CFOs have long cited the skills gap among their most pressing challenges. In a recent EY survey, 47% of CFOs said their current function lacks the right mix of capabilities to meet future priorities. This echoes research from leading recruitment specialist Robert Half UK, according to whom 74% of finance directors worry that the skills gap resulting from the mass retirement of the baby boomers will have a negative impact on their organizations over the next two years.
These finance leaders claim that there just aren't enough talented employees to fill every position, with the situation only going to deteriorate as baby boomers retire and the labor market tightens. Many CFOs believe that their current finance function lacks the capabilities to meet future demands driven by technology, with 69% of respondents to EY’s survey saying they see the finance leader role significantly changing as traditional finance tasks become automated or outsourced, and 65% claiming that standardizing and automating processes and building agility and quality into processes will be a significant priority for tomorrow’s finance function.
In the short term, they are right. In the long term, however, the idea that there is a skills gap simply does not gel with their acknowledgement of advances in AI, or at least it drastically underestimates the likely impact.
At the moment, the majority of the finance function’s time is spent creating and updating reports instead of on analysis and impacting the business at a strategic level. Automation can easily deal with this aspect of the finance function’s work, and as the technology gets cheaper it is hard to see how anybody will be spending time on reporting tasks in the future. In order to meet the potential of new technologies’ ability to reduce costs, manage risks and drive insights, finance leaders must indeed look at both improving their own technical knowledge and putting it at the heart of their hiring policy - with data analytics skills a particular need.
Three-fourths of managers listed in a Robert Half survey listed ‘identifying key data trends’ as a skill important to success, although only 46% said their teams possess that skill. Another McKinsey study reinforced this finding, projecting that ‘by 2018, the US alone may face a 50% to 60% gap between supply and requisite demand of deep analytic talent.’ However, data analysis is not a field that will go untouched by automation, and it is another area that machine learning algorithms will be more than capable of dealing with in the near future. In a recent KDnuggets poll - which asked when most expert-level Predictive Analytics/Data Science tasks currently done by human Data Scientists will be automated - 51% of respondents said that they expect many data science tasks to be automated the next decade. Only a quarter said they expect this to happen in over 50 years or never.
We are already seeing tremendous leaps forward in automated analytics, with many of academia’s sharpest minds having set their minds to the tasks. In one such project led by MIT researchers, they developed a Data Science Machine able to find patterns and design the feature set that came in the top third of teams in three data science competitions. In two of the three competitions, the predictions it made were 94% and 96% as accurate as the winning submissions from human teams, while in the third, it was 87%. Even more impressive, and even more likely to make it appeal to finance leaders, was that it was able to do it in mere hours, whereas the human teams typically spent months pouring over their prediction algorithms to produce each of its entries.
According to Ben Mulling, the CFO of Tente Casters, 'I can go out and find a good accountant, but I’m not looking for that. I’m looking for someone who’s going to fill manager roles in three to five years, somebody who thinks the right way. Nobody’s going to come in with all the skills you want, but it can be difficult to find a person who has the frame of mind to see things from a macro level and take charge’. These are the people that are difficult to find in the current job market'. What will they be managing though? Machines? Is that not an engineer? Would the manager he requires be managing the engineer or the machines? If a mistake is made, does the engineer get fired or do they look for new technology? And where do managers come from? How can they become managers if they have no people to manage?
Many claim that robots will really act as a companion to people - a view that seems either woefully optimistic or wilfully naive, with many companies likely to automate everything they can in order to cut costs. There is a certain sadism in asking people to start down a career path while knowing that many of the jobs they are learning will soon be automated. And it may be partly down to this realization that many are avoiding accounting. There are a lot of questions to be answered, and if we are to start trying to encourage people to enter finance, we are going to have to get a clearer idea of the skills they need and what their roles are likely to actually be, and communicate this to universities so that they can be educated accordingly.