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Human intelligence

The future of work: Learn, learn, then learn again

Brian Peccarelli  President, Tax & Accounting, Thomson Reuters

Brian Peccarelli  President, Tax & Accounting, Thomson Reuters

Discussions at Davos largely involve concerns about people rather than processes. Politicians and business people meet to talk about big issues such as poverty, social inclusion and about improving the quality of life for the world’s population. Yes, they talk about the global economy quite a bit and about social and political unrest, but mainly the conversation relates in the end to the quality of life experienced by the world’s population.

It is natural, then, that the future of work is mentioned a lot. There are a number of robotic devices on display performing various tasks – the dancing robot gained most of the headlines of course – and a lot of discussion at this year’s World Economic Forum was about what we do when the robots take over.

Are the workers of today collaborating or competing with the machines?

For me, this was particularly interesting when it comes to professional services. To be a professional, the reasoning goes, you need to study over a long period to amass the body of knowledge you need in order to practice. You qualify, you join a development program with your professional body, then you continue training throughout your career.

Today, much of that necessary knowledge is accessible instantly and easily. Artificial intelligence can interrogate that body of knowledge and apply it to situations in a limited but increasingly effective way. As computing power develops and data capacity grows, the ability of AI to perform some of these professional tasks will continue to grow.

Our society is still training its workforce in much the same way as it was 50 years ago. Much professional training is tightly task-oriented – it is about how to do a specific task. But where a task is specific and tightly defined, somebody is already writing an algorithm to do it more quickly, cheaply and efficiently.

The big question is what that means for the working population today. If you have a skilled trade, prepare for a future of regular retraining: robots are developing at speed and are after your job. If you have a profession, some of the basic but lucrative aspects of your work will become automated very soon, and then more sophisticated tasks will go the same way.

For professionals, then, the answer is not only continuous retraining, but also mastery of soft skills. The ability to develop and nurture client relationships, to apply your experience and knowledge to draw on the massive data resources at your disposal, to extract the right information and to apply it for the benefit and satisfaction of your client – all these skills will remain vital to a successful career.

This leads to one further point: how can we measure progress? At the moment gross domestic product (GDP) is the main means to compare the success (or not) of nations. But more than one delegate at Davos pointed to groups which have improved the quality of life for many millions of people, but whose efforts do not feature in any national accounts. These are not simply voluntary and charitable organizations, but also not-for-profit enterprises such as Wikipedia, which relies entirely on charitable donations. Wikipedia has made an enormous difference to millions of people’s quality of life. It is their first port of call for information – an extraordinary repository of user generated content about the world today. It has made no measurable impact on any country’s GDP however.

Surely we are measuring progress the wrong way, or we are using the wrong number as an indicator.

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