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.