www.pwc.co.uk/economics

Will robots reallysteal our jobs?
An international analysis of
the potential long term
impact of automation

Key findings: impact of automation
Financial services jobs could be relatively
vulnerable to automation in the shorter term,
while transport jobs are more vulnerable toautomation in the longer term

In the long run, less well educated workers
could be particularly exposed to automation,emphasising the importance of increased
investment in lifelong learning and retraining

Figure 1 – Potential job automation rates byindustry across waves

Figure 2 – Potential job automation rates by
education level across waves% of existing jobs at potential risk of automation
50%% of existing jobs at potential risk of automation

Transport
Financial services
All sectors
Health40%

Wave 1(to early 2020s)

Low education
Medium education
High education

30%

Wave 2(to late 2020s)

20%
10%

Wave 3(to mid-2030s)

0%Wave 1(to early 2020s)

Wave 2(to late 2020s)

Wave 3(to mid-2030s)

0%

10%

20%

30%

40%

Source: PwC estimates based on OECD PIAAC data (median values for 29 countries)

Source: PwC estimates based on OECD PIAAC data (median values for 29 countries)

Female workers could be more affected byautomation over the next decade, but male jobs
could be more at risk in the longer term

Waves

Description and impact

Wave 1:

Automation of simple
computational tasks andanalysis of structured data,
affecting data-driven sectors
such as financial services.

Figure 3 – Potential job automation rates bygender across waves

Algorithmic
wave (to early2020s)% of existing jobs at potential risk of automation
Wave 1(to early 2020s)

Wave 2:

Men
All
Women

Wave 2(to late 2020s)

Wave 3(to mid-2030s)

Augmentation
wave (to late
2020s)

Wave 3:
0%

5%

10%

15%

20%

25%

30%

Source: PwC estimates based on OECD PIAAC data (median values for 29 countries)

35%

Autonomous
wave (to mid2030s)

50%

Dynamic interaction withtechnology for clerical supportand decision making. Also
includes robotic tasks in semicontrolled environments such as
moving objects in warehouses.
Automation of physical labourand manual dexterity, andproblem solving in dynamic realworld situations that require
responsive actions, such as intransport and construction.

Will robots really steal our jobs?

Contents
1.

Summary

1

2.

Introduction

7

3.

How do potential automation rates vary by country?

9

3.1. Estimated potential automation rates across countries

9

3.2. The relative impact of industry composition and job automatability

10

3.3. Factors related to estimated automation levels

11

3.4. Impact on countries over time – the three waves of automation

14

3.5. Two important caveats – constraints on automation and new job creation

17

4.

Which industry sectors could see the highest rates ofautomation?

18

4.1. Total automation rates across industries18

4.2. Impact on industries over time20

4.3. Drivers of differences between industries

21

4.4. Which sectors are likely to see the largest jobs gains?

22

5.

23

Which occupations could see the highest rates of automation?

5.1. Total automation risk across occupation categories

23

5.2. Impact over time by occupation

24

5.3. Drivers of differences between occupations

25

5.4. Composition of industries by occupational category

26

6.

Why does the potential rate of job automation vary by typeof worker?

27

6.1. Total automation risk across workers

27

6.2. Potential automation rates by education level

30

7.

34

What are the public policy implications?

7.1. Education and skills

34

7.2. Job creation through increased public and private investment

34

7.3. Enhancing social safety nets

35

8. Implications for business: constraints, opportunities
and responsibilities

36

8.1. What constraints will need to be overcome to realise benefits for business?

36

8.2. AI’s impact on company value chains37

8.3. AI and healthcare provision38

8.4. Businesses need to help workers retrain and adapt to new technologies

39

8.5. Conclusion

39

Annex – technical methodology

40

References

41

Authors, contacts and services

43

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1. Summary
Artificial intelligence (AI), robotics and other forms of ‘smart automation’ are advancing at a rapid pace andhave the potential to bring great benefits to the economy, by boosting productivity and creating new and better
products and services. In an earlier study 1, we estimated that these technologies could contribute up to 14% toglobal GDP by 2030, equivalent to around $15 trillion at today’s values.
For advanced economies like the US, the EU and Japan, these technologies could hold the key to reversing
the slump in productivity growth seen since the global financial crisis. But they could also produce a lot of
disruption, not least to the jobs market. Indeed a recent global PwC survey 2 found that 37% of workers were
worried about the possibility of losing their jobs due to automation.
To explore this further we have analysed a dataset compiled by the OECD that looks in detail at the tasks
involved in the jobs of over 200,000 workers across 29 countries (27 from the OECD plus Singapore andRussia). Building on previous research by Frey and Osborne (Oxford University, 2013) 3 and Arntz, Gregory
and Zierahn (OECD, 2016) 4 we estimated the proportion of existing jobs that might be of high risk of
automation by the 2030s for:•Each of these 29 countries;•

Different industry sectors;•

Occupations within industries; and•Workers of different genders, ages and education levels.

We also identify how this process might unfold over the period to the 2030s in three overlapping waves:
1.

Algorithm wave: focused on automation of simple computational tasks and analysis of structured datain areas like finance, information and communications – this is already well underway.

2. Augmentation wave: focused on automation of repeatable tasks such as filling in forms, communicatingand exchanging information through dynamic technological support, and statistical analysis of
unstructured data in semi-controlled environments such as aerial drones and robots in warehouses – this isalso underway, but is likely to come to full maturity in the 2020s.
3. Autonomy wave: focused on automation of physical labour and manual dexterity, and problem solving in
dynamic real-world situations that require responsive actions, such as in manufacturing and transport (e.g.
driverless vehicles) – these technologies are under development already, but may only come to full maturity
on an economy-wide scale in the 2030s.
Our estimates are based primarily on the technical feasibility of automation, so in practice the actual extent of
automation may be less, due to a variety of economic, legal, regulatory and organisational constraints. Just
because something can be automated in theory does not mean it will be economically or politically viablein practice.

1 PwC, ‘Sizing the prize’ (2017): https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.
2 PwC, ‘Workforce of the future’ (2017): https://www.pwc.com/gx/en/services/people-organisation/publications/workforce-of-the-future.html.

3 Frey, C.B. and M.A. Osborne (2013), The Future of Employment: How Susceptible are Jobs to Computerisation?, University of Oxford.
4 Arntz, M. T. Gregory and U. Zierahn (2016), ‘The risk of automation for jobs in OECD countries: a comparative analysis’, OECD Social,

Employment and Migration Working Papers No 189.

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Furthermore, other analysis we have done 5 suggests that any job losses from automation are likely to bebroadly offset in the long run by new jobs created as a result of the larger and wealthier economy madepossible by these new technologies. We do not believe, contrary to some predictions, that automation will leadto mass technological unemployment by the 2030s any more than it has done in the decades since the digital
revolution began.
Nonetheless, automation will disrupt labour markets and it is interesting to look at the estimates we haveproduced to get an indication of the relative exposure of existing jobs to automation in different countries,
industry sectors, and categories of workers. We summarise the key findings in these three areas in turn below.
Potential impacts by country
As Figure 1.1 shows, the estimated proportion of existing jobs at high risk of automation by the early 2030s
varies significantly by country. These estimates range from only around 20-25% in some East Asian and Nordic
economies with relatively high average education levels, to over 40% in Eastern European economies whereindustrial production, which tends to be easier to automate, still accounts for a relatively high share of total
employment. Countries like the UK and the US, with services-dominated economies but also relatively long‘tails’ of lower skilled workers, could see intermediate levels of automation in the long run.
Figure 1.1 – Potential job automation rates by country across waves
Potential jobs at high risk of automation

Country

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

svk
svn
ltu
cze
ita
usa
fra
deu
aut
esp
pol
tur
irl
nld
gbr
cyp
bel
dnk
isr
chl
sgp
nor
swe
nzl
jpn
rus
grc
fin
kor
Algorithm wave

Augmentation wave

Autonomy wave

Source: PIAAC data, PwC analysis

5 This modelling was described in our report on the global economic impact of AI here: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.

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Figure 1.1 also shows how potential automation rates might evolve by country over our three waves ofautomation. Existing jobs in some countries with relatively low longer term automation rates, such as Japan,
may nonetheless be see relatively high automation rates in the shorter term given that algorithmic technologies
are already more widely used there.
The opposite is true for a country like Turkey, which may have relatively high exposure to later waves ofautomation that start to displace manual workers such as drivers and construction workers, but relatively
lower exposure in the short term.
Potential impacts by industry sector
We also see significant variations in potential automation levels between industry sectors, although the pattern
here also varies across different waves as Figure 1.2 illustrates.
Figure 1.2 – Potential rates of job automation by industry across waves
Potential jobs at high risk of automation
0%

10%

20%

30%

40%

50%

60%

Transportation and storage
Manufacturing
Construction
Administrative and support service
Wholesale and retail trade
Public administration and defence
Financial and insurance
Information and communication
Professional, scientific and technicalAccommodation and food service
Human health and social work
Education
Algorithm wave

Augmentation wave

Autonomy wave

Source: PIAAC data, PwC analysis

Transport stands out as a sector with particularly high potential for automation in the longer run as driverlessvehicles roll out at scale across economies, but this will be most evident in our third wave of autonomous
automation (which may only come to maturity in the 2030s). In the shorter term, sectors such as financial
services could be more exposed as algorithms outperform humans in an ever wider range of tasks involving
pure data analysis.

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Potential impacts by type of worker
Our analysis also highlights significant differences in the potential impact of automation across types of workers
and these will also vary across our three waves of automation as Figure 1.3 shows.
Figure 1.3 – Potential job automation rates by type of worker across waves
Potential jobs at high risk of automation
0%

10%

20%

30%

40%

50%

Sex

Male
FemaleEducation level

Age group

Young (<25)
Core (25-54)
Older (55+)

Low
MediumHigh
Algorithm wave

Augmentation wave

Autonomy wave

Source: PIAAC data, PwC analysis

The starkest results are those by education level, with much lower potential automation rates on average forhighly educated workers with graduate degrees or above, than for those with low to medium education levels.
This reflects the greater adaptability of more highly educated workers to technological changes and the fact thatthey are more likely to be in senior managerial roles that will still be needed to apply human judgement, as wellas to design and supervise AI-based systems. Such workers could see their wages increase due to the
productivity gains that these new technologies should bring.
Differences are less marked by age group, although some older workers could find it relatively harder to adapt
and retrain than younger cohorts. This may apply particularly to less well-educated men as we move into ourthird wave of autonomous automation in areas like driverless cars and other manual labour that has a relatively
high proportion of male workers at present. But female workers could be relatively harder hit in early waves ofautomation that apply, for example, to clerical roles.
Implications for public policy
The most obvious implication of our analysis is the need for increased investment in education and skills to
help people adapt to technological change throughout their careers. While increased training in digital skills
and STEM subjects 6 is one important element in this, it will also require retraining of, for example, truck
drivers to take jobs in services sectors where demand is high but automation is less easy due to theimportance of social skills and ‘the human touch’. Governments, business, trade unions and other organisations(e.g. the NHS and social care providers in the UK) all need to play their part here in helping people to adapt tothese new technologies 7. This will include training and retraining people in softer skills, such as creativity,
problem solving and flexibility. On-the-job training will be important here, for example through degree
6 Design and other creative skills may also be important here.
7 The recent UK government proposal in its November 2017 Budget for a new National Retraining Scheme involving both business andtrade unions is one example here. Further discussion of how people can be helped to adapt to new technologies is contained in our
Workforce of the Future report here: https://www.pwc.com/futureworkforce.

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apprenticeships that offer a mix of theoretical study and practical experience, and that are open to a wide rangeof people (including mature students) to promote social mobility.
In addition, it is important that aggregate demand levels are kept high so as to facilitate the creation of newjobs. One obvious way to do this at present is through increased infrastructure investment (including areas suchas housing where this is in short supply as in the UK). Such investment is needed to support longer term
growth, but can also create many new jobs in construction and related sectors 8. Governments can play a keyrole here both in funding some investment directly and in helping to lever in additional private investment.
It is also important to recognise that concerns about the possible loss of existing jobs should not lead countries
to miss out on opportunities to lead the way in developing these new technologies. If governments andbusinesses in one country do not invest in them, then they will just be developed elsewhere. Unless a country
blocks itself off from global trade and investment, which history shows would be extremely damaging
economically in the long run, the technologies will still come to all countries over time, so it is better to be at theforefront of this global race.
However, governments do have a key role in making sure that the great potential benefits from AI, robotics andrelated technologies are shared as broadly as possible across society. As well as investing more in education,
training and retraining, and protecting workers rights through appropriate legislation, governments should
consider using the tax proceeds from technology-driven growth to strengthen social safety nets for those wholose out from automation.
Universal basic income (UBI) is one idea that has been discussed here. The case for this remains to be proven,but it makes sense for governments to gather evidence from pilot schemes and microsimulation models to
inform future decisions on this and other options for sharing the benefits of technology more widely acrosssociety. Optimal solutions here may involve combining different ideas (e.g. UBI-type schemes with a degreeof conditionality related to working, learning, training, caring or doing some other form of socially valuable
activity to qualify for such benefits).
Implications for businessOur research on AI, robotics and related technologies shows their huge potential to boost productivity andcreate new and better products and services. There are large benefits to be reaped here by businesses in all
sectors, but the phasing of these may vary across different waves of automation (see Table 1.1). Of course,many businesses will need to start investing now for later waves, but they also need to focus on the shortterm gains already available through emerging technology and algorithmic methods to enhance data analysis
and customer service.
Table 1.1: Key impacts in the three waves of automation
Phase

Description

Tasks impacted

Industries impacted

Algorithm
wave

Automation of simple
computational tasks andanalysis of structured data,
affecting data-driven
sectors such as
financial services.

This includes manually conducting
mathematical calculations, or using
basic software packages and internet
searches. Despite increasingly
sophisticated machine learningalgorithms being available andincreasingly commoditised, it is thesemore fundamental computational job
tasks that will be most impacted first.

Data driven sectors likefinancial and insurance,
information andcommunication, andprofessional, scientificand technical services.

8 Of course, as our analysis shows, some construction jobs may also be automated in the long run to boost productivity, but if moreconstruction work is undertaken this will also boost the demand for human labour, particularly in the short to medium term.

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Phase

Description

Tasks impacted

Industries impacted

Augmentation
wave

Dynamic interaction
with technology forclerical support anddecision making. Also
includes robotic tasks
in semi-controlledenvironments suchas moving objects
in warehouses.

For example, routine tasks such as
filling in forms or exchanging
information, which includes thephysical transfer of information. It isalso likely to see a decreased need formany programming languages as
repeatable programmable tasks are
increasingly automated, and throughmachines themselves building andredesigning learning algorithms.

The financial and insurance
sector will continue to behighly impacted, along withother sectors with a higher
proportion of clerical support,
including public andadministration,
manufacturing, andtransport and storage.

Autonomy
wave

Automation of physicallabour and manual
dexterity, and problem
solving in dynamic realworld situations that
require responsive actions,such as in transportand manufacturing.

AI and robotics will further automate
routine tasks but also those tasks thatinvolve physical labour or manual
dexterity. This will include thesimulation of adaptive behaviour byautonomous agents.

Sectors like construction,water, sewage and waste
management, andtransportation and storage
with the advent of fullyautonomous vehicles
and robots.

Source: PwC analysis

Businesses also need to consider now how successive waves of AI-related technologies might further break
down barriers to entry in their sector and challenge existing business models. In addition to enhancing existing
propositions, it also allows business to offer the same proposition in a more cost effective way, which may beparticularly beneficial for small to medium sized businesses and start-ups. This will also create newopportunities for successful businesses to leverage their distinctive competencies in adjacent sectors. Given thefast pace of change, businesses need to be constantly experimenting with new technologies and creating options
that they can scale up quickly where successful.
Individuals also need to be more entrepreneurial, taking responsibility for their lifelong learning and seeking to
generate their own intellectual property and start new businesses. Much of the automation of the future may be
driven by these new businesses replacing or challenging established companies that find it harder to change.
At the same time, as we have argued in previous reports 9, businesses and other employers need to adopt a
responsible approach to AI, both as regards their customers (e.g. as regards data privacy) and their workers(e.g. helping them to develop the skills they need to prosper in an age of increasing automation and rapid
technological change).
By acting in this way 10, businesses and governments can help to maximise the benefits of AI and robotics while
minimising as far as possible the negative impacts of these disruptive technologies.

9 See our website for more details on Responsible AI: https://www.pwc.co.uk/services/audit-assurance/risk-assurance/services/

technology-risk/technology-risk-insights/accelerating-innovation-through-responsible-ai/responsible-ai-framework.html.

10 For more on this, see the following G20 Policy Insights paper by various PwC authors: http://www.g20-

insights.org/policy_briefs/accelerating-labour-market-transformation/.

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2. Introduction
The potential for disruption to labour markets due to advances in technology is not a new phenomenon. Most
famously, the Luddite protest movement of the early 19th century was a backlash by skilled handloom weavers
against the mechanisation of the British textile industry that emerged as part of the Industrial Revolution(including the Jacquard loom, which with its punch card system was in some respects a forerunner of themodern computer). But, in the long run, not only were there still many (if, on average, less skilled) jobs in the
new textile factories but, more important, the productivity gains from mechanisation created huge new wealth.
This in turn generated many more jobs across the UK economy in the long run than were initially lost in the
traditional handloom weaving industry.
The standard economic view for most of the last two centuries has therefore been that the Luddites were wrong
about the long-term benefits of the new technologies, even if they were right about the short-term impact on
their personal livelihoods. Anyone putting such arguments against new technologies has generally been
dismissed as believing in the ‘Luddite fallacy’.
However, over the past few years, fears of technology-driven job losses have re-emerged with advances in‘smart automation’ – the combination of AI, robotics and other digital technologies that is already producing
innovations like driverless cars and trucks, intelligent virtual assistants like Siri, Alexa and Cortana, andJapanese healthcare robots.
While traditional machines, including fixed location industrial robots, replaced our muscles (and those of otheranimals like horses and oxen), these new smart machines have the potential to replace our minds and to movearound freely in the world driven by a combination of advanced sensors, GPS tracking systems and deeplearning - if not now, then probably within the next decade or two. Will this just have the same effects as past
technological leaps – short term disruption more than offset by long term economic gains? Or is this something
more fundamental in terms of taking humans out of the loop not just in manufacturing and routine service
sector jobs, but more broadly across the economy? What exactly will humans have to offer employers if smart
machines can perform all or most of their essential tasks better in the future 11? In short, has the ‘Luddite fallacy’
finally come true?
This debate was given added urgency in 2013 when researchers at Oxford University (Frey and Osborne, 2013)
estimated that around 47% of total US employment had a ‘high risk of computerisation’ over the next couple ofdecades – i.e. by the early 2030s.
However, there are also dissenting voices. Notably, Arntz, Gregory and Zierahn (OECD, 2016) re-examined theresearch by Frey and Osborne and, using an extensive new OECD data set, came up with a much lower estimatethat only around 10% of jobs were under a ‘high risk 12 of computerisation’. This is based on the reasoning thatany predictions of job automation should consider the specific tasks that are involved in each job rather thanthe occupation as a whole 13.
In an earlier article in March 2017 14 we produced our own analysis of the potential effect of automation on jobs
with a focus on the UK. Using a more refined version of the OECD methodology, we concluded that up to 30%
of UK jobs could be impacted by automation by the 2030s. We also produced high level comparisons suggesting
somewhat lower potential automation rates in Japan and somewhat higher rates in Germany and the US.

11 Martin Ford, The Rise of the Robots (Oneworld Publications, 2015) is one particularly influential example of an author setting out thisargument in detail. Calum Chace (The Economic Singularity, 2016) also discusses these issues in depth.

12 In both studies, this is defined as an estimated probability of 70% or more. For comparability, we adopt the same definition of ‘highrisk’ in this report.

13 The importance of looking at tasks is also emphasised by Autor (2015).
14 ‘Will robots steal our jobs?’ PwC UK Economic Outlook, March 2017, available here: https://www.pwc.co.uk/economic-

services/ukeo/pwcukeo-section-4-automation-march-2017-v2.pdf.

