April 28, 2026 – 6th Future Skills & AI Workshop

Artificial Intelligence: Between Testability, Application, and Industrial Value Creation

On April 28, the sixth edition of the webinar on AI Use Cases & Future Skills took place. The event was organized by the Industry 4.0 Platform as part of the Interreg AT-SK project “Twin City Future Innovation Manufacturing Hub,” with a focus on Work 5.0.

The speakers were:

• Mateo Primorac, Founder & CEO, scopri.ai
• Sebastian Kreuter, Research Associate, Intelligent Maintenance and Product Development, Fraunhofer Austria Research GmbH
• Bernhard Nessler, Research Manager, Intelligent Systems and Certification for AI, Software Competence Center Hagenberg (SCCH)

The presentations examined artificial intelligence from three perspectives: scientific foundations and testability, innovative AI systems in industry, and specific applications in production.

Mateo Primorac of scopri.ai demonstrated how AI in industry can be further developed beyond traditional large language models (LLMs). While LLMs perform well on general tasks, they often reach their limits when it comes to highly specialized problems and industrial applications. Cognitive AI combines model intelligence with user interaction and specific contextual knowledge. This allows it to learn from the application and deliver more precise results for complex industrial scenarios. Using practical examples, he demonstrated how AI can analyze large volumes of patent and scientific data in a very short time and derive valuable insights for technology and market analysis. This significantly accelerates development processes and enables more informed strategic decisions. Looking ahead, he presented a knowledge graph-based AI system for CAD models that assesses product designs for patentability and technical risks as early as the initial development phases. In the future, design guidelines and standards will also be integrated to enable fully data-driven product development.

Sebastian Kreuter of Fraunhofer Austria presented use cases for AI in industrial production. Today, companies face challenges such as increasing product complexity, customization, and a multitude of regulatory requirements. AI systems help manage this complexity by consolidating and structuring data from various sources. One example is the automated analysis of specifications, in which customer requirements are extracted from documents and compared with internal standards. This allows potential errors or gaps to be identified early on. Other applications range from the analysis of technical drawings and CAD data to support for standards compliance checks and documentation processes.

Bernhard Nessler, SCCH, addressed the fundamental question of how AI systems can be reliably tested and what is meant by “representativeness” in this context. He pointed out that modern AI models do not operate on a rule-based basis but are based on statistical learning. As a result, AI must be evaluated not on the basis of correctness, but on the basis of statistical performance within a clearly defined application domain. A key challenge remains the precise definition of the test domain, as this is the only way to make robust and reproducible statements about the performance of AI systems.

The presentations made it clear that AI is not just a technological development, but represents a systemic transformation in research, development, and production.

May 28, 2026 – 7th Future Skills & AI Workshop

On May 28, the Industry 4.0 Platform hosted the seventh edition of its webinar on AI Use Cases & Future Skills, organized as part of the Interreg AT-SK project “Twin City Future Innovation Manufacturing Hub” under the Work 5.0 focus area.

Speakers Rania Wazir (leiwand.ai) and Thomas Doms ( TÜV AUSTRIA) provided key insights into how artificial intelligence can be designed to be safe, transparent, and trustworthy.

AI Standards as the Foundation for the AI Act

In her presentation, Rania Wazir, co-founder and CTO at leiwand.ai, explained the central role of standardization in the implementation of the European AI Act. The goal of the regulation is to ensure a high level of protection for health, safety, fundamental rights, and democracy. Standards are intended to help companies implement these requirements in practice.

The focus is on the so-called “Essential Requirements” of the AI Act, including risk management, data quality, transparency, human oversight, robustness, accuracy, and cybersecurity. Harmonized standards for these areas are currently being developed at the European level. It has become clear that standardization is a complex and time-consuming process. Although the European Commission commissioned the development of the standards as early as 2023, many documents are still undergoing coordination or public consultation.

The first standards are expected to be fully available starting in 2027. Translating the legal requirements of the AI Act into technical quality metrics is particularly important. Using “accuracy” as an example, it has been shown that the mere accuracy of an AI model is often insufficient. What matters far more is whether a system reliably performs its intended task while treating different user groups fairly. Standards help select appropriate metrics, make test results transparent, and ensure the comparability of AI systems. In doing so, they lay an important foundation for the development, evaluation, and certification of trustworthy AI.

AI at TÜV AUSTRIA

In his presentation, Thomas Doms, Head of Research, Development & Innovation at TÜV AUSTRIA, demonstrated how the group not only tests and evaluates artificial intelligence but also successfully implements it itself. Through its subsidiary Trustify, TÜV AUSTRIA—in collaboration with the SCCH (Software Competence Center Hagenberg)—helps companies design AI systems that are secure, compliant, and trustworthy. This includes audits, compliance assessments, and the evaluation of data protection, security, and quality requirements.

AI also plays an important role within the corporate group. In addition to standard solutions, TÜV AUSTRIA develops its own applications that streamline processes and support employees. Current examples of these applications include an AI-powered expert assistant for creating inspection reports, AI support in the Cyber Security Operations Center, and the new website assistant “Adam,” which helps customers find information and services. The basis for the development and evaluation of AI systems is the company’s own AI testing catalog, which includes approximately 400 evaluation criteria. The focus is on data quality, functional correctness, cybersecurity, transparency, and human oversight. One central principle remains constant: When it comes to safety-related decisions, humans retain responsibility. AI supports experts but does not replace them.

Der Vortrag betonte, dass vertrauenswürdige KI nicht nur leistungsfähige Technologie, sondern vor allem klare Prozesse, Qualitätsstandards und verantwortungsvollen Einsatz erfordert.