The advent of crowdsourcing was a game changer for the business world. It enabled companies to break up overwhelming data projects into thousands or even millions of tiny, individual tasks for human “workers” to complete (usually for pennies). It empowered businesses with a remote workforce to which to outsource data projects — projects that quickly became mind-numbing to any one human to work on for too long, and that computers could not readily handle (at the time). It was a breakthrough invention — but that was over a decade ago. And as has becomingly increasingly apparent in today’s world of artificial intelligence (AI), machine learning, personalization, and optimization, crowdsourcing in its original form is no longer adequate.
Traditional crowdsourcing was not designed, for example, to provide training data for machine learning models. It was never designed to automate critical business processes. It was never designed to handle complex tasks that require specialization and thereby enable employees to focus on their highest value.
Crowdsourcing has the potential to encompass all of these goals — but only in an evolved form of its original design.