This post was originally published in Jan 2022 and was updated in September 2025 with additional information on AI implementation in business, FAQs, and more.
According to a recent Billtrust/ IDC survey, one of the major aspirations for companies is to drive greater efficiency across their organizations and teams. Failing to consider and adopt AI and machine learning could have detrimental consequences for your business in the long term, incurring substantial costs related to invoice processing, collections calls, and reminders, as well as lockbox fees.
When it comes to AI, there’s no turning back. It will revolutionize businesses and processes, undoubtedly for the better. However, have you ever pondered how to maximize AI’s potential and leverage your data intelligently? The key lies in posing the right questions to equip yourself for AI implementation and extract the maximum benefits from your solution.
1. Define Your ‘Why’: Start with Clear Goals and Use Cases
AI is a powerful tool, not a magic wand. Before implementation, you must define what success looks like for your organization.
Developing specific use cases is the crucial first step. Go beyond broad goals like “increasing efficiency.” Ask targeted questions: Is your primary objective to reduce Days Sales Outstanding (DSO) by 15%? Or is it to cut manual cash application time by 40 hours per week? A clear, measurable target will guide your entire AI strategy and ensure you’re solving the right problem.
2. Ensure Your Data is AI-Ready
An AI model is only as smart as the data it learns from. “Garbage in, garbage out” has never been more true. Prioritizing data quality is non-negotiable. This means ensuring all your core AR data—invoices, payment remittances, credit memos, and collections notes—is digitized and structured. Are you still processing PDF invoices manually or deciphering remittance advice from emails? These unstructured sources must be transformed into clean, machine-readable data before any AI effort can succeed.
3. Understand the importance of a data lakehouse
High-quality data is useless if it’s siloed. You need a streamlined process to collect and integrate data from various sources like your invoicing system, CRM, and payment gateways. This is where a modern architecture like a data lakehouse becomes critical. Think of it as a single source of truth that combines the storage flexibility of a data lake with the management features of a data warehouse. By unifying your data, you enable the powerful, cross-functional analysis that unlocks true AI-driven intelligence.
4. Ask some tough questions about your existing AR processes
For quite some time, the payments industry has been dominated by the mindset of ‘If it’s not broken, don’t fix it.’ However, this outlook is evolving, driven by the ongoing digitalization and data explosion. This necessary transformation calls for a thorough evaluation of your current AR processes, which can yield valuable insights. When this data is meticulously analyzed, it reveals untapped efficiencies.
5. Determine the impact AI will have on your budget
AI technology can come with significant expenses. When collaborating with external vendors for your AR transformation, it’s crucial to be informed about the associated AI costs. Some vendors may add extra charges for AI within their products or offer AI as a separate, premium product. On the other hand, some vendors are integrating AI features into their solutions without increasing the overall product cost. Evaluate each vendor’s reputation and determine whether the proposed solution aligns with your existing infrastructure.
6. Prepare and involve your team early on
It goes without saying that, in order to get your processes and data sorted, you need to set aside time for people within your business. And that often means bringing everyone into the discussion and/or process sooner rather than later.
It’s fundamental to understand that AI implementation isn’t just about purchasing a tool and expecting it to work seamlessly with your existing processes. Instead, it’s a collaborative effort that requires active participation from your team. By involving people within your business early in the process, you’re not only acknowledging their expertise but also ensuring that the AI aligns with your specific needs.
Before implementing AI, it’s essential to clearly define the features and benefits the AI tool will provide to your team. This involves understanding how the AI will enhance your existing processes, whether it’s automating repetitive tasks, providing predictive insights, or improving decision-making.
Consider how the AI tool will impact your workflows. Will it make them more efficient? Will it allow your team to focus on higher-value tasks while automating routine work? Understanding the potential workflow improvements can help in setting realistic expectations and measuring the success of AI implementation.
7. Identify key metrics
To optimize your AR processes, it’s essential to pinpoint the Key Performance Indicators (KPIs) that hold the greatest significance for your department. These KPIs serve as vital metrics to track your AR’s efficiency and performance. Consider metrics such as Days Sales Outstanding (DSO) to gauge how quickly you’re collecting outstanding payments, aging receivables to monitor overdue invoices, cash application accuracy to ensure precise payment allocation, and customer payment trends to understand payment behaviors.
