Advances in artificial intelligence (AI) are reshaping how finance professionals manage customer interactions within Accounts Receivable (AR), a domain ideal for AI-driven innovation due to its repetitive nature and critical demand for accuracy and efficiency. Two key types of AI in AR (generative and agentic) are driving this transformation. However, it’s important to understand their distinct capabilities: simply automating tasks does not always equate to employing genuinely intelligent, autonomous agents.
Understanding Generative AI and Agentic AI in Accounts Receivable
Generative AI
Generative AI refers to artificial intelligence capable of creating original content (personalized emails, written communications, images, or code). Within AR, generative AI predominantly generates highly personalized, natural-sounding customer interactions. For instance, generative models like ChatGPT and Claude can craft tailored payment reminders or individualized outreach emails based on customer data and historical interactions, significantly enhancing customer engagement and responsiveness.
Agentic AI
Agentic AI takes AI capabilities further by autonomously performing tasks typically managed by human employees. These intelligent agents independently analyze data, make decisions, and execute specific actions such as automatically sending invoices, logging disputes, or answering routine customer queries. Essentially, agentic AI operates like an autonomous virtual AR team member , available around-the-clock to handle specific, designated, and defined responsibilities.
Agentic AI takes AI capabilities further by autonomously performing tasks typically managed by human employees.
Autonomous capabilities shouldn’t mean a loss of control. Aided processes precede unaided processes. Typically, AR professionals click an ‘approve’ button before client emails are sent, and guardrails are removed only after the machine has been trained and trusted to handle a variety of scenarios.
Approval buttons help humans train autonomous agents and maintain control over AI.
Distinguishing Basic Automation from Autonomous Agents
It's crucial, however, to distinguish true autonomous agents from basic automation tools or workflows. Automating straightforward tasks such as payment processing doesn't inherently mean you're employing intelligent, self-directed AI agents. Genuine agentic AI involves deeper decision-making, adaptive responses, and continuous learning from interactions to enhance its performance over time.
Generative and agentic AI, though distinct, closely complement each other. Generative AI enriches agentic AI by facilitating more human-like, nuanced customer interactions.
Learn more about the evolution of generative and agentic AI
Practical AI Applications in Accounts Receivable
Enhanced Customer Communication
- Personalized Messaging: Generative AI crafts unique messages at scale considering each customer’s payment history and past communications. This personalization fosters stronger relationships and encourages prompt action. For example, a customer with a consistent payment record might receive a friendly reminder, while a customer with a history of late payments might receive a message offering support and flexible payment options.
- Advanced Chatbots: AI-powered chatbots equipped with Natural Language Processing (NLP) can manage intricate customer inquiries, such as requests for invoice copies or explanations of late fees. This allows AR professionals to dedicate their time and expertise to resolving more complex customer issues and providing personalized support.
Efficient Dispute Management
Automated Resolution
Agentic AI will revolutionize AR dispute resolution by automating key tasks, such as identifying the reason for the dispute, gathering supporting information, and communicating with the customer. It would do this by using NLP and machine learning to log and categorize dispute issues, collect necessary documents, and analyze data to identify discrepancies. For routine disputes, agentic AI could autonomously take the necessary steps to resolve the issue. For complex disputes, AI could escalate the issue to human agents, providing a comprehensive overview and relevant documentation, and keeping a human in the loop when needed.
Agentic AI will revolutionize AR dispute resolution by automating key tasks
Clear Communication
Generative AI can be used to compose clear and concise updates to customers at every stage of the dispute resolution process, ensuring they are kept informed, and that the company maintains a professional image. If, for example, a customer disputes an invoice due to an error in the billing amount, generative AI could be used to draft an email response acknowledging receipt of the dispute, explaining that it is being investigated, and providing a timeline for resolution. The AI can also be used to generate follow-up emails to the customer with updates on the status of the dispute and the expected resolution date.
Proactive Customer Management
Predictive Analytics for Proactive Intervention
Advanced machine learning (classical AI) algorithms can analyze historical customer data, payment patterns, and external factors to predict potential late payments or disputes. This early identification empowers AR teams to proactively contact customers, offer tailored payment plans or discounts, and address any underlying issues before they escalate into collection problems. By mitigating risks and preventing payment delays, businesses can improve cash flow and reduce bad debt.
