which a company manages orders and warehousing, production, purchasing and finance. Artificial intelligence in ERP has two faces. The first is classic analytics – forecasts, detection of unusual transactions and order suggestions – and the second, newer one is assistants and agenci AI, which you instruct in plain language (“show orders delayed by supplier X”), and they look up the data or carry out several steps in the system.
Manufacturing is just one of the departments where AI is changing day-to-day work today. For a fuller picture, from sales to marketing and PR, see our guide to AI in business.
Six applications where AI in manufacturing delivers the most
The best results come from applications where the problem is frequent and costly, and at the same time well described in the data. Here are the six most common, along with what each one requires. Demand forecasting works in much the same way in retail, as you can see from the example of AI in e-commerce.
| Application | What AI does | What data is needed | Typical pitfall |
|---|---|---|---|
| AI quality control | Detects defects in images from cameras on the line | Thousands of images of good and defective parts, labelled by people | Too few examples of rare defects |
| Predictive maintenance | Predicts failure from vibration, temperature and current | Sensor history and records of past failures | No record of breakdowns, so the model has nothing to learn from |
| AI demand forecasting | Predicts orders, taking seasonality and promotions into account | Several years of sales history from the ERP | Sudden market shifts that never appeared in the history |
| AI production planning | Suggests the order of jobs to cut changeover times | Operation and changeover times, availability of machines and people | ERP data that does not match what happens on the shop floor |
| Purchasing and inventory | Suggests what to order and when | Stock levels, delivery times, consumption | Manual stock adjustments outside the system |
| Documents and finance | Reads invoices and warehouse documents, matches payments | Documents in electronic form, e.g. from KSeF | Treating the extracted data as infallible |
What do SAP, Microsoft and Comarch offer in artificial intelligence?
The largest ERP vendors are now building AI into their systems. They differ in scope and in how they bill for it, and when it comes to budget the latter is often more important than the former. Below is the situation as of September 2026, based on vendor documentation and descriptions.
| System | AI assistant | What it can do | How it is billed |
|---|---|---|---|
| SAP (cloud, incl. S/4HANA Cloud) | Joule | Answers questions about data, guides users through transactions, drafts documents; agents for tasks such as maintenance planning | Core features included in the cloud subscription; premium features consume SAP AI units purchased separately (ERP Research, July 2026) |
| Microsoft Dynamics 365 Business Central | Copilot and agents | Suggestions, summaries, agents handling selected processes | Some features have no consumption charges, others are billed by Microsoft based on consumption; the billing type is shown in the system (Microsoft Learn, August 2026) |
| Comarch ERP | ChatERP and AI agents | Searching and filtering data with text commands, invoice reading (OCR), verifying business partners in KSeF, anomaly detection | Depends on the product and contract; details from the vendor |
When do AI in ERP and in manufacturing make sense?
AI will not fix a process that does not work without it. It makes sense where the process works but is expensive, slow or dependent on the experience of a handful of people, as in these situations:
- A planner who builds the schedule in a spreadsheet because the ERP “doesn't understand” changeovers – and nobody else can do it while they are on holiday.
- A quality department that inspects hundreds of parts per shift by hand and lets defects slip through when tired.
- Maintenance that reacts to breakdowns instead of preventing them.
- A sales team that forecasts “by eye” every month, after which production catches up with overtime (here, help also comes from CRM with AI).
- Accounting and purchasing retyping data from documents, which we cover in more detail in the article on AI in company finance.
How to implement AI in manufacturing step by step
Industrial AI projects rarely fail because of the algorithm. They fail because of bad data, no owner on the plant side and no comparison with how things were before.
- STEP 01Choose one problem with a quantifiable cost
Downtime on one line, the complaint rate for one product, excess stock of one group of materials. Write down what it costs today, because that figure will be your baseline.
- STEP 02Check the data before you choose a tool
Are there failure records? Do ERP stock levels match the warehouse? Are defect images labelled? Without data, even the best model will predict nothing.
