Artificial Intelligence (AI) is reshaping Accounts Receivable (AR) management by offering automation, predictive analytics, and improved decision-making capabilities. According to Gartner, 58% of finance departments had incorporated AI at some level by the end of 2024, underscoring its growing influence in financial operations. But before integrating AI into AR, it’s critical for organizations to systematically assess their readiness. Financial leaders can effectively leverage AI in AR processes by adopting a structured, practical framework tailored to their organization’s specific needs.
Strategically evaluating AI readiness can unlock efficiencies, reduce costs, and enhance cash flow.
Step 1. Evaluate Core Pain Points and Strategic Goals
Start by examining your AR workflows closely. Typical inefficiencies include manual data entry, delayed invoice processing, errors in cash application, and ineffective collections processes. These inefficiencies increase Days Sales Outstanding (DSO), reduce cash flow, and degrade customer satisfaction. Clearly identifying these issues will help pinpoint precisely where AI can provide the greatest value to CFOs and AR teams.
Clearly define specific, measurable, achievable, relevant, and time-bound (SMART) goals for your AI initiatives. Align these targets closely with broader financial and strategic goals to ensure coherence and maximum return on investment.
Lessons Learned from Real-World Experiences
Set realistic, measurable expectations. For example, consider objectives such as reducing DSO by 15%, boosting collector productivity by 25%, or minimizing invoicing errors by 20%. Focus initially on high impact use cases where quick successes can validate your AI strategy. Clearly define realistic objectives and measure progress consistently.
Step 2. Analyze Your Technical Readiness
Data Quality and Consistency: AI’s effectiveness heavily relies on high-quality data. Examine the integrity, accuracy, completeness, and accessibility of your AR data. Establish strong data governance practices to ensure consistency and reliability, as poor data quality can significantly compromise AI-driven insights. Invest significantly in cleaning, standardizing, and consolidating AR data from all sources. High-quality data underpins accurate AI predictions and effective decision-making.
Lessons Learned from Real-World Experiences
Underestimating data preparation efforts can seriously undermine AI initiatives. Allocate sufficient resources upfront to ensure comprehensive data cleansing and readiness.
Centralize Data for Enhanced Visibility: Establish a centralized repository, or “single source of truth,” to ensure consistent and reliable data flows, critical for generating actionable AI insights.
Data Security and Privacy: Implement stringent security measures to protect sensitive financial data, ensuring strict compliance with data privacy laws such as GDPR and CCPA. Verify vendor compliance with industry-standard security protocols (e.g. ISO 27001, SOC 2, PCI DSS, HIPAA, etc.).
Compatibility: Assess your current finance tech stack (ERP systems, CRM tools, billing platforms, and payment gateways, etc.) for compatibility with AI solutions. Identify potential integration issues, particularly with legacy systems or isolated data repositories. Successful AI implementation requires seamless technological integration.
Lessons Learned from Real-World Experiences
Legacy systems often pose integration challenges. Use a phased, incremental approach, starting with pilot projects to troubleshoot and refine processes before full-scale implementation.
Don’t Forget Compliance, Governance, and Ethical Considerations: Stay abreast of current and emerging regulations, including GDPR, CCPA, SOX, PCI DSS, and AI-specific frameworks such as the EU AI Act. Select AI solutions with built-in compliance features and comprehensive audit capabilities. Be ready to create clear governance policies emphasizing transparency and explainability in AI decision-making processes. Maintain human oversight to manage critical decisions effectively and define clear escalation paths. Plan to regularly audit AI systems for biases to ensure equitable, fair, and ethical decision-making across your customer base.
Step 3. Gauge Team Readiness and Adaptability
Technology alone won’t deliver results – your team’s mindset, capabilities, and trust in the process will ultimately determine success. Before introducing AI into your AR operations, assess your team’s readiness across three dimensions: skills, sentiment, and structure.
Start with a Readiness Survey
Conduct an internal survey or readiness assessment to gauge:
- Current comfort with AR automation tools
- Understanding of AI’s capabilities and limitations
- Concerns about AI replacing or changing roles
- Training needs for data literacy, system navigation, or interpretation of AI-driven insights
This survey sets a baseline and gives your team a voice early in the process – both are crucial for change management.
