7 questions to ask before reading an AI talent-flow chart
Understand where talent-flow counts come from, what the ribbons mean, and which conclusions the data cannot support.
1. What is a move?
Check whether the chart counts confirmed announcements, inferred transitions, or all historical employer connections. Transfer separates curated reports from dated transitions inferred from collected careers. These should not be combined and described as confirmed hires.
2. Which people were collected?
An AI-team filter is different from a whole-company directory. The result depends on exact employer matches, role evidence and provider coverage. Read the scope before comparing two labs.
3. Which date controls the filter?
A reporting date, start month and end month are not interchangeable. For recent movement, make sure the filter uses the date that matches the question you are asking.
4. Does ribbon width mean headcount?
Ribbon width describes the number of recorded transitions in the selected sample. It does not measure total company headcount, recruiting spend, productivity or talent quality.
5. What is inside Others?
Grouping small categories makes a chart readable, but it can hide a wide mix of companies. Inspect the underlying connections before treating Others as one employer or one consistent kind of move.
6. Is location an actual relocation?
A profile's listed city is not proof that the person relocated when changing jobs. Remote work and outdated profiles complicate that inference. Location reports describe the listed locations associated with collected profiles.
7. Can I inspect the people?
An aggregate becomes more useful when it leads back to the individual records. Open the company, person and source links. If the evidence is incomplete, retain the uncertainty instead of inferring a precise industry trend.