D7.1 Survey of customer needs and HPC/AI maturity
Year: 2025
Type: article
Abstract: A customer needs and maturity survey was designed and launched to collect data on the needs of current, previous, and potential customers of the LUMI AI Factory (LUMI AIF) services. The data to be collected involved current state of High-Performance Computing (HPC) and Artificial Intelligence (AI) adoption, technical needs, barriers, and collaboration outlook. From mid-June to mid-August 2025, the survey was distributed among the consortium countries Finland, Norway, Denmark, Estonia, Poland, and the Czech Republic, resulting in 40 responses, incl. responses from these countries and other European countries. The respondents represented a broad mix of start-ups (30.8%), SMEs (30.8%), higher education institutions (HEI) (15.4%), large enterprises (15.4%), and research institutes (7.7%), with Finland, Czechia, Denmark, and Poland as the most represented countries providing a snapshot of current practice and immediate operational requirements in the rapidly evolving HPC/AI landscape. The content is structured around survey design, respondents’ organizational and technical profiles, and their day-to-day experience with AI and HPC systems. Key outcomes include strong demand for scalable GPU access, unified infrastructure supporting hybrid and cloud workloads, technical onboarding, and specialist support for integrating new hardware or platforms. Access to curated European data resources, straightforward funding pathways, and ongoing workforce training emerged as distinct needs, particularly from organizations scaling beyond pilot projects. Key findings reveal that AI adoption is highly mature, with 92.3% of organizations already using AI or machine learning technologies, compared to 53.8% using HPC. Moreover, 28.2% of organizations plan to introduce HPC in the near future, demonstrating a growing interest. Organizations self-assess their maturity levels as predominantly advanced (50%) or intermediate (35%), while only 15% identify as beginners. The main drivers of adoption include efficiency gains (72.5%), innovation (45%), and cost reduction. However, several barriers constrain broader integration, most notably budget constraints (65%), followed by data privacy issues (25%) and unclear business value (20%). In terms of technical needs, GPU computing stands out as the most critical requirement (79.5%), alongside AI inference services, data cleaning, and fast storage. Computational tasks focus on AI model training (85%), simulations and digital twins (47.5%), and large-scale data analysis (37.5%). Scalability, fast results, and reliability are the leading performance requirements. Skills and workforce readiness show promising trends: 69.2% of organizations already employ dedicated AI/HPC staff, and 61.5% are actively investing in workforce upskilling. Nonetheless, gaps remain in HPC operations (57.1%), AI/ML expertise (40%), cloud infrastructure (40%), and data science (37.1%). Collaboration is emerging but uneven. While 53.8% of organizations partner with external actors, awareness and use of competence centres and EU-funded HPC programmes remain limited. Only 33.3% of respondents reported participation in publicly funded programmes, while 41% had not engaged in any. Looking ahead, organizations highlight top priorities in infrastructure access (cloud, GPUs, scalable HPC), large model training and fine-tuning, commercialization of AI-based products, and domain-specific applications (e.g., agriculture, manufacturing, translation). Strategic goals also include workforce development, demonstrating ROI, and making societal contributions, such as addressing labour market challenges. These results offer practical, user-driven guidance for enhancing LUMI AI Factory services. The evidence highlights the importance of targeted infrastructure investments, community-focused training, and flexible, partnership-oriented approaches to ensure broad and sustainable AI/HPC adoption across Europe’s research and industrial sectors.
Authors: Michel-Schneider, Ulrike, Nosek, Vojtek, Pojsl, Jan
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W7087388905
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