Skip to main content
SpringerLink
Log in

Democratizing Enterprise AI Success Factors and Challenges: A Systematic Literature Review and a Proposed Framework

  • Conference paper
  • First Online:
Information Systems (EMCIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 437))

  • 2233 Accesses

Abstract

To democratize or not to democratize, this is not the problem anymore for the enterprises that consider democratizing their enterprise AI practice; the problem that these enterprises face nowadays, is how to successfully democratize their enterprise AI. In this paper we conduct a systematic literature review to provide an in-depth analysis of the success factors and the challenges of democratizing the artificial intelligence practices in the enterprises, we also build on this review and propose a framework for the enterprise AI democratization that suggests a set of the success factors and challenges. The research design of this paper is to conduct a systematic literature review by including 41 papers as an initial set of studies for review; we screen the papers and implement inclusion and quality checks on these studies, and we qualify 15 papers for the final review. The key findings of this paper, from the systematic literature review, list a set of success factors and challenges that enterprises should consider to strengthen or to avoid. We propose these factors in a form of proposed framework suggesting four categories: strategy, enterprise architecture, data, and trust. Because of the publication specification and limitation, we limited the scope of our primary studies to a limited set to match the constraints and limitations. The paper includes implications for the academic literature review and the extraction of factors that can impact the process of the enterprise artificial intelligence democratization, and the need to increase the awareness of the enterprise AI practices in order to overcome the challenges that might prevent enterprises from having a successful enterprise AI. While there are some efforts to assess and review the success factors and challenges of the AI practices in general, one of the major findings of the literature review conducted is that there is evident research gap in the literature on the perception and associated factors of artificial intelligence. This paper seeks to fill this gap.

T. Kaddoumi—Independent IT Expert.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jain, A., Ranjan, S.: Implications of emerging technologies on the future of work. IIMB Manag. Rev. 32(4), 448–454 (2020). https://doi.org/10.1016/j.iimb.2020.11.004

    Article  Google Scholar 

  2. Ulnicane, I., Eke, D., Knight, E., Ogoh, G., Stahl, B.: Good governance as a response to discontents? Déjà vu, or lessons for AI from other emerging technologies. Interdiscip. Sci. Rev. 46(1–2), 71–93 (2021). https://doi.org/10.1080/03080188.2020.1840220

    Article  Google Scholar 

  3. Finley, T.: The democratization of artificial intelligence: one library’s approach. Inf. Technol. Libr. 38(1), 8–13 (2019). https://doi.org/10.6017/ital.v38i1.10974

    Article  Google Scholar 

  4. Ahmed, N., Wahed, M.: The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research (2020). http://arxiv.org/abs/2010.15581

  5. Verma, S., Sharma, R., Deb, S., Maitra, D.: Artificial intelligence in marketing: Systematic review and future research direction. Int. J. Inf. Manag. Data Insights 1(1), 100002 (2021). https://doi.org/10.1016/j.jjimei.2020.100002

    Article  Google Scholar 

  6. Musikanski, L., Rakova, B., Bradbury, J., Phillips, R., Manson, M.: Artificial intelligence and community well-being: a proposal for an emerging area of research. Int. J. Commun. Well-Being 3(1), 39–55 (2020). https://doi.org/10.1007/s42413-019-00054-6

    Article  Google Scholar 

  7. Indriasari, E., Gaol, F.L., Matsuo, T.: Digital banking transformation: application of artificial intelligence and big data analytics for leveraging customer experience in the Indonesia banking sector. In: Proceedings of the 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019, pp. 863–868 (2019). https://doi.org/10.1109/IIAI-AAI.2019.00175

  8. Luce, L.: How AI is Revolutionizing the Fashion Industry (2019)

    Google Scholar 

  9. Unesco: Artificial intelligence in education: challenges and opportunities for sustainable development. Working Paper Education Policy, vol. 7, p. 46 (2019) https://en.unesco.org/themes/education-policy

  10. Snoeck, M., Stirna, J., Weigand, H., Proper, H.A.: Panel discussion: artificial intelligence meets enterprise modelling. In: CEUR Workshop Proceedings, vol. 2586, pp. 88–97 (2020)

    Google Scholar 

  11. Auth, G., Czarnecki, C., Bensberg, F.: Impact of robotic process automation on enterprise architectures. Lecture Notes Informatics (LNI), Proceedings - Series Gesellschaft fur Inform., vol. 295, pp. 59–65 (2019). https://doi.org/10.18420/inf2019_ws05

