Agent Pageant submission type
Maestro Mind (Maestro Orchestration Track)
Full name
AI-driven Soft Collection Agent
Team name
Agentic Recovery Lab
Team members
Marcin Więcek, Wojciech Ziębicki, Bogdan Nowopolski
How many agents do you use
Multiple agents
Industry category in which use case would best fit in (Select up to 2 industries)
Customer service
Insurance
Complexity level
Advanced
Summary (abstract)
AI-driven Soft Collection Agent automates early-stage debt collection by combining voice AI with agentic process orchestration powered by UiPath Maestro.
The solution proactively contacts customers with overdue payments, conducts structured conversations, and guides interactions toward clear outcomes such as payment promises, installment plans, or escalation when necessary.
By integrating enterprise systems such as SAP and CRM platforms, the agent uses customer context and payment history to personalize communication and improve recovery effectiveness.
This approach reduces operational costs, accelerates payment recovery, and creates a scalable architecture for building a family of voice-based enterprise agents.
Detailed problem statement
Many organizations struggle with inefficient early-stage debt collection.
When customers miss payments for recurring services such as energy, telecommunications, or subscription products, companies typically rely on manual call-center operations or repetitive reminder messages.
This approach creates several challenges:
• high operational costs of manual outbound calls
• delayed response to payment delays
• inconsistent communication quality
• limited scalability when the number of overdue accounts increases
In practice, thousands of similar conversations must be conducted every day, yet each one requires human time and effort. As a result, organizations often escalate cases to formal collection processes too late, which reduces the probability of recovery and increases customer friction.
The key challenge is how to scale personalized, compliant customer communication during the early stage of payment delays without significantly increasing operational workload.
Detailed solution
The proposed solution introduces an AI-driven Soft Collection Agent that automates early-stage debtor communication using agentic automation orchestrated by UiPath Maestro.
The system operates as a case-based orchestration workflow in which each debtor interaction is managed as a separate process instance. Customer and payment data are retrieved from enterprise systems such as SAP and CRM platforms, providing the agent with contextual information about the customer’s payment history and account status.
Based on this context, the agent generates a conversation plan and initiates a voice interaction with the customer using a voice AI layer powered by ElevenLabs and telecommunication infrastructure provided by Vonage.
During the call, the AI agent conducts a structured and empathetic conversation that informs the customer about the outstanding payment, responds to questions, and guides the interaction toward a clear outcome such as:
• a payment promise (Promise to Pay)
• an agreement on installment payments
• escalation to a human agent when necessary
After the interaction, the system captures and analyzes the conversation outcome, updates the CRM system, and determines the next best action within the collections process.
This architecture enables organizations to automate large volumes of debtor communication while maintaining compliance, auditability, and customer-friendly interaction. Additionally, the same architecture can be extended to build a broader family of voice-based enterprise agents for customer service, reminders, and other proactive communication scenarios.
Narrated video link (sample: https://bit.ly/4pvuNEL)
Expected impact of this automation
The Soft Collection Agent significantly improves the efficiency and scalability of early-stage debt collection processes.
Operational efficiency
AI voice agents can conduct thousands of debtor conversations simultaneously, reducing the workload of call center agents and automating repetitive outbound contact tasks.
Cost reduction
Automating early-stage collection calls can reduce the cost per contact by up to 60–80% compared to traditional manual call center operations.
Faster payment recovery
Proactive and timely engagement with customers increases the likelihood of payment before escalation to formal collection procedures, reducing the Days Sales Outstanding (DSO).
Improved customer experience
The AI agent conducts structured and empathetic conversations, ensuring consistent communication and reducing customer friction compared to aggressive collection practices.
Compliance and auditability
All interactions are recorded, logged, and traceable, ensuring compliance with internal policies and regulatory requirements.
Scalable enterprise architecture
The solution creates a reusable architecture that can support thousands of automated interactions daily and can be extended to build a broader family of voice-based enterprise agents for customer service, reminders, and proactive notifications.
UiPath products used (select up to 4 items)
UiPath Agent Builder
UiPath Integration Service
UiPath Maestro
UiPath Robots
Automation Applications
SAP, CREATIO, MS Outlook/Gmail, ElevenLabs, Vonage
Integration with external technologies
ElevenLabs, Vonage, Azure
Agentic solution architecture (file size up to 4 MB)
Sample inputs and outputs for solution execution
not required - inputs are generated in first step process
Other resources
Solution presenation, technical documentation, export of Maestro process are provided on the following drive: