EMCE @PoEM 2025

For full information and interactive material, please go to HERE (Google Drive)

Enterprise Modelling for Context Engineering
Workshop (EMCE)

@PoEM 2025

Organizers: Stijn Hoppenbrouwers1,3, Hend(Erik) Proper2, Vincent Wiegel1

1 HAN University of Applied Sciences, Arnhem, the Netherlands

2 TU Wien, Vienna, Austria

3 Radboud University, Nijmegen, the Netherlands

Over the past decades, enterprise modelling has established itself as a central practice in information systems research and organizational engineering. Enterprise models have served as pragmatic, semantic, and structural resources to support communication, requirements engineering, business–IT alignment, and organizational change. The discipline, and its perspective on the role of models, has evolved from a rigid “blue printing” perspective to one including a more coarse grained architectural perspective, while also including more flexible socio-technical approaches, reflecting the dynamic nature of modern enterprises (Sandkuhl et al., 2018; van Gils & Proper, 2018).

In parallel, the emerging field of context engineering (L. Mei et al, 2025) underscores the need to systematically represent, structure, and govern contextual knowledge as a foundation for advanced AI and digital transformation. While current AI excels at identifying common patterns in large-scale generic data, it struggles to include domain-specific, local, and transient knowledge. Addressing these limitations requires careful conceptualization of organizational knowledge —precisely defined, normative where necessary, and actionable for both human and machine actors (Dey, 2001; Bazire & Brézillon, 2005).

Using enterprise modelling for context engineering is not only useful for providing context-specific data to AI, but also for helping enterprises articulate their relation and positioning towards AI (perhaps in particular, towards agentive AI). Models can capture, for example, enterprise-specific terms and concepts, process descriptions, organizational and architectural principles, and the values, norms, and rules an enterprise chooses (or needs) to implement and uphold. In a world where AI systems may lack transparency or human comprehensibility, enterprise models can serve as a “fortress of human-defined meaning”—a conceptual foundation that both informs AI and protects the human and organizational meaningfulness the enterprise seeks to preserve (Jarrahi, 2018; European Commission, 2021).

As a concrete starting point, context engineering can be viewed as a next step beyond mere utilitarian ‘prompt engineering’ in the context of generative AI, using model-based, deliberate, up-to-date and highly situation-specific representations of organizations and their environments. This aligns with the long-standing ambitions of enterprise modelling: to provide structured, purposeful representations that support both analysis and guidance. The convergence of enterprise modelling and context engineering may thus open new avenues for integrating semantics, pragmatics, and normative frames into future socio-technical systems, including AI-enabled decision support and digital twins (S. Mihai et al., 2022).

The EMCE workshop invites participants to join us in exploring the strategic link between enterprise modelling and context engineering, enriching the practice of enterprise modelling with new goals, applications and principles. Using a semi-structured group facilitation setup, we will discuss and examine how established modelling practices may create a bridge to AI-enabled organizational intelligence and how context engineering can help enterprises to both enable and govern AI in ways that remain aligned with their principles, values, and aspirations.

Depending on the outcome of the workshop, we expect one or more papers will eventually result from the workshop, possibly to be presented at PoEM 2026.

If you are interested in participation, please register for PoEM 2025 but also mail stijn.hoppenbrouwers@han.nl. You can also post questions on this address.

References

van Gils, B., Proper, H.A. (2018). Enterprise Modelling in the Age of Digital Transformation. In: Buchmann, R., Karagiannis, D., Kirikova, M. (eds) The Practice of Enterprise Modeling. PoEM 2018. Lecture Notes in Business Information Processing, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-02302-7_16

Sandkuhl, K., Fill, H.-G., Hoppenbrouwers, S., Krogstie, J., Matthes, F., Opdahl, A. L., Schwabe, G., Uludag, Ö., & Winter, R. (2018). From expert discipline to common practice: A vision and research agenda for extending the reach of enterprise modeling. Business & Information Systems Engineering, 60(1), 69–80. https://doi.org/10.1007/s12599-017-0516-y

Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., Zhou, C., Mao, J., Xia, T., Guo, J., & Liu, S. (2025). A survey of context engineering for large language models. arXiv. https://doi.org/10.48550/arXiv.2507.13334

Bazire, M., & Brézillon, P. (2005). Understanding context before using it. In Modeling and Using Context (CONTEXT 2005) (pp. 29–40). Springer. https://doi.org/10.1007/11508373_3

Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7. https://doi.org/10.1007/s007790170019

European Commission. (2021). Proposal for a Regulation laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act). Brussels. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human‑AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Mihai, S., Bălăceanu, A., Bălăceanu, C., Apostol, A. C., Dobre, C., & Mavromoustakis, C. X. (2022). Digital twins: A survey on enabling technologies, challenges, trends, and future prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255–2291. https://doi.org/10.1109/COMST.2022.3208773