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At the same time, we also emphasised that various economic, legal and regulatory and organisational factors
mean that these potential risks may not lead to actual job displacement. In some cases, it would alter the natureof jobs significantly, but not displace humans entirely.
Furthermore, we emphasised that there were likely to be broadly offsetting job gains from the new technologies,
provided that the income and wealth gains from these advances were recycled into the economy. Thisqualitative judgement was backed up by later detailed quantitative modelling 15 that concluded that the net longterm job impact of automation would be likely to be neutral or even slightly positive 16. This will, however,require both business and governments to provide support to workers affected by these technological advances
to retrain and start new careers.
In this paper, we extend our March 2017 analysis of jobs at potential risk of automation to a much wider set ofcountries, using the OECD’s PIAAC database for 29 countries (27 from the OECD, plus Singapore and Russia).
In total, this covers the jobs of over 200,000 workers and so provides a much larger dataset to explore potential
impacts of automation by country, sector and type of worker. The additional data also allows us to provide amore robust analysis of the factors causing automation risk to vary across countries and sectors.
We also identify how this process might unfold over the period to the 2030s in three overlapping waves:
1.

Algorithm wave: focused on automation of simple computational tasks and analysis of structured datain areas like finance, information and communications – this is already well underway.

2. Augmentation wave: focused on automation of repeatable tasks such as filling in forms, communicatingand exchanging information through dynamic technological support, and statistical analysis of
unstructured data in semi-controlled environments such as aerial drones and robots in warehouses –this is also underway, but is likely to come to full maturity in the 2020s.
3. Autonomy wave: focused on automation of physical labour and manual dexterity, and problem solving in
dynamic real-world situations that require responsive actions, such as in manufacturing and transport (e.g.
driverless vehicles) – these technologies are under development already, but may only come to full maturity
on an economy-wide scale in the 2030s.
Report structure
The discussion in the rest of this report is structured as follows:•

Section 3 – How do potential automation rates vary by country?

•

Section 4 – Which industry sectors could see the highest levels of automation?

•

Section 5 – Which occupations could see the highest rates of automation?

•

Section 6 – Why does the potential rate of job automation vary by type of worker?

•

Section 7 – What are the public policy implications?

•

Section 8 – Implications for business – constraints, opportunities and responsibilities.

Further details of the methodology behind our analysis are contained in a technical annex at the end of thisreport, together with references to the studies cited.

15 This modelling was described in our report on the global impact of AI here: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.

16 Other reports such as ZEW (2016) and Acemoglu and Restrepo (2016) also highlights the job creating potential of these newtechnologies. However, an empirical study for the US manufacturing sector by Acemoglu and Restrepo (2017) found a net negative
impact on employment from industrial robots, so this remains an active area of debate.

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3. How do potential automation rates
vary by country?
Key findings• The estimated share of existing jobs that could potentially be automated by the 2030s varies widely acrosscountries from only around 22% in Finland and South Korea to up to 44% in Slovakia.
•

Countries with similar labour market performances and economic structures have broadly similar levels ofpotential automation. Four broad country groups emerge: a) Industrial economies with relatively inflexible
labour markets, which could see the highest automation rates; b) Services-dominated economies such as the
US and the UK with long tail of lower skilled workers and intermediate levels of potential automation; c)
Nordic countries with high employment rates and skill levels and relatively low levels of potential
automation; and d) East Asian nations with high levels of technological advancement and education, which
could see high short term automation rates in some sectors but lower longer term impacts.

•

Industry structure is important as Eastern European countries, for example, tend to have relatively highshares of employment in sectors such as manufacturing and transport that are projected to be relatively
easy to automate looking ahead to the 2030s.

•

Automation rates also differ across countries because ways of working differ. In particular, workers incountries such as Singapore and South Korea with more stringent educational requirements have greater
protection against automation in the long run. This is also true (particularly in Europe) for countries withhigher levels of education spending as a percentage of GDP.

•Country automation levels will evolve over time – jobs in more technologically advanced nations like Japanand South Korea may be at immediate risk as computational tasks are automated in the first algorithmic
wave. But workers in these nations could eventually face lower risks in the later waves of automation that
displace manual jobs and could have a greater impact on workers in other countries with lower average skill
levels and/or large manufacturing bases.

3.1. Estimated potential automation rates across countries
The methodology for estimating potential future automation rates that we previously developed 17 was refinedand applied across the set of 29 countries for which OECD PIAAC data are publicly available (27 from the OECDplus Singapore and Russia). This revealed a range of estimates across countries for the share of existing jobs
with potential high rates of automation by the 2030s, as shown in Figure 3.1. Notably some Eastern European
countries such as Slovakia (44%) and Slovenia (42%) face relatively high potential automation rates, whilst
Nordic countries such as Finland (22%) and Asian countries such as South Korea (22%) have relatively lower
shares of existing jobs that are potentially automatable.

17 PwC (2017), UK Economic Outlook: Will robots steal our jobs? Available here: https://www.pwc.co.uk/economic-

services/ukeo/pwcukeo-section-4-automation-march-2017-v2.pdf.

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Figure 3.1 – Potential rates of job automation by country
Potential jobs at high risk of automation

Country

0%

10%

20%

30%

40%

50%

svk
svn
ltu
cze
ita
usa
fra
deu
aut
esp
pol
tur
irl
nld
gbr
cyp
bel
dnk
isr
chl
sgp
nor
swe
nzl
jpn
rus
grc
fin
kor

Source: PIAAC data, PwC analysis

3.2. The relative impact of industry composition andjob automatability
The overall automation rate estimates in Figure 3.1 reflect two key factors:
1.

The share of employment in each country across industry sectors; and2. The relative automatability of jobs in each country on a sector by sector basis.
As a result of differences in labour market structures, education and skills levels, and government polices acrossthe countries, the relative impact of these two components varies between countries (see Figure 3.2), which
gives rise to differences in estimated automation levels. Looking at Figure 3.2, we can distinguish four broadcountry groups:
Industrial economies – for example, Germany, Slovakia and Italy, which could see relatively higher
automation rates in the long run. These countries are typically characterised by jobs that are relatively more
automatable and (relative to the OECD average) more concentrated in industry sectors with higher potential
automation rates (as discussed further in Section 4 below).
Services-dominated economies – for example, the US, UK, France and the Netherlands, which have jobs
that are on average relatively more automatable based on their characteristics, but also a greater concentration
on services sectors that tend to be less automatable on average than industrial sectors.

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Asian countries – for example, Japan, South Korea, Singapore and Russia, which have jobs that are relatively
less automatable overall but with relatively high concentrations of employment in industrial sectors withrelatively high potential automation rates.
Nordic countries – for example Finland, Sweden and Norway (in addition to New Zealand and Greece
outside this region). These countries have jobs that are on average relatively less automatable and in industry
sectors with relatively lower potential automation rates.
Figure 3.2 – Potential impact across countries by employment shares and automatability of jobs

Relative impact from the automatabilityof jobs (0-10 index)

10
svk

9

ltu

usa

8
7
gbr

isr

pol

nld
dnk

5
nor

4
3

cze

cyp
swe

sgp
jpn

rus

grc

2

tur

bel
chl

fin

nzl

svn

deu

aut

irl
6

ita

fra

esp

kor

1
0
0

1

2

3

4

5

6

7

8

9

10

Relative impact from employment shares across industries (0-10 index)
Source: PIAAC data, PwC analysis

There are also some relationships here between estimated automation risks in different countries and theirperformances on PwC’s labour market indices – the Young Workers Index (YWI) 18, the Women in Work Index(WWI) 19 and the Golden Age Index (GAI) for older workers 20. European countries such as Slovakia, Slovenia,
Czech Republic and Italy have repeatedly appeared towards the bottom or lower middle of the rankings on all ofthese labour market indices. This indicates relatively higher NEET (not in education, employment or training)rates for younger people and lower engagement of women and older people in the workforce. Similarly, New
Zealand and Israel, along with the Nordic countries, have been high performers on all of our indices due torelatively high employment rates and education and skill levels across all major demographic groups.

3.3. Factors related to estimated automation levels
Countries that have an increased concentration of labour in more industrial sectors, rather than in the service
sectors, tend to have higher potential automation rates (other things being equal). For example, countries witha higher share of employment in the manufacturing sector such as Czech Republic (29%) and Slovenia (28%)
are estimated to have an increased potential job automation rate, see Figure 3.3. These jobs are characterised bya greater proportion of manual or routine work that is typically more susceptible to automation (as discussed
further in Section 4 below).

18 PwC (2017), Young Workers Index: The $1.2 trillion prize from empowering young workers in an age of automation. Available here:

https://www.pwc.co.uk/services/economics-policy/insights/young-workers-index.html.

19 PwC (2017), Women in Work Index: Closing the gender pay gap. Available here: https://www.pwc.co.uk/services/economics-

policy/insights/women-in-work-index.html.

20 PwC (2017), Golden Age Index: The potential $2 trillion prize from longer working lives. Available here:

https://www.pwc.co.uk/services/economics-policy/insights/golden-age-index.html.

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Figure 3.3 – Relative impact from employment shares across industries e.g. manufacturing
Potential jobs at high risk of automation

50%

40%

30%
ρ = 0.536
20%

10%

0%
0%

5%

10%

15%

20%

25%

30%

Manufacturing employment share
Source: PIAAC data, PwC analysis

The relative impact from the automatability of jobs is instead dependent on a wider range of determinants,
such as the level of training, education, and skills required for those jobs. For example, countries with ahigher proportion of labour employed in jobs with a high level of educational requirements, such asSingapore (60%) and Russia (57%), are estimated to have lower potential automation rates (see Figure 3.4).
Notably, this is a stronger effect than the proportion of labour with high education levels alone (e.g. degreelevel, correlation (r) = -0.35 vs. r = -0.55 for high educational job requirements).
Figure 3.4 – Relative impact from the automatability of jobs e.g. educational job requirements

Potential jobs at high risk of automation

50%

40%

30%

20%
ρ = (0.554)
10%

0%
0%

10%

20%

30%

40%

50%

60%

70%

High educational job requirements
Source: UNDP HDI data, PwC analysis

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Furthermore, for European countries there are strong negative correlations between the potential share ofexisting jobs at high risk of automation and country education metrics, such as government expenditure on
education as a percentage of GDP (r=-0.77) and pupil-t0-teacher ratios in primary school (r=0.53) 21. This
relationship is not so strong for Asian countries that have proportionally lower education spend and higher
pupil-to-teacher ratios than in Europe. However, these Asian countries nonetheless achieve high educational
outcomes, notably for STEM subjects, so the underlying negative relationship between high education and low
automatability also holds here even if different metrics need to be used to show this relationship in Asia.
In addition to the share of employment and automatability of jobs, one other factor that may impact Asian
countries more is the current technological level and the extent to which job automation has already takenplace, which is also an important factor in future automation rates. Figure 3.5 shows a negative correlation
between the potential jobs at high risk of automation, adjusted to account for industry composition, against thedensity of industrial robots in the country 22. This suggests that workforces in more technologically advanced
countries such as Japan, South Korea and Singapore that are increasingly working alongside robots havealready adjusted to automation to some degree and so may be at lower future risk. Instead they may be wellplaced to reap the benefits of automation in terms of higher productivity and real wages.
Figure 3.5 – Relationship between density of industrial robots and industry-adjusted job automation rates

Industry-adjusted job automation risk

50%

svk

svn

40%

ita usa

fra

cze

deu

esp
dnk

gbr
30%

nld

aut

bel
sgp
jpn

fin

20%

kor

swe

ρ = (0.596)
10%

0%
1.7

1.9

2.1

2.3

2.5

2.7

2.9

log (industrial robots per 10,000 employees in the manufacturing industry)
Source: International Federation of Robots, PwC analysis

21 Data sourced from the United Nations Development Programme. Government expenditure on education (% of GDP 2010-2014);

Pupil-to-teacher ratio (2010-2014).

