Why crunch numbers when you can crunch time? Discover how AI & Automation in 2024 are transforming the accounting function of finance from transactional to analytical!
of Activities Dedicated to Analysis & Action
of Tasks Repetitive in Nature
Reduction in Manual Activities with AI & Automation
Shorter Cycle-time thanks to AI & Automation
For this article, we look back into one of our conversations—which we found extremely relevant for today's accountants—with Abhishek Gupta, Executive Director at EY, who has more than 20 years of experience in the industry transforming Fortune 500 accounting practices.
Albert Einstein once said, “the measure of intelligence is the ability to change”. This quote perfectly captures why today’s accounting teams must transform.
Firstly, over 50% of finance tasks are repetitive, often due to siloed systems and rapid organizational growth outpacing finance functions. This results in adding more personnel while clinging to outdated processes, which isn’t simply scalable.
Secondly, not all organizations have the budget or appetite for advanced technologies that could streamline these processes.
Let’s trace back to the roots of the problem!
Finance professionals often lack real-time insights because they spend too much time on manual tasks like general ledger entries and account reconciliations.
This focus on routine tasks prevents them from identifying the root causes of critical issues, such as unmatched balances, data inconsistencies across systems, or discrepancies in financial statements. As a result, analysis and action account for less than 10% of accounting activities, limiting the accounting team’s ability to provide strategic input.
While rushing to complete their close tasks, accounting teams rarely get any time to focus on understanding the data and recognizing patterns to support the bigger financial decisions taken by CFOs and investors. By the time they share their financial reports, the information is already stale and it’s time to start another close cycle.
Because manual tasks take up so much time, companies are quickly adopting AI and automation technologies in finance. Over the past decade, these technologies have greatly improved, solving many business problems that finance teams have faced for a long time.
As we look at the journey towards Autonomous Finance, we’ll see how moving from simple desktop automations to advanced AI solutions is helping finance professionals shift their focus from repetitive tasks to strategic analysis.
Also Read: Is Record-to-Report Automation Threatening 20% of Finance Roles? (link)
You can’t do anything in finance without technology now. That’s a fact. It’s new-gen finance with the help of which finance teams are evolving.
Many excel-based processes were primarily meant for someone actually doing the processes on their local desktop. This aimed to make work easier by using simple automations like Excel functions such as VLOOKUP and macros—these are basic automations.
With RPAs, we saw many players entering the market, providing solutions from an overall finance function perspective. This wasn’t just dedicated to small desktop-based automation but was function-based.
In finance, subprocesses like invoice processing, month-end closing, and billing often involve repetitive tasks. Initially, automation solutions used screen scraping technology to mimic human actions in ERP systems.
For example, RPA could move data between applications by imitating clicks. However, RPA had a major limitation: it couldn’t analyze unstructured data and lacked intelligence.
IPA, a more advanced solution, could process unstructured data.
Imagine a finance team that needs to analyze expense reports from various departments. These reports are often in different formats, such as PDFs, emails, and spreadsheets.
Without IPA:
With IPA:
The adoption curve became steeper, almost exponential, as organizations rapidly moved from IPA to Hyper Automation and GenAI. While the transition from desktop-based automation to IPA was gradual, the shift from IPA to GenAI has been remarkably fast.
Hyper automation takes IPA a step further with predictive analytics. It processes historical data to forecast future trends, incorporating both internal organizational data and external factors like micro and macroeconomic indicators, demographics, and potential disruptive events (such as COVID-19) to predict industry forecasts.
GenAI generates recommendations, commentary, and text. Unlike previous technologies, it’s not just data-based but also text-based. For example, when preparing quarterly 10-K reports in the US, extensive commentary is required to justify the numbers.
GenAI can analyze the data, create the complete report based on the data in your ERP system, and generate reasoning for trends—like explaining quarter-over-quarter sales increases. This eliminates the need for analysts to manually analyze data and provide commentary.
Two areas where companies today are looking to improve their Record-to-Report (R2R) process using AI are cost and accuracy in the month-end closing process.
This includes reducing audit observations, reducing manual errors in reporting, and decreasing the cycle time of the month-end close process.
According to IBM Institute of Business Value:
Having explored how AI and automation are transforming the financial close process, let's examine a real-world example that illustrates the benefits of streamlining the month-end close.
This case study demonstrates how these technological advancements can be applied in practice to address common challenges in financial operations.
An Anglo-Swedish multinational pharmaceutical and biopharmaceutical company faced similar challenges. Their month-end close cycle time was around workday 9, indicating an inefficient process.
They had to perform numerous manual journal entries in their system, and errors often occurred. For example, invoices frequently failed to pass through the subledger system, requiring manual information entry.
Let's dive deeper into their problem statement before exploring the solution EY provided and the resulting outcomes.
This British multinational, with a revenue of £16B and around 61,000 employees, was growing rapidly and launching numerous products. The finance organization needed to keep pace and provide enhanced business partnership and analytics for faster, better decision-making.
Problem Statement:
Solution:
Results:
Well... we don't want to sound outright dismissive about why many organizations still haven't become part of the AI revolution. There are many constraints such as cost, skill gaps, technology limitations, and use case challenges.
However, these shouldn’t be the excuses for not finding a solution that best addresses your concerns in accounting procedures.
In short, the time is now!
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