Research on redesigning organisational workflows around AI, including operating model change, role redesign and measurable productivity outcomes.
AI workflow redesign is the deliberate restructuring of organisational processes around AI capability — rethinking who does what, when and how when AI takes on tasks previously performed by humans or manual systems. It is distinct from AI automation, which tends to replace specific steps within an existing process. Workflow redesign asks a more fundamental question: given that AI can perform task X, what should the overall process now look like? Effective AI workflow redesign produces operating models where AI and human capability are combined at the appropriate points — with AI handling pattern recognition, data processing and decision support at scale, and humans handling judgement, accountability and contextual interpretation.
AI workflow redesign is fundamentally an operating model intervention. It changes roles, reporting lines, performance metrics and the skills required to do work — making it a significant change management challenge as well as a technical one. Strategen AI's research examines the change management approaches most effective in AI workflow redesign contexts, including how to maintain productivity during transition, how to design workforce development programmes that match the pace of change, and how to sequence workflow changes to manage risk. Research covers role redesign methodologies, workflow mapping, performance measurement for AI-augmented processes and the organisational design choices that determine whether redesigned workflows become self-sustaining.
The APIG framework is the methodological foundation for Strategen AI's workflow redesign practice. The Actors dimension maps how roles and capabilities must change; the Practices dimension designs new process steps, decision points and human-AI interfaces; the Infrastructure dimension ensures data and system architecture supports the redesigned workflow; and the Governance dimension embeds accountability, audit and oversight mechanisms the new workflow requires. Research in this hub provides the empirical evidence base for APIG-led workflow redesign, examining what works across sectors and use-case types.
AI shifts destination management from reactive promotion to predictive intelligence, governance, and proactive visitor stewardship.
Category: Destination Intelligence | Year:
Australia needs sovereign AI infrastructure and models to reduce foreign dependency and retain strategic value.
Category: AI Sovereignty, National Capability | Year:
Agency as a Service reframes AI adoption as outsourcing organisational judgement, decision rights, and accountability.
Category: AI Governance | Year:
AI leadership appointments are governance decisions requiring better mandates, panels, and assessment criteria.
Category: Recruitment, AI Strategy, Operating Model | Year:
Agentic AI exhausts PPT by redistributing agency across humans, systems, practices, infrastructure and governance.
Category: AI Transformation Frameworks, Sociotechnical AI Governance | Year: