Blog · Published · Updated
AI in manufacturing: observation or cause?
Short answer
AI orders descriptions of production events before the team's review and keeps the difference between the operator's observation, the action taken and a confirmed cause. Similar words in several notes do not prove that the stoppages have the same reason, so the result is a set of questions for the review.
- net for implementation for a team of up to 10 people, paid once
- from €1,150
- from start to work of the entire team
- 2 weeks
- after this time, we measure the effect on the same tasks
- 30 days
- conversation with an engineer and a written plan within 2 business days
- €0
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Author: Syntalith · 3 min read
Statistics
Manufacturing and AI: statistics 2026
0.9 mo.
payback of a €1,150 training day for 10 people saving 1 hour a week each: €1,511 of time recovered against €178 of licences a month. Scenario.
40%
According to a study published in Science (2023), ChatGPT cut the time 453 professionals spent on writing tasks by 40%.
Source: Noy and Zhang, Science (2023) ↗
17.3%
According to Eurostat (2025), 17.3% of EU manufacturing companies used AI.
Source: Eurostat, AI by NACE activity (isoc_eb_ain2) (2025) ↗
20.0%
According to Eurostat (2025), 20.0% of EU companies with at least 10 employees used AI.
Source: Eurostat, 20% of EU enterprises use AI technologies (2025) ↗
90%
According to the European Commission (2026), 90% of companies using AI say that people take the decisions, and 2% of decisions are fully automated.
Source: European Commission, Flash Eurobarometer 578 (2026) ↗
Figures checked: 22 October 2026
Start with a copy of the report. NIST SP 800-82 describes the security of industrial control systems, and working on a copy of the notes needs no access to the production network.
- 01Observationwhat the operator saw
- 02Actionwhat was done on the shift
- 03Causeconfirmed or not
- 04Groupsimilar descriptions from different shifts
- 05Questionwhat to check at the review
Grouped descriptions explain nothing yet
Suppose a fictional set contains three entries: „no material”, „waiting for a component delivery” and „sensor reports material missing”. The first two may concern the availability of material. The third describes a signal whose cause has not been established yet. A shared category „supply problem” could send the conversation to the wrong team.
The draft summary should show the original notes and the proposed category. For the third entry, leave a question: was material really missing, or does the report concern how the measurement works? The answer has to come from the documentation or from a person who knows the event.
What does a review with the team need?
Agree the category list together with the users of the report. Some descriptions may stay in the category „to be clarified”. Shrinking it by guessing improves the look of the chart and weakens the basis for decisions.
Keep the link between the entry and the machine, the shift and the event. Merging two descriptions of the same stoppage is a different task from grouping similar events. We describe the first problem in the guide to a downtime report without duplicates.
When you assess the result, ask whether it helps the next conversation. The manager should see which cases need clarifying with the operator, maintenance or planning. The summary must never change a machine's instructions or confirm that it works properly on its own.
When the dispute is about the number of parts made, start by reconciling production and quality data. A part that comes back after rework is no new product. Such a report orders the quantities; explaining the cause of the rework still needs a separate record.
Start with a copy of the report
The first exercise can use fictional notes or historical notes cleared for use. Also prepare descriptions that sound alike but need different categories. A person who knows the process assesses the proposals without treating the model's reasoning as evidence.
Record wrong assignments, the number of cases set aside for clarification and the time needed for corrections. We present no results of a completed test here. If the team cannot agree the categories by hand, first clarify what they mean.
This exercise concerns document analysis. NIST stresses that operational technology has particular requirements for reliability, performance and physical safety. A connection to machines needs a separate project and an assessment by the responsible specialists. Description of the NIST SP 800-82 Rev. 3 guide, checked on 9 October 2026.
Syntalith can agree team training on reading records and checking AI conclusions. Syntalith prices: an automation, app or agent from €3,000 net, training from €1,150 net a day (pricing). The workshop does not cover machine diagnostics. For the call, bring a description of a question the current report does not let you answer, and say who can assess its sources.
What it costs
- Time saved
- 10 people × 1 h a week × 4.33 = 43.3 h
- Value of that time
- €1,511
- Licences
- $200 (€178)
- Left after licences
- €1,333 a month
Payback of a €1,150 training day
0.9 months
The hours saved are the scenario's assumption, which we check by timing tasks before the training and after 30 days. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat. Claude Team seats at $20 per person a month, at the ECB rate of 1.1205 USD per euro on 22 October 2026.
- The time of the task before the training and after 30 days
- The corrections made by the person checking the result
- Tasks where a participant still needs help
- Licences and tools cleared for work
Where to start
We run the workshop as part of team training, from €1,150 net a day. For the free review, a description of one task the team does every week and the tools it uses is enough. You receive the written training programme within 2 working days.
„I already use AI.”
Your people too. Just no rules.
From our conversations with companies
5 na 10 employees paste sensitive data into private AI accounts: contracts, invoices, customer data.
In ESET survey (2026)
1 na 10 admits this in the survey.
Customer data pasted into a private AI account without an data processing agreement is a violation of the GDPR.