22 International Federation of Robotics – https://ifr.org/ifr-press-releases/news/world-robotics-report-2016.

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3.4. Impact on countries over time – the three waves of automation
The impact of the automation process is expected to vary over-time as automation encroaches on increasingly
human-like capabilities, as illustrated in Figure 3.6 for the four country groups we discussed earlier in thissection. On average across the 29 countries covered, the share of jobs at potential high risk of automation isestimated to be around 3% by the early 2020s, but this rises to around 20% by the late 2020s, and around 30%
by the mid-2030s. The precise timings shown in Figure 3.6 (and subsequent charts of this kind in the report)
are subject to many uncertainties, but give some indication of how automation might have its effect on different
groups of countries over time.
Figure 3.6 – Potential impact of job automation over-time across the four country groups

Potential jobs athigh risk of automation

50%

40%

30%

20%

10%

0%
2019

2021

2023

2025

Industrial

2027

2029

Service

Nordic

2031

2033

2035

2037

Asian

Source: PIAAC data, PwC analysis

As mentioned in the introduction, this automation process can be characterised as involving three overlapping
waves, which we refer to as: 1) an Algorithm wave, 2) an Augmentation wave, and 3) and Autonomy wave.
Algorithm wave – The first wave of automation, which is already well underway, is primarily an automationof simple computational tasks and analysis of structured data (see Figure 3.7). This includes manuallyconducting mathematical calculations, or using basic software packages and internet searches. Increasingly
sophisticated applications for processing big data and running machine learning algorithms are available to themarket and being commoditised. However, it is these more fundamental computational job tasks that will bemost impacted first.
Augmentation wave – The second wave of automation is expected to involve a more dynamic change to how
many job tasks are conducted, in particular those that are routine and repeatable. For example, routine tasks
such as filling in forms or exchanging information, which includes the physical transfer of information, will
increasingly be augmented by technology. It is also likely to see a decreased need for many programming
languages as repeatable programmable tasks are increasingly automated, and through machines themselvesbuilding and redesigning learning algorithms. This will also involve further advances in robotics, althoughgenerally these will not be fully autonomous during this period but will operate with the assistance of human
workers and augment their capabilities. The impacts of this second wave are expected to emerge on an
economy-wide scale during the course of the 2020s.
Autonomy wave – The third wave of automation is one of autonomous AI and robotics that will further
automate routine tasks but also those tasks that involve physical labour or manual dexterity. Problem solving
will increasingly extend from analytical modelling of structured data to problem solving in dynamic real-world
situations that also requires responsive actions to be taken. This will include the simulation of adaptive
behaviour by autonomous agents, such as in factories or in transport. The full impacts of this third wave are
only expected to emerge on an economy-wide scale in the 2030s, even though some of these technologies are
already being piloted now.

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Figure 3.7 – Task automation across the three waves
Proportion of tasks from high risk jobs
0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Manual tasks

Routine tasks

Computation

Management

Social skills

Literacy skills
Algorithm wave

Augmentation wave

Automony wave

Source: PIAAC data, PwC analysis

As these three waves play out, different regions of the world are expected to see relatively greater impacts at
different points in time, as illustrated in Figure 3.8. For example, a greater impact is expected in Asian
countries at first as the Algorithm wave predominates. However, both industrial and services-dominatedeconomies are then expected to face greater impacts in the longer term as the Augmentation and Autonomy
waves ripple through economies.
Figure 3.8 – The rank order of potential impact over-time across the four country groups

Rank order of potential
impact of job automation

1

2

3

4
2019

2021

2023
Industrial

2025

2027
Service2029
Nordic

2031

2033

2035

2037

Asian

Source: PIAAC data, PwC analysis

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In Table 3.1 below we set out estimates of how the proportion of jobs at risk of automation in different
countries might evolve over the three waves.
Table 3.1 – Estimated share of jobs at potential high risk of automation across countries for each of thethree waves: Algorithm wave, Augmentation eave and Autonomy wave
Country

Algorithm wave (%)

Augmentation wave (%)

Autonomy wave (%)

Slovakia

4

25

44

Slovenia

3

24

42

Lithuania

4

26

42

Czech Republic

3

25

40

Italy

4

23

39

USA

5

26

38

France42237Germany

3

23

37

Austria

3

22

34

Spain

3

21

34

Poland2

18

33

Turkey1

1433

Ireland2

19

31

Netherlands

4

21

31

UK

2

20

30

Cyprus2

1930

Belgium

4

18

30

Denmark3

1930

Israel

3

19

29

Chile

1

13

27

Singapore

4

18

26

Norway3

18

25

Sweden3

17

25

New Zealand

2

16

24

Japan

4

16

24

Russia2

1223

Greece2

1323Finland2

16

22South Korea

2

12

22

Note: figures shown are cumulative so those in the final column include the estimated impacts from all three waves of automation.
Source: PIAAC data, PwC analysis

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3.5. Two important caveats – constraints on automation and newjob creation
When considering these and other results in this report, however, it is important to bear in mind, first, that
there could be a variety of economic, legal and regulatory and organisational constraints that mean that
automation does not proceed as fast as projected here. We discuss these constraints further in Section 8 below.
Second, we also believe that new technologies like AI and robotics will create many new jobs. Some of these
new jobs will relate directly to these new technologies, but most will just result from the general boost to
productivity, incomes and wealth that these technologies will bring. As these additional incomes are spent, thiswill generate additional demand for labour and so new jobs, as such technologies have done throughout history.
Our other research 23 suggests that the net long term effect on employment in advanced economies like the US
and the EU may be broadly neutral, although it is harder to quantify new job creation than it is to estimate theproportion of existing jobs at risk of automation (precisely because those jobs exist now and we therefore know
a lot about their characteristics). We can, however, gain some more insight into potential areas of job losses andgains by considering how automatability varies by industry sector, which is the subject of the next section ofthis report.

23 PwC (2018), The macroeconomic impact of artificial intelligence

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4. Which industry sectors could see
the highest rates of automation?
Key findings• Potential automation risk varies widely across industry sectors. Transportation and storage andmanufacturing are estimated to have the highest share of existing jobs that could potentially be automatedby the 2030s at around 52% and 45% respectively. Human health and education are the major sectors withthe lowest estimated future automation rates, and corresponding potential for net job gains in the long run.
•Industries are likely to follow different paths of automation over time – data-driven industries such as
financial services and information management will be most affected in the short term as algorithmic
technologies are developed. In the longer run, the advent of driverless vehicles and other types ofautonomous machines will impact sectors such as transport and construction.

•

An industry’s task composition and educational requirements are the primary drivers behind its
automatability. Industries where large number of workers are engaged in relatively routine tasks are likely
to see more automation. Less automatable sectors have a greater proportion of time spent on social andliteracy-based tasks, and also have higher average educational requirements.

4.1. Total automation rates across industries
The estimated share of existing jobs with potential high rates of automation varies widely across industry
sectors, from a median across countries of 52% for transportation and storage to just 8% for the education
sector (see Figure 4.1 – error bars in this and other similar charts in this report show the variation acrosscountries in our estimates of potential automation rates by industry).
However, in terms of absolute numbers of jobs that could be automated, the greatest impact might be felt in the
manufacturing sector (with an estimated automatability of 45%) as this has a median employment share acrosscountries of 14%, as compared to only 5% in transport and storage. The wholesale and retail trade sector has amoderately high automatability estimate at 34% (with a median employment share of 14%), whilst health andsocial work has relatively lower potential automatability at 21% (with a median employment share of 11%).
Figure 4.1 –Share of jobs with potential high automation rates by industry
Potential jobs at high risk of automation
0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Transportation and storage
Manufacturing
Construction
Administrative and support service
Wholesale and retail trade
Public administration and defence
Financial and insurance
Information and communication
Professional, scientific and technicalAccommodation and food service
Human health and social work
Education
Source: PIAAC data, PwC analysis

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The fact that automatability in a given industry sector varies across countries is illustrated by the more detailed
figures in Table 4.1 for five of the largest sectors by employment.
Table 4.1. Share of jobs with potential high automation rates for the top 5 industries by employment share,across countries.
Country

Manufacturing(%)

Wholesale andretail trade(%)

Human healthand social work(%)

Education

Construction

(%)

(%)

Slovakia

58

43

34

14

42

Slovenia

57

35

31

13

53

Lithuania

55

39

27

26

58

Czech Republic

55

33

38

10

36

Italy

55

35

29

17

44

USA

53

51

28

12

34

France

53

41

29

17

41

Germany

49

43

24

9

39

Austria

48

37

26

9

51

Spain

45

35

26

8

42

Poland

50

31

21

9

48

Turkey

45

26

36

8

40

Ireland

50

39

17

7

33

Netherlands

46

35

24

8

36

UK

45

42

18

8

23

Cyprus

38

35

14

6

42

Belgium

45

28

19

10

43

Denmark

46

33

17

9

44

Israel

42

34

14

8

42

Chile

32

27

23

13

29

Singapore

33

38

19

9

26

Norway

33

34

16

6

35

Sweden

45

26

22

4

28

New Zealand

36

32

16

6

23

Japan

32

27

10

6

29

Russia

33

21

8

5

45

Greece

35

23

20

3

25

Finland

41

22

9

4

35

South Korea

31

24

12

6

31

Employment
share (median)

14.4

13.7

10.7

8.7

7.6

Source: PIAAC data, PwC analysis

As the colour coding in the table shows, there are some clear common patterns across countries between thosewith high automatability (red colour) and low automatability (green colour), but also some differences between
countries within sectors.

