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Speeding up Explorative BPM with Lightweight IT: the Case of Machine Learning

Author

Listed:
  • Casper Solheim Bojer

    (Aalborg University)

  • Bendik Bygstad

    (University of Oslo)

  • Egil Øvrelid

    (University of Oslo)

Abstract

In the modern digital age, companies need to be able to quickly explore the process innovation affordances of digital technologies. This includes exploration of Machine Learning (ML), which when embedded in processes can augment or automate decisions. BPM research suggests using lightweight IT (Bygstad, Journal of Information Technology, 32(2), 180–193 2017) for digital process innovation, but existing research provides conflicting views on whether ML is lightweight or heavyweight. We therefore address the research question “How can Lightweight IT contribute to explorative BPM for embedded ML?” by analyzing four action cases from a large Danish manufacturer. We contribute to explorative BPM by showing that lightweight ML considerably speeds up opportunity assessment and technical implementation in the exploration process thus reducing process innovation latency. We furthermore show that succesful lightweight ML requires the presence of two enabling factors: 1) loose coupling of the IT infrastructure, and 2) extensive use of building blocks to reduce custom development.

Suggested Citation

  • Casper Solheim Bojer & Bendik Bygstad & Egil Øvrelid, 2025. "Speeding up Explorative BPM with Lightweight IT: the Case of Machine Learning," Information Systems Frontiers, Springer, vol. 27(2), pages 823-840, April.
    • Handle: RePEc:spr:infosf:v:27:y:2025:i:2:d:10.1007_s10796-024-10474-1
    DOI: 10.1007/s10796-024-10474-1
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