Build a Communication Plan
A proactive, transparent communication strategy helps demystify AI and build trust. Here’s a sample approach:
Phase 1: Awareness & Education
- Host a town hall or team meeting to explain why AI is being explored
- Share real-world examples from your industry to show success stories
- Emphasize that AI augments – not replaces – human decision-making
Phase 2: Engagement & Input
- Share initial findings from the readiness survey
- Invite feedback on pain points and wish-list capabilities
- Identify champions within the team who can act as early adopters and internal advocates
Phase 3: Training & Implementation
- Deliver focused training aligned to each role (e.g., collectors, analysts, cash app teams)
- Pair new tools with real use cases so team members see tangible benefits
- Create a feedback loop to refine implementation in real time
Tips for Success
- Avoid jargon. Use plain language when introducing AI concepts.
- Create a safe space for questions. Let your team express concerns without fear.
- Celebrate quick wins. Even small improvements (like reduced time spent on manual matching) can boost morale and confidence in the new system.
- Consider incentives. One Billtrust client found success in challenging their AR professionals to improve their personal cash application match rates by training the machine learning platform – thus reducing the number of exceptions.
Lessons Learned from Real-World Experiences
Teams that are brought into the process early—and shown how AI can reduce repetitive work—adapt faster and deliver stronger results. One client used short, scenario-based workshops to train AR staff on AI tools, which increased engagement and improved rollout success.
Step 4. Evaluate AI in AR Software
Comprehensive AI Functionality: Choose platforms capable of addressing multiple AR functions (e.g. credit assessment, invoice management, collections prioritization, cash application automation, and predictive analytics) to achieve holistic improvements across the AR lifecycle. Ensure AI solutions include robust analytics and reporting tools that provide actionable insights, track key performance indicators, and clearly measure ROI. Choose intuitive, user-friendly tools to ensure rapid adoption, minimize resistance, and quickly achieve productivity improvements.
Services: Consider what services accompany the AR automation software as it relates to solution customization, integration services, and a partner in helping you reach out to payers to encourage the use of digital invoices, electronic payments, and online payment portals.
Prioritize Seamless Integration: Opt for AI solutions that integrate easily into your existing financial ecosystem. Solutions should enhance rather than disrupt current workflows. Common integration challenges include disruptions to daily operations, unexpected downtime, and costly data migration efforts, making seamless integration a critical selection factor. For example, Billtrust offers connectors with 40+ ERPs and financial institutions.
Evaluate Proven Success and ROI: Seek AI vendors with demonstrable successes and measurable outcomes. Consider asking specific questions such as:
- What measurable improvements in DSO have other clients experienced?
- Can productivity gains be clearly quantified?
- What metrics demonstrate reductions in errors and operational risks?
Detailed case studies and clear references from comparable organizations can provide deeper insights into potential vendor effectiveness.
When using Billtrust’s AI finance tools, companies recognize productivity gains of up to 80%, DSO improvements of up to 50%, and cash application match rates of +95%.
- International cinema chain Kinepolis reduced its DSO by 13 days.
- Manufacturer Acushnet used cash application automation to achieve matching accuracy rates of +99.9%, even with complex multi-line remittances.
- White Cap, a building materials supplier, recognized productivity gains equivalent to 3 full-time employees and saved $36,000 within the first few months of automating their AR processes using AI.
AI Investment Thrives Amidst Economic Uncertainty
Despite macro-economic trends putting downward pressure on financial outlooks and corporate spending, investments in AI automation are still on the rise. According to a Billtrust survey of 550 finance professionals, 67% are allocating more than 10% of their 2025 budget to AI automation, even in the current economic climate.
This is a clear indication that AI has become a fundamental requirement of financial leadership. The advantages of AI extend beyond mere automation and productivity improvements. The survey reveals that 90% of financial decision-makers now depend on AI for financial decisions, and 83% acknowledge that AI has positively impacted their approach to managing financial risk.
Step 5. Prepare to Continuously Improve and Optimize
AI solutions require ongoing monitoring and continuous refinement post-implementation. Regularly evaluate performance to maintain accuracy, relevance, and alignment with organizational goals.
Financial leaders who systematically evaluate and enhance AI readiness in AR management stand to dramatically improve operational efficiency, cash flow, and customer satisfaction. Organizations that strategically integrate AI into their AR operations, emphasizing strong governance, precise data management, and ongoing refinement, can significantly enhance their operational effectiveness and strengthen their financial performance.
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.