Don't worry, we won't tell anyone :)
What do we see in companies that "already use"
- Only ChatGPT or Claude accounts, without plugins, skills, integrations and MCP servers with company tools.
- Company programs are not connected to AI. People still click through systems and Excel, and enter chat data manually.
- Lack of company standards: everyone writes instructions in their own way, no one shares what works.
- No rules for personal data, GDPR and company secrets.
- No limits and no visibility: no one knows who uses AI, how much and for what.
62% Polish employees use AI at work · 27% companies have written rules for working with AI.
Source: ESET and DAGMA, Cyberportrait of Polish business 2026 (March 2026, 1,026 people) ↗
ChatGPT or Claude space review
Accounts, permissions, rules for working with data and 5 tasks set for the team. 1 day.
€450 net
Company accounts, rules of working with data and team training in 2 weeks, from €1,150 net.View implementation for the team →
„Is my data safe?”
Yes. You choose where they go: from your company account to the model on your server.
00Employee's private account
Customer and company data ends up in an account that the company cannot see or control. There is no data processing agreement, so it is a violation of the GDPR. When an employee leaves, the chat history goes with them.
01ChatGPT or Claude corporate account
The provider does not use your data to train models. A copy of the queries remains in his logs for up to 30 days.
02API with zero retention (ZDR)
The provider processes the request and does not save anything. It requires his consent, we prepare the application together.
03Processing in the EU
Queries are counted and stored in Europe: OpenAI in the EU region, Claude via clouds with the EU region.
04Model at your place
The open model runs on your server or in your cloud in the EU. The data does not go outside the company.
What we do with every implementation
- Confidentiality agreement and data processing agreement before we receive any data.
- You choose the cases for the prototype and we anonymize them upon request.
- The data stays in the EU and we delete it after the test.
- Corporate accounts with an administrator on your side.
When data cannot leave the company
The open model runs on your server or in your cloud in the EU. To choose from:
- White-tailed eagle
- Polish model, SpeakLeash and ACK Cyfronet AGH
- PLLUM
- Polish model, HIVE consortium commissioned by the Ministry of Digitization
- Qwen
- Alibaba
- GLM
- Zhipu AI
- DeepSeek
- DeepSeek
We select a model for the task and check it on your cases. We run it, tune it and maintain it.
Source: Bielik and PLLuM, ACK Cyfronet AGH ↗ · PLLuM, Ministry of Digitization ↗
Do you have a lot of your own data? We will fine-tune the small model.
- 1 Your examples
- 2 tuning
- 3 test on new cases
- 4 model at your place
When you have thousands of similar emails, documents or reports, we teach a small open model based on your examples. It works for you, responds quickly and costs less to maintain than a large model. We measure quality on issues that the model has not seen while learning.
Przemysław and Piotr did it in large programming and consulting companies. Artem did it before and does it today at Syntalith. It's about models on your own infrastructure, tuning and quality measurement.
You choose the protection level during the interview. We sign the data processing agreement before we see any data.Schedule a free analysis →
Price
Three packages. You know the price right away.
Team of up to 10 people
from €1,150
net, paid once · 2 weeks
Company, from 11 to 40 people
from €2,300
net, paid once · 3–4 weeks
Organization, over 40 people
from €4,600
net, paid once · 6–8 weeks
- The same packages once you have licenses. To start, an inspection of the space in 1 day is enough: €450 net.
- You buy Claude Team licenses directly from Anthropic, without our markup: USD 20 per person per month for annual payment or USD 25 for monthly payment, from 2 to 150 people.
From conversation to measurement
01 · 30 min
Free process review€0
An engineer picks 3 to 5 of your team's tasks with you. Written plan within 2 working days.
02 · 2 weeks
Rolloutfrom €1,150
Accounts and roles, rules for working with data, training on your team's tasks. You buy licences directly from the provider.
03 · after 30 days
Measurementincluded
We compare task times before and after. We name in writing the tasks worth handing to a system.
or call+48 888 78 48 78WhatsAppMon–Fri 9:00–17:00 CET
Worked example · Scenario
How we count the payback: production events
We run the same count for your plant in the free review.
€1,815
a month for 12 h a week of report preparation
Payback: 1.9 mo.
The hours for each firm size and checking in 15% of the old time are our assumptions. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat, for companies with 10 or more employees. We measured the model cost in our report system: a commentary with a check of the figures costs about €0.0002, converted at the NBP rate of 21 October 2026 and the ECB rate of 1.1205 USD per euro on 22 October 2026.
See the full calculation →Questions
How does AI help with reviewing production events?
It orders the operators' notes into observations, actions and confirmed causes, and prepares questions for the review with the team.
Can AI find the cause of a stoppage?
No. It groups similar descriptions, but shared words do not prove a shared cause. The team confirms the cause at the review.
How much does this training cost?
At Syntalith, from €1,150 net for a workshop day for the team. Claude Team seats cost $20 per person a month. With 10 people and 1 hour a week each, the payback is 0.9 months.
