Understanding AI as an opportunity to intelligently evaluate and use large amounts of data.

Artificial intelligence and machine learning are considered by 79% of German companies to be the most important future technologies.

Do you share this opinion?

Nevertheless, 40% of all companies have no AI projects planned so far. Thus, opportunities remain unused. Are you interested in introducing AI in your company, but want to use a secure roadmap and avoid risks?


„Artificial intelligence will revolutionize knowledge management in the future."

Dr. Jan Hendrik Schoenke
+49 (0)541 | 200 690-333


Dr. Jan Hendrik Schoenke is the Business Development Manager within LMIS AG and responsible for all topics around AI. He´s our expert to explain the options of these innovative technologies.

1What must be given, so that AI is used sensibly?
For AI to be used sensibly, three things must be given for every project. First, there must be a problem. This sounds banal, but without a problem definition even an AI cannot find a solution. Secondly, there must not be a simple solution to the problem using classical methods. Even if AI is a powerful tool, it is only worth using it for problems that have been difficult so far. Third, every AI project needs data. This is especially true for machine learning as a part of AI. A good database does not have to be extensive, but above all it must contain the right data. Only if the data describe the problem adequately, AI can provide a good solution. Good entry points into the topic of AI are therefore exactly where the data is located. In concrete terms, this can be machine data for typical applications such as predictive maintenance, image data for optical quality control, but also FAQs for generating chatbots. Here AI brings a new quality to digitization and can open up new interactions, especially in knowledge management.
2Which project is best suited as the first AI application?
The best first step is always where the data is already available or can be made available very easily. My heart beats for technical solutions in the field of automation and I prefer solutions where human and machine interact.

An ideal entry point for AI in a company is for me the area of knowledge management. In other words, the digitalization of operational and production knowledge. Often a large part of the knowledge is stored in the heads of experienced employees and is only available to the company when the respective employee is present.

Using AI, this knowledge can be easily documented and made permanently available to all employees. By observing the human interactions, the AI can additionally deepen and constantly update this knowledge. That would be Machine Learning. Here we have focused on image recognition and processing in order to be able to detect objects reliably and provide them with additional information. This results in data records for each object, which can be managed and optimized by the AI.

Everyone can imagine the added value of such a solution in the quality assurance of production processes. This is, for example, also the basic idea behind Dynamian AI, one of our current IT products with corresponding AI and AR extensions.
3What does that mean exactly?
Compared to the possibilities offered by AI, classic digital solutions work within a very narrowly defined framework that is fixed by programming. The lever in using AI lies in the database, as it is much easier to expand and adapt. I will illustrate this with the example of object recognition. A classic system is programmed to recognize a certain object, let's say a pair of pliers, in order to link, for example, instructions for use or work instructions with it. In an AI system it is sufficient to be presented with images of the object to be recognized. From this it learns to recognize it by itself and can be used much more flexible for information supply, because no new programming is required, only new images.
4How can employees get the right information?
This is a very good question and it allows us to focus on embedding AI solutions into existing IT systems or standalone products.

A stand alone AI module for object recognition does not create added value, AI always requires a complete application. In our own product development Dynamian for work instructions, we use AI in exactly this way to simplify the cooperation between human and machine. This again shows the great advantage of AI and the use of image processing.

Cooperative models are characterized by strong interactions between humans and AI. They enable employees to acquire knowledge very quickly with the help of images and supplementary texts. The availability for the employees is an important field of the AI application, because onboarding-processes and internal trainings can be covered.

It doesn´t matter whether it is knowledge in production, quality assurance, sales or any other department. The cooperative approach also ensures better acceptance by all those involved parties. Who wouldn't be happy if the long search for assembly instructions and other documents is finally resolved.
5Why is AI your passion?
During my studies, I was fascinated by technical computer science, where we built and programmed adaptive robots. Today, AI is more exciting than ever before, as technological maturity has increased enormously in many areas. Today we can not only process enormous amounts of data and create impressive solutions from it. AI is getting better and better at absorbing human knowledge, supplementing it and interacting with us. This is the basis for future innovation.
Data Science
Knowledge Management
Knowledge Management
Efficiency increase
Efficiency increase
Quality assurance
Quality assurance
Augmented Reality nutzen 75 % unserer Kunden

Our customers already consider artificial intelligence as a relevant success factor for their sustainable business success. 79% of German companies already consider AI to be very important. Thus, Artificial Intelligence is considered a technology of highest relevance.


We love exchange and knowledge transfer. Our experts have published their knowledge and experiences from practice:

(Disclaimer: Some of the publications may still be in the publication process.)

Barenkamp, M. (2022): Identifikation der Urheber von Cyberattacken mithilfe künstlicher Intelligenz.
In: Wirtschaftsinformatik und Management.

Barenkamp, M. (2022): Datenschutz, -sicherheit und Servicekomfort moderner Anwendungen der Künstlichen Intelligenz.
In: Wirtschaftsinformatik und Management, 14(1), pp. 20-26.

Barenkamp, M. (2021). Die Softies der künstlichen Intelligenz.
In: Wirtschaftsinformatik & Management.

Barenkamp, M. (2021). Warum die Erde einen digitalen Zwilling bekommt.
In: Wirtschaftsinformatik & Management.

Butz, R., Schulz, R., Hommersom, A. & van Eekelen, M. (2021). What is understandable in Bayesian network explanations?.
In: Explainable Artificial Intelligence in Healthcare.

Barenkamp, M. (2020). A New IoT Gateway for Artificial Intelligence in Agriculture.
In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, pp. 1–5.

Barenkamp, M. (2020). Künstliche Intelligenz in der Softwareentwicklung.
In: Wirtschaftsinformatik & Management, 12(2), pp. 120–129.

Barenkamp, M. (2020). UI Generierung aus Handschriften im Design Sprint Prozess.
In: Informatik Spektrum, 43(3), pp. 211–219.

Barenkamp, M. & Niemöller, D. (2020). ARchitecture — Insights From Theory and Practice.
In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, pp. 1–6.

Barenkamp, M., Rebstadt, J. & Thomas, O. (2020). Applications of AI in classical software engineering.
In: AI Perspect, 2(1), pp. 1–15.

Butz, R., Hommersom, A. & van Eekelen, M. (2018). Explaining the most probable explanation.
In: Scalable Uncertainty Management (Best Student Paper Award).

Rabinowicz. S, Butz. R, Hommerson. A & Williams. M. (2018). CSBN: a hybrid approach for survival time prediction with missing data.
In: Advanced Analytics and Learning on Temporal Data.

Rabinowicz. S, Butz. R, Hommerson. A & Williams. M. (2017). A prognostic model of glioblastoma multiforme using survival bayesian networks.
In: Artificial Intelligence in Medicine.

Luebbers, D., Grimmer, U. & Jarke, M. (2003). Systematic development of data mining-based data quality tools.
In: Proceedings of the 29th international conference on Very large data bases, 29, VLDB Endowment, pp. 548–559.



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