By the end of this course, you will: 

• Apply Systems Thinking to examine workflows as interconnected systems of people, information, decisions, technology, controls and outcomes.

• Identify workflow opportunities for AI by distinguishing between isolated task automation and end-to-end workflow redesign.

• Diagnose workflow friction such as duplicated effort, fragmented information, delayed decisions, rework and recurring workarounds.

• Use Generative AI as an innovation partner to interpret evidence, compare methods, generate workflow alternatives, challenge assumptions and stress-test proposed solutions.

• Design AI-enabled future-state workflows that specifies AI and human responsibilities, information requirements, decision ownership, controls, approvals and exception handling.

• Develop an AI-Enabled Workflow Innovation Blueprint through a hands-on, cross-functional simulation, translating analysis and design decisions into a practical workflow proposal.

Through this course, you will be empowered with:

• Approach AI adoption as an organisational design challenge rather than a software deployment exercise. 

• Identify where AI can reduce friction, improve visibility, support judgement and strengthen coordination across functions. 

• Prevent automation from reproducing existing workflow weaknesses, unclear ownership and fragmented data. 

• Establish clearer boundaries between AI assistance, human judgement, management accountability and formal approval. 

• Design more resilient workflows that respond effectively to changing information, exceptions and operational disruptions. 

• Engage colleagues across functions in the joint design of AI-enabled work. 

• Translate an AI concept into a structured and explainable workflow proposal that can support further experimentation, piloting and implementation. 

1. Understand AI-Enabled Workflows as a System

• Why isolated AI automation may fail to improve overall performance

• Viewing workflows as a system of inputs, information, handoffs, decisions, controls and outcomes

• Distinguishing workflow friction, recurring workarounds, bottlenecks and downstream consequences

• Identifying the difference between digitising the existing process and innovating the way work is organised 

2. Frame AI-enabled innovation using guiding ideas

• Clarifying the purpose and intended value of the workflow innovation 

• Formulating guiding ideas such as one trusted source of information, prevention at source, exception-driven workflow and human accountability with AI assistance 

• Defining design principles and non-negotiable conditions 

• Aligning workflow innovation with customer value, organisational purpose and operational outcomes 

• Establishing what the innovation is not intended to automate or replace 

3. Apply theories, methods and tools with Generative AI

• Use Generative AI to analyse the current workflow, compare innovation methods and identify improvement opportunities 

• Map workflow handoffs, stakeholder requirements, information dependencies and decision rights 

• Diagnose root causes, workflow friction, recurring workarounds and potential failure points 

• Apply methods such as human-in-the-loop design, validation, exception handling and pre-mortem testing 

• Evaluate AI-generated recommendations through human review, modification and rejection

Operational Case Simulation:

Participants will work on a cross-functional operational case using Generative AI and selected innovation methods to identify workflow issues, requirements and opportunities for improvement.

4. Design innovations in workflow infrastructure

• Design the future product or service journey from initiation to completion 

• Define what AI should assist with—and where human judgement, approval and accountability must remain 

• Creating, validation points, controls, escalation triggers and exception pathways 

• Build traceability, auditability and appropriate decision records 

• Considering practical implementation constraints, platform limitations and adoption risks 

Operational Case Simulation:

Participants will design alternative workflows ranging from low-change to more transformative options. Teams will compare the alternatives against accuracy, speed, cost, implementation difficulty, accountability and risk before selecting or combining a preferred design.

Note: No programming experience is required. Participants should have basic familiarity with a mainstream Generative AI application and bring a laptop for the class.