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4.2. Impact on industries over time
The automation process is also expected to affect industries differently over time, as shown in Figure 4.2.
For example, the financial and insurance sector has the highest share of existing jobs at potential high risk of
automation in the Algorithm wave at 8%, but then peaks at just over 30% in the early 2030s as we move into
the Autonomy wave. In contrast, the transport and storage and manufacturing sectors have lower potential
automation rates in the Algorithm wave, but this picks up to higher levels by the time of the Autonomy wave inthe 2030s (by which time use of driverless vehicles is likely to become more widespread across the economy).
Figure 4.2 – Potential impact of job automation over time across industry sectors
Potential jobs at high risk of automation

60%

50%

40%

30%

20%

10%

0%
2019

2021

2023

2025

2027

2029

Transportation and storage
Wholesale and retail trade
Human health and social work

2031

2033

2035

2037

Manufacturing
Financial and insurance
Education

Source: PIAAC data, PwC analysis

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4.3. Drivers of differences between industries
One of the main drivers of a sector being potentially more automatable is the composition of tasks involvedin jobs in that sector. Workers in sectors such as manufacturing and transport and storage spend a larger
proportion of their time engaged in manual tasks and in conducting simple administrative and routine tasks,
as shown in the left hand pane of Figure 4.3 for manufacturing. In the long term these tasks are most likely to
be automated by machines that are increasingly able to replace human labour, and carry out tasks at muchhigher speed and levels of accuracy and efficiency.
Figure 4.3 – Task composition for manufacturing, financial and insurance, and education sectors.
Manufacturing

Financial and insurance

Education

2%
12%

4% 6%
20%

7%

10%

16%
28%

21%
26%

22%
26%

31%
12%

Compared to average
0%

100%

Manual tasks

Compared to average
200%

124%

0%
Manual tasks

Routine tasks

108%

Routine tasks

Computation

101%

Computation

Management

88%

Management

Social skills

86%

Literacy skills

23%

20%

63%

100%

12%

Compared to average
200%0%Manual tasks

34%

100%

200%

64%

Routine tasks

90%

Computation

102%

103%

Management

92%

Social skills

114%

Social skills

Literacy skills

119%

Literacy skills

98%
163%

148%
202%

Source: PIAAC data, PwC analysis

However, as noted above, industries follow different paths of automation over time, and data-driven industries
such as the financial and insurance sector (and others such as the ‘information and communication’ and the‘professional, scientific and technical’ sectors) may be most automatable in the short term. Workers in these
sectors typically spend a disproportionately larger amount of their time engaged in simple computationaltasks (see middle panel of Figure 4.3 for finance/insurance).
In contrast, relatively low automatability sectors such as human health and social work, and education (seeright hand panel in Figure 4.3) have more focus on social skills, empathy and creativity, which are more difficult
to directly replace by a machine even allowing for potential technological advances over the next 10-20 years.

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4.4. Which sectors are likely to see the largest jobs gains?
We mentioned above that our previous research suggests that, at the macroeconomic level, the job losses fromautomation are likely to be broadly offset by job gains arising from new technologies like AI and robotics. This
will include some totally new jobs in areas relating specifically to these technologies, which will be relatively
highly skilled and highly paid, but probably relatively small in number based on past experience 24.
However, the largest job gains will be in sectors where these new technologies boost demand, either directly or
indirectly, through increasing income and wealth. As these additional incomes are spent on goods and services,
so this will generate increased demand for labour. It is difficult to put precise numbers on what kind of jobs
these will be, but we would anticipate them being concentrated in non-tradable service sectors such as healthand education that a richer, and older, society is likely to demand more of, and which are less readily
automatable according to our analysis. In the case of education, the increased demand with an ageing
population and rapid technological change may not be from the young but rather from older people wanting
to retrain for new careers later in life, or just to study for personal fulfilment in retirement. While some of this
could be delivered digitally, there is still likely to be strong demand for human teachers, coaches and mentors to
help guide people through this process (whether in person or online). As average incomes grow, there will also
be increased demand for a range of other jobs providing personal services (e.g. cleaning, household chores andrepairs, personal trainers and shoppers, and the digital platforms providing these services).
In addition, the government should benefit from increased tax revenues from the higher incomes and profitsthat these new technologies will generate. These additional tax revenues could fund higher public spending on
health and education to support additional jobs in these areas, but could also be directed into increased
investment in infrastructure, which would both support the supply side of the economy and create new jobs inconstruction and related sectors. While construction may well be more automatable than health or education by
the 2030s, we would still expect there to do be considerable human employment in this sector on supervisory
jobs and those that require multi-tasking and flexibility rather following set routines.
The public policy implications are discussed further in Section 7 below. Before that, however, we look in thenext section at how different occupations may be affected by automation.

24 As discussed, for example, in Frey and Hawksworth (PwC, 2015)

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5. Which occupations could see thehighest rates of automation?
Key findings• Potential automation rates vary widely by occupation – machine operators and assemblers could face a riskof over 60% by the 2030s, while professionals, senior officials and senior managers may face only around a
10% risk of automation. These variations stem from the different kinds of tasks performed in different
occupations and their varied educational requirements.
•

Workers in different occupations are likely to be impacted differently over time – technicians and clerical
workers could be most heavily affected in the algorithmic and augmentation waves where machines
overtake humans in firstly simple computational tasks and eventually routine, information processing tasks.
However, in the longer run, machine operators and assemblers may be the most exposed to automation.

•

Occupations typically vary more in their automatability than industries, which reflects the fact that they are
typically more concentrated in their task composition than industries. However, a given occupation could
see different automation rates in different industries and countries depending on factors such as theaverage education level of workers, and the practices of labour division and specialisation from country tocountry.

5.1. Total automation risk across occupation categories
In addition to the overall impact on industries, potential rates of automation also vary across occupational
categories. For example, our estimates suggest a median long run automation rate of up to around 64% formachine operators and assemblers, as compared to a median rate of just 6% for senior officials and senior
managers (see Figure 5.1). Machine operators and assemblers are most over-represented in the transportationand storage sector, accounting for on average 43% of the employment in that sector, followed by 20% for the
manufacturing sector.
Figure 5.1 – Share of jobs with potential high rates of automation by industry
Potential jobs at high risk of automation
0%

10%

20%

30%

40%

50%

60%

70%

80%

90% 100%

8. Machine operators and assemblers
4. Clerical workers
9. Elementary occupations
7. Craft and related trades workers
3. Technicians and associate professionals
5. Service and sales workers
6. Skilled agricultural and fishery workers
2. Professionals
1. Senior officials and managers
Source: PIAAC data, PwC analysis

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5.2. Impact over time by occupation
The impact of the automation process shows notable differences between occupations over time (see Figure5.2). In particular, clerical workers are estimated to face the highest potential impacts in the short to medium
term. This includes: general and keyboard clerks, customer services clerks, numerical and material recording
clerks, and other clerical support workers. The proportion of these clerical jobs at potential high risk of
automation is estimated at 10% in the Algorithm wave, rising sharply to 49% in the Augmentation wave of the2020s (but with only a slight further rise to 54% in the Autonomy wave of the 2030s, which would hit otheroccupations such as machine operators and assemblers more).
Figure 5.2 – Potential impact of job automation over time across occupational categories
Potential jobs at high risk of automation

70%
60%
50%
40%
30%
20%
10%
0%
2019

2021

2023

2025

2027

2029

2031

2033

2035

Managers and professionals

Technicians and associate professionals

Clerical workers

Service and sales workers

Craft and related trades workers

Machine operators and assemblers

2037

Source: PIAAC data, PwC analysis

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5.3. Drivers of differences between occupations
The estimated differences in potential automation rates across occupational categories is much greater thanacross industries. For example, machine operators and assemblers have a long run estimated automation ratethat is over 10 percentage points greater than the most automatable industry during the Autonomy wave (whichis the transportation and storage sector). For these workers, the tasks conducted are primarily manual androutine tasks, which account for approximately two-thirds of their working activity (see left hand panel in
Figure 5.3). This concentration of labour into this particular set of tasks makes their work more automatable.
Figure 5.3 – Task composition for machine operators and assemblers, clerical workers and professionals
Machine operators and assemblers

Clerical workers

2%

Professionals

3%

8%

7%

12%

12%
28%

8%

19%

21%

27%
25%

33%

6%

23%
35%

Compared to average
0%

100%

Computation
Management

Compared to average
200%

Manual tasks

168%

Routine tasks

17%

15%

124%
51%
84%

0%
Manual tasks
Routine tasks

100%

0%

200%
Manual tasks

75%
114%

Computation
Management

Compared to average

128%
99%

Routine tasks
Computation
Management

Social skills

58%

Social skills

84%

Social skills

Literacy skills

45%

Literacy skills

79%

Literacy skills

100%

200%

48%
93%
142%
92%
128%
189%

Source: PIAAC data, PwC analysis

Clerical workers, likewise, could face much higher automation rates in the Algorithm and Augmentation waves
than the most automatable industry in that period (the financial and insurance sector). These clerical workers
inherently undertake work that is most characteristic of the Augmentation wave – routine processes, simpler
computational tasks and exchanging information.
Professionals, as well as senior officials and senior managers, are estimated to be at the lowest risk of
automation throughout the three waves. They are more likely to be engaged in social skills, literacy skills andmore complex computational tasks that are less automatable (see right hand panel in Figure 5.3). They also
tend to be relatively highly educated and this will help them to adapt to new waves of technology so as to remaincomplementary to machines, rather than being replaced by them. The nature of their work may change
significantly over time (as it did previously with the advent of personal computers and later the internet),
but they are less likely to find themselves displaced entirely by autonomous machines than a driver, factory
worker or clerk.