In addition to these core KPIs, it’s crucial to take into account the investment your team puts into the AR solution and the resources required from your IT teams. This comprehensive evaluation allows you to calculate the ROI for the solution you’re contemplating. Understanding the relationship between these KPIs and the ROI helps you make informed decisions regarding the effectiveness and sustainability of the solution in improving your AR operations.
8. Watch out for automation and AI claims
In today’s market, “AI” is often used as a buzzword. It’s crucial to distinguish true AI from simple, rules-based automation.
When evaluating vendors, ask them to “show, don’t just tell.” Request a live demo using your own sample data to see the technology in action. Does it simply follow a pre-set script, or does it demonstrate genuine learning, prediction, or intelligent decision-making? True AI should adapt and provide insights, not just automate a checklist.
Read the blog → Predictive AI in accounts receivable: Understanding machine-generated financial forecasts and advice
From Insight to Impact: How AI Drives Measurable Results
Strategic AI implementation is more than an operational upgrade; it’s a catalyst for business transformation. By moving from reactive tasks to proactive, data-driven strategy, you can control costs, accelerate cash flow, and build stronger customer relationships. Here’s how AI delivers tangible benefits across key business functions:
Finance and accounts receivable
AI-powered AR automation transforms your financial operations by streamlining manual processes and providing predictive insights. Smart cash application technology automatically matches payments to invoices with industry-leading accuracy, reducing processing time and eliminating costly errors. Predictive analytics help you forecast cash flow more accurately, while automated collections workflows prioritize high-risk accounts and personalize customer outreach.
These capabilities help you reduce Days Sales Outstanding (DSO), lower operational costs, and free up your team to focus on strategic initiatives that drive business growth.
Customer service and support
AI enhances customer experiences through intelligent automation and real-time support capabilities. Automated customer portals provide 24/7 access to account information, payment options, and dispute resolution tools. Smart chatbots handle routine inquiries instantly, while predictive analytics identify potential issues before they impact customer relationships.
This approach reduces support costs while delivering the self-service options your customers expect, strengthening relationships and improving satisfaction scores.
Operations and workflow management
Operational AI optimizes your business processes by identifying bottlenecks, predicting demand patterns, and automating routine tasks. Machine learning algorithms analyze historical data to improve inventory management, reduce waste, and prevent supply chain disruptions. Automated workflows ensure consistent processes while providing real-time visibility into performance metrics.
These improvements help you reduce overhead costs and scale operations without proportional increases in resources.
Sales and marketing performance
AI-driven sales and marketing tools help you identify the most promising prospects and personalize engagement strategies. Predictive analytics analyze customer behavior patterns to optimize campaign timing and messaging. Lead scoring algorithms prioritize sales efforts on accounts most likely to convert, while automated nurturing campaigns maintain engagement throughout the sales cycle.
This targeted approach accelerates revenue generation while reducing marketing costs and improving conversion rates across all channels.
Curious about what AI can do in the world of accounts receivables?
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Frequently Asked Questions
Check out the FAQs for general questions. Find helpful answers quickly to get the information you need.
How is AI implemented in business?
AI implementation helps you control costs, accelerate cash flow, and improve customer satisfaction across your organization. You can automate repetitive tasks through intelligent workflows, enhance forecasting with predictive analytics, and personalize customer interactions based on payment behaviors and preferences. In accounts receivable specifically, AI streamlines invoice processing, optimizes payment matching, and automates collections outreach to deliver measurable improvements in cash flow.
How does AI improve accounts receivable (AR) processes?
AI transforms your AR operations by automating manual tasks and providing predictive insights that accelerate cash flow. Smart cash application technology matches payments to invoices with exceptional accuracy, while predictive analytics help you identify which customers are likely to pay late. Automated collections workflows prioritize high-risk accounts and personalize outreach based on customer payment history. These capabilities help you reduce DSO, lower operational costs, and improve customer relationships through more targeted, professional interactions.
What challenges should businesses consider before AI implementation?
Before implementing AI, you should establish clear goals that align with your business objectives and ensure your data quality meets the standards needed for effective AI performance. Consider the total cost of AI solutions, including any premium charges some vendors may add. Involve your team early in the process to ensure buy-in and proper training. Be cautious of vendors making broad automation claims – ask specific questions about capabilities and results. Focus on identifying the key metrics that will demonstrate ROI and success in your specific use case.
