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Unlocking AI-Based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption

In: Artificial Intelligence, Data, and Decision-Making

Author

Listed:
  • Timo Grüneke

    (University of Bayreuth
    FIM Research Center
    Branch Business & Information Systems Engineering of the Fraunhofer FIT)

  • Tobias Guggenberger

    (University of Bayreuth
    FIM Research Center
    Branch Business & Information Systems Engineering of the Fraunhofer FIT)

  • Jakob Nusser

    (University of Bayreuth)

  • Anna Maria Oberländer

    (University of Bayreuth
    FIM Research Center
    Branch Business & Information Systems Engineering of the Fraunhofer FIT)

  • Jan Stramm

    (University of Bayreuth
    FIM Research Center
    Branch Business & Information Systems Engineering of the Fraunhofer FIT
    Frankfurt University of Applied Sciences)

  • Alexander Varrentrapp

    (University of Bayreuth)

Abstract

Small and medium-sized enterprises (SME) thrive on knowledge-intensive operations, making effective knowledge management critical to their success. Contemporary developments, such as semantic search applications (SSA) leveraging artificial intelligence (AI), promise significant benefits for knowledge management. However, the adoption of such AI-based applications in the context of SME remains still notably limited. Building upon the groundwork laid by previous research on the socio-technical dimensions of AI adoption, we thus investigate the adoption of SSA in a multiple case study within the German manufacturing sector. Hence, contextualizing the adoption of SSA in SMEs using a grounded theoretical framework. Our findings highlight the intricate interplay of organizational readiness, external support, and user satisfaction in facilitating SSA adoption. We believe our framework holds significant potential to guide the adoption of SSA and thus offers valuable insights for navigating the complexities of harnessing the potential of AI-based applications for effective knowledge management in SMEs.

Suggested Citation

  • Timo Grüneke & Tobias Guggenberger & Jakob Nusser & Anna Maria Oberländer & Jan Stramm & Alexander Varrentrapp, 2026. "Unlocking AI-Based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Artificial Intelligence, Data, and Decision-Making, pages 169-185, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08480-4_12
    DOI: 10.1007/978-3-032-08480-4_12
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