Document Open Access Logo

AI-Enabling Requirements: An Industrial Experience Report from a Data-Driven Recruitment System

Authors Sylwia Kopczyńska , Mirosław Ochodek , Eryk Kosmala , Jędrzej Musiał , Rafał Mroziewski, Dominik Nitychoruk

PDF

File

Thumbnail PDF
PDF
LIPIcs.ESEM.2026.92.pdf
  • Filesize: 0.75 MB
  • 13 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Software and its engineering → Requirements analysis
  • Computing methodologies → Machine learning
Keywords
  • requirements engineering
  • AI-based systems
  • AI-enabling requirements
  • industry case study
  • data-driven recruitment

Metrics

Abstract

Requirements Engineering commonly distinguishes between functional and non-functional requirements. In AI-based systems, an orthogonal perspective might also be worth considering: whether requirements concern AI components or not.
This paper reports on a 2.5-year R&D project building MONEKTO, a holistic data-driven recruitment system for a Polish temporary employment agency. The system integrates three data streams: recruitment process events, candidate and job order profiles, and employment histories - to support both candidate-order matching and employee retention prediction. 
Drawing on project logs covering June 2023 to January 2026, we identified three categories of requirements in AI-based systems: Non-AI-based requirements, AI-based requirements, and AI-enabling requirements. 
We show how these categories differ in their lifecycle: while traditional requirements stabilize after implementation, AI-enabling requirements emerge incrementally and require continuous investment throughout the system lifetime. We report five observations on the emergent nature of AI-enabling requirements and derive seven practitioner lessons for RE in AI-based system projects.

Cite As Get BibTex

Sylwia Kopczyńska, Mirosław Ochodek, Eryk Kosmala, Jędrzej Musiał, Rafał Mroziewski, and Dominik Nitychoruk. AI-Enabling Requirements: An Industrial Experience Report from a Data-Driven Recruitment System. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 92:1-92:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ESEM.2026.92

Author Details

Sylwia Kopczyńska
  • Poznan University of Technology, Poznań, Poland
Mirosław Ochodek
  • Poznan University of Technology, Poznań, Poland
Eryk Kosmala
  • Poznan University of Technology, Poznań, Poland
Jędrzej Musiał
  • Poznan University of Technology, Poznań, Poland
Rafał Mroziewski
  • MONDI sp. z o.o., Poznań, Poland
Dominik Nitychoruk
  • MONDI sp. z o.o., Poznań, Poland

Funding

This work was supported by the European Funds for a Modern Economy 2021-2027 (FENG) under project no. FENG.01.01-IP.02-0857/23: "MONEKTO - System wspomagający proces rekrutacji kandydatów oraz monitorowania pracy pracowników wykorzystujący sztuczną inteligencję".

References

  1. Wajid Ali and Abdul Zahid Khan. Factors influencing readiness for artificial intelligence: A systematic literature review. Data Science and Management, 8, 2024. Google Scholar
  2. Sulaiman Alsheibani, Yen Cheung, and Chris Messom. Artificial intelligence adoption: AI-readiness at firm-level. In Proceedings of the 22nd Pacific Asia Conference on Information Systems (PACIS), 2018. Google Scholar
  3. Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. Software engineering for machine learning: A case study. In Proc. of the 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 291-300. IEEE, 2019. URL: https://doi.org/10.1109/ICSE-SEIP.2019.00042.
  4. Hrvoje Belani, Marin Vukovic, and Željka Car. Requirements engineering challenges in building ai-based complex systems. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pages 252-255, 2019. Google Scholar
  5. Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to rank: From pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine Learning (ICML), pages 129-136, 2007. URL: https://doi.org/10.1145/1273496.1273513.
  6. European Parliament and Council of the European Union. Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (artificial intelligence act). Official Journal of the European Union, 2024. Google Scholar
  7. Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad, Olof Eriksson, Jennifer Linder, Padmini Subbiah, Shameer Kumar Pradhan, and Sagar Tungal. Requirement engineering challenges for ai-intense systems development. 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), pages 89-96, 2021. Google Scholar
  8. ISO/IEC. ISO/IEC 25010:2023 - systems and software engineering - systems and software quality requirements and evaluation (SQuaRE) - product quality model. International Organization for Standardization, 2023. Google Scholar
  9. Jan Jöhnk, Malte Weißert, and Katrin Wyrtki. Ready or not, AI comes - an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1):5-20, 2021. URL: https://doi.org/10.1007/S12599-020-00676-7.
  10. Rick Kazman, Mark Klein, Mario Barbacci, Tom Longstaff, Howard Lipson, and Jeromy Carriere. The architecture tradeoff analysis method. In Proc. of the Fourth IEEE Internat. Conference on Engineering of Complex Computer Systems, pages 68-78. IEEE, 1998. Google Scholar
  11. Sylwia Kopczyńska, Jerzy Nawrocki, and Mirosław Ochodek. An empirical study on catalog of non-functional requirement templates: Usefulness and maintenance issues. Information and Software Technology, 103:75-91, 2018. URL: https://doi.org/10.1016/J.INFSOF.2018.06.009.
  12. Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl. Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11:31866-31879, 2023. URL: https://doi.org/10.1109/ACCESS.2023.3262138.
  13. Jay Lee, Hanqi Su, Dai-Yan Ji, and Takanobu Minami. Engineering artificial intelligence: Framework, challenges, and future direction. arXiv preprint arXiv:2504.02269, 2025. Google Scholar
  14. Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Neural Information Processing Systems, volume 30, 2017. Google Scholar
  15. Nadia Nahar, Shurui Zhou, Grace Lewis, and Christian Kästner. Collaboration challenges in building ML-enabled systems: Communication, documentation, engineering, and process. In Proc. of the 44th Intl Conference on Software Engineering (ICSE). ACM, 2022. URL: https://doi.org/10.1145/3510003.3510209.
  16. Luisa Pumplun, Christoph Tauchert, and Margareta Heidt. A new organizational chassis for artificial intelligence: Exploring organizational readiness factors. In Proceedings of the 27th European Conference on Information Systems (ECIS), 2019. Google Scholar
  17. Mona Rahimi and Jane Cleland-Huang. Toward requirements specification for machine-learned components. In Proceedings of the 27th International Requirements Engineering Conference Workshops (REW), pages 241-244. IEEE, 2019. URL: https://doi.org/10.1109/REW.2019.00049.
  18. Per Runeson and Martin Höst. Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering, 14(2):131-164, 2009. URL: https://doi.org/10.1007/S10664-008-9102-8.
  19. Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M. Aroyo. "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1-15. ACM, 2021. URL: https://doi.org/10.1145/3411764.3445518.
  20. D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems, 2015. Google Scholar
  21. Andreas Vogelsang and Markus Borg. Requirements engineering for machine learning: Perspectives from data scientists. In Proc. of the 6th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), pages 1-8. IEEE, 2019. Google Scholar
  22. Robert K. Yin. Case Study Research and Applications: Design and Methods. SAGE Publications, 6th edition, 2018. Google Scholar
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact