This day-to-day reality at work is in painfully sharp contrast to finance professionals’ personal lives, where everything from managing their household finances to shopping online to composing a text message has transformed into more automatic and personalized experiences. This is thanks in large part to artificial intelligence — more specifically, machine learning and natural language processing.
The disparity between professional and personal does not need to exist because this same technology exists within ERP systems today. Much of the continued reliance by finance and accounting teams on manual processes at work can be attributed to inertia and comfort, versus a lack of options. AI and its offshoots are still intimidating concepts for many people, regardless of their profession. It can be difficult to determine where and how to implement these innovative technologies in practical ways.
The move to the cloud in ERP systems has enabled new ways to embed AI, machine learning and natural language processing as a value-add to customers. ERP technology providers have a significant opportunity to educate their customers on how these embedded technologies will not only make day-to-day financial operations and management easier but also propel finance and accounting teams into the future with newfound strategic value.
What are the practical ways in which these technological innovations can move from buzz to beneficial? Pairing the right processes and workflows with AI, rather than trying to force it across the board, can create a competitive advantage and enhance the strategic value of finance and accounting professionals.
Let’s look at some key examples of how AI can be applied to financial functions within ERP.
Automate manual tasks
Machine learning plays a huge role in automating repeat, mundane tasks, and can free finance and accounting professionals to focus on value-added activities that will drive their business forward. As an example, machine learning can detect when a user is performing the same task over and over and offer to automate it in the future. Entry and approval of purchase orders, invoices and payments typically consume a significant number of hours for multiple full-time employees within a middle-market or large corporate organization. Month- or year-end close activities are also highly time-consuming. While these activities can require human intervention from time to time, rules can be put in place to create straight-through processing in the majority of cases and only route outlier scenarios to staff or senior management for their review or action.
Machine learning-driven automation results in cost reduction, increased staff productivity and overall operational efficiency gains. Because they’re spending less time re-keying information or pushing paper around the organization, team members will have more time to spend collaborating with one another, solving problems they were previously too busy for, and further optimizing the finance function.
Deliver richer, more actionable insights
Machine learning makes it possible to analyze massive amounts of clean data and detect patterns and other relationships to make predictions — even from potentially disparate sources — with incredible speed and accuracy. Machine learning embedded in the ERP system can examine historical data from multiple internal finance functions and external sources such as bank accounts to help an organization more accurately predict cash flow. This technology can look at an organization’s production and sales patterns to improve inventory management, or examine payables and receivables data to find discrepancies in regular bills or make suggestions about when to pay a supplier.
The full extent of actionable insights that a finance function could obtain by implementing AI for data analysis is difficult to quantify. Needless to say, leveraging the technology in this way will ultimately enable finance professionals to increase the overall financial health of their business. Improved cash management, optimized payables, and receivables strategies, and reduced risk of making fraudulent or erroneous payments to business partners or employees are all hugely beneficial outcomes and are just the tip of the iceberg.
Improve the experience for end-users and external business partners
The use cases above all underscore the myriad ways in which machine learning can make daily work in the ERP system less frustrating and more gratifying for finance and accounting professionals. Natural language processing, which powers digital assistants like Siri and Alexa, is another technology that can make ERP system interactions more human and intuitive. Natural language processing can help users more easily search and interact with massive amounts of data that may be stored across applications or other silos, with a simple voice-based search.
This technology could also be applied to analyze conversations between trading partners and work in conjunction with AI to suggest the next steps after a conversation has concluded. For example, if an accounts receivable clerk reaches out to a customer requesting payment of an invoice that the customer then claims to have not received, natural language processing and machine learning could work together to automatically type and send an email from the accounts receivable clerk that resends the invoice in question without it bogging down a team member’s inbox.
The benefits of these elegant, time-saving processes go beyond efficiency and productivity. As opposed to job replacement, providing employees with this level of technical assistance can help businesses attract and retain valuable talent.
We are still in the early stages of AI and machine learning’s impact on many financial and business management tools, including ERP. There is no question these technologies will ultimately transform work for the masses the same way they have transformed our consumer lives. How long will adoption take?
The answer likely varies by company size and industry. The turn of the decade will be an interesting time period for watching end users’ experiences with these technologies and the benefits realized, as this will help set the tone for further adoption within core ERP functions and beyond. For their own part, ERP companies should be focused on driving usage of embedded AI and machine learning across their customer base and ensuring their AI/ML strategy considers the capabilities and data from any broadly adopted plug-and-play technology partners in their ecosystem.
Source: Accounting Today