Process Ownership in the Context of Process Intelligence:  A Systematic Literature Review

Riyan Ainur Rahman; Nur Aeni Hidayah

doi:10.30871/jaic.v10i5.13871

Authors

Riyan Ainur Rahman UIN Syarif HIdayatullah Jakarta
Nur Aeni Hidayah UIN Syarif Hidayatullah Jakarta

DOI:

https://doi.org/10.30871/jaic.v10i5.13871

Keywords:

Process Ownership, Process Intelligence, Process Mining, Business Process Management, Artificial Intelligence

Abstract

Business Process Management (BPM) has been transformed by Process Intelligence, encompassing process mining, business process analytics, and Artificial Intelligence (AI). While the technological dimensions of Process Intelligence are well studied, the organizational implications, particularly the evolving roles, responsibilities, and competencies of process owners, remain fragmented and underexplored. Prior systematic reviews have addressed either process ownership in conventional BPM or the technical aspects of Process Intelligence in isolation, leaving a gap in understanding how Process Intelligence capabilities reshape process ownership as an accountability structure. To address this gap, this study conducts a Systematic Literature Review following the PRISMA 2020 guidelines, guided by four research questions covering publication characteristics, role evolution, conceptual themes, and research gaps. From an initial pool of 1,408 publications across two databases, 40 studies met the selection and quality criteria and were included in the final synthesis. A three-stage thematic coding process identified four overarching themes: data-driven process governance, human–AI collaboration, continuous process improvement, and organizational capability. Findings indicate a shift from technology-oriented discussions toward governance-oriented perspectives, with process owners evolving from administrative coordinators into data-driven decision makers operating within a Human-in-the-Loop framework. Three research gaps were identified: the absence of integrated conceptual frameworks, limited longitudinal and cross-sector empirical evidence, and underdeveloped governance mechanisms for AI-assisted decision-making. This study contributes a theoretically grounded synthesis of the process ownership–Process Intelligence nexus, offering conceptual insights for researchers and practical guidance for organizations implementing data-driven BPM.

Downloads

Download data is not yet available.

References

[1] H. Lizano-Mora, P. R. Palos-Sanchez, And M. Aguayo-Camacho, “The Evolution Of Business Process Management: A Bibliometric Analysis,” Ieee Access, Vol. 9, Pp. 51088–51105, Jan. 2021, Doi: 10.1109/Access.2021.3066340.

[2] W. Van Der Aalst And J. Carmona, Process Mining Handbook. 2022. Doi: 10.1007/978-3-031-08848-3.

[3] M. Dumas Et Al., “Ai-Augmented Business Process Management Systems: A Research Manifesto,” Acm Transactions On Management Information Systems, Vol. 14, No. 1, Pp. 1–19, Jan. 2023, Doi: 10.1145/3576047.

[4] S. Weinzierl, S. Zilker, S. Dunzer, And M. Matzner, “Machine Learning In Business Process Management: A Systematic Literature Review,” Expert Systems With Applications, Vol. 253, P. 124181, May 2024, Doi: 10.1016/J.Eswa.2024.124181.

[5] M. Abbasi Et Al., “A Review Of Ai And Machine Learning Contribution In Business Process Management (Process Enhancement And Process Improvement Approaches),” Business Process Management Journal, Vol. 31, No. 4, Pp. 1414–1452, Nov. 2024, Doi: 10.1108/Bpmj-07-2024-0555.

[6] A. Zebec And M. I. Štemberger, “Creating Ai Business Value Through Bpm Capabilities,” Business Process Management Journal, Vol. 30, No. 8, Pp. 1–26, Apr. 2024, Doi: 10.1108/Bpmj-07-2023-0566.

[7] M. Kohlbacher And S. Gruenwald, “Process Ownership, Process Performance Measurement And Firm Performance,” International Journal Of Productivity And Performance Management, Vol. 60, No. 7, Pp. 709–720, Sep. 2011, Doi: 10.1108/17410401111167799.

[8] K. B. Danilova, “Process Owners In Business Process Management: A Systematic Literature Review,” Business Process Management Journal, Vol. 25, No. 6, Pp. 1377–1412, Dec. 2018, Doi: 10.1108/Bpmj-05-2017-0123.

[9] M. J. Page Et Al., “The Prisma 2020 Statement: An Updated Guideline For Reporting Systematic Reviews,” Bmj, Vol. 372, P. N71, Mar. 2021, Doi: 10.1136/Bmj.N71.

