The AI implementation strategy for collections in 2026
This is the second in a series examining implementation of AI in collections and loan servicing. In the previous edition, we established what AI agents are and how they differ from traditional automation. This installment provides a practical AI implementation strategy: identifying where to start, assessing readiness, and building the prerequisites for successful implementation of AI agents.
In this blog: A complete AI implementation strategy covering target selection, operational readiness assessment, and building the prerequisites for successful implementation of AI agents.
Key insights:
Successful AI implementation strategy begins with specific operational pain points, not broad transformation goals
Implementation of AI agents requires cleaner operational environments than human agents
Three prerequisites determine readiness: documented processes, accessible data, and system connectivity
Your AI implementation strategy should include remediation timelines for addressing gaps
Why your AI implementation strategy matters?
The implementation of AI in operations is inevitable. The strategic question for leadership is not whether to implement AI, but where to implement it first.
You cannot implement AI across an entire enterprise overnight. Your AI implementation strategy must define a scope. Identify the specific operational bottlenecks where implementation of AI agents delivers maximum value. Verify that your infrastructure can support this implementation.
The economics make this urgent. US-based contact center agents cost between $25-50 per hour for basic operations, with fully loaded costs (including benefits, facilities, technology, and management overhead) reaching $40,000-50,000 per agent annually.
Organizations with strong AI implementation strategies report average cost reductions of 30-40% in operational expenses, with early adopters achieving ROI of $3.70 for every dollar invested in implementation of AI.
This guide provides a framework for developing your AI implementation strategy.
Phase 1: Identifying implementation targets
An effective AI implementation strategy starts by targeting repetitive friction points that drain margins. The implementation of AI is most successful when focused on tasks that were previously impossible to automate because the technology lacked reasoning capabilities.
AI Agent Deployment Targets
1
High-Volume Consumer Engagement
Omnichannel AI Agent
The Symptom
Cannot scale contact capacity without scaling headcount. Peak periods create backlogs. After-hours inquiries go unanswered.
The Problem
Human agents handle every interaction regardless of complexity. At $7.16 per contact, routine inquiries represent a significant cost center.
Deployment Target
Omnichannel AI agent across voice, SMS, email, and chat. Handles routine interactions end-to-end, escalates complex cases to humans.
The Impact
14% increase in issue resolution per hour. Handle 13.8% more inquiries per interaction.
2
Portfolio Intelligence & Prioritization
Intelligence Agent
The Symptom
All accounts treated the same. Outreach timing follows dialer rules, not consumer behavior. Channel selection driven by agent availability.
The Problem
Data exists but sits unused. Payment patterns, communication preferences, previous interactions—all available but not informing strategy.
Deployment Target
Intelligence agent that analyzes behavior patterns, segments portfolio by propensity and channel affinity, recommends contact strategy.
The Impact
Shifts strategy from volume-based to precision-based outreach. Improves right-party contact rates and payment yields.
3
Back-Office Processing
Processing Agent
The Symptom
Manual work creates bottlenecks. Mail sits in queues. Documents require human review before routing. Case files need manual assembly.
The Problem
Back-office staff spend time on pattern recognition tasks. Up to 41% of contact center time goes to repetitive administrative tasks.
Deployment Target
Processing agent for document intake, OCR, routing, and case preparation. Reads incoming mail, matches to accounts, flags exceptions.
The Impact
55% reduction in processing time. 90% increase in accuracy. Shifts back-office to exception handling.
4
Agent Support & Documentation
Augmentation Agent
The Symptom
Post-call wrap-up time is high. Notes are inconsistent. Disposition codes are unreliable. Best agents spend significant time on documentation.
The Problem
Documentation competes with production. Agents rush notes to take the next call. Quality suffers. Downstream processes break down.
Deployment Target
Augmentation agent that listens to calls, generates notes in real-time, suggests disposition codes, schedules follow-up actions.
