Why are knowledge workers still doing routine work?

By Brian Safron

Have you ever wondered why skilled workers often spend valuable time doing routine work when there are more automation technologies than ever to automate these repetitive tasks? Is it really the best use of resources for experienced employees to spend time on tasks that could be easily automated? Probably not, if you’re trying to improve productivity or customer service.

So, why are some companies slow to adopt automation software that could help employees be more productive —  that could free them from lower value tasks and assist them in higher value activities? In most situations, in order to go beyond just rote functions and calculations, the software must include some degree of embedded intelligence.  And most automation software lacks that critical capability.

Consider a loan officer named Lisa. As shown in the graphic below, Lisa’s major activities include: 1) inputting data from document and forms; 2) meeting with her current clients; 3) finding and building relationships with new clients; 4) resolving problems, issues and exceptions related to loans; 5) creating compliance and financial reports; and 6) managing the end-to-end loan process and documentation.  

A day in the life of a finance professional

Figure 1. A day in the life of a finance professional

Like most employees, Lisa does whatever it takes to get the job done. Her dilemma is that many of her activities don’t add value, so that much of her time is spent on low value work with little time left for high value work.  

The knowledge worker’s dilemma

Figure 2. The knowledge worker’s dilemma

To make knowledge workers more productive, automation should free them up to focus on inherently human strengths (for example, strategy, judgment, creativity, empathy) while machines should support them by performing repetitive, data-intensive (and often mind-numbing) tasks.  As shown in Figure 3, the goal of intelligent automation is for Lisa to spend more time finding new clients and meeting with current clients (as seen on the right) than she does inputting data and managing loan approvals (on the left).  

How Lisa spends her time before and after intelligent automation — size of circle equates to time spent

Figure 3. How Lisa spends her time before and after intelligent automation size of circle equates to time spent

Using intelligent automation to offload repetitive tasks and assist knowledge workers with their skilled work is what we call “digital labor,” which can be divided into the following three segments:

  • Digital clerk: Intelligent automation can function as a “digital clerk” by performing repetitive, mundane tasks. For example, Lisa shouldn’t spend her time inputting data from documents, but since inputting data requires a certain amount of intelligence, such as knowing which fields on a scanned document belong to which fields in a software application, Lisa and her colleagues often end up inputting the data manually.
  • Digital advisor: Intelligent automation can act as a “digital advisor” by assisting knowledge workers so they can spend more time performing uniquely human activities. For example, when Lisa plans a meeting with clients, intelligent automation should be able to gather the appropriate information and predict which loan products would be most beneficial to the client and therefore most likely to be accepted.
  • Digital self-service: Intelligent automation can enable “digital self-service” by providing end users a nuanced experience equal to, and often faster than, the equivalent nonautomated experience. Customers and other end users often prefer being empowered to serve themselves — on their own schedule — as long as the automation can intelligently navigate the variations and decisions inherent in the interaction. By offloading work directly to end users, employees are freed up to focus on those high-value, mission-critical interactions that require human empathy, creativity and judgment.  
Figure 4. Digital labor — supporting the full spectrum of digital work

Figure 4. Digital labor supporting the full spectrum of digital work

Digital labor mirrors human labor in its ability to perform a variety of skills. The skills are as varied as extracting information, making decisions and anticipating actions. To be effective, the skills must be performed in the specific context of the work being done. Because intelligent automation must mimic human intelligence in order to perform these skills in the context of the work, we call the skills “moments of intelligence.” Six key moments of intelligence are shown below.  

Figure 5. “Moments of intelligence” — skills that can be automated in the specific context of the work being performed

Figure 5. “Moments of intelligence” skills that can be automated in the specific context of the work being performed

In a recent publication, Gartner stated that “by 2022, one in five workers engaged in mostly nonroutine tasks will rely on AI to do their jobs.”[1] Human expertise is always in short supply, and for years we’ve looked for ways to make our experts more productive and take unnecessary work off their plates. Intelligent automation offers a solution for finally getting closer to that goal.

Note: We’re in the process of developing a new automation capability –  IBM Business Automation Intelligence with Watson™ – designed to improve productivity across the full spectrum of digital work. We’re running an early access program to incorporate as much customer feedback as possible into the development of the offering. If you’re interested, click the link above to join the program.

[1] Gartner Magic Quadrant for Intelligent Business Process Management Suites, 30 January 2019 - ID G00345694

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