You've rolled out AI tools, opened up access and invested in training. Today's AI Working Lab takes on the harder call: which AI initiatives are improving the work enough to scale, and which to change or stop.
We're working a realistic case together. AI helped one team deliver faster, but the organization still has to decide where to invest. The question on the table: what would need to be true for that team's success to carry over to another?
Three questions guide the work:
- What's holding people back? Inconsistent workflows, low trust in AI outputs, or something else.
- What evidence justifies expanding? Faster turnaround, better quality, less rework.
- Where do you invest when time and resources are limited? And what are you willing to give up?
The goal: a practical way to assess whether an AI initiative is improving the work, and the evidence to decide whether to scale it, change it, or stop it.
Takeaways from the community will follow after the Lab. Weighing an AI initiative of your own? Add it to the pre-chat + post-chat thread on the Idea Exchange.
What we learn in these Labs feeds In Search of Organizational Excellence, ELE's fourth book. 📌 In Search of Organizational Excellence — 4th Book Project 
Have an AI decision your team is stuck on? Submit it as a talent challenge at Shape the Calendar.
