Stories Not Scores: What Ratings and Transcripts Can't Tell You

Wednesday, September 30, 2026 1:00 PM to 1:45 PM · 45 min. (America/New_York)
Leveraging AI and Emerging Technologies

Information

There are three ways to learn how someone shows up at work: score them, watch the work, or ask the people around them. Until AI, only the first one scaled. Now all three scale, and each one answers something different.

Ratings are built for comparison: many people, many dimensions, tracked over time. Observation establishes what someone actually did in a call, a meeting, or a document. Both are useful. Neither tells you how someone came across. You can read every transcript and still not know what the people in the room walked away thinking. For that, you have to ask them.

This session explores what each source can and cannot tell you, and how to match the source to the development question in front of you. We'll dig into cases where a program leaned on a source that couldn’t produce the awareness it promised, including the common one: trying to change how a leader lands with their team using rating data. You’ll leave with a one-page guide for matching feedback sources to development questions, and a fast way to audit the programs you already run.
Learning Objective 1:
Describe what ratings, observation, and perception-based feedback can each tell you about how someone shows up at work, and what each one misses.
Learning Objective 2:
Identify which source your current feedback and development programs rely on, and whether it can produce the awareness the program is meant to create.
Learning Objective 3:
Match common development questions to the source suited to answer them, using a one-page reference you can apply back at the office.
Session Type
Solution Session

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