AI in a manufacturing plant without a revolution – where to start?
Implementing AI in a manufacturing company now seems to be a natural process that most growing organizations are striving for. Meanwhile, many companies still approach this topic with great caution. Where do these doubts come from? Artificial intelligence in a manufacturing plant is often associated with a costly revolution that requires massive technological and organizational changes. However, the automation of production processes using AI does not necessarily mean radical changes or the need to overhaul the entire way the company operates. How can you implement AI in your company without turning the organization upside down? A guide in which we discuss the definition, principles, and main tools of lean manufacturing.
Why are manufacturing companies hesitant to implement artificial intelligence?
As data cited by *Rzeczpospolita* based on a report by the Polish Agency for Enterprise Development (PARP) shows, as many as 77% of Polish companies do not currently use artificial intelligence and have no plans to implement it. Many organizations view AI as a significant risk rather than an opportunity for growth. This is particularly evident in companies with a traditional business model, where the prevailing belief is that implementing artificial intelligence entails high costs, the need to engage external specialists, and a loss of control over processes. Concerns regarding data security and investment financing also arise. According to PARP data, as many as 65% of companies cite security as one of the main barriers to AI implementation, and 63% fear that implementation costs will be too high.
Many companies view the use of artificial intelligence in a manufacturing facility as a massive, complex, and unpredictable undertaking. Managers often assume that implementing AI in a company will require costly process redesign, infrastructure replacement, or a temporary halt to production. And it is precisely this misconception about the scale of the changes involved that most often prevents any action from being taken.
On the other hand, expert analyses point to the growing importance of technological trends such as AI agents. Autonomous systems designed to perform complex tasks independently support employees in planning and personalizing activities or quickly searching through dispersed data. And it is precisely this type of solution support from AI assistants that we offer at noSilo.
Where should you start when implementing AI in a manufacturing facility?
The best approach to implementing AI in manufacturing is to start with a single area that can be easily tested without risking disruption to the entire plant’s operations. It’s best to begin with organizational issues, such as the flow of information and documentation or access to knowledge.
Companies most often start with processes that:
- involve many repetitive tasks,
- require frequent information searches,
- rely on documentation,
- cause time losses or downtime,
- can be tested on a small scale.
However, for AI to work effectively in manufacturing, the organization must have structured and standardized knowledge. The foundation of implementation is therefore properly prepared data, such as work instructions, SOPs, procedures, health and safety materials, regulations, and technical documentation. The more centralized and organized the information is, the easier it is for AI tools to leverage organizational knowledge and support employees in their daily work.
What is safe AI based on internal data?
One of the biggest concerns regarding AI in manufacturing remains data security. Companies do not want to risk a leak of proprietary knowledge or a violation of GDPR regulations. Security concerns may stem, among other things, from the way publicly available AI tools operate, where data entered into chatbots is sometimes used by the service provider, for example, to improve the language model. In such cases, there is no full control over how the information is processed.
AI solutions designed for manufacturing are engineered to ensure data security. In noSilo, one such safeguard is the lack of an internet connection- the AI assistants in the system generate responses to user queries solely based on information stored in a central Knowledge Base created by the organization. The lack of access to an external network ensures two-way security, as unverified data does not enter the platform, and no information is sent outside the company’s local network.
Additionally, AI assistants in the noSilo system operate exclusively within the scope of data assigned to a specific user and the role they perform within the company.
How does the Knowledge Assistant support employees on a daily basis?
The noSilo Knowledge Assistant significantly simplifies access to information. The tool searches through company documents to generate accurate and clear answers to questions.
Knowledge Assistant:
- searches for information in the organization’s materials,
- answers specific questions about processes,
- provides a link to the source of information,
- and reduces the number of repetitive questions directed at leaders and support departments.
This use of AI in manufacturing translates to shorter downtime, greater employee autonomy, and faster completion of daily tasks.
How does the Manager Assistant make team management easier?
The noSilo Manager Assistant uses artificial intelligence to analyze data on employee competencies, training, and documents. Based on this, it generates summaries and recommendations to support decision-making.
Among other things, the tool helps with:
- identifying competency gaps,
- planning employee development,
- analyzing team replaceability,
- preparing periodic evaluations,
- planning promotions.
A major advantage is the ability to quickly generate practical summaries. Managers do not have to analyze multiple data sources on their own a single query is enough to receive ready – made conclusions and recommendations for action.
What does the step-by-step implementation of AI look like?
The successful implementation of AI in a manufacturing company should be carried out in phases. How can this be done step by step?
Implementing AI in a manufacturing company doesn’t have to mean a costly overhaul or a complete restructuring of the entire facility. The best results come from gradually introducing solutions in less critical areas of operations. AI assistants can support both employees and managers in their daily access to information, data analysis, and streamlining organizational processes – all while ensuring full security and the ability to scale the project freely in the future.
FAQ
The most valuable data is that which describes the day–to–day operations of the organization. This may include SOPs, job descriptions, technical documentation, quality procedures, health and safety materials, training information, and employee competency data. The more organized and up–to–date the documents are, the more effective the AI tools will be.
Employees are more likely to accept AI in a manufacturing company when they see the real benefits of using it. It’s important to reassure employees that artificial intelligence isn’t meant to replace them, but rather to support them in performing their tasks and reduce repetitive work.
It’s a good idea to start preparing for AI implementation by organizing your data and selecting a single process that can be streamlined without interfering with critical areas of production. Many companies opt for organizational improvements, such as facilitating access to knowledge, searching through documentation, or analyzing data.
Employees are more likely to accept AI in a manufacturing company when they see the real benefits of using it. It’s important to reassure employees that artificial intelligence isn’t meant to replace them, but rather to support them in performing their tasks and reduce repetitive work.
Companies usually begin automation with organizational processes related to information flow, documentation, and data analysis. These are areas where AI support can be implemented quickly and without risking the operational continuity of the entire facility.
Bibliography:
https://cyfrowa.rp.pl/ai/art44198501-firmy-obawiaja-sie-ai-najnowsze-dane-nie-pozostawiaja-zludzen;
https://www.ey.com/pl_pl/insights/cybersecurity/bezpieczenstwo-danych-w-czasach-ai-ai-fy24;
https://raportstrategiczny.iab.org.pl/sztuczna-inteligencja/.
Mariusz zna się na HR jak mało kto, ale ciągnie go też do przemysłu i technologii. Pisze o cyfrowych narzędziach tak, żeby każdy – od specjalisty HR po kierownika zmiany – wiedział, jak mogą ułatwić codzienną pracę.
Łączy wiedzę o procesach personalnych z rozumieniem przemysłowych realiów, dzięki czemu potrafi pokazać, jak narzędzia HR-owe działają w środku fabryki, a nie tylko w teorii. Człowiek-orkiestra, który potrafi połączyć onboarding, rozwój kompetencji i przemysłowe realia w jedną, wciągającą opowieść.
