Teams have adopted AI to write user stories, summarize meetings, and draft code. Yet many leaders are asking a hard question: why hasn't delivery sped up? The answer lies in the value stream. When most of a work item's life is spent waiting, making the working steps faster barely moves the needle.

In this interactive workshop, we'll use Value Stream Management to find where AI genuinely changes outcomes. You'll place AI interventions on a realistic value stream map, calculate their effect on end-to-end flow time, and see why targeting wait states, handoffs, and decision delays often beats automating tasks. We'll also use flow metrics as an honest test of AI ROI and examine new risks, such as review backlogs created by AI-generated work.

You'll leave with a practical framework for deciding where AI belongs in a value stream, and the numbers to defend that decision.

Additional Information for Evaluators:
Why this session. Most AI content for BAs and PMs focuses on personal productivity with prompts and tools. This session addresses the question organizations are now facing after adoption: whether AI is improving delivery or just activity or 'busyness'. It gives practitioners a measurable, defensible way to direct AI investment.

The idea for this was borne from the IIBA Columbus September session. The participants there noted they are leaning in more to VSM, while also being measured on their AI tool usage. It sounded as though the community could use guidance on both VSMs, as well as how to incorporate AI into their daily routines better.

I am submitting 2 distinct workshop ideas that are designed to be delivered independently; however, together they could be a great package for anyone seeking a deeper dive into VSM, flow and practical applications of AI.

Learning Objectives:
By the end of this session, participants will be able to:
1. Explain why reducing active work time has limited effect on flow time when wait time dominates.
2. Calculate the projected change in flow time and flow efficiency from a proposed AI intervention.
3. Compare AI interventions that target active work with those that target wait states and handoffs.
4. Assess an AI investment's impact using flow metrics rather than activity or output measures.
5. Design a prioritized set of AI interventions for a value stream, including controls for the risks they introduce.