How to build a SaaS dunning management with AI

There are roughly 15–25 dedicated dunning management platforms (Churn Buster, Recurly's dunning, Stripe Billing, Zuora, etc.), plus another 50+ payment recovery tools that touch dunning as a secondary feature. The common failure pattern: most dunning tools are reactive rule engines that require manual tuning per merchant—they don't learn or adapt. The actual gap isn't dunning logic; it's personalized, intent-aware recovery. Most existing tools blast generic retry sequences and retry strategies without understanding *why* a card declined or *when* a customer is most likely to re-engage. Here's where AI wins: predictive modeling on churn signals, natural language payment failure explanations, dynamic retry sequencing based on customer lifetime value and behavior patterns, and genuinely smart email/SMS timing that doesn't feel like spam. A new vibecoder should focus on two things: (1) making AI-driven retry optimization transparent and explainable to finance teams (they need to audit and trust it), and (2) integrating deeply with one payment processor at a time—go vertical on Stripe first, nail it, then expand. The edge case that nobody owns well: micro-recovery campaigns targeting specific failure modes (e.g., expired cards vs. fraud blocks vs. insufficient funds) with personalized messaging, not one-size-fits-all flows.