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. This mismatch shows up especially often for consulting firms, where fit depends as much on scope and expertise match as on budget.
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](/testing-an-ai-agent-before-launch/).