"A loss is only acceptable if it's a present investment into future profit." Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth (Business Essentials)
Every business, at the end of the day (or should I say a year), is about profit and loss. Having losses is only okay if there's a path to profitability and a clear vision of a profitable business model. From the national and global economy to all activities under CSR and ESG, everything is subject to the commercial viability of the enterprises. And it is every CFOs perpetual struggle to tangibly justify investments with returns and establish the company’s profitability, keep shareholders content, and bring in more investments. That said, finance is a high-risk and compliance-intensive discipline due to which it does not readily welcome emerging technologies. It is, consequently, behind most organizational functions in the adoption of innovation such as robotic process automation (RPA) and artificial intelligence (AI)/machine learning (ML). Reports suggest that 85% of all banks are still at the strategy stage when it comes to leveraging AI in new products/services development.
Indeed, software applications for order-to-cash (O2C), procure-to-pay (P2P), record-to-report (R2R), regulatory compliance, and performance management requirements are pervasive in finance. However, they are used in disparate and siloed manner, leaving ample scope for manual intervention even where it can be easily avoided. This, in turn, reflects as inaccuracy and inefficiency across various financial functions, especially in accounting. It is, therefore, not surprising that the CFOs have many sleepless nights during the fiscal year-end matching numbers and stressing over the book closure deadline.
Making a case for automation in finance
Any manual process and disparate/siloed software entail the risk of human error, and so is the case in all financial functions. Omissions, overlooking, and inaccurate entries into the application are common errors. Finance professionals all over the world typically end up wasting much of their time on inefficient, manual processes and siloed/disparate technological tools, resulting in shadow costs and inflated opex. This is the main reason behind the annual battle of negotiation between auditors and finance executives, in which the latter have to defend their decision to approve post-closure adjustments. On the other hand, automation of tedious tasks has proven to eliminate redundancies, improve accuracy, and improved visibility due to audit trail of automated operations. Also employee satisfaction allows employees to focus on higher value-added tasks that eventually expedites the whole process across industries that have more openly embraced it.
The priority list for automating your enterprise finance may seem long and daunting. However, leaders who have persisted on pursuing automation have also achieved the desired results.
Synchronised, automated solutions easily identify and flag errors that even multiple rounds of human scrutiny fail to find. However, there’s a catch. There’s simple automation and there’s strategic or progressive automation. Simple automation is the use of technology to take as much load off human shoulders as possible. This approach lacks vision and may end up yielding subpar results. In fact, nearly half of all automation initiatives in finance fail to improve efficiency and throughput to the utter dismay of the CFOs.
This happens when automation initiatives are implemented as a break-fix rather than a strategic enabler that aligns with the overall business objective. This brings me to AI/ML in finance, which enables your software solution to learn from the detected errors rather than just simplifying and expediting tedious tasks using software. As such, the algorithm updates itself with each error detection and becomes incrementally more intelligent over time. This not only improves the error detection capability of the software but reduces errors as well. This relieves your finance team of redundant tasks allowing them to devote their time and effort to core and higher-value activities where human intervention is imperative.
Paving the way for AI/ML-powered automation in finance
AI/ML-based automation will not replace humans in finance, it will ensure that technological and human capabilities are used complementarily and optimally. AI/ML makes it possible to create human interfaces while keeping the implementation simple to program, easy to understand and highly maintainable. Strategically implemented, end-to-end automation in O2C, P2P, R2R, payroll administration and other functions, can provide a prioritized list of tasks for the day to your employees. Machine learning in finance can provide a greater opportunity to evaluate external and internal data and identify the business drivers. They can address the exceptions and anomalies already highlighted based on factors such as timing, vendor information, and the general ledger code.
That said, for your organization to reap the benefits of automation in finance, you need to address the innate human resistance to change. Inculcating a culture of actively recommending technological or process upgrades in employees is crucial for successful implementation of any innovative practice. On the other hand, you should gradually phase out traditional, top-down approaches to investment governance for want of greater flexibility. Here, budgeting, and periodic mapping of benefits with investments will be critical to overcome the old investment governance approach and get the approval on a comprehensive automation strategy.
Warren Buffett said, “Someone is sitting in the shade today because someone planted a tree a long time ago.”
The priority list for automating your enterprise finance may seem long and daunting. However, leaders who have persisted in pursuing automation have also achieved the desired results. Their automation initiatives are improving the efficiency of their department. Their team members are also adding more value to their respective organizations by taking up responsibilities that require human intervention. The need is to start with identifying the right set of use cases and then an efficient machine learning services partner can help to develop and implement the right models by focusing on specific data and business domain. This after a thorough understanding of the expected output that is going to be extracted from different sources, the data can accordingly be transformed to get the desired results. With a technology major like HCLTech by your side, you can also successfully implement end-to-end automation of your finance functions. making them more productive, profitable, compliant and resilient to disruptions.