Why Consumer Trust Will Shape AI Adoption in Debt Collection

This year, a Gartner study reported that 50% of surveyed customers said interactions are easier when companies use generative AI. At the same time, 87% said providing an option to reach a human agent is essential when companies use GenAI for customer service.

These findings point toward an important shift in the AI conversation: artificial intelligence is getting better at talking to consumers. That does not automatically mean consumers want every conversation handled by AI.

Karan Sood, who leads AI Solutions and Products at EXL, identified this predicament as an overlooked dimension of AI adoption during an Applying AI Podcast with Adam Parks and Mike Walsh.

“The second axis, which gets ignored, is human behavior,” Sood highlighted.

Contrary to popular belief, the future of conversational AI will depend on two curves developing simultaneously: AI capability and consumer readiness.

AI Capability Is Only Half the Adoption Equation

Conversational AI is advancing across several interconnected technologies.

Language models are becoming more capable of understanding context. Intent classification is improving. Speech-to-text systems are becoming more accurate. Text-to-speech technology is producing increasingly natural voices.

Each improvement expands the range of conversations that AI can potentially handle. But technical capability should not be confused with customer acceptance.

An organization may eventually possess the technology to automate most of an interaction while still discovering that some consumers prefer human assistance in particular situations.

Recent customer behavior reinforces that distinction.

Gartner found that customers are approximately three times more likely to use third-party GenAI tools than company-provided chatbots when resolving customer service issues. Use of third-party GenAI for service interactions has nearly doubled over the past year, while company chatbot use has remained statistically unchanged since 2022.

Consumers are clearly becoming comfortable with AI. The challenge is that they may not always want to use AI in the way organizations expect.

The Capability-Comfort Framework

For receivables leaders, AI adoption can be evaluated through what might be called the Capability-Comfort Framework.

The first dimension is capability: what can the AI reliably accomplish?

This includes comprehension, intent recognition, response generation, transactional functionality, compliance controls, and the ability to complete increasingly complex workflows.

The second dimension is comfort: what will the consumer willingly allow AI to accomplish?

Comfort can depend on the type of interaction, complexity of the request, previous experience with automated systems, perceived consequences of an error, and availability of human assistance.

The strongest AI strategy sits at the intersection.

High capability combined with low consumer comfort creates technically impressive systems that customers may resist. High consumer comfort paired with insufficient capability creates experiences consumers are willing to try but may abandon because the technology cannot reliably complete the task.

High capability and high comfort create the conditions for scalable adoption.

Human Access Can Become an AI Guardrail

Organizations sometimes frame human escalation as evidence that an AI system failed.

That definition deserves reconsideration. A well-designed escalation can be evidence that the system worked exactly as intended.

Gartner’s August 2026 research found that customers who are unwilling to engage with AI most commonly identified the ability to switch to a human as something that could change their minds. The research recommends allowing AI to understand intent and attempt resolution when confidence is high while preserving a clear path to human support.

A system should handle interactions it is equipped to manage while recognizing when complexity, uncertainty, consumer preference, or organizational policy requires a person.

Human access can therefore operate as a practical AI guardrail for debt collection. It protects the consumer experience without eliminating the efficiencies AI can provide.

AI-to-AI Conversations May Be the Next Behavioral Shift

The next phase could move beyond consumers communicating directly with business AI altogether.

Consumers may eventually create personal AI agents capable of communicating with company agents on their behalf. That scenario is becoming easier to imagine as third-party AI becomes part of everyday customer behavior.

If a personal agent eventually communicates with a collection agency’s AI system, several operational questions emerge.

How will authorization be established?
How will identity be verified?
Which information can an automated representative access?
How should required disclosures work?
When should the consumer personally enter the interaction?

These questions are not necessarily immediate implementation requirements. They illustrate how quickly the definition of a “customer interaction” could change, especially in the receivables industry.

Measure Acceptance Alongside Automation

This leads to a practical measurement framework for AI-driven collections. Traditional automation metrics emphasize containment, efficiency, cost reduction, and completion rates.

Those measures should remain, but organizations can expand them with behavioral indicators.

Adoption: Are consumers choosing to use AI-enabled channels?
Completion: Can consumers successfully accomplish their objective?
Escalation: Why are conversations transferring to human representatives?
Re-engagement: Do consumers willingly use the AI channel again?
Preference: Which types of consumers and interactions demonstrate greater acceptance of automation?

Together, these metrics provide a clearer picture of whether AI is creating an experience consumers actually value.

The Winning AI Strategy Will Follow the Consumer

AI adoption in debt collection will continue improving. Voice interactions will become more natural. Intent recognition will become stronger. AI will become capable of completing a greater percentage of customer journeys.

Consumer behavior will evolve alongside it. The organizations positioned to benefit will be those that understand both curves.

That means building AI capable of taking meaningful action while preserving human access, measuring consumer acceptance alongside operational efficiency, and preparing for a future in which customers may interact with financial organizations through their own AI agents.

The future is a new relationship between consumers, humans, and intelligent systems.

For more perspectives on AI guardrails, changing consumer behavior, and the future of conversational AI in receivables, explore the full Applying AI conversation and additional content on The Receivables Info.

About Adam Parks

Adam Parks has become a voice for the accounts receivable industry. With almost 20 years working in debt portfolio purchasing, debt sales, consulting, and technology systems, Adam now produces industry news, hosts hundreds of episodes of the Receivables Podcast, and manages branding, websites, and marketing for over 100 companies within the industry.