Digital transformation in finance has been a priority for several years, and the COVID-19 pandemic accelerated the process even further.
In Gartner’s recent report “Top Priorities for Finance Leaders in 2021” 69% of board directors stated that the effects of the COVID-19 pandemic are accelerating their digital business initiatives. IT and technology have the highest expected budgetary increase in 2021 making digitalization one of the top CFO priorities in 2021.
Where to start?
Digital transformation assumes the adoption of new technologies which lead to changes in processes, company culture, and increased productivity. The majority of companies start the process with fragmented data sources and various legacy systems in place. The situation in the finance department at an average company might look something like this:
There is an ERP and maybe it’s in the cloud (60% of companies still use on-premise solutions, while 64% are planning or they’re in the middle of a migration to the cloud). If not, the lower cost of ownership and the availability to the workforce might be a reason to consider the cloud option in the near future.
Budgeting and forecasting are probably done using spreadsheet tools, as is the case in 72% of enterprises. The process involves a lot of manual work, represents a big security risk, and an incredible waste of time. This could be one of the first areas to digitalize and see immediate results.
Our imaginary company probably doesn’t use rolling forecasts (only about 25% of companies do) and it probably won’t be able to implement them if the main budgeting and forecasting tool are spreadsheets. Rolling forecasts have been shown to save 50% of the time on budget preparation and increase overall profitability up to 10% which is a great incentive to investigate their potential.
Reporting is probably done manually combining multiple data sources. The reports often come too late and the numbers are questionable.
This is just a rough overview of some core systems and issues finance teams are facing to make sense of the data and steer the company in the right direction. The majority of companies are still a long way from the Single Source of Truth or real-time and data-driven decision-making. While a complete digital transformation is a lengthy and complex process that might take a few years, small-scale pilot projects could bring immediate benefits and get the ball rolling.
Pick a specific area where the effects will be immediate and the risk is manageable. Think about the automation of simple and time-consuming tasks (rolling forecasts, management reports) or even some that are complex for humans but rather simple for software like top-down budgeting or integrated scenario planning.
Automation vs. AI
In the context of finance digitalization, automation and AI are often mentioned together, but it’s important to make the distinction since they are essentially quite different.
The goal of automation is to replace human labor in manual, repetitive, and voluminous tasks. It’s software that needs manual configuration, a simple set of rules of what it should do, and when set up properly it can perform these tasks faster and more accurately than humans. Automation increases productivity and gives your team members the time to perform more complex and strategic tasks.
AI as an umbrella term generally means technology that can mimic human behavior. It uses machine learning to process large amounts of data and draw conclusions without additional programming or specific instructions. Or as the awesome blog summed it up nicely:
“Automated machines collate data; AI systems ‘understand’ it.”
When considering implementing either one it's important to know that automation should come first, as explained 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 paralyzed. 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.”
Priority areas for automation in finance
McKinsey Global Institute’s automation research shows that currently available technology “can fully automate 42 percent of finance activities and mostly automate a further 19 percent”. The graph below shows a detailed breakdown of the potential for automation in specific areas.
Opportunities for automation vary, but they're present in almost all functions.
Let’s drill down to some specific tasks to identify the low-hanging fruits ready for automation:
Budgeting & forecasting - These processes usually take a lot of time and (manual) effort. Every department is involved and it can take months to get to the final version of the annual budget. Using software to distribute budgets top-down and automatically roll out a forecast for the next month could significantly shorten the back and forth communication in the budgeting process and improve the accuracy of the data.
Reporting - Gartner research suggests that almost 90% of corporate controllers will be using robotic process automation for financial and management reports. On-demand and real-time reporting is becoming the norm. The latest data should be available through a user-friendly interface for anyone in the team who needs it.
The future of jobs in finance
Automation will have a tremendous impact on every profession. While less than 5% of jobs are expected to be completely automated almost every position will go through some degree of automation. Three things will happen as a result:
Some positions will go out of existence
Increased productivity and efficiency on remaining positions
Creation of completely new jobs
In general, finance professionals can expect to spend more time on strategic and value-adding tasks.
In his book Deep Work, Cal Newport describes 3 types of professionals he sees thriving in the digital world. One of them are high-skilled workers, or as he explains it:
“In other words, those with the oracular ability to work with and tease valuable results out of increasingly complex machines will thrive. Tyler Cowen summarizes this reality more bluntly: “The key question will be: are you good at working with intelligent machines or not?”
In my next blog post, I’ll go into more detail on what digital skills will finance professionals need to thrive in this new environment. In the meantime, if you have any questions, feel free to contact me via email or LinkedIn.