AI for production operators – answers without leaving the workstation
AI for production operators provides quick and easy access to the knowledge needed during their daily work. A knowledge assistant for production operators helps them find answers in company documents without having to interrupt their work or leave their workstation to search for information. AI in manufacturing significantly reduces the time spent searching for information and minimizes errors resulting from the use of outdated sources. How can AI be used in a production operator’s work to get answers to questions faster?
Why do production operators waste time looking for information?
Today, artificial intelligence in manufacturing is used not only for process automation and data analysis. More and more companies are also recognizing the potential of AI for front-line workers, who can use new technologies directly while performing their daily duties. This also applies to operators a production machine operator can use AI, for example, to quickly find information needed to operate the machine, carry out procedures, or solve ongoing problems.
In most production facilities, the problem is not a lack of knowledge, but rather access to it. Instructions, procedures, and other documents exist, but in practice, finding them often takes longer than it should. As a result, instead of performing their duties, production line operators:
- looks through the manual,
- searches for the right document on the network drive,
- calls the shift leader,
- asks a more experienced coworker,
- pauses their work to make sure they’re following the procedure step by step.
Situations like this happen every day and are usually caused by a few factors:
- Scattered documentation. Documents are stored in various systems, folders, binders, or applications, causing production line operators to spend valuable time searching for them.
- Instructions that are too long. Finding a single parameter in a comprehensive manual often requires reviewing many pages of documentation.
- Uncertainty about which version of a document is current. The proliferation of new versions of the same manual causes operators to lose confidence in the currency of the document they are consulting.
- Reliance on more experienced employees. Shift leaders and experienced operators regularly answer the same questions instead of performing other tasks during that time.
- Lack of access to knowledge at the operator’s workstation. Even a short break to check the manual means stopping work or having to step away from the machine.
How can AI help an operator without them having to leave their workstation?
AI supports production operators by making it easier to access knowledge on the job. The noSilo Knowledge Assistant is a chatbot that answers questions based on the company’s knowledge base. How does it work?
- A production machine operator enters a question into the app.
- The AI assistant for production operators searches through the company’s documents.
- The intelligent tool selects the most relevant information and generates a brief answer, along with a reference to the source document.
This approach can support both experienced employees and those just starting out in production. However, it requires a properly prepared knowledge base the sources must be up–to–date and well–organized. Therefore, the implementation of AI in production should be viewed not as a replacement for existing documentation, but as a new way of utilizing the knowledge the organization already possesses.
Question asked in the app
The AI knowledge assistant is easy to use. All the operator has to do is ask a question in a natural way, such as:
- What are the startup parameters for machine X?
- What is the sequence for retooling this line?
- What does error code X mean?
The knowledge assistant will search the company’s database, provide an accurate answer, and provide a link to the source.
Response based on company documents
Simply using AI is not enough. To maximize the technology’s potential, it must be supervised. In the case of the Knowledge Assistant, this supervision is provided by the database of sources on which the tool bases its responses.
The Knowledge Assistant can use materials such as:
- job descriptions,
- SOPs,
- quality procedures,
- health and safety instructions,
- technical documentation,
- machine operating instructions,
- regulations.
The closed knowledge base available to the chatbot ensures that the response complies with the policies in place at a specific organization.
Link to the source
In addition to the answer itself, the ability to verify it is also important. The noSilo knowledge assistant identifies the document and the specific passage on which it based its answer. The operator can go to the source at any time and check the full context of the entries.
How is a Knowledge Assistant different from a regular chatbot?
Although both solutions use artificial intelligence, their applications are completely different. Public chatbots respond based on the model’s knowledge or information available on the internet. A knowledge assistant generates responses based on documents from a specific organization.
- Closed knowledge base. The assistant uses only documents provided by the organization, ensuring that responses comply with internal procedures.
- No internet access. The operator receives information based on documentation regarding specific resources used at its facility. This eliminates the risk of generating random responses based on publicly available online sources.
- Roles and Permissions. The AI assistant for production workers takes user roles into account and displays only information consistent with the permissions granted to each user.
- Message indicating that no information is available. If the Knowledge Assistant cannot find the information in the knowledge base, it will display an appropriate message instead of generating a random response.
In what situations can a production operator use AI?
An AI assistant on the production floor proves useful in any situation where an operator needs a quick answer based on current documentation. Below are a few examples of such situations.
What are the benefits of quick access to knowledge on the production floor?
The benefits of implementing AI for production operators stem from the reduction in the time needed to find the right information. Both operators and leaders or specialists experience the benefits of artificial intelligence.
The most important benefits include:
- shorter time spent searching for information,
- reduced downtime,
- greater autonomy for operators,
- lower risk of using outdated instructions,
- faster onboarding of new employees,
- reduced workload for experienced employees,
- greater consistency in processes.
How to Implement AI for Manufacturing Operators, Step by Step?
The effectiveness of the Knowledge Assistant depends primarily on the quality of the data it works with. Therefore, it’s best to begin the implementation by organizing the documentation and only then make the solution available to users.
FAQ
No. In this application, the role of AI is to facilitate access to the knowledge stored in company documents. The operator remains responsible for performing tasks in accordance with applicable procedures.
The noSilo platform allows you to create a central knowledge base based on your organization’s documents. The knowledge assistant searches this database and prepares answers, citing the source.
The most valuable documents are those that operators use on a daily basis. These primarily include workstation instructions, SOPs, quality procedures, health and safety instructions, technical documentation, machine operating manuals, checklists, work standards, and materials used during the onboarding of new employees.
Yes. New employees often ask similar questions about machine operation, safety rules, and applicable procedures. A knowledge assistant helps them quickly find answers in company documentation, making the learning process easier and reducing the workload on more experienced employees.
It is worth evaluating the effects of implementation based on specific metrics. These may include, among others:
● the time needed to find information,
● the number of questions directed to shift leaders,
● the time required to train new operators,
● the number of errors resulting from non-compliance with procedures,
● the frequency of use of the Knowledge Assistant,
● user feedback on the quality of responses.
Bibliography:
https://www.microsoft.com/en-us/industry/blog/wp-content/uploads/sites/12/2025/06/Microsoft_Insights_on_Workforce_Transformation_EN_US.pdf;
https://www.mckinsey.com/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/the%20state%20of%20organizations/2026/the-state-of-organizations-2026.pdf?hctky=16424856&hdpid=b8488b3f-7ad4-4f0c-9fc9-57ca8433807e&hlkid=42b91ecd9586438c8a2dbce697b6dbc1&;
https://hai.stanford.edu/assets/files/hai_ai-index-report-2024_chapter4.pdf.
Mariusz knows HR like few others, but he’s also drawn to industry and technology. He writes about digital tools in a way that ensures everyone - from HR specialists to shift managers - understands how they can make day-to-day work easier.
He combines knowledge of HR processes with an understanding of industrial realities, allowing him to demonstrate how HR tools work on the factory floor, not just in theory. A jack-of-all-trades who can weave onboarding, skills development, and industrial realities into a single, compelling narrative.
