As AI algorithms--and the computing power that drives them--improve year-on-year, their ability to positively transform the world in which we live is unquestionable. In fact, PwC predicts that AI could contribute up to $15.7 trillion to the global economy by 2030. Indeed, as many as one-in-five (20 percent) of the 1,000 US organisations recently surveyed by PwC had plans to implement AI enterprise-wide in 2019. The PwC research also reveals how companies are increasingly initiating AI models at the very core of their production processes, in a bid to enhance operational decision-making and provide forward-looking intelligence to people in every function throughout the business. To many, this move to AI is no surprise. After all, robots have been used for years in many manufacturing disciplines, so the progression to AI seems like a logical next step.

Most AI giants on the internet rely on the continuous collection of personal data from their users, primarily to build and maintain machine-learning models. These models are often core to the value proposition of these companies, providing recommendations, behavioural analytics and consumer insights not only to their own services, but to associated advertising networks. This practice, however, comes at a cost to individuals. The repeated delivery of ads by third-party services creates excessive bandwidth and energy usage, something consumers are noticing as ongoing data collection and analysis by background apps slows their internet connection. And, as many recent cases have shown, there are now serious privacy concerns from excessive data collection and the resulting exposure from linkages of personal data across different services.