Using AI for Process Analysis and Workflow Improvement

AI is quickly becoming a behind-the-scenes accelerator for how work actually gets done. This workshop takes a practical look at how AI can support process analysis, workflow design, documentation, and optimization across teams.

In this workshop, participants will have the opportunity to examine existing workflows, identify inefficiencies, and use AI to propose improvements, draft process documentation, and support decision-making. Emphasis is placed on human judgment, transparency, and responsible use rather than automation-for-automation¿s-sake.

The workshop also addresses governance considerations, including data sensitivity, validation of AI outputs, and aligning AI-assisted improvements with organizational standards and stakeholder expectations.

Objectives

Participants will have the opportunity to learn how to:
1. Describe how AI can support process analysis and workflow improvement
2. Identify high-friction steps, bottlenecks, and manual effort within existing workflows
3. Use structured prompts to analyze, summarize, and map processes
4. Apply AI to draft process documentation, SOPs, and workflow diagrams (text-based)
5. Evaluate AI-generated suggestions for feasibility, risk, and accuracy
6. Compare AI-supported workflows with current-state processes
7. Identify low-risk, high-impact opportunities for AI-assisted improvement
8. Apply basic guardrails for responsible AI use in operational contexts
9. Collaborate more effectively with stakeholders using AI-supported artifacts
10. Outline a practical next-step plan for improving one real workflow
 

Topics Covered

Session 1 Agenda

Module 1: AI and Process Thinking
- AI's role in process improvement, continuous improvement, and operations
- Why AI surfaces symptoms faster than root causes
- Structuring workflows for meaningful AI input
- Prompt patterns for:
  - Step analysis
  - Bottleneck detection
  - Handoff evaluation

Module 2: Current-State Analysis with AI
- Describing real workflows clearly and consistently
- Using AI to:
  - Identify inefficiencies
  - Surface hidden assumptions
  - Highlight risk and rework
- Validating AI output with human experience
- Refining prompts for better insights
- Extended Activity: Analyze a real workflow from your own work or a provided case

Module 3: Designing Future-State Workflows
- Using AI to generate multiple improvement options
- Comparing tradeoffs (speed, risk, effort, impact)
- Choosing realistic improvements over "perfect" ones
- Drafting future-state workflow summaries

Session 2 Agenda

Module 4: Workflow Documentation and Communication
- Using AI to draft:
  - SOPs
  - Process guides
  - Stakeholder summaries
- Improving clarity, consistency, and usability
- Making AI-generated documentation review-ready
- Aligning documentation with organizational standards
- Activity: Create a polished workflow artifact suitable for sharing

Module 5: Automation Awareness and Risk Management
- Improvement versus automation
- Where AI-assisted automation makes sense (and where it doesn¿t)
- Common failure patterns in AI-suggested automation
- Keeping accountability clear

Module 6: Governance and Implementation Planning
- Privacy and data protection considerations
- Documenting AI-assisted decisions
- Establishing lightweight guardrails
- Aligning with leadership, IT, and compliance
- Defining success measures and pilot criteria
- Capstone Activity: Develop a simple, defensible plan for improving one workflow using AI support