Artificial Intelligence (AI) is hailed as the \u201cnext big thing,\u201d but how far away is it from full adoption? Operationalising AI-powered models demands new investment, new skills, and a more collaborative way of working.\r\n\r\nWhen it gains momentum, AI has the potential to change the financial world and how finance teams work.\u00a0\r\n\r\nBut we are a long way from that at the moment.\u00a0\r\n\r\nAI is a broad term, and can be defined very differently, depending on your background and whether you\u2019re a \u201ctechie.\u201d\u00a0\r\n\r\nAI has many strands to it. A financial (non-technical) audience should understand (at a base level) how it slots into automation and process optimisation when planning for the future.\u00a0\r\n\r\nWhy? Well, through machine learning and neural networks, AI has the ability to recognise patterns in complex data sets far quicker and with far greater accuracy than a human being. But operationalising these patterns also requires complementary technology. The entire transition will be gradual, as AI is only one cog in a big tech-powered machine focused around intelligent automation.\u00a0\u00a0\r\nWhere AI ranks against other things\r\nToday, AI isn\u2019t even top of finance\u2019s new technology wish list. According to a survey cited in Gartner\u2019s Magic Quadrant for Cloud Financial Planning and Analysis Solutions, forty-six percent of respondents said predictive analytics is where they intended to invest the most money over the next couple of years. The second ranked technology was robotic process automation (43%), followed by artificial intelligence\/machine learning (35%).\r\n\r\nThis means the finance world thinks AI is important, but not as important as the building blocks needed to enable it. Forward thinking organisations are developing a specific plan of action, which starts with data analytics and automation, followed by process automation, and finally AI.\u00a0\r\nBuilding automation blocks around AI\r\nTo be ready for full-blown AI, finance teams need to review what they have right now.\u00a0\r\n\r\nFirst things, first. You line up and enhance existing software focusing on the low-hanging fruit: data automation.\r\n\r\nFinance teams \u2013 like many business teams \u2013 can suffer from clunky, time-intensive processes that do not support any type of progressive technology. To plan for the future with AI in mind, a finance team needs to review these promptly to ensure data automation and data quality is made a priority.\u00a0\r\n\r\nAnyone working in finance knows that manually dumping data into Excel and manipulating it is error-prone, not to mention unbelievably time-intensive.\u00a0\r\n\r\nIt is these types of use cases that need the boost. This process can be fully automated with financial reporting software. As a result, accountants can drill from summary data into balances, journals, or subledgers to investigate variances and fix reconciliation and data quality issues. This means less time spent on data collection and manipulation, as analysis takes centre stage.\r\n\r\nAs you can imagine, the productivity gains are huge, and reporting automation is becoming the norm in finance.\u00a0\r\n\r\nOnce over this automation hurdle, finance teams should start thinking about the next stage of automation, called \u201crobotic process automation\u201d (RPA).\r\n\r\nAccording to the IEEE Standards Association (IEEE SA), RPA refers to the use of a \u201cpreconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.\u201d\u00a0\r\n\r\nThis appears a long-winded definition, but it does reflect what\u2019s starting to happen within finance teams.\u00a0\u00a0\r\n\r\nRPA is very effective. Studies indicate it can reduce repetitive data entry tasks by 80 percent in accounts payable, financial close, and tax accounting. RPA is able to read data from one source and then automatically enter it into an ERP system. A financial or operational report is only as good as the data inside the ERP system. RPA can help quickly ensure that data is both accurate and exactly where it needs to be, leading to further productivity gains.\r\n\r\nRPA should not be mistaken for AI though. RPA is only mimicking human behaviour, not \u201cthinking\u201d like a human. Nevertheless, RPA is a conduit to enabling AI in the future, so we strongly recommend that finance teams build RPA into their technology plans.\u00a0\r\nEnter AI\r\nRPA sets a finance team up neatly for something called \u201cintelligent automation.\u201d Intelligent automation is a combination of process-driven tasks (RPA) and data-driven tasks (AI). AI is powering the process, but it is not completing it alone.\u00a0\r\n\r\nAI understands the meaning of data, whereas RPA focuses purely on a process. Take invoices: An RPA process could be programmed to understand a specific way of working within strict parameters. If you introduce a new supplier, invoice template, different tax rates, or any new data point, RPA cannot deal with it alone. To ensure everything runs smoothly, you need AI to make sense of this new information and decide how to handle it by \u201cthinking\u201d for itself.\r\n\r\nWhile finance has proven to be an early adopter of AI in comparison to other areas, they are still more focused on the earlier phases of automation. Combine these with AI and you have a type of automation with the potential to transform a finance team and give it a huge competitive edge in the coming years.\r\n\r\n\r\n\r\nRichard Sampson is the SVP EMEA at financial reporting specialist, insightsoftware.