Why should AI be used in manufacturing?
Company data as a strategic lever
By utilizing data across the organization and linking machine and process data with other operational metrics, companies can solve complex questions, gain competitive advantages, and unlock numerous use cases, such as:
How does the use of "Material A" affect energy consumption?
What parameter settings are important for achieving the best product quality?
How has the number of defective products changed in recent months?
Alleviate the skilled labor shortage
Artificial intelligence increases work efficiency and improves productivity:
New employees can be trained faster when AI models have access to documentation and process flows.
Repetitive tasks such as creating reports in Power BI or dashboards in Grafana can be made more efficient with AI.
Employees build expertise faster and can focus on higher value-adding tasks.
Webinar On-Demand
KI Use Cases auf dem Shopfloor erfolgreich umsetzen
Paving the way for successful AI projects in production.
5 common challenges industrial AI projects face
Insufficient data quality
One of the biggest hurdles in using AI applications in manufacturing is insufficient data quality. A large part of the data is generated through manual entries in Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP) systems, or Excel spreadsheets, but this leads to inaccuracies and errors when entering or transferring data, resulting in a weak foundation for the use of AI algorithms.
The result: The models provide unreliable results. To still obtain meaningful analyses, a high manual effort for data preparation and cleaning is required.
Sensitive data must be protected and secured within the company
Lack of IT or data science skills
Knowledge of data management, AI, and data analysis is necessary for project development and implementation. However, there are hardly any people with this background in manufacturing. This gap hinders the future-proof development, deployment, and maintenance of AI systems. Efforts to educate existing employees are essential but can be hindered by lack of time, limited access to training resources, and modern equipment.
Traditional procurement processes
A frequently overlooked problem is the classic purchasing model: long-term contracts, extensive specifications, and the expectation that systems will remain unchanged for years. However, AI technologies are advancing rapidly – what is current today may be outdated tomorrow. Instead of rigid contracts, flexible, adaptable procurement models the new standard because these can keep pace with rapid developments. Procurement and sourcing teams need to adjust their processes to support AI purchase models to avoid slowing down innovation potential.
Webinar On-Demand
The most important steps for starting an AI project
Successfully implementing AI applications on the shop floor.
What foundational approaches are there for establishing AI in production?
The challenges are real – but not insurmountable. Those who want to successfully implement AI projects need a pragmatic approach that emphasizes security, data quality, and flexibility without getting lost in complex structures or high costs. Let's look at three general strategies:
Training your own AI models: Unrealistic for (almost) every organization
The idea of developing a large language model (Foundation Model) is attractive until you consider the high hardware costs, immense computing resources, and the enormous manpower required make this approach successful. Even for large corporations training AI models from scratch is often unappealing.
For small and medium-sized enterprises, this is practically hopeless. Even though the recently published model by Deepseek showed that it is possible to create a similarly powerful model with relatively low effort, the costs for the last training run were still around 5.6 million US dollars.
In-house training and hosting: Achievable with a long-term vision
An alternative is the fine-tuning of existing models using your proprietary data. By using in-house hosting, data security can be ensured. The selection of models is constantly growing, and every month new, more powerful versions are added. However, the use of these models is not 'plug-and-play'; it still requires expert knowledge and regular adjustments as the data set grows.
Companies can outsource these data management tasks to specialized service providers but should keep in mind that a bit of technical expertise and personnel resources are still required.
Integration of existing models: Easy but with limitations
Major providers like OpenAI, Anthropic, Microsoft, and Google enable companies to utilize advanced AI models without having to host or train the models themselves. A prominent example is the OpenAI Platform, which provides direct access to powerful models via simple APIs. At the same time, companies can legally ensure through "No-Data-Use" agreements that their data does not contribute to the global training of the algorithms.
This approach is already implemented in many software products, such as Microsoft Office, Salesforce, or SAP. Here, AI features come into play, but they often can only access data from the respective system – which limits the possibilities of application.
How does the data get into the AI model?
A promising approach is the structural integration via APIs. Here, so-called “agents” or orchestrators operate in the background: they detect when the model needs external data and query predefined data sources for it. A large language model (LLM) – for example, via the OpenAI Platform – internally formulates a structured query, sends it to the company database or a specialized data service, and subsequently processes the results.
In order for this process to run smoothly, companies need to rethink their data landscape and adopt an API-first tech stack.
The tech stack for Industrial Artificial Intelligence in manufacturing companies
To keep up with the pace of innovation in artificial intelligence, it is worth looking at software companies, where value creation takes place entirely digitally and the use of new software for product optimization is the order of the day.
In the software industry, the term technology stack is often used to refer to the totality of all technologies, tools, and infrastructures that a company uses to develop and operate a software solution or a digital product.
What does a technology stack for manufacturing companies look like?
For companies with a physical value chain, the technology stack can be divided into four levels:
Data Layer
This is where machine, production, and quality data are collected in a normalized and retrievable format.
Data Layer
This is where machine, production, and quality data are collected in a normalized and retrievable format.
API Layer (Data Access)
Access to the data is done through clearly documented interfaces – instead of direct database access.
AI Layer (Models and Orchestration)
Central AI models use, for example, retrieval-augmented methods or prompt techniques to query and process relevant data. These are also referred to as agents or agentic AI models.
Application and User Interface Level
Various frontends, integrations (e.g., Excel, Power BI, production control systems) or chat interfaces through which employees can interact with the systems in natural language or graphically.
Blog
AI or K.O.: The modular tech stack and its significance for AI in manufacturing
How the modular tech stack provides flexibility and scalability for AI in manufacturing.
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How does ENLYZE support this transformation?
To realize the full value of Industrial AI, a centralized, automated data platform is needed that ensures high data quality and enables informed decisions based on validated data – without tying up valuable personnel with manual data management tasks. This is exactly where ENLYZE Manufacturing Data Platform comes in:
