Workplace AI: What can we learn from the BYOD trend

‘Bring your own device’ (BYOD) started trending over a decade ago, but many IT organizations didn’t fully support the concept until a few years past. Instead, some banned BYOD, and some adopted a limited BYOD policy.

Despite the ban on consumer technologies at work, employees—including business executives—worked on their personal devices anyway. IT had to reckon with the impact on security and find solutions to manage new devices and operating systems. Mobile device management (MDM) was created to help manage BYOD. Then, to support additional consumer device types and applications, unified endpoint management hit the scene. As we look back, what started as a nuisance for IT turned out to be a powerful movement that benefits both employees and businesses.

IT now knows that empowering employees with secure and seamless access to any app on any device results in higher performing employees and businesses. According to Forbes Insights research, empowered employees across EMEA:

• • • Spend 16 percent less time on manual processes. 
    • Collaborate better (an increase of 15 percent against traditional employees).
    • Are twice as likely to say apps are important in accelerating decision making.
    • Are five times more likely to report gains in productivity.

Fast forward to today and the infiltration of consumer AI technologies in business. The employee demand is similar to BYOD, but the opportunity could be tremendous.

A powerful example for AI and machine learning comes from Siza, a Dutch healthcare organisation which supports people with physical, mental or multiple disabilities and people with autism or non-congenital brain injuries. The company makes significant technology investments to empower these people to better organize their own lives.

“We often talk about augmented healthcare as the new thing to ensure that we can build more technology into the healthcare processes,” says Jorrit Ebben, chief strategy and innovation officer at Siza. “What we try to do is introduce more data-driven decisions for our healthcare professionals—as an aid for them to do even better work than they do now.”

Ebben recalls a specific use case of a patient who uses a wheelchair with an integrated respirator. If the wheelchair or respirator fails, the patient could be in life-threatening danger. AI can enable nursing staff to respond faster to these situations.

“We can use artificial intelligence and also machine learning to predict whether his respirator’s still going to work or not. And that’s the kind of stuff that we want to introduce to make sure that we can help him earlier than he knows that he needs help,” Ebben says.