Faculty/School

Faculty of Science

School of Information Systems

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Primary Supervisor

Dr Rehan Syed
Position
Senior Lecturer in Information Systems (Process Science)
Division / Faculty
Faculty of Science

Overview

As AI systems increasingly make or heavily influence process-level decisions within organisations, a critical misalignment is emerging between traditional BPM governance structures and the opacity, autonomy, and adaptability of AI agents. BPM has historically governed process decisions through clearly defined ownership, version control, audit trails, and human accountability. However, when AI-driven automation handles decisions at speed and scale, these governance mechanisms often lag or fail entirely. Research has confirmed that many organisations cannot even produce a complete AI audit trail for their deployed systems. This governance gap creates conditions for unaccountable automation — processes that operate within organisations but outside meaningful human oversight. The EU AI Act, ISO/IEC 42001, and Australia's National AI Plan (published December 2025) each attempt to impose accountability frameworks, yet significant tensions remain between BPM governance maturity and AI governance imperatives.

Research Question: How do existing BPM governance frameworks fail to accommodate the accountability and explainability demands of AI-driven process automation, and what theoretical and practical mechanisms can bridge this gap?

Research engagement

A critical realist mixed-methods design is well-suited to this topic; exploring the context–mechanism–outcome (CMO) configurations that produce governance failures will require a deep understanding of the  literature and conceptualisation of key configurations.

Research activities

  • Systematic literature review mapping the intersection of BPM governance and AI governance frameworks (ISO/IEC 42001, EU AI Act, NIST AI RMF)
  • Multiple case studies across regulated industries (e.g., finance, healthcare, government), examining audit trail practices, process ownership models, and accountability escalations
  • Expert interviews with BPM leads, AI governance officers, and internal auditors to surface structural and institutional barriers
  • Documentary analysis of governance policies, AI incident logs, and process change records

Research skills

Critical analysis, writing, literature search and management of data, innovative thinking and conceptualisation.

Outcomes

  • A taxonomy of governance gaps at the intersection of BPM and AI governance
  • A CMO framework (Critical Realist) explaining why and how governance failures occur in AI-embedded BPM environments
  • A prescriptive framework for "dual transparency" in BPM — operationalising both technical explainability and operational auditability
  • A peer-reviewed journal article targeted at a Q1 journal or BPM Conference/AIS Conference  (ACIS, ECIS, ICIS, PACIS)

Skills and experience

Must have a good understanding of process modelling (IAB203). diligence, creative thinking, and must be well motivated to enjoy the research topic,

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Y Block, Virtual

Additional information

Student researcher will receive hands-on training on research methodologies and qualitative data analysis.

Keywords

Contact

Dr Rehan Syed

0456036386

r.syed@qut.edu.au