Agile transformations don’t fail because of bad frameworks, bad people, or whatever excuse is trending on social media. They fail because we make decisions with incomplete, incorrect, or overwhelming amounts of data. In response, we fill in the blanks with assumptions and appoint an ‘Agile expert’ to “fix” things—often with little connection to what’s actually happening in the organization.

From change readiness to ongoing feedback, we fall into the trap of meaningless metrics—not because they’re useful, but because they’re easy to measure and make pretty in a status report. Meanwhile, the real insights—the patterns, sentiment, and system-level feedback that truly matter—get buried under noise.

Gimmicks aside, the real power of AI in Agile isn’t about building an AI coach or chatbot. It’s about having an extra brain that can sift through vast amounts of feedback, sentiment, and data patterns in ways no human ever could.

In this session, we’ll explore:

- Sentiment Analysis – How are people actually experiencing change?
- Pattern Recognition – What past feedback patterns can we learn from?
- Separating Signal from Noise – AI processes the data, but humans make the decisions.
- Creating Safety – With AI as a co-pilot, real, meaningful data can no longer be suppressed by change and project managers.

Context matters. So does safety. Employees today see through corporate spin faster than ever. AI gives us the ability to establish continual feedback loops—from readiness to engagement to employee-driven insights—providing a clearer, real-time picture of how transformation is actually unfolding.
For change practitioners, our role isn’t to blindly follow AI insights—it’s to use them to continually make sense of our context so we can navigate change in a way that actually works.