When OmPrompt began in 2004, the application of Artificial Intelligence to everyday devices and processes was almost a pie-in-the-sky dream. From the smallest smartphone to the biggest supercomputer – every corner of enterprise and personal computing is embedded with some part of AI.
And for a good reason – combining AI with machine learning and robotic process automation (RPA) reduces errors, improves spend, boosts efficiency and accuracy across a multitude of business functionalities.
This can be seen quite clearly across the often complex order-to-cash cycle – an essential part of any business along the supply chain. It’s where a company and a customer meet, where orders are created and fulfilled, invoices are sent, and payments are processed.
Since our inception all those years ago, many companies have improved their order to cash cycle using a multitude of software and expertise in combination with AI. We’ve learned that it’s not what you apply, but where you apply it and when.
Order Processing Automation Benefits
With orders originating from various sources (ecommerce, email, fax, text and EDI connections), it’s a complex cycle in many instances. Many businesses have worked hard to automate where they can. When processes like invoice creation, dispute resolution and payment settlement are added on top as manual endeavours, it slows the entire thing down.
Customer service teams who should be on the ground floor with customers are instead spending their days keying in manual data, despite automated processes that come before and after them.
Some, but not all, order management platforms can address these issues. OmPrompt, for example, streamlines the OTC cycle from top to bottom – enabling customer service teams to ensure customer needs are met.
Many organisations have implemented order to cash automation, but there’s always room for improvement. Some may automate things like documentation ingestion, invoice creation and dispute resolution but leave other activities as manual processes. For example, staff may still be comparing invoice numbers against current orders or matching payments and remittances to ensure what’s being automated is coming out correctly.
This doesn’t need to be the case.
AI+ML=100% Order to Cash
Data is currency. And proper automation ensures that currency is spent and distributed correctly. It does that by analysing all the information your fed and identifying patterns that humans cannot. AI can then streamline these patterns and automate them, saving both time and money.
Here are a few ways OmPrompt’s AI brain works in this regard:
Resolving payment discrepancies quickly is a sure way to keep customers happy. However, if a manual consideration of each dispute slows that process, it’s not optimally using customer service’s time and leaving the customer waiting longer than they should be.
What we know, however, is most OTC disputes don’t need any human intervention. It’s usually down to being incorrectly billed or an item missing from delivery. A customer service rep may have relevant data about the sale like the cost and products ordered – but usually don’t have the authority to make any further decisions above, helping to identify and recognise a claim’s validity.
OmPrompt’s AI technology speeds up resolution by recognising patterns and immediately assessing the validity and scale of a claim. A business can then easily prioritise disputes based on which need human intervention, speeding up the entire process. If it’s a case of a case missing, or an incorrect bill, this can be flagged on the customer’s account and easily rectified with the push of a button.
Traditionally speaking, invoicing as a process is one that’s manual and slow, with paper invoices moving from person to person internally for approval before being sent to a customer who then sends it back with payment. At its most basic level, the automation of this process means customers get their orders faster, and companies reach their money quicker.
But AI can be implemented further to streamline compliance regulations. Different regions mean different payments, some needing special authority, stamps or clearance before payment is finalised. Keeping up with this manually is cumbersome.
AI learns what each customer needs to receive an order and automates this process going forward. Automated cash collection is also faster, with an AI engine knowing which customer is best to contact and at what time.
It can also play a role in fraud detection because identifying patterns or anomalies is what a machine learning engine does. Usually, an invoice is printed, scanned and then emailed or sent in the post. No one is looking at the invoice’s data. AI, however, analyses each data set as it goes through and cross-referencing it to past invoices. If it detects an issue, it can be flagged for further examination.
For customers receiving invoices, AI can ensure that nothing is incorrect, that they’re being billed the correct amount for the right products. It can also proactively verify invoice data and banking information before allowing a payment to be completed.
Despite all the world’s technological advancement, in 2021, most orders still come in via email. Most companies employ someone to sift through the emails, extract order data like customer identifiers and order numbers, and then forward the emails to the correct department.
Much of this can be done automatically.
An RPA bot can identify data within the email, recognise the products being ordered, determine whether there are duplicates and then forward it to where it needs to go. It can also resolve how data is represented: are dates from the customer’s ERP represented as dashes rather than dots? Does it keep zeroes in? American or European spelling? Format discrepancies can be uniformed and then processed in an instant. Thanks to a Business Rules Engine, the RPA bot will always remember changes that have been made and instantly apply them to orders going forward.
Artificial intelligence, machine learning, and robotic process automation all inherently do the same thing – ingest information, learn from it, apply it going forward. Meaning, they can enable individual processes to learn from one another and automatically boost their performance over time.
Any process that relies on a human investigation or moving of data is ready to be automated. Resources and imagination only limit the potential!
Next week we’ll delve into the considerations in planning any organisation must think about before applying AI further into their order-to-cash cycle. Subscribe below to receive that article directly to your inbox!