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According
to a leading business consultancy, 3-14% of the global workforce will need
to switch to a different occupation within the next 10-15 years, and all
workers will need to adapt as their occupations evolve alongside
increasingly capable machines. Automation – or ‘embodied artificial
intelligence’ (AI) – is one aspect of the disruptive effects of technology
on the labour market. ‘Disembodied AI’, like the algorithms running in our
smartphones, is another.
Dr
Stella Pachidi from Cambridge Judge Business School believes that some of
the most fundamental changes are happening as a result of the
‘algorithmication’ of jobs that are dependent on data rather than on
production – the so-called knowledge economy. Algorithms are capable of
learning from data to undertake tasks that previously needed human
judgement, such as reading legal contracts, analysing medical scans and
gathering market intelligence.
‘In
many cases, they can outperform humans,’ says Pachidi. ‘Organisations are
attracted to using algorithms because they want to make choices based on
what they consider is “perfect information”, as well as to reduce costs and
enhance productivity.’
‘But
these enhancements are not without consequences,’ says Pachidi. ‘If routine
cognitive tasks are taken over by AI, how do professions develop their
future experts?’ she asks. ‘One way of learning about a job is “legitimate
peripheral participation” – a novice stands next to experts and learns by observation.
If this isn’t happening, then you need to find new ways to learn.’
Another
issue is the extent to which the technology influences or even controls the
workforce. For over two years, Pachidi monitored a telecommunications
company. ‘The way telecoms salespeople work is through personal and
frequent contact with clients, using the benefit of experience to assess a
situation and reach a decision. However, the company had started using a[n]
… algorithm that defined when account managers should contact certain
customers about which kinds of campaigns and what to offer them.’
The
algorithm – usually built by external designers – often becomes the keeper
of knowledge, she explains. In cases like this, Pachidi believes, a
short-sighted view begins to creep into working practices whereby workers
learn through the ‘algorithm’s eyes’ and become dependent on its
instructions. Alternative explorations – where experimentation and human
instinct lead to progress and new ideas – are effectively discouraged.
Pachidi
and colleagues even observed people developing strategies to make the
algorithm work to their own advantage. ‘We are seeing cases where workers
feed the algorithm with false data to reach their targets,’ she reports.
It’s
scenarios like these that many researchers are working to avoid. Their
objective is to make AI technologies more trustworthy and transparent, so
that organisations and individuals understand how AI decisions are made. In
the meantime, says Pachidi, ‘We need to make sure we fully understand the dilemmas
that this new world raises regarding expertise, occupational boundaries and
control.’
Economist
Professor Hamish Low believes that the future of work will involve major
transitions across the whole life course for everyone: ‘The traditional
trajectory of full-time education followed by full-time work followed by a
pensioned retirement is a thing of the past,’ says Low. Instead, he envisages
a multistage employment life: one where retraining happens across the life
course, and where multiple jobs and no job happen by choice at different
stages.
On
the subject of job losses, Low believes the predictions are founded on a
fallacy: ‘It assumes that the number of jobs is fixed. If in 30 years, half
of 100 jobs are being carried out by robots, that doesn’t mean we are left
with just 50 jobs for humans. The number of jobs will increase: we would
expect there to be 150 jobs.’
Dr
Ewan McGaughey, at Cambridge’s Centre for Business Research and King’s College
London, agrees that ‘apocalyptic’ views about the future of work are
misguided. ‘It’s the laws that restrict the supply of capital to the job
market, not the advent of new technologies that causes unemployment.’
His
recently published research answers the question of whether automation, AI
and robotics will mean a ‘jobless future’ by looking at the causes of
unemployment. ‘History is clear that change can mean redundancies. But
social policies can tackle this through retraining and redeployment.’
He
adds: ‘If there is going to be change to jobs as a result of AI and
robotics then I’d like to see governments seizing the opportunity to
improve policy to enforce good job security. We can “reprogramme” the law
to prepare for a fairer future of work and leisure.’ McGaughey’s findings
are a call to arms to leaders of organisations, governments and banks to
pre-empt the coming changes with bold new policies that guarantee full
employment, fair incomes and a thriving economic democracy.
‘The
promises of these new technologies are astounding. They deliver humankind
the capacity to live in a way that nobody could have once imagined,’ he
adds. ‘Just as the industrial revolution brought people past subsistence
agriculture, and the corporate revolution enabled mass production, a third
revolution has been pronounced. But it will not only be one of technology.
The next revolution will be social.’
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