[10] M. Imran, S. Hamid, And M. A. Ismail, “Advancing Process Audits With Process Mining: A Systematic Review Of Trends, Challenges, And Opportunities,” Ieee Access, Vol. 11, Pp. 68340–68357, Jan. 2023, Doi: 10.1109/Access.2023.3292117.

[11] A. Mamudu, W. Bandara, M. T. Wynn, And S. J. J. Leemans, “Process Mining Success Factors And Their Interrelationships,” Business & Information Systems Engineering, Vol. 67, No. 3, Pp. 389–408, Mar. 2024, Doi: 10.1007/S12599-024-00860-Z.

[12] L. Marcus, S. J. Schmid, F. Friedrich, M. Röglinger, And P. Grindemann, “Navigating The Landscape Of Organizational Process Mining Setups,” Business & Information Systems Engineering, Vol. 68, No. 1, Pp. 137–158, Nov. 2024, Doi: 10.1007/S12599-024-00908-0.

[13] N. Martin Et Al., “Opportunities And Challenges For Process Mining In Organizations: Results Of A Delphi Study,” Business & Information Systems Engineering, Vol. 63, No. 5, Pp. 511–527, Oct. 2021, Doi: 10.1007/S12599-021-00720-0.

[14] T. Grisold, J. V. Brocke, S. Gross, J. Mendling, M. Röglinger, And K. Stelzl, “Digital Innovation And Business Process Management: Opportunities And Challenges As Perceived By Practitioners,” Communications Of The Association For Information Systems, Vol. 49, No. 1, Pp. 556–571, Jan. 2021, Doi: 10.17705/1cais.04927.

[15] G. D. Kerpedzhiev, U. M. König, M. Röglinger, And M. Rosemann, “An Exploration Into Future Business Process Management Capabilities In View Of Digitalization,” Business & Information Systems Engineering, Vol. 63, No. 2, Pp. 83–96, Mar. 2020, Doi: 10.1007/S12599-020-00637-0.

[16] A. Mamudu, W. Bandara, S. J. J. Leemans, And M. T. Wynn, “A Process Mining Impacts Framework,” Business Process Management Journal, Vol. 29, No. 3, Pp. 690–709, Feb. 2023, Doi: 10.1108/Bpmj-09-2022-0453.

[17] J. V. Brocke, M. Jans, J. Mendling, And H. A. Reijers, “A Five-Level Framework For Research On Process Mining,” Business & Information Systems Engineering, Vol. 63, No. 5, Pp. 483–490, Sep. 2021, Doi: 10.1007/S12599-021-00718-8.

[18] K. Lazaros, A. G. Vrahatis, And S. Kotsiantis, “Human-In-The-Loop Artificial Intelligence: A Systematic Review Of Concepts, Methods, And Applications,” Entropy, Vol. 28, No. 4, P. 377, Mar. 2026, Doi: 10.3390/E28040377.

[19] J. Rott, M. Böhm, And H. Krcmar, “Laying The Ground For Future Cross-Organizational Process Mining Research And Application: A Literature Review,” Business Process Management Journal, Vol. 30, No. 8, Pp. 144–206, Jun. 2024, Doi: 10.1108/Bpmj-04-2023-0296.

[20] M. R. Da Silva, N. De Souza Mayrink Resende, And E. P. Da Silva-Dias, “Connections Between Governance And Business Process Management – Bpm,” International Journal For Innovation Education And Research, Vol. 10, No. 4, Pp. 221–235, Apr. 2022, Doi: 10.31686/Ijier.Vol10.Iss4.3733.

[21] C. A. Kesici, N. Ozkan, S. Taşkesenlioglu, And T. G. Erdogan, “A Systematic Literature Review Of Studies Comparing Process Mining Tools,” International Journal Of Information Technology And Computer Science, Vol. 14, No. 5, Pp. 1–14, Oct. 2022, Doi: 10.5815/Ijitcs.2022.05.01.

[22] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, Jan. 2006, doi: 10.1191/1478088706qp063oa.

[23] J. Thomas and A. Harden, “Methods for the thematic synthesis of qualitative research in systematic reviews,” BMC Medical Research Methodology, vol. 8, no. 1, p. 45, Jul. 2008, doi: 10.1186/1471-2288-8-45.