- STEP 03Start with what you already have in your ERP
Check which AI features your system has and how they are billed, because sometimes the first step does not require a new purchase, just enabling and configuring a feature the company is already paying for.
- STEP 04Appoint an owner on the shop floor
A maintenance, quality or planning manager who will assess the AI’s suggestions and decide what to do with them. Without them, the model becomes a report nobody reads.
- STEP 05A pilot with a control group
Several months on one line or product group, compared with a line without AI or with the same months of the previous year. Correlation is not impact. A drop in downtime in summer may simply be due to a lower workload.
- STEP 06Scaling and maintenance
After a successful pilot, you scale up the project and decide who will monitor the quality of the model when machines, raw materials or the product range change.
How much does artificial intelligence in manufacturing cost and what drives the budget?
There is no single price for “AI for the factory”. The budget is made up of several items, and their proportions depend on whether you use features built into your ERP or build your own solution.
| Item | What it depends on | Notes |
|---|---|---|
| AI features in ERP | System version, licence edition, billing model | In SAP some features consume AI units; in Business Central some are billed by consumption |
| Sensors and cameras | Number of machines and workstations, age of the machine fleet | Older machines often need retrofitting |
| Data integration | Number of systems (ERP, manufacturing execution system, quality, warehouse) | Usually the largest item in the project |
| Integrator or implementation partner | Scope of work, industry experience | Compare offers by how they measure results |
| Plant staff time | Labelling data, assessing suggestions, training | Often left out of the budget |
| Model maintenance | Changes to the product range, machines, raw materials | An unattended model loses accuracy |
New rules for manufacturers of machinery with AI
For machine manufacturers the key date is 20 January 2027. From that day the EU Machinery Regulation 2023/1230 applies, replacing the Machinery Directive. In its September 2024 overview, PARP (the Polish Agency for Enterprise Development) points out that the new rules introduce the concept of machinery with evolving behaviour, i.e. equipped with self-learning systems. The manufacturer must assess the risks arising from changes the system may introduce into how the machine operates, and safety-function software with machine learning requires new conformity assessment procedures.
The second act is the AI Act. AI systems built into products covered by sector-specific legislation may be classified as high-risk systems, and the amending Regulation (EU) 2026/1744, the so-called digital omnibus, published on 24 July 2026, postponed these obligations to 2 August 2028. For machinery the change goes further: according to a Eurogip overview from August 2026, AI performing safety functions in a machine is assessed primarily under the Machinery Regulation, and AI requirements are to be added to it by a delegated act. The 20 January 2027 deadline has not changed. We track the current state of the law in our overview of The AI Act in Poland. Machine manufacturers should plan for compliance now, because designing a new machine takes longer than the time that is left.
Data matters more than the model
The board most often asks which AI system to choose. A better question is: is our data good enough for any system to make sense of it? Check whether stock levels and times in the ERP match reality, whether shop-floor events (breakdowns, changeovers, shortages) end up in one place, and whether the data has a single owner.
AI assistants in ERP, such as Joule or ChatERP, answer based on the data in the system. When the data is wrong, the answer sounds convincing but is still wrong – and we describe exactly this phenomenon when discussing AI hallucinations. Where knowledge from documents is needed (manuals, process sheets, procedures), what works well is the approach we describe in the article on AI on company knowledge. For everyday analysis outside the ERP, many teams still rely on spreadsheets – and you can find out what AI can do there today in the article on AI in Excel.
How a manufacturing company can talk about AI – and what not to promise
Implementing AI is also a communication matter, because B2B customers ask about quality and on-time delivery, candidates about a modern workplace, and the media look for concrete stories from industry. A story about AI works when it has measured figures and is honest about what does not work yet. It does harm when it promises a “factory of the future” without proof. In B2B, one untrue sentence in a proposal can cost you a contract.
Plan the change communication together with the project. Decide what you will tell the workforce (AI does not replace people, it helps with specific tasks), what you will tell customers and what you will tell the trade media. We describe the basics of building a credible image in the article on how to build a brand image, and you can read about how Commplace® supports industrial companies on the page industrial marketing.
