Artificial intelligence and machine learning (AI/ML) are revolutionizing the business landscape across all sectors by enhancing various business activities like content creation and data analysis.
As businesses invest in tools like ChatGPT to boost their marketing, sales, and operational performance, they’ll want to also remember there are back-office processes that are just as important to invest in.
In this post, we’ll examine how and why businesses should use AI/ML capabilities to improve their accounts payable (AP) and accounts receivable (AR) processes to enhance their cash flow.
A healthy and predictable cash flow should always be a top priority for any business. It keeps operations running smoothly, ensures good financial stability, and enables businesses to seize new opportunities. In previous blogs, we’ve explored the dangers of legacy AR processes, and the benefits automation can have on a firm's cash flow. As automation is a precursor to advanced technologies like AI/ML, it only makes sense that setting up more intelligent insights would enable even greater control over how money flows in and out of your business.
Unfortunately, due to an unrelenting combination of pop culture and overhyped product marketing, many seasoned financial professionals remain unsure of how best to harness the potential of AI/ML. However, when it comes to AR and AP, there are compelling reasons to embrace these new technologies and ai systems. All businesses must contend with both AR and AP (whether they want to or not!). The early track record of AI/ML in helping Finance teams leverage more accurate insights and recommendations to improve these historically manual and error-prone processes is indeed promising.
Firms of all sizes are sending and receiving dozens, hundreds, or even thousands of invoices on a monthly basis. Each interaction brings with it a deep and rich set of both behavioral and financial data. By incorporating technology into your AR and AP processes, you cannot only shave away hours of labor, you’ll also optimize strategies to capture the key metric you are fundamentally looking for (e.g., getting paid faster, processing incoming invoices quickly in order to selectively take advantage of early pay discounts, etc.).
Let's start with the most basic model associated with financial process automation – optical character recognition (OCR). Instead of manually scanning or importing invoices, OCR translates figures and characters automatically and triggers the creation of all necessary AP entries and workflows. As OCR is limited in terms of how it understands language and intent, AI/ML introduces a technology called natural language processing (NLP). NLP not only recognizes but deciphers strings of words to “understand” statements, intents, and associated activity.
Pairing NLP with OCR for invoice ingestion can eliminate the need to manually translate and code unstructured invoices or take action from “free form” commentary from the invoice. This provides complex automation around matching and coding with deep automated context for reconciliation. Often, there are back and forth exchanges during payment cycles, usually done through email. NLP also offers the ability to analyze correspondence and automatically update notes on unpaid status or expected payment status.
Lastly, one of the most advanced and popular forms of AI is a large language model/generative AI. LLM/GenAI allows users to write commands as if they were speaking to an individual and receive results in the form of generated content. This type of model can be used to generate emails summarizing total due and past due amounts with links to pay for the associated invoice. It can also be used to generate invoices, AR and AP reports, and answer general questions rather than going through typical reporting methods such as writing “Pull up past due invoices over X amount” in a command bar.
Predictive forecasting and scoring compare current activities to historical metrics, ranking the likelihood of a favorable outcome. This technology could be used in AR to predict payment patterns, assigning labels to customers such as 'fast payer,’ 'slow payer,’ or 'at-risk payer,’ and measuring their impact on project financials. The insights derived from predictive forecasting and scoring can guide users towards specific actions that would yield the greatest results.
Prescriptive AI models offer businesses recommended courses of action based on historical performance. They provide valuable market context, benchmarking data that helps users improve their processes in line with market best practices. This type of AI/ML insight could greatly help AP and AR processes, especially in areas like invoice design recommendations, support for outbound communication cadence, and decisions on which invoices to pay and when.
AI/ML holds immense potential for businesses to evolve their AP and AR processes, positively impacting cash flow and overall business growth. As these technologies continue to advance it is important to ensure that your firm's billings and collections program is AI ready. A good first step is to ask the following questions:
If you answered yes to all the above, then your firm has laid the proper foundation for its cash flow to be optimized with AI/ML. If you didn’t, consider evaluating a solution that not only centralizes business activities and data, but also incorporates automation into your processes. This will allow you to generate AI/ML insights that are more accurate and have a greater impact on your firm’s cash flow and growth.
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