Updates on Artificial Intelligence

Generative AI is revolutionizing business innovation by streamlining processes and driving efficiency. Learn about the four distinct levels of Generative AI integration - exploration, refinement, automation, and optimization - and how they can guide your organization toward AI-enabled transformation. This structured roadmap ensures sustainable adoption, measurable results, and a future-proofed enterprise in the age of complex technologies.

This whitepaper explores the potential of Large Language Models (LLMs) in the development of GenAI-powered chatbots.

Globalization and digitalization demand customized, high-performance products with swift development cycles. Agile methods, successful in software, are expanding into hardware development with iterative cycles or "sprints," delivering tangible, feedback-ready increments.

Whitepaper on Explainable AI: XAI is specifically designed to facilitate transparency, enhance the understandability of AI decisions, and foster trust in AI utilization.

Discover how AI is transforming project management by automating workflows and improving efficiency by 38%. Our analysis reveals significant potential in resource management, planning, and data updating. Learn about three approaches to integrating AI into your company to enhance project quality and decision-making.

Discover generative AI's potential! Bloomberg Intelligence foresees a €1.2 trillion market by 2032. While ChatGPT showcases tech, it's crucial to differentiate the application from the model. Companies can thrive by identifying tailored generative AI scenarios. The market offers opportunities in hardware, software, and inference devices.

ISO 8800 is soon to be released – it will play an important role when it comes to the question of how to ensure that AI-powered road vehicles operate safely and reliably. In this interview, Sascha Hackmann, our Head of Automated Driving, investigates the significance of AI technology in automotive industry and the crucial questions concerning the intersection of AI and the safety of road vehicles.

Where lie the key differences in the AI development process between classical software and AI? This is what our AI experts Dr. Marc Großerüschkamp and Mina Khosravifard discuss in this interview. They focus especially on the role of data and the security of AI systems.

What concrete steps can you take to harness AI and machine learning for your own production? Read part two of our interview "Predictive Quality - Opportunities of AI in Production". Christopher Seinecke and Dr. Marc Großerüschkamp take a closer look at the technical context, answer the question of the relevance of the data an AI learns from, and address concerns that the use of AI raises.

Jan Zutter, Head of Systems Engineering at INVENSITY, and Christian Dahm, Systems Engineering Consultant at INVENSITY, discuss the advantages and potential risks of switching to and using MBSE in development. They explore the question of how safety management can benefit from a model-based development approach.

What impact does hybrid working have on collaboration in innovation and change projects? And how can good leadership keep employee productivity consistently high and perhaps even increase it? Read our latest blog-post to find answers to these questions.

Where do the potentials of AI applications lie, especially in system and machine operation as well as in production, and also for medium-sized companies? Dr. Marc Großerüschkamp and Christopher Seinecke discuss this question in this expert interview.