Since 2022, AI has been the dominating story in tech after the public launch of ChatGPT. In the first days of AI beginning in 2022, AI-enablement was the introduction for many to how to utilize this technology for operational purposes. With an AI-enabled present, we are looking to emerging AI use cases like predictive and prescriptive analytics, and eventually, to AI-enabled automation and decision-making.
For many organizations looking to modernize their financial operations, there’s a lot to consider.
The Changing Roles of Finance
The finance industry, and accounting specifically, have been slow to adopt new technology. Accounts Payable (AP) and Accounts Receivable (AR) have historically involved significant manual efforts: data entry and manual reports in AP and chasing after invoices in AR. Now, both of these functions can realize significant efficiency gains with the use of automation and AI technology to manage cash flow and maintain relationships.
Businesses like SquareWorks Consulting and ezyCollect are creating opportunities for organizations to easily access AI tools as part of their established process when managing the books through NetSuite. By integrating AI directly into the solutions companies are already using to manage their AP and AR, they can see the effects on their business without having to invest in significant employee training.
What Does AP and AR Automation Look Like?
The current state of AP and AR without automation is occupied by manual data entry, naturally error-prone with only partial automation.
Accounts Payable:
- While some companies have managed to remove some of the manual efforts, there are still very time-heavy tasks, prone to errors, manually managing invoices, sending out for approvals, stuffing paper checks into envelopes.
- The most significant change is that when building automation into your accounts payable processes, at SquareWorks we don’t just automate everything, but replace bad processes with technology, fundamentally changing the job.
- For example, a function like invoice capture to input specific dollar values can be automated with a high degree of confidence and automatically process the amount and then submit for approval.
- By automating these rote jobs, the job of an AP professional then becomes one of reviewing anomalies and working strategically to see how AP can be optimized overall, focusing on cash flow versus managing a constant flow of paperwork.
Accounts Receivable:
- Traditionally, AR is a follow-up role to manage the relationships between you and your vendors, while walking the tightrope to ensure your company gets paid.
- Where ezyCollect sees a huge impact with AI in accounts receivable is predictive use cases: calculating risk levels of different customers based on internal and external data, allowing for the creation of custom automation and workflows.
- AI can also be used to establish where the human touch is the most important or effective; high-value customers who benefit from a soft touch, or high-risk customers who require more frequent follow-up.
- Predictive analysis to help organizations avoid late payments from occurring, by helping client onboard new debtors or businesses they want to extend credit, using predictions based on hard data rather than references; this ensures better business decisions are made upfront, such as setting up specific payment plans based on risk levels.
The Importance of Keeping a Human in the Loop
At the end of the day, B2B AI has to be a completely trustworthy endeavor; eliminating bad processes is impossible unless you are entirely confident in the capabilities of your AI tool. In the three years since ChatGPT came to the public forefront, we have seen huge leaps in the reliability of AI.
One of the most important aspects of automation is knowing when and where to keep a human in the loop.
Getting paid faster can be a huge upside to automating AR and using AI-enabled workflows for predictions and speed up processes, but it cannot eliminate the human interaction. The human interaction is crucial to keeping a competitive edge, especially as good customer service is increasingly measured by this factor.
The human factor is also important with managing expense accruals, a time-sensitive and significant source of manual efforts within AP. AI can help predict what expenses should be on the books based on open purchase orders and historic information, without having to individually reach out to every single vendor for every single open transaction. While keeping the human in the loop, we try to improve the human aspect of the job in a significant way by saving time.
The Evolution of Deep Learning in Finance
The creation of large language models (LLMs) to power deep learning models has changed the game for small and medium businesses looking to utilize this technology for themselves. Niche businesses like SquareWorks and ezyCollect now have the ability to tap into these models and power their own independent products and produce more reliable results, which are then more easily accessible by other organizations.
Within NetSuite, AI-enabled tools can be deployed directly out of the box with no separate systems needed; the technologies available via deep learning models are available right at your fingertips.
AR utilizes deep learning to establish a silent partner within the finance operation, one which can adapt in real-time based on the data it’s constantly ingesting. Eventually, the AR partner will sit in the background of every finance operation.
Developing Trust in your AI
While it’s easy to talk about predicting behaviors and automating processes with AI insights, it does require a high level of trust. When AI is coming up with suggestions and automating processes on its own, it’s providing strategic foresight into your organization’s finances and proactively managing financial operations, allowing humans to focus on the interpersonal relationship side—as long as you don’t have to double check everything for errors.
A model that can learn dynamically from your data, without needing constant human intervention to provide answers, is certainly the ideal.
It’s never been easier to deploy AI within a business. This is why it’s so important to ensure the human touch remains integral to your businesses’ financial operations and processes when using AI. Don’t just automate your processes with AI for the sake of it; fix bad processes to optimize your financials and accelerate growth.
If you’re interested in a rewatch of our webinar, AI in B2B Finance: Smarter AP & AR for Better Insights and Workflows, click here to download.
