By Adam Devine, VP Product Marketing, WorkFusion
Predictable, monotonous data work costs the United States, Canada and Europe over $10 trillion of human intelligence each year, and that number is growing. You know the kind of work: a data analyst sitting in front of two screens, one flashing an endless stream of unstructured content, the other with an insatiable database or excel sheet, hand-jamming data from a PDF or website into cells…one painstaking attribute at a time. “Why am I doing this? Why can’t this be automated?” asks every single data analyst at a financial services business on the face of the planet. Smart machines are finally mature enough to take over this thankless work, but you as executives and software buyers have a decision to make between the role you give these smart machines in your businesses.
As John Markoff points out in his excellent recent book, Machines of Loving Grace, there are two camps in the smart machines world: those who are designing systems to recreate and surpass human intelligence, and those who wish to elevate human intelligence with smart machines. These camps even have names. The former advocates artificial intelligence (AI), and the latter believes that the role of smart machines is human intelligence augmentation (IA). What’s interesting, Markoff points out, is that the differences in these two warring camps is not their product but their application.
Singularity – the hyped notion that machines will one day have super-human intelligence and take over the world – is more or less fiction. Even Chris Gerdes, the head of Stanford’s Center for Automotive Research (CARS), arguably the most advanced laboratory for developing autonomous car technology, opines that, “the more research we do with human beings, the more impressed I am with our eyes and our brains.” Recreating and surpassing human intelligence is no small feat, and if this ever happens, it will not be in our lifetime.
What has the potential to take place in our lifetime is massive scale technological unemployment. Smart machines are now capable of watching knowledge workers do their day-to-day work, identify patterns in it, and non-intrusively train automation to perform predictable tasks. The commonly held belief among engineers is that what can be automated should be automated. And truly, if a machine can do a task, is it a task from which a human could derive true job satisfaction? Smart machines can and should remove the $10 trillion of predictable knowledge work that millions of data analysts spend about 30% of their days on, but it’s the both the responsibility and the opportunity for business leaders to use this newfound capacity not just to lower the bottom line but to raise the topline. The more repetitive work that smart machines automate, the more creative, innovative, and revenue-generating work people should add to their workdays.
What’s an example of work that’s being automated, you ask? Take for example invoicing. Ever business in the world either sends or receives invoices, and usually both in very high volumes. Because there is no single, ubiquitous invoice format, it’s challenging for rules-based or robotic automation to turn these documents in automated action. There are simply too many exceptions, and exceptions require either people or IT projects. Machine learning can watch analysts annotate invoices and name each attribute on a corpus of documents, and that work becomes a training set for automation, which can be applied to many more formats of documents. Thus is born durable, reliable automation. The same watch-and-learn technique can be used for the majority of the work of regulatory compliance, like AML, BCBS 239, and MIFID.
What’s an example of the sort of work that people should do with their abundance of time when machines have automated 30% (according to HBR) of their workday, you ask? Sharpening their subject matter expertise and developing new products for customers and customer service (because nothing is more defeating to a needy customer than a machine voice or a stock answer to a unique question). As much of the financial sector becomes industrialized and commoditized, businesses will differentiate on products, pricing and services. Smart machines can help businesses move the needle in each category, but it’s the creativity and ingenuity of people that will ultimately put a business into first place.
The automation of more and more work that we once believed only people can do is an inevitable reality, and we should welcome the opportunity to rid our days of data collection and extraction. But the entire automation of valuable jobs and the elimination of the human touch in enterprise business is not inevitable. It’s up to business leaders to use smart machines thoughtfully to augment intelligence, not artificially replace it.