Updates on Artificial Intelligence

In this whitepaper, we explore the implications of the EU AI Act for businesses, detailing how organizations can navigate compliance through risk-based AI system classification, employee training, and the implementation of governance structures. Special attention is given to AI literacy as a key enabler - not only for regulatory alignment but also for driving innovation and operational efficiency across the enterprise.

AI is transforming project reporting by cutting down manual effort and enhancing accuracy. Using Microsoft Copilot, teams can automate the extraction of project status, risks, and next steps directly from meeting notes and project plans. Especially in multi-project environments or tight reporting cycles, this approach reduces workload, supports strategic focus, and increases reporting efficiency. Learn how structured data and well-designed prompts enable Copilot to deliver clear, actionable reports in seconds.

As part of the SALSA research project, INVENSITY conducted a TARA following ISO/SAE 21434 to ensure cybersecurity for external Human-Machine Interfaces (eHMI) in autonomous vehicles. By identifying assets, analyzing threats, and prioritizing risks early in development, this approach strengthens both safety in mixed traffic and public trust in automated driving. The analysis highlights how proactive, security-by-design thinking is essential for resilient and socially accepted vehicle technologies.

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.