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Future of Work

The Way We Work: Tools, Technology and Trust

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The Way We Work: Tools, Technology and Trust

Personal assistants (PA) were rendered redundant when modern tools, like Microsoft Office, hit the market. At first, it was cool...

7 Minutes Read

Personal assistants (PA) were rendered redundant when modern tools, like Microsoft Office, hit the market. At first, it was cool to adopt an independent, DIY-approach to work. But now the dust has settled on Office, workers bemoan the arduous meeting-booking system and the endless barrage of unnecessary email.

Office workers now spend roughly one-third of the year completing administrative or repetitive tasks, which amounts to an annual cost of $2.9 trillion in lost productivity, in the U.S. alone.

Workers are drowning in repetitive, rote work. At the same time (subsequently and surely) they are suffering from high stress levels, which have risen 20% in the last three decades and a loss of creativity: Despite over half (53%) of Brits believing they are creative thinkers, a worrying four out of 10 (42%) claimed to be too busy to spend time coming up with the next big invention.

How do we buy back time at work? One solution could be a redefinition of one of the most famous trademarks of work today: the 9-5. Shifting from an 8x5 model to a 10x4 model could be the jolt the workforce needs to rethink how work gets done.

But the roots of old ways of working go deeper. When we surveyed over 2,000 senior leaders about the skills they need in five years as part of our ‘Work Ahead’ research, without exception, every skill was a human skill — and they needed an average of 15% more of all of them. How do we free ourselves of monotonous work and free our capacity for creative, innovative work that pivots around uniquely human skills?

First, we have to learn to partner better with machines. PAs are a thing of the past, but Robotic Personal Assistants (RPA) are already here. Robotic assistants like Google’s Duplex voice bot (for reservations) and x.ai’s Amy Ingram (for scheduling meetings) are yielding huge productivity gains. Experian, for example, reported a 10x increase in productivity 12 months after RPA deployment. It’s this kind of partnership with machines that’s cause for celebration, not fear. In What To Do When Machines Do Everything we stated that the majority of work (75%) is going to be augmented by machines – not replaced. To really appreciate how machines will enhance work, we have to stop defining work in job roles. Instead, we have to break work down into a task-based understanding. Only with this view can we see that very few whole jobs are being automated, but in actual fact only certain tasks will be carried out by machine (e.g. data entry) and other tasks will remain uniquely human (e.g. complex problem solving).

The collaboration with machines goes both ways. Not only do machines help humans but we too have to support better practice and design as technology advances. Stories of data breaches hit the headlines on a daily basis – Bad Machines need Good Human Beings. It’s no mean feat, but many companies have started to publish their principles for the use of AI or set up ethics boards to make decisions on the ethical use of AI (a lesson learnt the hard way at Google: Pick your ethics board members wisely.) Policy is fighting hard to keep up. 29 countries around the world have published at least one piece of AI governance policy.

Humans also have to learn to collaborate better with each other, shifting from the command-and-control hierarchy to the flatter, more dynamic wirearchy. Facebook’s Zuckerberg famously sits in among his staff in an effort to promote transparency and equality across the company. New data-driven technologies like organizational network analysis (ONA) provide x-ray vision into the realities of modern work, uncovering dynamic networks of connected nodes, free of predefined priorities or ranks. (Far more useful that the static org chart).

For humans and machines (and humans collectively) to collaborate better, trust is of paramount importance. The Future of Work is often thought of as a daunting place – but greater transparency can relieve this pressure. Insurer Aviva, for example, recently released a note to its 16,000 workers in the UK saying, “If you think your job could be automated, we want to know. If we agree, we’ll retrain you for another role at the firm.”

The ultimate physical manifestation of a trust-based human-human/ human-machine über-network can be seen in the shift from buying to leasing. It used to be that ownership (house, car, land) was a status-symbol. But ownership is fast becoming a bug in late-stage Western capitalism, not a feature. New models of shared ownership and leasing are rising (think Zipcar, WeWork) -- but they’re dependent above all else on trust.

Do you trust another human with your car? Drivy allows you to rent your car to a stranger near you. Do you trust algorithms to recommend the right suit for your big interview? StichFix does “algorithmic fashion design” and uses complex algorithms to recommend new styles. Do you trust augmented reality to judge your shape and size accurately? MTailor’s revolutionary measurement technology claims to measure your size (using your device) with 20% more accuracy than a human tailor.

Work has always been an integral part of human identity, which makes old ways of working even harder to shake off. How will we get out of the habit of 9-5, when “honey, I’m home” still rolls off the tongue?

It’s high time to make the transition from old ways of working to new ways of working. From sitting atop a hierarchy, to creating a sense of belonging in among co-workers. From obsessing over job title to focusing on adaptability and willingness to learn. From fearing the bot to excitement about work transformed.

Our latest book, From/To: Everything You Wanted to Know About the Future of Your Work but were Afraid to Ask deep-dives into 42 shifts of where work has come from and where it’s going to – including those highlighted here.


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