For example, let’s consider a large scale industrial manufacturer based in the United States with Accounts Receivable of $1 Billion. In 2020, the manufacturing industry averaged 51 days for Accounts Receivable to be collected.
Let’s assume the company used a Generative AI automation tool to automate the collection, processing and approval of purchase orders, generation of invoices and routine follow up of collections. If post-automation is able to conservatively reduce its average accounts receivable collection period to 51 days (or roughly 10% improvement) over $100 Million of capital would be unlocked for the business. With high interest rates, this $100 Million of additional capital can have profound effects not only on the leverage of a company, but on the interest paid during inflationary periods.
The company can use this cash to pay its suppliers (and possibly take advantage of discounts), potentially reduce its own discounts given to customers for early payment and invest in growth opportunities without needing to borrow additional funds or tie up as much cash in working capital. The reduced need to rely on short-term lending makes the organization more agile, and better prepared for any potential recessionary environments that may occur in the future
So why haven’t more businesses automated more of their accounts receivable processes to date? One word: exceptions.
Processes within accounts receivable are document heavy and can have numerous rules and idiosyncrasies depending on the customer. Differences in payment terms, variations in formats across documents and errors often throw exceptions that break legacy automation tools like RPA. Fixing and maintaining these processes with legacy automation can cost 5X in services the cost of automation licenses making the ROI on many processes low or negative.
However, Generative AI Automation like Kognitos, and the introduction of Conversational Exception Handling changes this dynamic. With Conversational Exception Handling, operations and finance team members within AR are able to quickly resolve any errors that occur without any needed training on an automation tool. If exceptions are recurring, the AR processor can teach Kognitos with simple English instructions how to handle a situation in the future. This removes the need for developers and the traditional 5X cost to maintain automations, leading to higher, positive ROI. Here is an example of such Conversational Exception Handling: Conversational Exception Handling In Claims
Businesses rightfully should focus on the benefits of cost reduction when implementing automation, but also should consider second-order effects on the organization’s finances. In the high interest rate environment of today, with inflation persisting throughout the economy, reducing accounts receivable in days is critical. Automating the manual steps, error handling and collection follow ups of the AR process not only eliminates labor cost in the business, but can free up hundreds of millions of dollars in cash and reduce interest expense from short term borrowing. Conversational exception handling makes automating these exception heavy processes possible, and should be a key focus for automation efforts in 2023.
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Our clients achieved:
- 75%manual data entry eliminated
- 30 hourssaved on invoicing per week
- 2 millionreceipts analyzed per year