The Impact
Workers save an average of 1 hour per day, with frequent users reclaiming 5-9 hours weekly.
5
Compliance & Quality Oversight
QA Agent
The Symptom
QA team audits a fraction of interactions. Compliance violations surface through complaints, not monitoring. Coaching is reactive.
The Problem
Structural visibility gap. Cannot identify training needs, flag at-risk agents, or defend against litigation when oversight is sampling-based.
Deployment Target
QA agent that monitors 100% of interactions across all channels in real-time. Flags regulatory risks, scores agent performance.
The Impact
75% improvement in fraud/violation detection. 20% reduction in false positives.
Phase 2: Assessing implementation readiness
Once you have identified an implementation target, validate that your operation can support it.
Successful implementation of AI agents requires cleaner operational environments than human agents. A human can navigate ambiguity by asking a colleague. An AI agent cannot.
Your AI implementation strategy must account for three prerequisites:
AI Implementation Prerequisites
1
Codified Processes
The Requirement
Documented Standard Operating Procedures for the workflow you want to automate.
The Test
Can you hand a new hire a written guide covering decision trees, authority limits, escalation paths, and exception handling?
The Gap
If your process relies on tribal knowledge, the AI will fail. Unwritten rules cannot be encoded into an agent.
If Not Ready
Shadow top performers, map decision trees, codify exception handling. Create training materials for human agents first.
2
Accessible, Structured Data
The Requirement
Consumer data, interaction history, and operational outcomes must be accessible and consistently structured.
The Test
Can you pull a report showing payment history, contact history, commitments, and account status—all structured, not buried in free-text notes?
The Gap
If data lives in siloed systems or unstructured notes, the agent operates blind. Organizations report 10-15% efficiency gains from data quality improvements alone.
If Not Ready
Audit data sources, implement structured disposition codes, migrate critical data to structured fields. Establish data quality metrics.
3
System Connectivity
The Requirement
Core systems must allow external software to read data and execute actions in real-time.
The Test
Can external software query your System of Record, update codes, schedule follow-ups, and trigger communications in real-time—not batch files?
The Gap
Without real-time integration, the agent creates more work than it eliminates. 60% of implementations face integration challenges with legacy systems.
If Not Ready
Document architecture, identify API availability, build integration middleware. Pilot read/write capabilities in test environment first.
The readiness checklist
Use this interactive checklist for a quick self-assessment. Select all items that apply to your organization to receive a readiness score and personalized recommendation.
AI Agent Readiness Checklist
AI Agent Readiness Checklist
Select items that apply to your organization
Process Readiness
Written SOPs exist for target workflow
Decision logic covers 90%+ of scenarios
Exception handling is documented
Data Readiness
Consumer data is accessible across systems
Interaction history is captured and structured
Outcome codes are specific and consistently used
Historical data supports pattern analysis
System Readiness
Core systems have API access
External applications can read account data
External applications can write updates
Data updates propagate in real-time
Organizational Readiness
Executive sponsor identified
Cross-functional team assigned
Success metrics defined
Control group methodology established
0/15items ready
Summary
Before moving to vendor selection, your AI implementation strategy should answer three questions:
Which bottleneck are we solving? Consumer engagement, portfolio intelligence, back-office processing, agent support, or compliance oversight?
Is the workflow documented? Or does it rely on institutional knowledge that has never been written down?
Can external software access our systems? Read data, write updates, and execute actions in real-time?
If you cannot answer these questions clearly, you are not ready to evaluate vendors. You are ready to build operational foundations.
The organizations achieving 30-40% cost reductions and $3.70 ROI per dollar invested did not skip these prerequisites. They built strong foundations for implementation of AI, then deployed agents on solid ground.
Your competitive advantage in 2025 will not come from being the first to implement AI. It will come from having a thoughtful AI implementation strategy that ensures effective implementation.
Next in this series: Vendor evaluation frameworks: how to separate real AI agents from rebranded chatbots when reviewing RFP responses.
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