Early identification empowers AR teams to proactively contact customers, offer tailored payment plans or discounts, and address any underlying issues before they escalate.
Personalized Customer Interactions for Enhanced Satisfaction
AI-driven analytics allow AR departments to segment customers based on their payment history, preferences, and behavior. This segmentation allows for personalized communication and support, such as automated reminders tailored to individual customer needs, targeted offers for early payment incentives, and customized dispute resolution processes. By delivering a more personalized and responsive experience, businesses can foster stronger customer relationships, increase loyalty, and minimize friction in the payment process.
Intuitive Self-Service Options for Convenience and Efficiency
AI-powered agents and self-service portals can provide customers with 24/7 access to account information, payment options, and dispute resolution tools. These intuitive platforms can answer frequently asked questions, guide customers through the payment process, and offer instant support – reducing the need for manual intervention by AR staff. By automating routine tasks and empowering customers to manage their accounts independently, businesses can streamline AR operations, improve efficiency, and enhance the overall customer experience.
10 Best Practices for Implementing AI in AR
Finance teams considering generative and agentic AI should adhere to the following guidelines for successful integration:
- Define Clear Objectives: Clearly outline specific AR challenges you aim to address with AI, such as reducing dispute resolution time or enhancing customer satisfaction.
- Prioritize Data Quality: Ensure the availability of high-quality, consolidated data from ERP, CRM, and email platforms to empower effective AI-driven decision-making.
- Start Small, Then Scale: Begin with pilot projects to evaluate AI capabilities within controlled scenarios before wider deployment.
- Ensure Smooth Integration: Choose AI solutions that integrate seamlessly with existing financial systems via APIs or built-in connectors.
- Maintain Human Oversight: Clearly define escalation procedures for AI-handled tasks, ensuring complex issues are quickly routed to human specialists.
- Invest in Training: Educate your team about the role of AI in augmenting their tasks rather than replacing human roles.
- Prioritize Transparency: Opt for AI solutions capable of clearly explaining decisions, thereby fostering trust among employees and customers alike.
- Security and Compliance: Confirm your AI solutions adhere strictly to relevant data privacy regulations (e.g., GDPR, CCPA) and robust cybersecurity standards.
- Continuous Optimization: Regularly monitor and measure AI performance, using insights gathered to refine processes and improve outcomes.
- Incorporate Customer Feedback: Regularly collect and utilize customer feedback to continuously enhance AI interactions and overall satisfaction.
Buyer Tip: Choose AI solutions that integrate seamlessly with existing financial systems via APIs or built-in connectors.
The Bottom Line
Generative and agentic AI represent transformative advancements for AR departments, dramatically improving customer interactions, automating routine tasks, and proactively addressing issues. Organizations leveraging these AI finance tools and technologies can experience substantial benefits, including reducing Days Sales Outstanding (DSO) and accelerating payment cycles. Further, AI-driven solutions have shown as much as an 81% accuracy in predicting invoice payments, potentially saving organizations significant amounts monthly. While successful implementation requires thoughtful planning, quality data, seamless integration, and continued human oversight, effectively adopting generative and agentic AI can transform AR from a routine operational task into a strategic financial advantage.
About the Author
Glenn Hopper is an author, speaker, and lecturer on the intersection of AI and corporate finance. He recently published the book AI Mastery for Finance Professionals. He is the Head of AI Research and Development at Eventus Advisory Group. He holds a Master of Liberal Arts from Harvard University and an MBA from Regis University.
FAQ
Agentic AI refers to artificial intelligence systems designed to autonomously perform tasks, make decisions, and achieve goals without human intervention.
AI finance tools use advanced algorithms and machine learning to automate tasks like matching payments to invoices, handling complex scenarios, and reducing manual intervention. .
Agentic AI in AR improves efficiency, enhances customer interactions, automates routine tasks, and proactively addresses issues, leading to better cash flow and reduced bad debt.