The webinar, hosted by Mike Gibb of AccountsRecovery.net and sponsored by Connect International, explored how collector compensation must evolve in an AI, portal, and omnichannel world. With routine payments increasingly handled through digital channels and self-service portals, panelists emphasized that traditional compensation models based on calls made and dollars collected are no longer sufficient. Instead, compensation structures must reward quality outcomes, adaptability, and behaviors that align with digital-first strategies.
Aaron Newell highlighted the shift to pooled environments to foster teamwork, noting, “Everybody wants paid for anything they may have touched… we changed to an office pool to foster a big team environment.” Meg Scotty described her agency’s evolving quota system, adjusted monthly using AI insights, and stressed the importance of aligning compensation with skill development. Jake Richards emphasized that digital funnels leave collectors with more complex cases, requiring metrics that measure quality over quantity. Rick Bonitzer underscored the value of “pre-scheduled revenue” and explained how agents directing consumers to portals still receive commission on future payments, ensuring buy-in.
Ken Rubin of Connect International added that nearshore agents are also adapting to complex calls in the AI-driven environment, reflecting the industry-wide shift toward technology-enabled collections.
🧠 Key Takeaways:
- Reevaluate metrics: Move beyond call counts and raw collections to focus on dispute resolution, conversion rates, and sustainable payment plans.
- Align incentives with digital adoption: Ensure compensation encourages agents to drive consumers toward self-service portals and auto-pay options.
- Introduce flexible structures: Consider pooled environments and monthly quota adjustments informed by AI to balance fairness and adaptability.
This session reinforced that compensation models must evolve to reflect the realities of digital-first collections, rewarding agents for quality, adaptability, and collaboration in an AI-augmented environment.
