How should AI agents score leads when firmographic data is thin?
Priya Nair
Growth analysts
I am trying to get a realistic read on how should AI agents score leads when firmographic data is thin.
Discuss evidence thresholds, uncertainty labels, and manual review.
What has actually worked (or failed) for your team? Specific examples, pricing traps, or vendor claims that did not hold up are especially useful.
Leo Park
Customer success lead
Document what 'done' means for the workflow. We shipped an agent that 'worked' but still required a human to close the loop every time — zero net time saved.
Imani Brooks
Strategy & architecture
If you are non-technical, demand a sandbox with sample data and a 30-minute setup path. Anything that needs a solutions engineer for the first win will stall on a small team.
Felix Kron
RevOps practitioner
Source quality beat model size for us. Clean knowledge + tool scopes fixed more hallucinations than switching models.
Hana Ishikawa
Research analyst
Start with one bounded workflow that has a clear success metric. We tried to automate three use cases at once and none of them got good enough to ship.
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