The future of finance: Exploring the role of AI

01. Introduction

CFOs and other finance leaders have plenty on their plates in 2023.

Gone are the days when all they had to worry about were balance sheets, P&Ls and management reports. Today’s finance leaders are operating in volatile and uncertain times. Ankur Agrawal from McKinsey recently wrote about the blend of opportunity and challenge that modern CFOs face.

Discussing McKinsey’s famous biennial survey
of CFOs, he noted that the role and specifically the responsibilities have broadened in recent years. More functions and departments report to the CFO than ever, with more CFOs now taking responsibility for such areas as procurement, cybersecurity and even talent acquisition and retention.

Finance leaders are at the forefront of these conversations, and must not only identify the optimal tech stack but must also be clear about the return 
on investment.

Today’s data-rich environment means there is more information to be analyzed than ever - but with their wider roles, there is also less time for finance leaders and CFOs to do so. This is part of the reason the industry is so excited about AI. AI tools can analyze vast quantities of data, and have the power to identify trends that the human eye could easily miss. That means better insights, more accurate reports and forecasts and better-informed strategic decisions. Also, AI tools can cut a swathe through routine tasks like report creation and even customer enquiries, leaving human brains free to focus on more interesting and rewarding value-adding activities.

02. AI - back
to basics

Before we dive too deep down the rabbit hole, 
it is as well to be clear what we are talking about when we discuss Artificial Intelligence.

We think of AI as a disruptive technology, and rightly so, but that does not mean it is conceptually new. It is simply the speed of development that is liable to catch businesses napping.

Understanding AI and its uses does not demand in-depth understanding of coding. A little technical knowledge is useful, but it is not obligatory, just as you don’t need to understand the technicalities of internal combustion engines and electric motors to make fleet decisions. The important thing to understand about AI is that it is just another tool, a means to an end. To start to conceptualize how AI applications can best be employed, therefore, the first step is to be crystal clear on the business strategy and challenges that it can be used to address. Otherwise, it is a classic case of Alice and the Cheshire Cat.

So what do we mean by AI and how can it help us reach our desired destination or achieve our strategic goal? In short, any software or program that exhibits what we would call intellectual quality that we would associate with cognition is an example of AI. So in other words, this is software that can learn, solve problems, understand language or exhibit some sort of creativity.

From a business perspective, we can immediately draw out some specific examples of AI in action:

In this era of Siri and Alexa, we encounter AI every day. However, the somewhat scripted responses seem more “A” than “I”, using simple voice-recognition NLP to select an appropriate resource. We actually encounter far more powerful AI, but in more subtle ways. When you browse your social media news feed or Google Discovery over breakfast, the news stories at the top are all on the topics that interest you. It’s not by coincidence, AI uses your browsing history to anticipate your needs and interests.

Similarly, navigation apps learn our daily routines so they can recommend the best time to leave for work and what route to take.

The latter is a particularly pertinent example as it shows how AI can analyze data and make the best recommendation, saving humans from having to work it out for themselves. In this case, it is quite a simple process based primarily on the latest traffic reports on different roads. But this kind of decision-making extends far beyond the preserve of handy consumer gadgets.

AI can offer both faster and more reliable decision-making. Faster because it can analyze thousands of data records in a second and more reliable because it can therefore base its recommendations on a far larger data set. The implications are significant. Take medicine as an example.

A doctor might spend an hour consulting a dozen similar cases when attempting to diagnose an unusual case.  AI can analyze thousands of cases much faster. Not only does it mean a quicker and more accurate diagnosis, it frees up an hour of the doctor’s time that can be spent treating patients.

Key takeaways

03. How generative
AI helps 
finance teams

There are more similarities than might be immediately obvious between the doctor in our previous example and the average CFO or senior finance manager. We mentioned earlier that finance leaders have a broader remit than ever before. The right AI tools can take routine tasks from their shoulders, effectively transforming their day to day activities into more supervisory and strategic roles.

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