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5.4. Composition of industries by occupational category
The share of employment across occupations varies between industry sectors, which accounts for some of the variation in
the overall estimated rate of automation by sector. For example, machine operators and assemblers typically account foraround 20% of occupations in the manufacturing sector, but are negligible in both the financial and insurance andeducation sectors (see Figure 5.4). Instead, the financial and insurance sector is over-represented in clerical workers (25%)
and associate professionals (26%), whereas education sector staff are primarily teaching professionals (70%).
Figure 5.4 – Employment share across occupations
Employment share
1. Senior officials and managers
2. Professionals
3. Technicians and associate professionals
4. Clerical workers
5. Service and sales workers
6. Skilled agricultural and fishery workers
7. Craft and related trades workers
8. Machine operators and assemblers
9. Elementary occupations
0%
Manufacturing

10%

20%

30%

Financial and insurance

40%

50%

60%

70%

80%

Education

Source: PIAAC data, PwC analysis

However, not all occupations are the same across different industry sectors. For example, professionals and associate
professionals are not only over-represented in the health and social work sector (31% and 24%) compared to the wholesale
and retail trade sector (5% and 10%), but also have a significantly lower estimated risk of automation (Figure 5.5). In
contrast, service and sales workers are over-represented in the wholesale and retail trade sector relative to the health sector(46% vs. 27%), but face a roughly equivalent risk of automation in the two sectors.
Figure 5.5 – Potential impact of job automation by occupation: Human health and social work vs.
wholesale and retail trade

Increased automation risk inhuman health and social work30%
4. Clerical workers

20%
10%

8. Machine operators
and assemblers

5. Service and sales
workers

3. Technicians andassociate
professionals

0%
(10)%
(20)%
(30)%
(30)%

9. Elementary
occupations

1. Senior officials andmanagers

(20)%

(10)%

0%

10%

2. Professionals

20%

30%

Increased employment share in human health and social work
Source: PIAAC data, PwC analysis

These sectoral and occupational variations are driven by differences in ways of working such as the educational
requirements of jobs and the characteristics of the workers employed. In the next section, we look in more detail at how
different types of workers may be affected by automation.

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6. Why does the potential rate of job
automation vary by type of worker?
Key findings• Potential automation risk varies significantly across different types of workers. Males may face ahigher automation risk (34%) than females (26%) in the long run because they are more likely to be
employed in manual-task-focused sectors such as manufacturing (13%) and transportation and storage(6%). In comparison, female employment in these sectors is relatively lower as women tend to be moreconcentrated in sectors such as education and health requiring more personal and social skills that tend to
be less automatable.
•

Automation risk is prevalent for all age groups, but the differences are less marked. Despite the risks facingsome young workers, they are potentially well positioned to capitalise on the new opportunities from digitaltechnologies if they can acquire relevant training. Similarly, older workers also need to equip themselveswith a skillset that complements the digital workplaces of future.

•

On average, males with low levels of education face the highest long term risk of automation of over 50%.
For both genders and across all age groups, highly educated workers consistently have lower automation
risks in the long run. This reflects the fact that their roles involve skills of supervision and intellectual
reasoning that will still be needed alongside AI-based systems. Higher levels of education also allow
workers flexibility to move around different occupations and industries and thus potentially escapeautomation risks.

•

Different worker types are impacted differently over time by successive automation waves – highly educatedwomen performing clerical tasks and highly educated men in analytical jobs could, for example, be
relatively vulnerable in the short term. But, eventually, less educated men may face the highest risks as
autonomous machines are deployed that are capable of independently performing manual tasks such as
driving, as well as many factory and warehouse jobs that currently employ a higher proportion of men
than women.

6.1. Total automation risk across workers
The estimated share of existing jobs at high risk of automation by the 2030s is greater for male workers, with a
median automation rate estimate across countries of 34%, as compared to 26% for female workers (see Figure6.1). This is primarily because male workers are typically over-represented in highly automatable sectors suchas transportation and storage, manufacturing and construction, whereas female workers are typically overrepresented in the health and social work and education sectors that have relatively low estimated future
automation rates (see Figure 6.2).

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Figure 6.1 – Share of jobs with potential high rates of automation by gender and age group
Potential jobs at high risk of automation

Age group

Sex

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Male
FemaleYoung (<25)
Core (25-54)
Older (55+)
Young males
Core males
Older males
Young females
Core females
Older females

Source: PIAAC data, PwC analysis

Overall, as Figure 6.1 shows, there is not much difference in the potential rate of automation between age
groups. There is, however, a notably greater potential rate of automation for young males (46%) than for young
females (20%). For both males and females there is a greater representation of young workers (less than 25
years old) in the wholesale and retail trade, and accommodation and food service sectors, as shown in Figure
6.2. However, across industry sectors young males are broadly represented to the same extent as core and older
males, and young females are broadly represented to the same extent as core and older females.
Instead it appears that young male and young female workers differ in the type of jobs they do within
industries. For example, in the wholesale and retail trade sector, young males are more likely to be craft andrelated trades workers than young females (22% vs. ~0%), whereas young females are more likely to be service
and sales workers (82% to 44%), as shown in Figure 6.3.
Figure 6.2 – Difference in employment shares across industries by sex and age group
Difference in employment share
Transportation and storage
Manufacturing
Construction
Administrative and support service
Wholesale and retail trade
Public administration and defence
Financial and insurance
Information and communication
Professional, scientific and technicalAccommodation and food service
Human health and social work
Education
(15)%

(10)%

(5)%
Male vs. female

0%

5%

10%

15%

Young vs. core age group

Source: PIAAC data, PwC analysis

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Figure 6.3 –Employment shares across occupations in the wholesale and retail trade sector
Employment share
1. Senior officials and managers
2. Professionals
3. Technicians and associate professionals
4. Clerical workers
5. Service and sales workers
6. Skilled agricultural and fishery workers
7. Craft and related trades workers
8. Machine operators and assemblers
9. Elementary occupations
0%

20%

40%

60%

Young males

80%

100%

Young females

Source: PIAAC data, PwC analysis

However, across the waves, female workers of all ages could be impacted more heavily at first, with an increased
potential rate of automation during the Algorithm and Augmentation waves (see Figure 6.4). This is primarily
driven by a greater proportion of women employed as clerical workers across industries.
Figure 6.4 – Potential impact of job automation over time across workers by age groups
Potential jobs at high risk of automation

60%

50%

40%

30%

20%

10%

0%
2019

2021

2023

2025

2027

2029

2031

2033

2035

Young males

Core males

Older males

Young females

Core females

Older females

2037

Source: PIAAC data, PwC analysis

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6.2. Potential automation rates by education level
The greatest difference in the share of jobs with potential high rates of automation relates to the educationlevels of workers. Those with low levels of education (e.g. GCSE level equivalent or lower in the UK) andmedium levels of education have notably higher estimated median automation rates across countries (44% and
36% respectively), compared to those with higher levels of education, such as university graduates (11%), as
shown in Figure 6.5. Workers with high education levels are over-represented in the professional, scientific andtechnical, and education sectors (see Figure 6.6), which tend to be less automatable on average.
Figure 6.5 – Share of jobs with potential high rates of automation by gender and education level
Potential jobs at high risk of automation

Education level

0%

20%

40%

60%

80%

100%

Low
MediumHigh

FemaleMale

Low educatedMed. educatedHigh educated

Low educatedMed. educatedHigh educatedSource: PIAAC data, PwC analysis

Males and females with high education levels have similar estimated rates of automation in the long run (11%and 12% respectively). Highly educated male workers are more likely to be employed in the information andcommunications sector (males: 9% vs. females: 4%), whereas highly educated female workers are more likely tobe employed in education (females: 29% vs. males: 14%).
In contrast, for workers with only a low level of education there is a notable difference between males andfemales. Men with low education levels face an increased estimated risk of automation (52%) compared to low
educated women (29%). The greatest difference is between the type of occupations, with low educated maleworkers over-represented as craft and related trades workers and machine operators and assemblers, whereas
low educated female workers are over-represented as service and sales workers and in elementary occupations,such as cleaners and helpers (see Figure 6.6). Notably, male and female workers with high education levels also
show a greater similarity in their employment shares across occupations.

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Figure 6.6 – Employment shares across industries for workers across education levels
Employment share
Transportation and storage
Manufacturing
Construction
Administrative and support service
Wholesale and retail trade
Public administration and defence
Financial and insurance
Information and communication
Professional, scientific and technicalAccommodation and food service
Human health and social work
Education
0%

5%

10%

Low education

15%

20%

Medium education

25%

High education

Source: PIAAC data, PwC analysis

Figure 6.7 – Difference in employment shares across occupations for males vs. females
Increased employment share for males
1. Senior officials and managers
2. Professionals
3. Technicians and associate professionals
4. Clerical workers
5. Service and sales workers
6. Skilled agricultural and fishery workers
7. Craft and related trades workers
8. Machine operators and assemblers
9. Elementary occupations
(40)%

(30)%

(20)%

(10)%
Low educated

0%

10%

20%

30%

High educatedSource: PIAAC data, PwC analysis

Over time the education level of workers also plays a striking role, as Figure 6.8 shows. Low and medium
educated male workers are least impacted in the Algorithm wave, as computational tasks typically forms asmaller proportion of their daily activity. However, by the end of Augmentation wave, the potential jobs at highrisk of automation are comparable between male and female workers with either a low or medium education. In
the final Autonomy wave, low educated males are expected to be at a much greater risk as manual and routine
tasks (including driving) become more heavily automated across the economy.

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Figure 6.8 – Potential impact of job automation over time across workers by education level
Potential jobs at high risk of automation

60%

50%

40%

30%

20%

10%

0%
2019

2021

2023

2025

2027

2029

2031

2033

2035

Low educated males

Med. educated males

High educated males

Low educated females

Med. educated females

High educated females

2037

Source: PIAAC data, PwC analysis

The results described in this section are averages across all countries in our sample, and there are somevariations by country as the detailed estimates in Table 6.1 show. However, the broad patterns seen bygender, age and education appear to be reasonably consistent across countries.
Table 6.1. Share of jobs with potential high rates of automation by worker characteristics, across countries
CountrySex

Age group

Education level

Female
(%)

Male
(%)

Young
(%)

Core
(%)

Older
(%)

Low
(%)

Medium
(%)

High
(%)

Slovakia

39

48

47

42

46

54

53

18

Slovenia

35

49

50

41

45

63

47

13

Lithuania

30

55

50

40

43

57

50

21

Czech Republic

38

42

40

38

45

51

47

11

Italy

32

44

42

39

39

45

43

16

USA

37

39

39

37

40

47

46

21

France

32

41

42

35

40

51

41

14

Germany

34

39

44

35

36

48

43

10

Austria

32

37

41

32

36

46

36

21

Spain

28

39

33

34

32

44

39

14

Poland

24

39

35

30

38

49

42

14

Turkey

19

36

41

30

35

38

35

7

Ireland

27

35

30

31

33

38

39

11

Netherlands

28

33

34

28

34

47

36

10

UK

26

34

32

28

36

47

35

12

Cyprus

27

33

28

30

32

38

38

12

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CountrySex

Age group

Education level

Female
(%)

Male
(%)

Young
(%)

Core
(%)

Older
(%)

Low
(%)

Medium
(%)

High
(%)

Belgium

23

36

39

27

33

45

33

10

Denmark

26

33

26

27

36

41

33

10

Israel

26

31

35

26

31

44

36

13

Chile

21

32

28

27

29

35

29

5

Singapore

28

24

24

23

33

46

30

10

Norway

22

28

26

22

31

40

31

9

Sweden

20

30

25

22

30

40

28

7

New Zealand

23

25

31

22

26

39

29

11

Japan

22

25

30

25

21

31

28

12

Russia

13

33

21

22

28

39

31

11

Greece

18

27

19

25

20

24

30

10

Finland

16

29

17

21

26

39

27

6

South Korea

18

24

30

21

20

24

26

9

Source: PIAAC data, PwC analysis

The fact that potential automation rates vary widely across different types of workers immediately raises theprospect that, even if new technologies like AI and robotics are good for the economy as a whole, there could be
important distributional effects. This in turn raises issues for public policy, as discussed in the next section ofthis report.

