A practical guide to implementing AI in collections

Everyone wants to adopt AI in collections. Few teams have a clear answer for where to start, how fast to move, or how to introduce new capabilities without adding risk to one of the most heavily scrutinized parts of the business.

This guide breaks down the approach that's actually working: start with a defined operational problem, run a contained pilot, build governance in from day one, and expand only when the results, and the organization, are ready.

What's inside

• The four stages of AI maturity in collections, from ad hoc experiments to fully autonomous processes, and what matters most at each stage
• How to identify a high value use case by naming the operational problem first, not the technology
• What to align on internally, business owner, success metrics, compliance sign off, and data readiness, before a single vendor conversation
• How to scope a pilot that actually answers a question instead of producing a vague sense that a tool "seems useful"
• The five governance principles that should be built into any AI pilot from the start, including grounding, human accountability, and audit trails
• A concrete checklist for knowing when a pilot is ready to expand, and why extending it is often the smarter call
• A pre-launch checklist to run before greenlighting any new AI use case