One of the hottest topics in the business and technological world right now is the rise of artificial intelligence. Set to transform every aspect of society, business, and industry, companies continue to innovate, and we are approaching an AI-driven world at breakneck speed. But how will AI—and its ability to learn (machine learning) affect the future of business, finance, and decision-making? How will it fit into an ERP strategy?
A brief primer on artificial intelligence
Two of the most-hyped terms since the rise of cloud computing in the 2010s have been artificial intelligence and machine learning. Applying efficiency to business operations through automation of boring and repetitive tasks, AI can analyze and present information on vast swaths of data and do so in much less time than a human.
However, there are many different ways to apply AI to different tasks, with one of the most important of these being the financial world.
More than hype
The tech industry is no stranger to hype—neither is finance. However, as businesses and finance teams process an ever-expanding amount of data, humans are becoming less and less efficient at tracking, measuring, and analyzing it. As the speed of business increases, however, companies are turning to robots for the ‘grunt work’ in the finance department and empowering people to do more with the information they have available—all as a result of artificial intelligence.
Capabilities like data integration and blending, data catalog, data preparation, data enrichment, governance, discovery, and visualization, are now designed as a modern continuum to support enhanced business decisions.
The many ‘flavors’ of artificial intelligence
As it continues to expand, there are many ‘flavors’ of AI coming into play that will change the way your software operates now and in the future. According to the Harvard Business Review, there are currently seven key areas in which AI is gaining a foothold:
Robotic process automation (RPA)
The automation of basic tasks using business logic and rules. RPA “bots” are used for highly repetitive tasks such as those used to process transactions, manipulate data, respond to queries, and communicate with other systems. Some question whether RPA qualifies as AI. Under the above definition, it does.
Machine learning: Getting robots to think like humans
Machine learning is a key subset of AI, which originated with the idea that machines could be taught to learn in ways similar to how humans learn.
A way of achieving AI without complex programming, rules, and decision trees. Instead, with machine learning, data—often in very large amounts—is fed into an algorithm so the algorithm can train itself and learn.
Deep learning is a subset of machine learning. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep artificial neural networks are a set of algorithms that have set new records in accuracy for many important problems, such as image recognition, sound recognition, recommender systems, etc.
A field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images and deep learning models, machines can accurately identify and classify objects—and then react to what they “see.”
Natural language processing (NLP)
A subset of AI that helps computers understand, interpret, and manipulate human language.
Machine learning techniques that make it possible for human users to understand, appropriately trust, and effectively manage AI. Various organizations, including the Defense Advanced Research Projects Agency, or DARPA, are working on this.
A term coined by Gartner to refer to technology platforms that automate and enhance IT operations by
- using analytics and machine learning to analyze big data collected from various IT operations tools and devices in order to
- automatically spot and react to issues in real time.
How AI will affect Enterprise Resource Planning
Although set to enter nearly every aspect of personal and professional lives, ERP vendors have recently begun to integrate AI into their products. Whether it allows for better support through natural language processing, faster analysis and smarter decision making, or more productivity and happiness at your workforce, the introduction of AI technology into ERP is something that will deliver benefits in the near and long-term.
From RPA to AI
Based on a survey of 64 corporate controllers at companies with greater than $1 billion in revenue, Gartner found that 50% of controllers and their teams are either in the process of implementing RPA (31%) or are in what it calls “operational” mode (19%). And within just two years, Gartner expects 88% of such controllership functions to be in one of those two buckets.
However, it’s much bigger than that. McKinsey found that the activity is not confined to large companies—businesses across the size spectrum are piloting this flexible, promising software to automate workflows like procure-to-pay, order-to-cash, and record-to-report.
Depending on the scenario, robotic process automation may or may not be included officially as a part of AI, but did play a major role in getting vendors to where they are today. RPA is designed for business-rules-based, non-subjective, repetitive tasks; AI does more.
Making AI work for you
As you look toward the future, AI should be at least on the CFO radar—even if your only foray into it is through embedded AI. AI uses the power of today’s computer systems to perform tasks that normally require human intelligence. ML uses algorithms that allow financial software applications to learn by being trained to identify key patterns. Both have matured to the point where now they have become key financial management technologies used by the modern CFO.
While most ERP systems feature dashboards that can provide real-time information on everything from inventory turnover to employee productivity, dashboards can’t tell you when to re-order inventory or hire your next employee. This is where AI and ML can be an essential tool. Using your ERP system, you can quickly gather data and generate reports with the push of a button. But ML can take data gathering one step further and help CFOs reduce repetitive tasks and make more strategic business decisions.
Choosing an ERP with the future in mind
When it comes to selecting ERP, you are looking for a product that will last you up to, and possibly exceeding, a decade—something that grows with your business and positions your company for success. While many companies have forayed into the AI world, Acumatica Cloud ERP is actively evolving their AI platform. As demoed at Acumatica Summit 2019, the company continues to improve AI skills and with their recent acquisition by EQT Partners, will have the backing of a powerhouse and code base of another ERP vendor focused on ERP—IFS.
It’s an exciting time for ERP, and if you are looking to find your way into the cloud and onto an intelligent ERP platform, we invite you to learn more about Acumatica and NexTec.
NexTec Group is proud to work with Acumatica to deliver this powerful solution for our customers. As one of the leading partners, we have the skills to customize and deliver Acumatica, no matter your size, focus, or complexity. Get to know more about the update, the product, and our work with Acumatica, and when you’re ready to learn more, contact us for a free consultation.