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7. What are the public policy
implications?
Major new technologies always raise important public policy issues and the same is true for AI and robotics.
It is beyond the scope of this report to consider all of these in detail, but we provide an overview here of three
key areas:•

Boosting education and skills levels to help people of all ages to adjust to new technologies;•

Supporting job creation through government investment that can also help to lever in private
investment, notably in areas like infrastructure and housing; and•Enhancing social safety nets to support those who may find it difficult to adjust to new technologies.

7.1. Education and skills
Our analysis above has highlighted the key role of education as a driver of potential automation risk. Moreeducated and skilled workers will, on average, be better able to adjust to new technologies and benefit from thehigher real wages these will bring by boosting productivity. Less well educated workers will generally bear moreof the costs of automation, potentially widening further existing income and wealth inequalities. Increasing
their adaptability and skills will be critical to enabling these groups to share in the gains from new technologies
and work more effectively with them 25.
Government, working with employers and education providers, should therefore invest more in the types of
education and training that will be most useful to people in this increasingly automated world. Exactly how to
identify the skills that will be required and develop the training is much more complex of course – for many
people, this will involve an increased focus on vocational training 26 that is constantly updated over theirworking lives to stay one step ahead of the robots. It will also require more focus on STEM subjects (science,technology, engineering and mathematics) where countries like the US and the UK tend to lag behind leading
nations such as South Korea, Japan, Singapore and indeed India and China to an increasing degree.
There also needs to be better matching of workers to the new opportunities that will arise in an increasingly
digital economy. This will require effective programmes of retraining for older workers as well as help with job
search. Of course, workers also need to take personal responsibility for their lifelong learning and careerdevelopment, but governments and businesses need to support them in achieving these goals.

7.2. Job creation through increased public and private investment
Additional investment in education and skills will only be fully effective, however, if there are jobs available forpeople to do. This will require running the economy at a sufficient level of aggregate demand to maintain highemployment levels.
Governments can help with this by investing more in areas like housing and infrastructure that are beneficial to
the longer term productivity of the economy, but will also help to create jobs that cannot be fully automated.
The exact nature of the desirable investment will vary from country to country – in the UK, for example, asevere shortage of housing supply would suggest that housebuilding would be one priority, as the government
has recognised. Improvements in transport infrastructure are also much needed in the UK, but also in countries
such as the US and across much of Europe. In emerging economies, construction of power plants and extensionof communications networks may also be priorities. Governments may not be able to fund all of these projects,

25 For more on these issues, see this recent PwC report on human value in the digital age: https://www.pwc.nl/en/publicaties/human-value-in-the-digital-age.html.

26 An area where the UK lags well behind countries like Germany as highlighted in our 2017 Young Workers Index report, which isavailable here: http://www.pwc.co.uk/services/economics-policy/insights/young-workers-index.html.

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but they can help to lever in private funds through providing some government funding and/or government
guarantees for private sector borrowing.
Central and local government bodies also need to support digital sectors that can generate new jobs, forexample through place-based strategies 27 focused on university research centres, science parks and otherenablers of business growth. This place-based approach is, for example, one of the key themes in the UK
government’s new industrial strategy 28 and its wider devolution agenda. It also involves extending the latest
digital infrastructure beyond the major urban centres to facilitate small digital start-ups in other parts of thecountry. Similar approaches are likely to be appropriate in other advanced economies, though the details willvary across countries.

7.3. Enhancing social safety nets
To the extent that new technologies boost productivity, income and wealth, they should also boost tax
revenues 29. As well as being invested in education, skills and infrastructure as described above, there could
also be a case for spending more on stronger social safety nets for those not able to easily adapt to new
automation technologies.
This could be done by extending existing social security benefits, but more radical solutions include the idea of auniversal basic income (UBI). This is an old idea 30, but it has gained traction in Silicon Valley and elsewhere in
recent years as a potential way to maintain the incomes of those who lose out from automation and (to be hard
headed about it) whose consumption is important to keep the economy going. The problem with UBI schemes,
however, is that they involve paying a lot of public money to many people who do not need it, as well as thosethat do. As such the danger is that such schemes are either unaffordable or destroy incentives to work andgenerate wealth, or they need to be set too low to provide an effective safety net.
Nonetheless, we are now seeing practical trials of UBI schemes in a number of countries around the world
including Finland, the Netherlands, some US and Canadian states, India and Brazil. The details of theseschemes vary considerably, and it is beyond the scope of this report to review them in depth, but it seems likelythat more pilot schemes of this kind will emerge around the world and that they will come on to the policy
agenda in countries such as the UK as well. While UBI in its pure form may not be politically or economicallyattractive, some variants on it might be if they involve a greater degree of conditionality (e.g. requiring someform of paid or voluntary work, education and training, family caring responsibilities or similar activities toqualify for payments). Some aspects of the idea, such as providing a universal lifelong learning fund for each
person that they could draw down when they needed it, might also be worth considering further even if a full
UBI scheme is rejected.
For the moment, the first priority may be to gather an evidence base on the different options through pilot
schemes, detailed financial modelling and other studies. The optimal solutions will also vary by country
depending on local political, economic and social circumstances. But the broader question of how to deal withpossible widening income gaps arising in part from increased automation seems unlikely to go away.

27 For more on place-based strategies in the UK context, see also our 2017 Good Growth for Cities report:

https://www.pwc.co.uk/industries/government-public-sector/good-growth.html.

28 https://www.gov.uk/government/topical-events/the-uks-industrial-strategy.
29 Another idea here is the suggestion of Bill Gates to tax robots where these displace human labour. However, it is not clear that such a

specific tax on investment in robots would be economically efficient. Other labour-saving technologies do not face such specific taxes, so
why single robots out for such treatment and potentially lose productivity gains from such innovation and investment?
30 For more details on the history of the UBI idea and its pros and cons see Guy Standing, Basic Income: And How We Can Make It
Happen (Pelican Introductions, 2017).

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8. Implications for business:
constraints, opportunities
and responsibilities
Much of this report has focused on the potential impacts of automation for existing jobs, but we should notforget the huge potential benefits that technologies like AI and robotics can offer to the economy and tobusiness. In an earlier study, we estimated that additional investment in these technologies, over and above
current baseline levels, could contribute as much as 14% to global GDP by 2030 (or around 10% to UK GDP) 31.
From a business perspective, another recent PwC survey 32 found that 52% of CEOs worldwide are already
exploring the benefits of machines and humans working together. Automation opens up a range ofopportunities for businesses. Directly, this includes the ability to collect, store and analyse data at a scale andspeed that will allow firms to drive cost efficiencies and improve the quantity and quality of their products.
Indirectly, businesses across the economy could benefit from increases in demand created by higher
productivity growth and the positive spill-over effects of industry wide digitisation.

8.1. What constraints will need to be overcome to realise benefits
for business?
Of course, these gains will not come automatically or easily. There will be a variety of technological, economic,
legal and regulatory, and social constraints to be overcome to realise such benefits. For example:•

Technological constraints: For any benefits from automation to materialise, it first has to be technicallyfeasible to adopt the technology in practice. This goes beyond just developing the technology in a lab. It has
to be integrated and adapted into solutions before it can be deployed in a real world business situation.
Different countries have different rates of technological advancement and thus will have different speeds ofautomation. For instance, some developing countries may not have the basic communications
infrastructure needed to implement new technologies 33. On the other hand, some businesses in advanced
economies may have legacy systems that are well developed, but do not mesh easily with new techniques
like AI.

•

Economic constraints: Technology must have a business case for being adopted. In many companies,
the upfront cost of advanced automation technologies such as AI and robotics may make this a significantly
higher risk option than just expanding by using additional labour, particularly where this is relatively
flexible and/or low cost. Over time, the relative cost of AI and robotics should come down as with otherdigital technologies in the past, perhaps at a significant rate, but the exact timetable for this is not clear.

•

Legal and regulatory constraints: Data is fundamental to the functioning of AI. Businesses wishing to
adopt AI and related technologies will therefore need to deal with a range of data regulation issues such asprotection of individual data rights and privacy, incomplete data collection leading to learning mishaps, andmisuse of data sharing platforms. Machine optimisation rules also need to be regulated to prevent biases in
the insights that are generated using data. Besides tech firms involved in developing AI, there may also be aneed to rethink the existing regulatory structures in other industries involved in deploying AI. For instance,in the case of driverless cars, regulations on accident liabilities will need to be rethought and potentiallyredesigned to determine who among the human vehicle owner/driver, the car manufacturer, the provider ofsoftware, or some other supplier should bear or share responsibility for any accidents.

31 PwC, ‘Sizing the prize’ (2017): https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html
32 PwC Global CEO survey (2018): https://www.pwc.com/gx/en/ceo-agenda/ceosurvey/2017/gx/talent.html
33 Although some emerging economies, notably China, may be ahead of many high income countries in their mobile telecommunications

and digital infrastructure due in part to having installed this more recently.

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•

Social constraints: Individuals may not be willing to have robots or other smart machines replace
humans for all of their day-to-day interactions, especially for risky fields such as health provision or driving.
Societal concerns can be raised with regards to the potential rise in inequality as a result of automation astech companies and highly educated workers gain at the expense of other workers. Acceptance of AI bysociety will occur when people are convinced of its advantages over humans in particular applications andhistory suggests that this can be a long process.

