In an era of cloud computing, AI, and big data, why haven’t I encountered a single company where the financial close isn’t a manual, all-hands-on-deck fire drill?
Why are we still so dependent on sheer manpower to perform one of the most fundamental business processes? This is what I will explore in this article. Read on not only to get an understanding of why things are the way they are, but also how to have what I call a no-drama close.
From the outside and to non-Finance people, it may seem simple: add up the revenues, tally the costs, and produce the core financial statements. But as a data & AI strategist, when I look under the hood, I actually see a perfect storm of systemic issues that make this “simple” task a huge effort. Every single time.
Let’s look at some of the reasons why.
The good news for our Finance Bros is that the core of the problem, of course, isn’t finance; it’s the data flowing into finance. The finance team is often the last stop for data from dozens of systems: the CRM, the ERP, HR platforms, logistics software, etc. If that data is messy, inconsistent, or late, finance doesn’t get to do accounting. They have to do data janitorial work. They spend days, not hours, manually cleaning, validating, and reconciling numbers that should have been correct at the source. It’s the classic “garbage in, gospel out” problem, where they’re expected to produce pristine reports from flawed raw materials.
Finance professionals are, by nature and necessity, meticulous and risk-averse. They are the gatekeepers of the company’s financial integrity. This instinct for control is vital, but it can also show up as a deep-seated scepticism towards far-reaching automation. A process run by a machine can feel like a “black box.” Many finance leaders want their teams to be able to “touch and feel” the numbers. The result? A preference for the familiar, manual control of an Excel sheet over a more efficient, automated system they don’t fully trust yet.
Beyond the technical and process challenges lies the most powerful force in any organisation: inertia. People get comfortable with their routines. The month-end scramble, as stressful as it is, is a known quantity. The team knows how to handle it. Proposing a new system or a redesigned process means learning new skills and venturing into the unknown. Often, this change is considered a greater risk than simply struggling through the “busy week” one more time. It’s not laziness; it’s a natural aversion to change.
So how do we reach a point where scheduling a meeting during the close is actually possible? It requires a fundamental shift—moving beyond simply buying new software.
First, treat data like a product. This means enforcing rigorous data governance and quality standards at the source. Finance should be the consumer of high-quality data, not the fixer of everyone else’s data problems. That requires making business units accountable for the data their activities generate.
Second, automate intelligently—not just for automation’s sake. Before applying any technology, use tools like process mining to understand how the close actually happens today. Identify the real bottlenecks and redundancies. Then apply smart automation—such as AI for anomaly detection or machine learning to forecast accruals—to augment human capabilities, not merely replicate repetitive clicks.
Finally, reframe the mission of the finance team. The goal of digital transformation isn’t to do the same work faster; it’s to elevate the work itself. Position automation as the tool that frees finance professionals from routine tasks, enabling them to become true strategic business partners. This shifts the conversation from job security to career growth and impact.
