We are heading toward a workforce that integrates artificial intelligence (AI), cognitive computing, machine learning and virtual and immersive reality with people; a new kind of workforce diversity. And how we define the term work itself is going to shift as well.

This should be the year we all truly comprehend the way our workforce and work processes are changing. If we don’t, we’ll be behind the curve and dealing with the consequences, which is a disengaged and frustrated workforce, workplace anxiety, and a lack of clarity over who’s in charge of what, and more. With big data, the cloud, mobile and social, we still have the certain luxury of just dealing with people. We are adapting to massively expanded functions across multiple channels, while getting used to remote and virtual, and coming to respect the power of social and the opportunities in data. We have learned new best practices and embraced change, and for that we can pat ourselves on the back. But let’s not get complacent: that was child’s play compared to what’s coming.

As AI, cognitive and virtual joins our workforce, here’s the question: how we can best leverage this new reality for the benefit of people? The onus is on leaders and managers to anticipate changes and get ahead of them. So, put down your copy of Do Androids Dream of Electric Sheep and let’s get real. Here’s what we need to do:

  1. Stop dreaming. We once believed eclipses were caused by dragons. We still love dragons. But no more indulging in fantasy. Instead of considering the future, think of the now. Given the rapid change work is undergoing, it makes a lot more sense. So, as you head out to work after Alexa locks the house, Siri locates the morning’s meeting, and the GPS tells you the quickest route in a soothing Brit accent, accept that the future is now.
  2. Create support systems. To get in front of the transformation to a mixed workforce to better mitigate its affect on our people, we need to build some solid scaffolding. Don’t be so concerned with perception versus reality: we’ll all have our opinions. Concentrate on communicating. In terms of recruiting, that means anticipating confusion and setting better expectations. We may start to see job postings where there’s a human side — such as innovating or adding knowledge — and a machine side — such as repetitive administrative tasks. And managers can use mobile apps to take employees’ pulse can keep lines of communication open without interrupting the workday. Teams can create surveys to compile key data on workforce readiness or reservations. On the leading edge there are often more people who feel than people who know. That will change, but not as fast as we’d like.
  3. Resolve to stay human. We don’t really know if robots dream, but AI knows if we do. MIT’s Computer Science and Artificial Intelligence Laboratory recently announced they developed an AI algorithm that monitors our sleep with radio waves, and translate those signals to detect if we’re in REM sleep or not. There’s also a wearable AI system that can detect social anxiety in its wearer, predicting if a conversation is happy, sad or neutral based on the wearer’s vital signs and speech patterns. This could be a huge benefit for helping Asperger’s patients and could transform the field of social coaching. But do we need to frame the ethics of this in terms of the workforce? Potentially employers could detect engagement or productivity by using wearables as well. Each organization needs to consider the parameters of its own policy. Not everyone is going to want to implant microchips in their workers.
  4. Ramp up learning. One of the classic challenges HR often faces is executive buy-in. In this case, the workforce is likely changing due to C-Suite course changes, not HR; this is a massive and holistic transformation. Now the challenge is going to be making sure there is enough training in place. One instance: Microsoft recently announced AI for Earth, dedicated to AI-based projects that focus on climate change, agriculture, water, and biodiversity. The firm is committed to its development, and committed to training as one of the key elements. Not surprising, given that L&D is part of that company’s DNA. But even for an entirely different kind of business — such as a retail organization bringing in new customer learning technology — it’s one thing to have it. It’s far better to make sure your people know how to use it well enough to trust it.

We may not always agree with progress. It can get in the way of a good day’s work. And AI, designed to learn more the longer it exists, may well wind up disagreeing with us. In fact, one researcher says we can count on that: based on how it learns and what it learns, AI will definitely have opinions. It’s up to us to have the confidence and familiarity to either agree with them or not — and possibly to mediate between our people and our machines in a way that capitalizes on the best qualities both sides have to offer. Then again, let’s not think of us and them. That’s so 20th century.

A version of this post was first published on FOW Media.

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