Eventually, we can expect that all or most of these issues will be resolved, as with past waves of new technology
since the Industrial Revolution. But the timing of this is uncertain and will vary from country to country andcase to case. This means that actual job displacement of workers by automation may not reach the potential
levels indicated by our analysis, but also that the economic and business benefits of AI and robotics may takelonger to come through than hoped. In the long run, we would expect a significant proportion of these benefits
to be realised given the potential power of these technologies, but it is unlikely to be a smooth or easy process.
In the remainder of this section we look at some of the potential benefits of automation in relation to company
supply chains and also discuss potential applications to healthcare as a specific example where public attitudes
will be important to the speed of uptake of AI and robotics. Finally, we consider what business needs to do tohelp its workforce adapt to these technologies and, more generally, to adopt a responsible approach to AI.

8.2. AI’s impact on company value chains
Analysis in our 2017 Sizing the Prize report showed that firm productivity – how much a company can produce
using a given level of inputs – could be significantly boosted by AI technologies in many different ways. There
are applications for AI across the whole value chain. These will often take the form of software, systems andmachines that augment or assist the workforce and, in the process, make them more efficient and allow them toconcentrate on higher value activities. But, in some cases, such technology could eventually replace some or all
human workers altogether (although there will often be an intermediate stage first where humans work
alongside machines – and in some cases this may be better than either working alone 34)
Table 8.1 below outlines the impact that AI can have at each stage of a firm’s value chain and illustrates specificexamples across various industry sectors.
Table 8.1 – Applications and the impact of AI on productivity along the value chain
Value chain element

Impact of AI

ExamplesStrategy, business model,
products and services

Reduces the risk, time andcapital expended in theprocess of moving fromstrategy to execution.

•

Simulating market conditions for
production forecasts and pricing strategy.

•

Creating digital mock-ups of productfeatures based on historically successful
features/user preferences.

Reducing the runway
required before insights
are generated.

•

Drug repositioning – scanning scientificand clinical research data to identify otheruses for drugs already approved.

More output or better
quality output using
fewer resources.

•

Robotics automating assembly lines.

•

On-demand manufacturing: adjusting to
produce goods based on order specifics orturning on/off autonomously.

The ‘brains’ of a company’s operations,decision making about offerings,
pricing and go-to-market strategy.
R&D and innovation
Discovery of new information and trends.
Purchasing and production
Sourcing raw materials andmanufacturing.

34 As discussed further, for example, in a recent PwC report on human value in a digital age:https://www.pwc.nl/en/publicaties/human-value-in-the-digital-age.html.

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Value chain element

Impact of AI

ExamplesSupply chain and logistics

Reducing the time andresources required inthese processes.

•

Auto-ordering raw materials based on sales
patterns and known lead/production times.

•

Routing emergency vehicles to hospitals
based on case criticality, staffing, expertise,traffic and patient load.

Reducing the information
asymmetry between
producer and consumer andtailoring messaging
accordingly.

•

Personalised recommendations of products
and services.

•

AI chatbot customer service agents.

•

Call centre sales practice monitoring.

•

Adverse event monitoring inpharmaceuticals (trends in doctor visits,social media reporting etc.).

Getting production resources from
A to B and getting the final productto the customer.

Marketing, sales andcustomer service
Increasing customer engagement
and conversion of customers.
Enabling functions (finance,
IT, risk)
Back-office supporting activities.

Reducing costs andreducing risks including
with better planningand forecasting.

Source: PwC analysis in ‘Sizing the Prize’ report on AI (June 2017)8.3. AI and healthcare provision
Besides their impact on purely commercial activities, PwC’s previous analysis shows the opportunities that exist
for AI in public services such as health and social care provision. Robotics and AI will help to increase the focus
on preventative care and aim to transform every aspect of the medical ecosystem, including but not limited toearly detection, diagnosis, decision making, treatment and end of life care.
In a recent international poll of 12,000 people commissioned by PwC 35, the majority of respondents incountries such as Turkey, Nigeria and South Africa were willing to embrace AI and robots as part of their healthcare and even surgery, although the numbers were lower in the UK (see Figure 8.1).
Figure 8.1 – Willingness to have surgery performed by robots
80%
69%
70%

60%
51%

60%

44%

45%
44%

50%

40%
35%

40%

35%
30%

32%
27%

30%
20%
10%
0%

Minor Surgery

Major Surgery

35 https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health.html.

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As we discussed in Section 4 above, this does not mean that doctors, nurses and other workers will disappear
from the health service. With ageing populations across most advanced economies, demand for health andsocial care services will only continue to increase over the coming decades. This is likely to require both morehuman workers and greater use of AI, robotics and other advanced technologies to complement and boost the
productivity of these human workers. Countries like Japan with relatively rapidly ageing populations are
already leading the way here in their healthcare sectors.

8.4. Businesses need to help workers retrain and adapt to
new technologies
Businesses should also seek to use AI and robotics in a responsible way 36. This includes encouraging continued
innovation and research, but at the same time developing policies that protect customer data and help workers
and institutions adapt to the new demands posed by these technologies. This will include reconfiguring training
programs to help workers acquire both the digital and softer skills which will be demanded in the new age,
replacing legacy processes and systems with those that are more suited to handle newer technologies, and also
temporary support for those that lose out from the impact of automation. As discussed in the previous section,
this has to be coupled with nationwide policies by the government to ease the transition process for workers
displaced by automation.

8.5. Conclusion
This section has illustrated some of the potential benefits of AI and related technologies for business and society
more generally, but also some of the constraints that need to be overcome and the responsibilities this involves
for business.
As we have argued in past sections of this report, AI and robotics will be disruptive for labour markets and some
jobs will be displaced or fundamentally changed in nature. But many new jobs will also be created and the longterm net effect should be positive for the economy as a whole. Business and government need to work together
to help people through the transition to this brighter future and ensure that as many people as possible share in
the benefits from these new technologies.

36 See our Responsible AI website for more details: https://www.pwc.co.uk/services/audit-assurance/risk-

assurance/services/technology-risk/technology-risk-insights/accelerating-innovation-through-responsible-ai.html

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Annex – technical methodology
The methodology used in this study builds on previous research by Frey and Osborne (2013) 37, Arntz, Gregory
and Zierahn (2016) 38 and our previous research on this topic in PwC’s UK Economic Outlook (March 2017) 39.
In the original study by Frey and Osborne (hereafter ‘FO’) a sample of occupations taken from O*NET, an
online service developed for the US Department of Labor, were hand-labelled by machine learning experts at
Oxford University as strictly automatable or not automatable. Using a standardised set of features of an
occupation, FO were then able to use a machine learning algorithm to generate a ‘probability ofcomputerisation’ across US jobs, but crucially they generated only one prediction per occupation.
Using the same outputs from the FO study, Arntz, Gregory and Zierahn (hereafter ‘AGZ’) conducted theiranalyses on the OECD Programme for the International Assessment of Adult Competencies (‘PIAAC’) database,which includes more detailed data on the characteristics of both particular jobs and the individuals doing themthan was available to FO. This allows a critical distinction that it is not whole occupations that will be replacedby computers, algorithms and robots, but only particular tasks that are conducted as part of that occupation.
Furthermore, this allows for the fact that the same occupation may be more or less susceptible to automation indifferent workplaces.
The PwC automation rate algorithm developed in our earlier study (PwC, March 2017) involved first taking thelabels from the FO study and replicating the methodology from the AGZ study using the PIAAC dataset. The
methodology was then enhanced using additional data and a refined automation-rate prediction algorithm.
This model was initially trained on PIAAC data for the UK, US, Germany and Japan, but then extended to over
200,000 workers across 29 countries in the present study. This much larger sample size gives increased
confidence in our estimates of the relative automatability of jobs in different industry sectors and acrossdifferent types of workers (e.g. by age, gender or education level).
As a further extension in the present study, the initial set of labels, seeded from the study by FO, were simulated
across a range of scenarios that varied the automation-rate estimates associated with both tasks andoccupations. Feedback from computable general equilibrium (CGE) modelling of the economic impact of AI 40
then allowed predictions for the potential jobs at high risk of automation to vary over a projected timeframefrom 2018-2037. This formed the basis for the analysis of automation waves over time in the present study.
However, it should be emphasised that this is only intended to give a broad indication of how automation mightroll out across economies over time; our results should not be interpreted as precise point estimates forparticular future years.

37 Frey, C.B. and M.A. Osborne (2013), The Future of Employment: How Susceptible are Jobs to Computerization?, University of Oxford.
38 Arntz, M. T. Gregory and U. Zierahn (2016), ‘The risk of automation for jobs in OECD countries: a comparative analysis’, OECD Social,

Employment and Migration Working Papers No 189.

39 ‘Will robots steal our jobs?’ PwC UK Economic Outlook, March 2017, available here: https://www.pwc.co.uk/economic-

services/ukeo/pwcukeo-section-4-automation-march-2017-v2.pdf.

40 PwC (2017), Sizing the Prize: What’s the real value of AI for your business and how can you capitalise? Available here:

https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf.

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Authors, contacts and services
This report was written by John Hawksworth, Richard Berriman and Saloni Goel. Additional research
assistance was provided by Nick Parlett, Robyn Foyster, and Pearl Ho.
For more information on the report, please contact:
John Hawksworth
Chief Economist

Richard Berriman
Senior Manager – AI Team

E: john.c.hawksworth@pwc.com

E: richard.berriman@pwc.com

Many thanks to the many other PwC experts who contributed helpful comments or inputs, including Euan
Cameron, Rob McCargow, Nick Jones, Jonathan Gillham, Justine Brown and Anita Hagen.
PwC’s economics services
Our Economics practice’s consulting services combine strategic analysis of macroeconomic and microeconomic
trends with strong quantitative techniques across all industry sectors and public policy analysis. Further detailsof our services can be found on our website at: http://www.pwc.co.uk/economics-policy/index.jhtml
And please see our Economics in Business blog page to read our insights on current economic developments
and their implications for governments and business: http://pwc.blogs.com/economics_in_business/
PwC’s AI services
Our AI team supports our clients to navigate emerging technology, build AI-enabled solutions that underpin
business strategy, and are disrupting our own firm through changes that AI brings to the business.
For queries about our AI practice, please contact Euan Cameron:
Euan Cameron
Partner – UK AI Leader
E: euan.cameron@pwc.com

Intelligent Digital
Intelligent Digital is PwC's approach to balancing business understanding with technology innovation andhuman insight to help solve our client's important problems.
To learn more about our Intelligent Digital approach visit http://pwc.co.uk/intelligentdigital/

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