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This series facilitates an exchange of best practices from science and industry, focusing on the use of artificial intelligence (AI) in organizations, the skills required, and how companies can prepare for the challenges associated with AI.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#F4F1DF” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb _builder_version=”4.27.6″ _module_preset=”default” background_color=”#F4F1DF” global_colors_info=”{}”]<\/p>\n
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April 28, 2026 – 6th Future Skills & AI Workshop <\/strong><\/p>\n Artificial Intelligence: Between Testability, Application, and Industrial Value Creation<\/p>\n 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 \u201cTwin City Future Innovation Manufacturing Hub,\u201d<\/a> with a focus on Work 5.0.<\/p>\n The speakers were:<\/p>\n The presentations examined artificial intelligence from three perspectives: scientific foundations and testability, innovative AI systems in industry, and specific applications in production.<\/p>\n Mateo Primorac<\/strong> of scopri.ai<\/a> 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. <\/p>\n Sebastian Kreuter<\/strong> of Fraunhofer Austria<\/a> 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. <\/p>\n Bernhard Nessler<\/strong>, SCCH<\/a>, addressed the fundamental question of how AI systems can be reliably tested and what is meant by \u201crepresentativeness\u201d 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. <\/p>\n The presentations made it clear that AI is not just a technological development, but represents a systemic transformation in research, development, and production.<\/p>\n <\/p>\n [\/et_pb_blurb][et_pb_blurb _builder_version=”4.27.6″ _module_preset=”default” background_color=”#FFFFFF” global_colors_info=”{}”]<\/p>\n May 28, 2026 – 7th Future Skills & AI Workshop <\/strong><\/p>\n 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 \u201cTwin City Future Innovation Manufacturing Hub\u201d<\/a> under the Work 5.0 focus area.<\/p>\n Speakers Rania Wazir<\/strong> (leiwand.ai<\/a>) and Thomas Doms ( <\/strong>T\u00dcV AUSTRIA<\/a>) provided key insights into how artificial intelligence can be designed to be safe, transparent, and trustworthy.<\/p>\n AI Standards as the Foundation for the AI Act<\/strong><\/p>\n In her presentation, Rania Wazir, co-founder <\/strong>and CTO at leiwand.ai,<\/strong> 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. <\/p>\n The focus is on the so-called \u201cEssential Requirements\u201d 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. <\/p>\n 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 \u201caccuracy\u201d 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. <\/p>\n AI at T\u00dcV AUSTRIA<\/strong><\/p>\n In his presentation, Thomas Doms<\/strong>, Head of Research, Development & Innovation at T\u00dcV AUSTRIA, demonstrated how the group not only tests and evaluates artificial intelligence but also successfully implements it itself. Through its subsidiary Trustify, T\u00dcV AUSTRIA\u2014in collaboration with the SCCH (Software Competence Center Hagenberg)\u2014helps 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. <\/p>\n AI also plays an important role within the corporate group. In addition to standard solutions, T\u00dcV 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 \u201cAdam,\u201d which helps customers find information and services. The basis for the development and evaluation of AI systems is the company\u2019s 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. <\/p>\n Der Vortrag betonte, dass vertrauensw\u00fcrdige KI nicht nur leistungsf\u00e4hige Technologie, sondern vor allem klare Prozesse, Qualit\u00e4tsstandards und verantwortungsvollen Einsatz erfordert.<\/p>\n <\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_enable_color=”off” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text admin_label=”Headline 2 + Text” _builder_version=”4.27.6″ _module_preset=”default” background_color=”#FFFFFF” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n <\/p>\n [\/et_pb_text][et_pb_text admin_label=”Headline 2 + Text” _builder_version=”4.27.5″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n When used wisely, artificial intelligence can significantly enhance the innovative strength and competitiveness of Austrian and Slovak companies. The workshop series ‘AI and Skills’ highlights, on the one hand, the training and further education opportunities available in Austria, and on the other hand, addresses concrete industrial applications in classical AI, generative AI, and subsequently agent-based AI. Sharing information on skills offerings and presenting AI use cases helps other companies in Slovakia and Austria integrate these experiences and applications into their own business environments. <\/p>\n <\/p>\n [\/et_pb_text][et_pb_text admin_label=”Headline 3 + Text” _builder_version=”4.27.6″ _module_preset=”default” background_color=”#FFFFFF” global_colors_info=”{}”]<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Work 5.0 This series facilitates an exchange of best practices from science and industry, focusing on the use of artificial intelligence (AI) in organizations, the skills required, and how companies can prepare for the challenges associated with AI. 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 \u201cTwin City Future Innovation Manufacturing Hub,\u201d 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 […]<\/p>\n","protected":false},"author":4,"featured_media":2976,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[61],"tags":[],"class_list":["post-5988","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en"],"acf":[],"yoast_head":"\n\n
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