Artificial Intelligence (AI) technologies are promising to transform the forefront of many business operations. The technology is proving itself evidently beneficial in revolutionizing the workplace culture as well. AI tends to guide, organize and automate work while improving staff efficiency and productivity. Specifically, when AI is blended with automation, it maximizes the company profits by utilizing the minimum manpower yet in a right and creative manner. The technical improvements brought in by them contribute to the management of several tasks in the office that are achieved effortlessly and employees’ work becomes less tiring.
Market Analysis by Gartner:
Artificial intelligence (AI) will be widely adopted in office environments in a variety of ways over the next few years as businesses invest in digital workplace initiatives, said Gartner analysts in their 2019 analysis. The trend is expected to gather steam as voice-activated personal assistants that have proved a hit at home begin to make inroads in the office.
By 2025, the technology will “certainly be mainstream,” said Matthew Cain, vice president, and distinguished analyst at Gartner – even though privacy and security concerns have limited deployments so far. Cain was among the analysts who spoke at Gartner’s Digital Workplace Summit.
Gartner has separately predicted that consumer and business spending on smart speakers will pass $3.5 billion in 2021, with 25 percent of digital workers using an AI assistant on a daily basis within the next two years. A number of companies have already deployed the technology, though WeWork reportedly put its Alexa for Business deployment on hold last year.
Case Study 1:
The New York Foundling which is a charity that offers child welfare, adoption, and mental health services, was stuck in cut-and-paste hell in 2018. The 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.
Case Study 2:
When Matt Radwell, a customer support officer for a small local authority in the UK, first started answering queries from the area’s residents, it was a frustrating and time-consuming business. If a resident contacted Aylesbury Vale District Council, 40 miles north of London, about an issue like housing benefit in which he lacked expertise, Mr. Radwell might keep the caller waiting as long as 20 minutes. He had to find someone who could give him the relevant information.
Over the past two years, however, his job has been transformed. When a resident types a question into the council’s online chat facility, an advanced computer system starts reading it.
For around 40 percent of inquiries, the system — which has been trained to recognize residents’ questions by using machine learning, a form of artificial intelligence — presents Mr. Radwell and other customer support officers with a series of potential, pre-written responses. Each is labeled with an estimated probability of its being the correct choice. If one is appropriate, Mr. Radwell clicks on it, satisfying the resident far more quickly and easily than before.
The council’s machine learning system — provided by Digital Genius, a San Francisco-based specialist in customer service systems — has put it at the forefront of a transformation underway in millions of white-collar jobs worldwide.
Tom Davenport, a professor who studies information technology and management at Babson College says, “When paired with robotic process automation, AI significantly expands the number and types of tasks that software robots can perform.”
He also added, “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.”
Even if AI does transform robotic process automation, there will be risks involved, says Davenport of Babson College. The trend may feed a natural human tendency to use automation beyond its capabilities, and this could prove especially problematic when using machine learning methods that are difficult to interpret. “You could be using it to make important decisions like issuing a credit or preventing fraud,” he says. “And the algorithms making the decisions in the process may not be transparent to the managers having to make the decision to keep the automated system going.”
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.
Wil van der Aalst, a professor at RWTH Aachen University in Germany, who coined the term process mining and has pioneered research on the subject, notes that “the techniques involved are not what people think of today as AI, but older statistical techniques.” He says the machine learning algorithms that make headlines today tend to be best suited for specific tasks such as image or voice recognition.
Leslie Willcocks, a professor at the London School of Economics who specializes in business process automation, says the process automation industry has yet to fully take off. Obstacles include siloed data that software robots can’t easily access, incompatible infrastructure, and the fact that many offices have never used automation before. But he believes this is how AI will ultimately arrive inside most businesses.
Willcocks further added, “I am not seeing strong AI being deployed in the vast majority of businesses globally if you exclude the top technology companies. But there will be a turning point when businesses will deploy these technologies better. It may take five years, [but] it’s a slow train coming.”
Guy Kirkwood, the chief evangelist at UiPath, says the vision is for software robots to be a lot more like the AI of science fiction, eventually taking over how they are programmed from start to finish.
“The direction of travel—and we’re not there yet—is that we’ll be able to create self-building robots, where the system just watches what the human does, works out where there’s a repeatable activity, and works out what the optimum route is with all the variances and the exceptions,” he says. “That would be pretty cool.”