How to Take Your First Steps with AI in Debt Collection

Innovation doesn’t happen all at once—it happens step by step.

When it comes to operationalizing AI in collections, the first move is often the hardest. Many organizations talk about AI's potential, but fewer have a clear, actionable roadmap for getting started. Based on the insights we shared in "The AI Gameplan for Debt Collection Professionals," here's how leaders can take meaningful, measured steps toward AI-driven success.

You don't need a massive tech overhaul to get started with AI—you need a clear first step.

The conversation around AI can feel overwhelming. Compliance risks, data quality, model governance—there’s a lot at stake. But waiting for "perfect readiness" guarantees one thing: falling behind.

The organizations that succeed will be the ones willing to take thoughtful, calculated first steps. True innovation doesn't begin with massive overhauls; it begins with intentional, strategic movement in the right direction.

Step 1: Define Your Purpose for AI

Before buying a tool or writing a line of code, organizations must clearly articulate the purpose of adopting AI. Will the initiative be consumer-facing, improving user experience through negotiators, payment portals, and SMS workflows? Or will it drive internal efficiencies, such as auditing transactions, analyzing call quality, or prioritizing account workflows?

Defining this purpose shapes the entire roadmap. It impacts how you select vendors, build governance frameworks, train staff, and communicate with clients. Without a clear "why," AI adoption risks becoming fragmented and ineffective.

Additionally, understanding the purpose early allows organizations to establish realistic KPIs and success metrics. These metrics will be critical when it comes time to evaluate pilot programs and scale initiatives. Purpose gives AI projects both a north star and a measurable impact path.

Step 2: Prepare Your Data

You don't need millions of rows of perfect data to begin. The key is focusing on core, high-quality information. Start with 4 foundational fields such as:

• Consumer contact preferences
• Payment history
• Balance amounts
• Account age

Strip out protected class attributes like race, gender, and geographic proxies (e.g., zip code clustering) to reduce bias risk. Validate the integrity of the remaining fields—bad data equals bad predictions.

This early focus on data quality sets the stage for more powerful, trustworthy AI models down the line. Good data hygiene also minimizes the risk of introducing unintended biases or inaccuracies that could trigger compliance issues later.

Organizations should also document their data preparation steps meticulously. This creates an auditable trail and prepares the team for future regulatory inquiries regarding how models were trained and validated.

Step 3: Pilot One Use Case with a Control Group

Choosing one high-impact, low-risk use case allows organizations to learn without overwhelming their systems. Examples include:

• Smart email templates that adjust language based on consumer payment behavior.
• Predictive dialer schedules based on optimal call windows.

Select a small segment of accounts. Set up a control group that does not receive AI-driven interventions. Measure outcomes like contact rate, engagement rate, and dollars collected.

This scientific approach provides real evidence of success—or identifies early course corrections needed—before expanding AI use across broader portfolios. Without a control group, it's nearly impossible to distinguish correlation from causation in AI outcomes.

Critically, organizations should run pilots long enough to account for variability in consumer behavior and market conditions. Rushing to conclusions too soon can lead to inaccurate assessments and poor strategic decisions.

Step 4: Build Model Governance from Day One

Even modest pilots need strong governance. That includes:

• Documentation of all training data sources and cleaning steps.
• Bias audits and validation reporting pre- and post-deployment.
• Clear logs of AI-driven decisions, actions, and outcomes.

Regulatory scrutiny is only increasing, especially around AI and consumer finance. Early investment in model governance isn't just about compliance; it's about building sustainable operational resilience.

Establishing clear governance policies from day one enables organizations to create consistent practices as AI adoption scales. This prevents the costly retroactive compliance work that plagues many organizations later in their AI maturity journey.

Additionally, well-governed models build client confidence. Being able to show prospective clients a documented, responsible AI strategy can become a competitive advantage in an increasingly regulated market.

Step 5: Communicate Transparently

Transparency isn't just ethical—it's strategic.

When deploying AI, communicate proactively with clients, internal stakeholders, and compliance teams. Share:

• A governance and auditability overview.
• How consumer protections are embedded in the AI system.
• How human oversight remains active alongside automation.

Building trust through transparency earns client loyalty and helps organizations weather regulatory inquiries with confidence.

It’s also crucial to set appropriate expectations internally. Transparency about what AI can and cannot do ensures teams remain engaged and realistic, rather than feeling either threatened by automation or overly reliant on it.

Externally, clear communication about AI usage can serve as a brand differentiator—positioning the organization as one that innovates responsibly.

Final Thought: Action Creates Momentum

You don't have to solve everything at once. But you do have to start.

Each small step—purpose definition, data prep, single-use-case piloting—builds the skills, governance muscles, and cultural readiness needed to scale AI sustainably.

Organizations that delay adoption in pursuit of perfection risk falling behind. Those that start with thoughtful, measured steps position themselves to lead.

Where are you planning to take your first step with AI? I'd love to hear your thoughts.