Automation is often sold with fewer manual processes, more self-service, faster interactions, and lower operating costs. 

But what if removing every human touchpoint is the wrong objective?

The question emerges from my Receivables Podcast conversation with Nick Cherry of Phillips & Cohen Associates. While discussing the future of technology in collections, Cherry makes a point. PCA is exploring AI, automation, voice bots, and other technologies, but its approach is not built around eliminating people. It is built around using technology to support them.

In bereavement care in debt collection, efficiency matters, but so does recognizing when a sensitive conversation requires a person. 

Automation is not the Customer Experience

A digital portal can accept a payment at midnight. An automated system can process routine information without putting someone on hold. AI can help organize notes, analyze interactions, and make information easier for representatives to access.

These capabilities can make the process faster and easier, but technology alone does not define the customer experience. The quality of that experience still depends on how effectively the organization responds to the person behind the interaction. 

The importance of this balance becomes particularly clear in deceased account care, where conversations often unfold under deeply personal and sensitive circumstances.  The objective is to use technology to support employees instead of allowing technology to replace the human dimension of the interaction.

This goes beyond bereavement.

Consumers enter collection journeys with different circumstances, financial knowledge, communication preferences, levels of digital comfort, and reasons for needing assistance. A channel that removes friction for one person may create friction for another. A well-designed digital experience therefore needs more than automation; it needs choice.

The Off-Ramp May Be as Important as the Automation

Consumer-facing technology works best when convenience does not come at the expense of choice. 

Imagine a consumer begins an interaction through an automated channel. Everything works until they reach a question the system cannot adequately address, or one they simply do not feel comfortable handling with a machine.

What happens next?

A poorly designed journey can force consumers to start over by searching for a telephone number, navigating another menu, repeating account information, and explaining their situation to a representative who may have no visibility into what already happened digitally. 

The technology has technically worked, but the experience has failed.

A human-in-the-loop model asks a different question: How quickly can the system recognize that the consumer needs something different? This can turn escalation from a failure state into an intentional part of the customer journey. The ability to move seamlessly from digital convenience to human assistance in bereavement care in debt collection.

AI as a Traffic Controller, Not the Destination 

Instead of asking "Can AI handle this conversation?", forward-thinking leaders ask "Who or what is best equipped to handle it?"

  • Self-Service: Best for routine tasks customers prefer to complete independently.
  • AI Routing: Analyzes context to direct interactions to the right channel at the right moment.
  • Human Agents: Essential for sensitive, high-empathy scenarios (like deceased account care) or complex negotiations.

Build the Super Agent Before Replacing the Agent

Some of the most practical AI applications in collections may never speak directly to a consumer. Instead, they work behind the scenes to help representatives access relevant information faster, reduce manual documentation, and identify interactions that require closer review.

Continuous-learning tools can support faster employee development, while automated note-taking and AI-assisted quality assurance can reduce the administrative workload surrounding each interaction. This gives representatives more time to focus on work that depends on judgment, communication, and human understanding. 

Here, AI strengthens the capabilities of the person doing the work rather than simply replacing human tasks. 

This shifts the business case for AI away from headcount reduction alone. Productivity can also come from improving how employees work. For collections organizations, that could mean evaluating AI investments based on whether they shorten time to competency, reduce repetitive tasks, improve quality assurance, or give representatives better information at the point of interaction.

Better Automation Starts with Better Data

Once AI begins assisting with workflow and decision-making, the quality and structure of the information feeding those systems become more consequential.

Beyond saving time, automated documentation can convert conversations into structured information that supports reporting and future technology applications. Poorly organized data limits what AI systems can interpret and, ultimately, the value organizations can extract from them.

Collecting more information is not automatically better. Cherry cautions against building a “data mountain” without a clear purpose, arguing that information should be structured so it is reportable and capable of informing subsequent decisions.

This principle aligns with the broader approach of the NIST AI Risk Management Framework, which encourages organizations to manage AI risks throughout the design, deployment, use, and evaluation of AI systems, instead of treating governance as an afterthought.

Consumer Choice is an Automation Strategy

The collections industry's technology debate does not need to end with a choice between people and machines. A more useful question is whether each interaction is being handled through the channel best suited to the consumer's needs and the complexity of the situation.

Self-service can make routine tasks easier. AI can strengthen training, documentation, analysis, and routing. Human representatives can concentrate on interactions that require explanation, judgment, or greater sensitivity. 

The result is a different measure of technological maturity where the AI application becomes more meaningful to the customer experience in debt collection. 

 

For more perspectives on AI, consumer communication, operational strategy, and emerging technology across the receivables industry, explore ReceivablesInfo.com and the Receivables Podcast.

About Adam Parks

Adam Parks, MBA, is the Founder and CEO of Receivables Info and a recognized leader in the receivables management industry. With nearly two decades of experience spanning debt portfolio management, technology, consulting, marketing, and operations, he brings a practical perspective to industry transformation. As host of the Receivables Podcast and a former President of RMAI, Parks regularly explores emerging trends in AI, compliance, recovery strategy, technology, and operational performance.