  12. Winter, R., Fischer, R.: Essential layers, artifacts, and dependencies of enterprise architecture. In: Proceedings of the 2006 10th IEEE International Enterprise Distributed Object Computing Conference Workshops, EDOCW2006, no. May, pp. 30–38 (2006). https://doi.org/10.1109/EDOCW.2006.33

  13. Xiao, Y., Watson, M.: Guidance on conducting a systematic literature review. J. Plan. Educ. Res. 39(1), 93–112 (2019). https://doi.org/10.1177/0739456X17723971

    Article  Google Scholar 

  14. Manyika, J., Bughin, J.: The Promise and Challenge of the Age of Artificial Intelligence, no. October, p. 8. McKinsey Co. (2018)

    Google Scholar 

  15. Vuppalapati, C., Ilapakurti, A., Kedari, S., Vuppalapati, J., Kedari, S., Vuppalapati, R.: Democratization of AI, albeit constrained IoT devices & Tiny ML, for creating a sustainable food future. In: Proceedings of the 3rd International Conference on Inventive Computation Technologies, ICICT 2020, pp. 525–530 (2020). https://doi.org/10.1109/ICICT50521.2020.00089

  16. Zawislak, P.A., Alves, A.C., Tello-gamarra, J., Barbieux, D., Reichert, F.M.: Innovation capability: from technology development to transaction capability. 7(2), 14–27 (2012)

    Google Scholar 

  17. Pimpale, D.: Reshape enterprise using AI, data analytics, enterprises science and learning platforms. 3(1) (2019)

    Google Scholar 

  18. Patel, J.: The democratization of machine learning features. In: Proceedings of the 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science, IRI 2020, pp. 136–141 (2020). https://doi.org/10.1109/IRI49571.2020.00027

  19. Bellomarini, L., Fakhoury, D., Gottlob, G., Sallinger, E.: Knowledge graphs and enterprise AI: the promise of an enabling technology. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 26–37 (2019). https://doi.org/10.1109/ICDE.2019.00011

  20. Guenole, N., Feinzig, S.: The business case for AI in HR—with insights and tips on getting started. IBM WATSON Talent, p. 36 (2018). https://public.dhe.ibm.com/common/ssi/ecm/81/en/81019981usen/81019981-usen-00_81019981USEN.pdf

  21. Pandey, S.: ROI of AI: Effectiveness and Measurement, vol. 10, no. 05, pp. 749–761 (2021)

    Google Scholar 

  22. Chander, A., Srinivasan, R., Chelian, S., Wang, J., Uchino, K.: Working with beliefs: AI transparency in the enterprise. In: CEUR Workshop Proceedings, vol. 2068 (2018)

    Google Scholar 

  23. Larsson, S., Heintz, F.: Transparency in artificial intelligence. Internet Policy Rev. 9(2), 1–16 (2020). https://doi.org/10.14763/2020.2.1469

    Article  Google Scholar 

  24. Ding, R., Palomares, I., Wang, X., Yang, G., Liu, B.: Large-scale decision-making: characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective, vol. 59, no. January, pp. 84–102 (2020). https://doi.org/10.1016/j.inffus.2020.01.006

  25. Renggli, C., Rimanic, L., Gürel, N.M., Karlaš, B., Wu, W., Zhang, C.: A Data Quality-Driven View of MLOps, no. 1, pp. 1–12 (2021). http://arxiv.org/abs/2102.07750

  26. Kerzel, U.: Enterprise AI canvas integrating artificial intelligence into business. Appl. Artif. Intell. 35(1), 1–12 (2021). https://doi.org/10.1080/08839514.2020.1826146

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dubai, UAE

    Tarek Kaddoumi

  2. Aarhus University, Birk Centerpark 15, 7400, Herning, Denmark

    Torben Tambo

Authors
  1. Tarek Kaddoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Torben Tambo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Torben Tambo .

Editor information

Editors and Affiliations

  1. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kaddoumi, T., Tambo, T. (2022). Democratizing Enterprise AI Success Factors and Challenges: A Systematic Literature Review and a Proposed Framework. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-95947-0_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95946-3

  • Online ISBN: 978-3-030-95947-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation