Qualifying criteria that don't match what sales actually needs
Built without asking the sales team what actually predicts a good fit — the agent ends up scoring leads on criteria that sound reasonable but don't match reality. See What CRM Fields Should a Service Business Collect for how to define this correctly upfront.
No defined fallback for uncertainty
When a lead doesn't clearly fit either "qualified" or "not a fit," a poorly configured agent either force-fits it or drops it silently. A well-built one flags it for human review instead — see When Should AI Hand a Lead to a Human.
Vague qualifying questions
"Tell me about your project" produces vague answers that are hard to score consistently. Specific questions (timeline, budget band, business type) qualify far more reliably — see the Example exchange on the AI Sales Manager page for what a specific question sequence actually looks like.
Treating a bad fit as a dead end instead of a clear no
A lead that isn't a fit should get an honest, clear next step — not silence and not a forced sales push. See AI Sales Manager's own FAQ on this exact point.
Questions
Can these mistakes be fixed after launch?+
Yes — qualification logic can be refined based on real data, which is exactly what the post-launch monitoring phase in Revenue System is for.
Is this specific to LATYNEX's setups or general?+
General — these are common failure patterns across AI qualification setups, not unique to any one vendor.
How do you avoid these in your own builds?+
Testing against real, messy phrasing before launch — see How to Test an AI Agent Before Launch.