[24] D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, and P. Group, “Preferred reporting items for Systematic Reviews and Meta-Analyses: the PRISMA statement,” PLoS Medicine, vol. 6, no. 7, p. e1000097, Jul. 2009, doi: 10.1371/journal.pmed.1000097.

[25] W. Van Der Aalst et al., “Process Mining manifesto,” Lecture Notes in Business Information Processing, pp. 169–194, Jan. 2012, doi: 10.1007/978-3-642-28108-2_19.

[26] M. H. Jarrahi, “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making,” Business Horizons, vol. 61, no. 4, pp. 577–586, Apr. 2018, doi: 10.1016/j.bushor.2018.03.007.

[27] N. Diakopoulos, “Accountability in algorithmic decision making,” Communications of the ACM, vol. 59, no. 2, pp. 56–62, Jan. 2016, doi: 10.1145/2844110.

[28] D. Doneda and V. A. F. Almeida, “What is algorithm governance?,” IEEE Internet Computing, vol. 20, no. 4, pp. 60–63, Jul. 2016, doi: 10.1109/mic.2016.79.

[29] B. C. Cheong, “Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making,” Frontiers in Human Dynamics, vol. 6, Jul. 2024, doi: 10.3389/fhumd.2024.1421273.

[30] O. B. D. S. Moderno, A. C. Braz, and P. T. De Souza Nascimento, “Robotic process automation and artificial intelligence capabilities driving digital strategy: a resource-based view,” Business Process Management Journal, vol. 30, no. 1, pp. 105–134, Nov. 2023, doi: 10.1108/bpmj-08-2022-0409.

[31] Y. L. Antonucci, A. Fortune, and M. Kirchmer, “An examination of associations between business process management capabilities and the benefits of digitalization: all capabilities are not equal,” Business Process Management Journal, vol. 27, no. 1, pp. 124–144, Jul. 2020, doi: 10.1108/bpmj-02-2020-0079.

[32] M. Froger, F. Bénaben, S. Truptil, and N. Boissel-Dallier, “A non-linear business process management maturity framework to apprehend future challenges,” International Journal of Information Management, vol. 49, pp. 290–300, Jun. 2019, doi: 10.1016/j.ijinfomgt.2019.05.013.

[33] E. Gökalp and V. Martinez, “Digital transformation capability maturity model enabling the assessment of industrial manufacturers,” Computers in Industry, vol. 132, p. 103522, Jul. 2021, doi: 10.1016/j.compind.2021.103522.

[34] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” Journal of Business Research, vol. 133, pp. 285–296, May 2021, doi: 10.1016/j.jbusres.2021.04.070.

[35] A. Batool, D. Zowghi, and M. Bano, “AI governance: a systematic literature review,” AI And Ethics, vol. 5, no. 3, pp. 3265–3279, Jan. 2025, doi: 10.1007/s43681-024-00653-w.

[36] A. M. Barbala, G. K. Hanssen, and T. Sporsem, “Towards a common data-driven culture: A longitudinal study of the tensions and emerging solutions involved in becoming data-driven in a large public sector organization,” Journal of Systems and Software, vol. 218, p. 112185, Aug. 2024, doi: 10.1016/j.jss.2024.112185.

[37] M.-T. Huynh, “Individual Data-Driven mindset and Decision-Making performance: the mediating roles of effort and persistence,” Information Systems Frontiers, vol. 27, no. 6, pp. 2511–2538, Oct. 2025, doi: 10.1007/s10796-025-10647-6.

[38] A. E. Marquez-Chamorro, M. Resinas, and A. Ruiz-Cortes, “Predictive Monitoring of Business Processes: a survey,” IEEE Transactions on Services Computing, vol. 11, no. 6, pp. 962–977, Nov. 2017, doi: 10.1109/tsc.2017.2772256.

[39] W. Kratsch, J. Manderscheid, M. Röglinger, and J. Seyfried, “Machine learning in Business Process Monitoring: A comparison of deep learning and classical approaches used for outcome prediction,” Business & Information Systems Engineering, vol. 63, no. 3, pp. 261–276, Apr. 2020, doi: 10.1007/s12599-020-00645-0.

[40] M. Rosemann, J. V. Brocke, A. Van Looy, and F. Santoro, “Business process management in the age of AI – three essential drifts,” Information Systems and e-Business Management, vol. 22, no. 3, pp. 415–429, Sep. 2024, doi: 10.1007/s10257-024-00689-9.