In the focus area “Work 5.0” and as part of the Interreg AT-SK project “Twin City Future Innovation Manufacturing Hub,” the first workshop on “Future Skills & AI” took place at the end of October. 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.
The workshop began with a presentation by Julia Bock-Schappelwein (WIFO) on occupations at the intersection of demographics, greening, and digitalization, summarizing the key findings from the AMS report “Labour Market and Occupations 2030.” Transformation processes have always occurred, but what is unique today is the speed at which they unfold. Changing geopolitical conditions and events like the pandemic further accelerate shifts toward sustainability and digitalization. The demographic dimension intensifies these effects, as large age cohorts exit the workforce and are replaced by smaller ones. The adoption of new technologies requires specific skill sets, and greening is also increasing qualification demands and creating new job categories. Future skills not only include traditional competencies, but also key digital and transformative skills. The study evaluated occupational sectors based on three criteria – demographics, digitalization, and greening – and assessed how each is affected. Key findings include that digital technologies are more likely to support human labour than replace it; while earlier automation phases primarily replaced manual routine tasks, AI now increasingly affects cognitive routine tasks. Due to demographic trends, academic roles and engineering fields like materials, process engineering, biotechnology, and technical trades (e.g., metal and mechanical workers) are particularly affected. Executive Summary_Handout
Links to studies:
Labour Market Policy Measures in View of Greening the Economy
Labour Market and Occupation 2030
How Demographics, Digitalization, and Greening Affect Occupational Fields Differently
Theo Kopetzky (SCCH) illustrated how AI works at a fundamental level and how it is trained. AI encompasses machine learning, deep learning, and large language models, which are fast, intuitive systems but not rational or autonomous. AI models are trained and guided by humans based on pattern recognition in large data sets – they are not independent systems. For proper use and expectation setting, it’s critical to understand how AI works. AI never “learns by itself”- it is designed and trained by human engineers, who also define the system’s goals. Anything beyond that belongs to the realm of artificial life and science fiction. He also referenced the Trusted Artificial Intelligence white paper developed by SCCH, TÜV Austria, and JKU, which introduces an initial certification process for AI solutions. TÜV Austria AI Certification
Manuel Woschank (Montanuni Leoben) and Corina Pacher (TU Graz) addressed digital competencies in production and logistics, presenting the EU research project “EE4M – Engineering Education of the Future in Production and Logistics.” This empirical study examines future skills and systematic skill development. International research networks have long focused on improving production and logistics through technology. Sustainability has recently been added to the agenda, with digitalization supporting decarbonization efforts. Simultaneously, the human factor gains attention in the research on digital transformation, aiming to make jobs more attractive.
The EE4M project aims to advance engineering education in logistics and identify key topics for the future of production. Core technologies include material identification and tracking, augmented reality, digital twins, and human-machine collaboration systems that support decision-making. The study concludes that, beyond technical digital skills, digital transformation skills are essential. Implementing new technologies requires change management and is a leadership responsibility. Data science is also a crucial competency. The network around MUL and TU Graz offers customized continuing education packages that enable problem- and practice-based learning in labs and promote smart, future-oriented learning. Executive Summary Handout – Projects: EE4M, SME4.0, SME 5.0
Michael Ginner and Axel Sonntag (KPMG) provided insight into how to develop an AI strategy for industrial companies. Based on a long-running annual study initiated by KPMG a decade ago, 64% of surveyed CEOs plan to invest in AI regardless of economic conditions, while 60% fear their staff are not adequately prepared. Legal, ethical, and regulatory aspects – especially EU frameworks – are equally relevant as the technical considerations. KPMG identifies four strategic focus areas: Where to apply AI within the organization; Organizational context, compliance policies, and processes; Required technologies and tools; Creating employee understanding and a supportive culture.
A structured approach is recommended to select the right use cases, as ideas often exceed available resources. The goal is to identify the highest value use cases. An initial assessment matrix compares implementation effort with potential value. Selected use cases are then evaluated in detail with relevant departments and validated technically. This leads to a prioritized AI roadmap.
Thomas Doms (TÜV Austria) and Wolfgang Wilke (Data Intelligence) shared how they implement AI models and presented concrete use cases. TÜV Austria, in collaboration with others, established a certification and the Trustify competence center to help companies meet quality standards and AI Act requirements. Implementation starts with identifying the problem and defining goals, metrics, and data sources. Suitable models are selected and trained with data. Testing, evaluation, and continuous monitoring are essential to ensure valid results.
Use Cases: Rise Device for fatigue monitoring of steel bridges using acoustic sensors to detect material stress from passing trains. Monitoring PV module degradation using image processing and spectral analysis to extend lifespan. Fiber production monitoring using sensor-guided lines, providing real-time quality data across the production chain. TÜV Austria supports companies with step-by-step guides that help data scientists and engineers maintain data quality standards.
Finally, Andreas Dieminger and Christian Wallmann (Welser Profile) connected the dots between transformation, changing job roles, required competencies, and AI use cases. Welser Profile specializes in custom steel profiles and emphasizes its pioneering spirit and commitment to people, demonstrated by its newly established apprenticeship center. Continuous investment in employee and leadership development is essential for managing transformation, requiring lifelong learning. Leaders need entrepreneurial skills, and employees must be adaptable, with strong learning and problem-solving abilities. AI plays a key role at Welser Profile, with various ongoing pilots and implemented use cases: Chatbots help gather documents on internal systems, compliance, and safety. Everyone can create or propose chatbots for organizational use. Machine learning predicts steel demand and payment flows. Internal knowledge graphs map organizational elements and their relationships to identify disruptions, machine loads, and material sources briefly. In collaboration with Ars Electronica, a “Test-before-Invest” project used computer vision to compare cross-sectional profiles in production. Executive Summary_Handout
Many thanks to all speakers and participants. Recordings and presentations are available in the members’ area of the platform.
