AI for Accounts Receivable: Why You Should Fix Your Current Systems Before Investing

Before diving into AI investments for Accounts Receivable (AR) operations, it’s crucial to first optimize what you already have. At present, AI has not demonstrated a strong return on investment (ROI) in AR, and it may introduce unnecessary complexity and development costs without delivering significant benefits. Here’s how to approach this:

Before AI Investment…

• Fix Your Current AR Processes: Before taking on any system upgrades, the first step is to evaluate your organizational setup, policies and the processes used. In doing so, you will be able to correct sub-optimal operations that are considered “low hanging fruit” and can be improved without any investment whatsoever. You can get a consultant to look at it for you, or do it yourself, but do not not skip this step.
• Maximize System Capabilities: Many AR systems, even those that are decades old, are still capable but often underutilized. Significant performance improvements can be achieved without new software. Start by reviewing your current system to ensure it’s being used as intended. Are you fully leveraging its features? We frequently see companies underutilize their AR systems. Once you understand the full capabilities of your existing system, ensure your team is properly trained. 
• Evaluate Integration Challenges: Integrating AI with existing AR systems, especially legacy ones, might require significant customization and re-engineering. This could lead to additional costs and delays, complicating the implementation process.

Compare Solutions and Cost

• Consider Targeted SaaS Solutions vs Full Overhauls: If your basic financial and AR systems are workable, but some areas need automation, consider targeted, cloud-based “bolt-ons” that will integrate with your system. These solutions are optimized for specific tasks and often deliver the desired improvements without the need for a comprehensive overhaul.
  
• • Examples of targeted SaaS solutions (available in a package or individually) include:
    • Data aggregation for Auto Cash processing
    • Bots for customer portal access
    • Bots for proofs of delivery
    • Collection Automation
    • Automated Deduction Processing
    • Credit Monitoring
  • These tools utilize flexible rules, multi-variable matching for cash application, BOTs that import outside data, automated deduction reconciliation, and autonomous collections—without requiring AI or a complete system change.
• Complete a Cost-Benefit Analysis: Before committing to any technology, it’s essential to perform a thorough cost-benefit analysis for AI. Consider not only the financial costs but also the potential impact on operations, employee morale, and customer relationships. AI implementation requires significant upfront investment, and the return may be uncertain. Also, consider the one-year plus work and disruption to get almost anything completed.

Evaluate Scalability, Compliance, and Human Oversight

• Assess Scalability: AI might be more beneficial for large enterprises with massive data volumes, but for small to mid-sized businesses, the investment might not be justified. Consider whether the scale of your operations warrants the use of AI or if existing systems can suffice.
• Consider Regulatory and Compliance Challenges: Financial data is highly sensitive, and AI systems must comply with relevant regulations. The risk of non-compliance can lead to significant legal and financial repercussions, so it’s crucial to ensure any AI solution adheres to these standards.
• Maintain Human Oversight: While AI can automate many tasks, financial decision-making often requires human judgment. Over-reliance on AI could lead to errors that might go unnoticed without human intervention, particularly in complex or unusual cases.

Plan For Organizational Change

• Manage Change Effectively: Implementing AI is not just a technological upgrade but a cultural shift. It requires buy-in from all levels of the organization, rethinking processes, retraining employees, and managing resistance to change. The complexity of managing such a transformation could outweigh the potential benefits.
• Plan for Future-Proofing: While AI may not be necessary now, the landscape of financial operations is evolving. Stay informed about developments in AI technology and remain agile enough to adopt AI when and if it becomes truly advantageous.

AI for Accounts Receivable – Is It Necessary (or even desirable)?

While AI holds transformative potential in many areas, its application in financial accounting should be approached with caution. Consider whether the AR function is critical enough to justify the time, money, and risk associated with AI investments. The speculative returns may not outweigh the costs. In addition, you may consider using a professional outsourcing firm that already has optimized software and experienced staff.

Risks: A practical and serious concern is ensuring that debit and credit transactions are accurately matched to prevent cascading mismatches over time, which will undermine the reliability of your accounts. Additionally, constructing a verifiable AI decision audit trail poses significant challenges.  The “AI made me do it” will not work.

In the B2B environment (except for extreme high-volume operations) AR operations are generally predictable and resource-efficient. A mature, rules-based AR system can often achieve near-optimal results without the risks and investment associated with AI.

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