How much of your work is reviewing and restating other people's work?
According to a recent study by Adobe, in which 1019 employed participants self-reported on heir time spent on daily work tasks and their attitudes toward AI in the workplace, workers could be spending up to 60% of their workweek on summarizing and communicating stuff other people have already created.
Per the write-up of the study: "According to our survey, U.S. employees spend, on average, 24 hours and 54 mins on editing, summarizing, reading, and creating documents per week … 30% of respondents identified time constraints as the primary challenge they encounter with document-related tasks."
One of the challenges of knowledge work is that it's not often easily automated. The very nature of knowledge work relies less on successfully and rapidly executing on a process, and more on bringing something to the work that is not easily replicated or automated. No Jitter recently ran a primer on knowledge work, AI and productivity, and laid out the difference between information work and knowledge work:
According to this IBM article, “a knowledge worker is a professional who generates value for the organization with their expertise, critical thinking and interpersonal skills.” The IBM article goes on to state that knowledge workers are different from information workers. According to IBM, an information worker applies information to perform a task while “in the hierarchy of today’s workplace, knowledge workers oversee[s] the daily work of the information worker.”
So the opportunity for Gen AI-powered solutions, according to IBM, is to enable knowledge workers to “quickly gather information about a topic, search for solutions to business problems and flesh out innovative ideas.”
The goal of quickly gathering and sorting information -- which Adobe purports its Acrobat AI Assistant can do -- is a good use of generative AI and AI assistants. It does the initial scut work and the second step of the initial sorting and discarding -- work that can be simultaneously tedious and mentally exhausting from all the judgment calls on why and how something should stay in the "pay attention to this" pile.
What is interesting about the Adobe release is how it's examining the effect people's work routines have on their overall feelings about their work itself: "71% of employees reported feeling burnt out or overwhelmed when tasked with processing and comprehending information in documents, such as reading lengthy proposals."
Addressing overwhelm is part of examining overall productivity and how to balance high performance levels with a minimum of stress. The formal study of how to optimize employee performance without undue stress really took off in the 20th century thanks to early industrial engineers like Frederick Winslow Taylor (known for his pioneering work in time study) and the team of Frank Bunker Gilbreth and Lillian Moller Gilbreth (known for their pioneering work in motion study). That laid the foundation for scientific management, which theorizes that by analyzing processes, one can make them more efficient and therefore more productive.
Although scientific management petered out about a hundred years ago, the central idea that workplace processes could be improved, thereby improving organizational productivity, persisted. We've got kaizen, or the idea that continuously improving business activities at every level of an organization leads to reduced waste (of effort and resources) and improved outcomes. And there are the schools of personal productivity that promise people frameworks for boosting their work performance, like David Allen's Getting Things Done or the Pomodoro Technique.
The point is, how to work is inextricably part of what people do on the job, and it's often the source of stress. And there's been a landrush in collaborative workspace platforms -- helped along by the widespread embrace of hybrid work -- and one of the primary competitive differentiators among these shared workspaces has been how AI will help define and optimize people's workloads.
AI might be able to improve how people work. And that, in turn, might change how people feel about their work -- and about the AI they're unsure will complicate or eliminate their jobs.