The recent 60 Minutes segment on Amazon.com CEO Jeff Bezos drew some serious attention over the revelation that the company is working on an uber delivery service using drones to drop items at consumers’ doorsteps in 30 minutes or less. While delivery-by-drone is far from imminent, it certainly got people buzzing about the role of machine vs. man in our every day lives.
Two weeks later, The Boston Sunday Globe carried not one, but two stories focusing on the potential for apps, robots and computers to handle jobs and tasks traditionally performed by humans. The common denominator is, of course, automation and the vast – and at times, scary – potential it offers to businesses and consumers who embrace new technologies to get things done faster, more efficiently and more cost effectively.
The first Globe article, written by economist Doug Handler, carried the very dramatic headline, “Will your job be replaced by an app?” and identified the many occupations – bank teller, travel agent, receptionist – that either no longer exist, are shrinking rapidly in number and are generally on the “endangered species list.” While not exactly a news flash, Handler’s piece noted that automation is, of course, good for a company’s bottom line, but he also encouraged professionals to develop new skill sets to remain relevant.
Scott Kirsner’s “Innovation Economy” column was juxtaposed against Handler’s. Reading just the headline – “It’s tough getting robots to act human” – might elicit a sigh of relief. The piece quotes MIT Robotics professor Seth Teller who noted, “The obvious analogy goes back to the 1980s and personal computers, when individuals first got access to computing technology,” says Teller. “We’re not quite there yet with robots, but we will see more of these machines in homes, hospitals, public spaces and workplaces.”
So as 2014 begins, are our jobs about to be replaced by apps or will robots never learn the soft skills required by millions of knowledge economy workers? As always, the truth lies somewhere in the middle. Most companies still need to figure out – against the backdrop of the rapid advancement of disruptive technologies and cloud-based delivery platforms. where the future of work will go and how to make the new landscape work for their business.
While machine-based technologies like Artificial Intelligence and automation are well suited for certain applications, they have many limitations including the inability to evaluate nuance, uncertainty and risk in ways that only human intelligence can do. Despite the enormous recent advances in technology, computers still have problems dealing with some basic data processing in areas such as search relevance testing, content editing and summarization, data research, data classification and rich media transcription, just to name a few. So while it plays a major role in accelerating the path to achieving competitive advantage, technology does not eliminate the need for human intervention.
We’ve witnessed this transformation first-hand as more enterprises embrace business process crowdsourcing, which blends automation with people. Leveraging a secure, cloud-based task management platform, businesses increasingly tap into an educated and pre-qualified managed crowd of more than 100,000 professionals worldwide, capable of delivering enterprise quality results by reinserting the human touch lost through an entirely automated approach.
In this technologically-driven, always-on world often dominated by super computers, smart phones, apps for everything – and maybe one day delivery drones – it is naïve to think there is no room for automation to help the enterprise increase productivity. Help being the operative word, of course. Prior attempts to create entirely automated processes have been decidedly mixed, particularly in areas such as Big Data, translation and testing. While machines may be good for building widgets on an assembly line, there’s no substitution for the power of the human brain when it comes to nuanced decision making. (At least not that we know about!)
What forward-looking strategies have your organization utilized? Did they meet expectations or fall short of performance metrics? What did you learn?