In 2018, the New York Foundling, a charity that offers child welfare, adoption, and mental health services, was stuck in cut-and-paste hell. Clinician
In 2018, the New York Foundling, a charity that offers child welfare, adoption, and mental health services, was stuck in cut-and-paste hell.
Clinicians and admin staff were spending hours transferring text between different documents and databases to meet varied legal requirements. Arik Hill, the charity’s chief information officer, blames the data entry drudgery for an annual staff turnover of 42 percent at the time. “We are not a very glamorous industry,” says Hill. “We are really only just moving on from paper clinical records.”
Since then, the New York Foundling has automated much of this grunt work using what are known as software robots—simple programs hand-crafted to perform dull tasks. Often, the programs are built by recording and mimicking a user’s keystrokes, such as copying a field of text from one database and pasting it into another, eliminating hours of repetitive-stress-inducing work.
“It was mind-blowing,” says Hill, who says turnover has fallen to 17 percent.
To automate the work, the New York Foundling got help from UiPath, a so-called robotic process automation company. That project didn’t require any real machine intelligence.
But in January, UiPath began upgrading its army of software bots to use powerful new artificial intelligence algorithms. It thinks this will let them take on more complex and challenging tasks, such as transcription or sorting images, across more offices. Ultimately, the company hopes software robots will gradually learn how to automate repetitive work for themselves.
In other words, if artificial intelligence is going to disrupt white-collar work, then this may be how it begins.
“When paired with robotic process automation, AI significantly expands the number and types of tasks that software robots can perform,” says Tom Davenport, a professor who studies information technology and management at Babson College.
Consider a company that needs to summarize long-winded, handwritten notes. AI algorithms that perform character recognition and natural language processing could read the cursive and summarize the text, before a software robot inputs the text into, say, a website. The latest version of UiPath’s software includes a range of off-the-shelf machine learning tools. It is also now possible for users to add their own machine learning models to a robotic process.
With all the AI hype, it’s notable that so little has found its way into modern offices. But the automation that is there, which simply repeats a person’s clicking and typing, is still useful. The technology is mostly used by banks, telcos, insurers, and other companies with legacy systems; market researcher Gartner estimates the industry generated roughly $1.3 billion in revenue in 2019.
Simple software automation is eliminating some particularly repetitive jobs, such as basic data entry, which are often already done overseas. In call centers, fewer people are needed to fill out forms if software can be programmed to open the right documents, find the right fields, and enter text. At the New York Foundling, Hill’s software allowed him to redirect eight workers to other tasks.
But Davenport says software robots that use AI could displace more jobs, especially if we head into a recession. “Companies will use it for substantial headcount and cost reductions,” he says.
Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy and the author of several books exploring the impact of technology on the workforce, says robotic process automation will mostly affect middle-skilled office workers, meaning admin work that requires some training.
But it won’t happen overnight. He says it took many years for simple software robots, which are essentially descended from screen-scrapers and simple coding tools, to affect office work. “The lesson is just how long it takes for even a relatively simple technology to have an impact on business, because of the hard work it takes to implement it reliably in complex environments,” Brynjolfsson notes.