Automation is becoming increasingly important to the finance function in our rapidly changing world. In this interview with Tom Willman, principal and global practice leader at The Hackett Group, we look at the impact of automation and machine learning on finance.
Businesses have learned many lessons from the global pandemic, including how investments in digital acceleration allowed them to more rapidly adapt to sudden change. And within companies, the finance function often plays an important decision-making role in digital transformation efforts.
I spoke with Tom Willman, principal and global practice leader at The Hackett Group, to get his thoughts on how emerging technologies such as automation and machine learning (ML) support this renewed thirst for digital transformation.
We hear the terms automation and ML used almost interchangeably. What are the key differences between the two concepts?
They are different, but the line is definitely blurring as ML becomes incorporated into many automation solutions. Automation in its simplest form is the use of technology to perform repetitive, rules-based tasks that would otherwise be performed by a human—think ERP, RPA, OCR, chatbots, etc.
Machine learning moves beyond automating tasks and begins to enhance the judgment or decision making of humans by using algorithms to analyze data and provide insight. Over time the application “learns” and refines its algorithms to improve the quality of insights and/or process outcomes. ML technology is frequently incorporated or integrated with automation applications to enhance the quality of the process, insights into process performance, and insights to guide future actions and decisions.
Why is automation so important to finance, and why now?
The pace of automation in finance has been accelerating for years. A significant driver of this acceleration has been the need for finance to evolve its role in the organization as a strategic advisor to the business, focused on enabling them to execute their strategies. Finance has looked to automation to reduce the resource commitment required to execute basic transaction processing, close the books, and perform other mechanical, repetitive activities—note that transaction processing represents 56% of all finance resources in the typical finance organization—and redeploy that freed up capacity to more strategic and value added work.
Then COVID hit, and our research showed that the lack of process automation was cited as the biggest barrier to responding effectively to the crisis. The pandemic has really been a catalyst to accelerate the pace of automation even further to improve (finance’s) agility and resilience in responding to future disruptions.
Finance has talked for decades about better processes and automation. What’s the biggest barrier for change?
While resistance to change presents a constant challenge to any type of change initiative, our annual key issues research shows that other factors are also at play here. Complexity of the existing process and technology environment make it difficult to automate for many companies. Focusing on elimination of work, simplification and standardization of processes, and rationalization of systems are critical steps in any automation journey.
Shortages and deficiencies in critical skills in areas like analytics, emerging technologies, process redesign, design thinking, and change management represent another significant barrier to change. Overcommitment—taking on more initiatives that can be resourced effectively—is also a problem which underscores the importance of prioritization and demand management.
“Finance will look more to technology and data analysis skills as it boosts digital acceleration.”
Automation is often seen as the job killer, but that’s not the full story for finance, is it?
I agree that automation is not intended to be a job killer. Of course there are certain types of jobs that will be eliminated due to automation, but you have to look at the net impact because there will be many types of new jobs created and new capabilities required.
I’m confident that for those whose jobs are eliminated—and even those whose aren’t but are looking for new challenges—automation will represent an opportunity for employees to develop new skills and experiences and prepare themselves for new and different roles. There’s a real opportunity to move from manual work into something more strategic.
What does that mean from a skills perspective, and how finance thinks about talent?
Finance will look more to technology and data analysis skills as it boosts digital acceleration. That means finding existing employees that are willing and enthusiastic about taking on new challenges and learning new skills, as well as recruiting and hiring people with different backgrounds—computer science, industrial engineering, data science, business analytics, etc.
Other non-finance and accounting related skills are at a premium as well, such as the ability to tell the story behind the numbers, communicating with impact, executive presence, influencing and negotiation. These skills will only grow in importance as automation changes the nature of work we do.
How do finance leaders work out which areas of the business they should be automating, and which should remain manual for now?
The starting point when deciding what to automate is, does this process suit itself to automation? Is it repetitive and rules based? Are the inputs digitized? How complex is it? How long would it take? How risky would it be to implement?
The next decision point is how to prioritize automation projects for good use cases. We strongly recommend that companies adopt a formal process to evaluate the relative merit of potential automation projects and prioritize them based on expected quantitative and qualitative benefits and costs to implement. This enables a more disciplined approach to determining what to automate and when.
Historically, putting a hard-dollar value on transformation outcomes is a challenge for finance due to its traditional focus on cost reduction. . . the latter is sometimes not as significant as expected or takes a long time to materialize. Meanwhile, the value of less-tangible outcomes has been often overlooked. While costs and FTE hours saved remain important metrics, indirect impacts like better decisions, reduced errors, improved brand awareness, better customer engagement, and risk reduction also have value and should be considered.
To learn more, check out: Looking Past COVID-19: Five Critical Messages for CFOs
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