How does a finance team build a strategy around AI?

As Artificial Intelligence (AI) gains momentum and becomes more sophisticated, it will change the financial world and how finance teams work.

AI is a broad term, so understanding how it slots into financial technology and the role of automation is important when planning an AI strategy that is fit for the future.

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 can manage. When this capability is harnessed with other complementary technologies, AI has the potential to transform business processes, introducing huge efficiencies.

AI is much further off than people think in terms of full-blown adoption. The transition will be gradual, as AI is only part of the story. That means strategically planning your adoption of AI as the technology matures.

Today, AI isn’t even top of finance’s new technology wish list. According to a survey cited in Gartner’s 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 before 2021. The second ranked technology was robotic process automation (43%), followed by artificial intelligence/machine learning (35%).

This is rather telling, because it indicates a specific plan of action which starts with data analytics and automation, followed by process automation, and finally AI.

So how do you plan for the future?

First things, first. You line up and enhance existing software focusing on the low-hanging fruit: data automation.

To plan for the future with AI in mind, a finance team needs to review any clunky, manual, and error-prone processes that are in need of a refresh to ensure data automation and data quality is made a priority.

Anyone working in finance knows that manually dumping data into Excel and manipulating it is error-prone, not to mention unbelievably time-intensive. In recent years, this process has been fully automated with financial reporting software, making real-time data access possible throughout the month. Accountants can drill from summary data into balances, journals, or subledgers to investigate variances and fix reconciliation issues.

The productivity gains are huge, and we’re seeing the majority of finance teams reporting in this way, no matter the organisation size or ERP system.

Once data automation and analysis is the “new norm” for a company, finance teams should start thinking about process automation in the guise of robotic process automation (RPA).

According to the IEEE Standards Association (IEEE SA), RPA refers to the use of a “preconfigured 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.”

This looks like a long-winded explanation, but it very much reflects what’s starting to happen within finance teams.

RPA 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.

However, don’t mistake this type of tech for AI. RPA is only mimicking human behaviour, not “thinking” 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 future technology plans.

AI takes centre stage

RPA sets the stage to something called “intelligent automation.” Intelligent automation is a combination of process-driven tasks (RPA) and data-driven tasks (AI).

AI understands the meaning of data, whereas RPA focuses purely on a process. Take invoices: That process is programmed to understand a specific way of working in strict parameters. If you introduce a new supplier, invoice template, different tax rates, or any new data point, RPA is flummoxed. You need AI to make sense of this new information and how to handle it by “thinking” for itself.

Once the initial steps of automation (data and process) are managed internally, you’ve set the stage for AI to shine. The applications of AI in finance are already numerous, and it shows particular promise as a supporting technology for risk management and compliance.

While finance has proven to be an early adopter of AI in comparison to other industries, AI as mainstream is still a while away. An increased demand for understanding data patterns has directly contributed to the growth in demand for AI, and the future of finance is going to be heavily influenced by AI’s ability to set the stage for increasing competitiveness.

Richard Sampson is the SVP EMEA at financial reporting specialist, insightsoftware.

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