WE CAN USE AI.
Artificial intelligence (AI) is a key factor for sustainable business success and is used to generate added value in the shortest possible time. Our experience shows that the majority of AI projects pay off in less than two years.
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?
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.
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:
Barenkamp, M. (2021). Why the earth is getting a digital twin.
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). Artificial intelligence in software development.
In: Wirtschaftsinformatik & Management, 12(2), pp. 120–129.
Barenkamp, M. (2020). UI generation from handwriting in the design sprint process.
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.