When to implement AI in manufacturing and when to wait
A good moment is when you are planning next year's budget and during a quieter production season, because a pilot needs time from plant staff, and at the peak of orders that time is not there. For machine manufacturers, the calendar is set by regulation. Only a few months remain until 20 January 2027.
It is a good time if you have a recurring, costly problem, several years of data and someone who will lead the project on the plant side. Wait if you are in the middle of replacing your ERP (new system first, then AI), if data sits in spreadsheets on private drives, or if nobody can say what the problem AI is supposed to solve costs today. When choosing a contractor, use the questions from the article AI software house or AI agency, and if you are considering an assistant in your office suite for administrative teams, take a look at the guide on Copilot for business.
Five ways to make an AI project at a plant stall
A company buys an AI platform “because the competition has one” without specifying what should improve. It pays for licences and integration, but has no result it could show the board.
A model trained on incorrect stock levels and incomplete breakdown records produces forecasts nobody trusts, so after a few months the team goes back to spreadsheets.
The project is run by IT alone, and the production manager finds out about it by email. AI suggestions are ignored because nobody is responsible for the decisions.
AI agents in the ERP are rolled out to all users without a cost simulation. The bill exceeds the entire saving from the pilot.
A machine manufacturer adds self-learning features without a new risk assessment and, after 20 January 2027, runs into problems with conformity assessment and CE marking.
What manufacturing company boards ask us
How is artificial intelligence used in manufacturing?
Most often for camera-based quality control, predicting machine failures, demand forecasting, production planning and reading documents in the ERP system. Each of these applications requires data the model can learn from.
What is artificial intelligence in ERP systems?
These are features built into business management systems that forecast, detect unusual data and respond to instructions written in plain language. Examples include Joule in SAP, Copilot in Microsoft Dynamics 365 Business Central and ChatERP in Comarch systems.
How many companies in Poland use AI?
According to Eurostat, in 2025 8.4% of Polish companies with at least 10 employees used AI technologies. The EU average was 20.0%, and Poland was among the countries with the lowest share.
How much does implementing AI in a factory cost?
It depends on whether you use features built into your ERP or build your own solution with sensors and data integration. The biggest costs are usually data integration, retrofitting machines and plant staff time, and some AI features in ERP are billed based on consumption.
Does the new Machinery Regulation apply to AI?
Yes. Regulation (EU) 2023/1230, applicable from 20 January 2027, covers machinery with self-learning systems and requires an assessment of the risks associated with changes in how it operates. Safety-function software with machine learning is subject to new conformity assessment procedures.
Where should you start with AI in manufacturing?
Start with one problem with a quantifiable cost, for example downtime on one line. Then check whether you have data that describes it, and run a pilot compared with a period or line without AI.
Will AI replace the production planner?
It will not. AI suggests the order of jobs and warns about problems. The decision is made by a person who knows the constraints of the shop floor, the people and the customers, so it is best treated as support for an experienced planner.
Sources
- SOURCEEurostat — 20% of EU enterprises use AI technologies · 11 December 2025
- SOURCEPARP — The new Machinery Regulation: key changes for businesses · 2 September 2024
- SOURCE
- SOURCEERP Research — SAP Joule explained: what it does, cost and limitations · reviewed 22 July 2026
- SOURCEMicrosoft Learn — Business Central: understand how each Copilot and agent capability is billed · updated 27 August 2026
- SOURCEComarch — Artificial intelligence (AI) in Comarch products · accessed 24 September 2026
- SOURCEEUR-Lex: Regulation (EU) 2026/1744 (Digital Omnibus on AI), Polish text · of 8 July 2026, OJ EU of 24 July 2026, accessed 24 September 2026
- SOURCE
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Commplace® starts with diagnosis and measurement: we identify the processes with the greatest potential, help plan a pilot and help you communicate the change to your workforce and customers.
Sebastian Kopiej, CEO of Commplace®. In public relations since 1996. Written with the help of AI tools and editorially verified by the author. Data current as of 24 September 2026.