AI and automation in finance - how to get the basics right (2/2)
- Summary:
- Before making the leap to AI and automation, finance leaders have to get the basics right with their data and their people, suggests Workday's Tim Wakeford
Any technology that can reduce manual input and the associated human errors for transaction processing and governance, risk, and control (GRC) will free up finance professionals for more strategic work. Repetitive processes involving large volumes of data are ripe for automation — especially in areas where improvements in analytics or speed would be an advantage, such as GRC.
Yet, before making the leap to AI, finance leaders have work to do with their own data, in terms of getting to grips with analytics and ensuring the integrity and quality of their own information. As Deborah O’Neill, a partner in Oliver Wyman’s Digital and Financial Services practices, explains in a Harvard Business Review article.
Companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralysed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.
Getting your data in order
In terms of automation, CFOs should ask themselves if there are opportunities to automate in areas that eat up valuable resources and slow down operations. Some of these areas include planning, budgeting and forecasting, financial reporting, operational accounting, allocations and adjustments, reconciliations, intercompany transactions, and close. In other words, a large portion of finance’s workload can benefit from automation.
Once key finance processes are automated, CFOs need to develop structured analytics and centralise data processes, so that the way data is collected is standardized and entered only once. The shift away from legacy on-premise systems to the cloud means that all systems lead back to “one source of truth,” updates apply to the entire system, and decisions are based on a single view of data.
In a 2016 EY survey, 57% of CFO leaders agreed that building skills in predictive and prescriptive analytics is critical for the future. Consider that there are a number of upcoming changes under IFRS and US GAAP. These include implementing changes to revenue recognition accounting standards, leases, and financial instruments, and understanding how these changes impact the entire business, not just finance.
Auditors regularly consider external data sources to understand risks, plan the audit, and confirm company assertions. To incorporate AI into their audit methodology, auditors need to understand systematically how those data sets are structured; how they differ from one industry, client, or source system; and how to transform the data reliably for use in their solutions.
Preparing people and technology for AI
Striking the balance between emerging technologies and an organization’s most important asset — its people — is going to be key for the future of finance. With finance being one of the functions most impacted by automation, CFOs must remember that the success of any technology will always depend on the capabilities of the people using it. As highlighted above, industry experts have spoken positively about the potential for financial professionals to move into more strategic data interpretation roles as the machines take over the more manual, tedious aspects of the work.
One question remains. Why would a business not take this opportunity to transform its finance function and deploy the latest cloud-based applications on a technology platform that was built to support constant change? The days of customizations and endless add-ons to integrate a vendor’s technology stack seem outdated at best, and now is the time for change. CFOs should have the mind-set to be continually re-evaluating the systems they are using and ensuring they meet the needs of the business.
