Most talent decisions feel urgent. And when the stakes are high, urgency can push leaders into hasty, reactive judgment before the right signals are fully understood.

This session introduces a structured reframe for people who make decisions about people: separate the moment you collect workforce signals from the moment you act on them. That sequence — signal before decision — is the core idea behind the Workforce Signal Detector, a practical decision-support model designed to help leaders slow down, notice what is changing, and make better-informed talent decisions.

Think of it as a smoke detector for your talent environment: always scanning, neutral in what it detects, and designed to alert you before the room fills with smoke.

In this 60-minute session, participants will see the model demonstrated through a live talent scenario. We will explore how external workforce data sources such as Lightcast, BLS, WisConomy, and WARN can be used as early-warning signals, and how AI can help flag meaningful workforce changes before leaders are forced into reactive decisions.

This introductory session is designed for HR, talent, learning, workforce, and business leaders who want a more disciplined way to make people decisions. Participants will evaluate the model, discuss its relevance to their own work, and consider whether a deeper follow-up design charrette would be useful for a specific talent or workforce challenge.

If you have ever made a talent decision that looked right in the room but wrong in the rearview mirror, this session is for you.

What happens next? This post captures our live framework. In a few days, we will update this page with the real-time breakthroughs, questions, and insights shared by our executive peers during the conference dialogue. Stay tuned!

Introduction to Data-Driven People Decisions: A Design Charrette Interest Session

Login