Automation has been talked about quite a bit for many years now. The reactions to the potential that automation carries have been mixed. Some are afraid of what it might mean for their jobs. Others are relieved that it will take their more tedious tasks off their plate so they can do something more valuable with their time.
HR is in a unique position to find advantage with automation. Recent research sought to take a snapshot of how HR professionals are feeling about automation. The research, conducted by Catalytic, is called “The Real State of Automation Progress Report.” I recently spoke with Jeff Grisenthwaite, VP of Product at Catalytic, about the results.
HR Daily Advisor: Do you have any insight into why so many (89%) HR professionals are optimistic that automation will make their jobs easier? Do you have a sense of how optimistic they are?
Grisenthwaite: HR professionals see the difference between what they actually do and where they would ideally spend their time. According to our research, HR professionals rate the following as top types of work that would experience the most positive impact if automated: entering and updating data in systems, tracking progress and following up with people, and working in spreadsheets and creating reports.
Automating these time-consuming manual tasks means HR would have more time to devote to the most human elements of the job, which could explain the optimism we’re seeing reported. In fact, our data show that if automation freed up 40% of HR professionals’ time, they would choose to focus on the following components of their jobs: employee training (53%), recruitment (41%), and employer-employee relations (35%).
The promises and hype around artificial intelligence (AI) in particular are also likely a factor in the optimistic outlook. With the advent of AI and automation, digital platforms and capabilities are being developed faster than ever.
HR Daily Advisor: Do you believe that their optimism is well placed? Why?
Grisenthwaite: HR, more so than many other lines of business, is well suited for automation and digitization. There is a significant amount of routine work required to execute on HR strategies. Simple things like gathering information from multiple people, putting together documents or reports, and grabbing data from one system to update another (swivel chair work) are all prime targets for automation.
Additionally, HR best practices for years have been advancing more quickly than HR technology. There is pent-up demand to reimagine HR processes with modern tech capabilities like machine learning, natural language processing, and application programming interface (API) integrations.
HR Daily Advisor: Your findings show that only 27% of HR professionals feel their department has the skills to seamlessly adopt automation tools. How much of a problem is that for HR? How can HR personnel help get their department ready for adoption?
Grisenthwaite: HR leaders need to define the right roles and goals of their automation strategy. Not everyone on the HR team is going to be best suited to build automations. The Mayo Clinic’s HR department, for example, developed a formal automation model that also puts parameters in place to help them scale automation in an organized, thoughtful way across the business. The HR tech team organized a core group of three to four people who are dedicated to helping HR leaders and employees not only scope and prioritize automation projects but also build and maintain them. By remaining focused on practical process improvement, the Mayo Clinic’s HR team members see increased quality with every process they review and build.
Within the greater HR ecosystem, there are several sources for some HR experts to take advantage of learning programs relative to AI and automation upskilling and new skilling for the future of work.
HR Daily Advisor: HR technology has always been sold as a way for HR to free up its time to do the really important work. What were your findings in this area?
Grisenthwaite: Our findings are really telling about the types of work HR professionals want to see automated, which provides powerful information about what they’re looking for in HR technologies.
That said, it’s important to note that while many HR technologies have automation capabilities, most are not full-fledged automation platforms that can help with processes in different departments and business units and interact with multiple systems. Some platforms are able to automate HR processes while serving other functions within the business—leading to efficiency and productivity gains for the entire organization in the process. Furthermore, most HR departments have unique needs that extend beyond the rigid confines of their existing HR systems. With a modern workflow automation platform, HR departments have the flexibility to define processes and automated steps in ways that conform to their particular organizations.
HR Daily Advisor: Do you agree that HR professionals really would have more time to do their other work if automation took over the more mundane work?
Grisenthwaite: Absolutely. As an example, the Mayo Clinic’s HR team has gained over 40,000 hours of timesavings in a single year (its first full year automating work). the department has been able to deliver more value in areas like succession planning and leadership development by being able to expand the programs to involve more people and be more robust, as well as handle administrative processes like certification renewals and payroll reimbursement. This has allowed HR to focus on content creation and delivery instead of administrative work.
HR Daily Advisor: Do you have any concerns about how automation tools might be misused?
Grisenthwaite: Business leaders have a responsibility to put the proper team structure and controls in place. With the right governance and clear visibility into what is being automated, there is no reason to think the tools will be misused. Centralizing all the automations within a cloud platform makes oversight much simpler for both HR and IT. When applying machine learning within HR processes, our recommendation is to start out using the machine learning models to make recommendations to people and continue to let people make the final decision unless the model reaches a high level of confidence and accuracy.