PwC | AI Analysis, Sizing the Prize

Sizing the prize
What’s the real value of AI for
your business and how can
you capitalise?
+26%
+14%
Artificial intelligence (AI) is a source of both huge excitement
and apprehension. What are the real opportunities and threats
for your business? Drawing on a detailed analysis of the business
impact of AI, we identify the most valuable commercial opening in
your market and how to take advantage of them.
www.pwc.com/AI
PwC research shows
global GDP could be
up to 14% higher in
2030 as a result of
AI – the equivalent of
an additional $15.7
trillion – making it the
biggest commercial
opportunity in today’s
fast changing economy.
The greatest gains
from AI are likely to be
in China (boost of up
to 26% GDP in 2030)
and North America
(potential 14% boost).
The biggest sector
gains will be in retail,
financial services
and healthcare as AI
increases productivity,
product quality and
consumption.

Human in the
loop
Hardwired
/specific
systems
Adaptive
systems
In our broad definition, AI is a collective
term for computer systems that can sense
their environment, think, learn, and take
action in response to what they’re sensing and
their objectives.
Forms of AI in use today include digital
assistants, chatbots and machine learning
amongst others.
Automated intelligence: Automation of
manual/cognitive and routine/non-
routine tasks.
Assisted intelligence: Helping people to
perform tasks faster and better.
Augmented intelligence: Helping people to
make better decisions.
Autonomous intelligence: Automating
decision making processes without human
intervention.
As humans and machines collaborate more
closely, and AI innovations come out of the
research lab and into the mainstream, the
transformational possibilities are staggering.
Defining AI
Assisted
Intelligence
AI systems that
assist humans in
making decisions
or taking actions.
Hard-wired
systems that do
not learn from
their interactions.
Augmented
Intelligence
AI systems that
augment human
decision making
and continuously
learn from their
interactions with
humans and the
environment.
Automation
Automation of
manual and
cognitive tasks that
are either routine
or non-routine.
This does not
involve new ways
of doing things
– it automates
existing tasks.
No human in
the loop
Autonomous
Intelligence
AI systems
that can adapt
to different
situations and can
act autonomously
without human
assistance.
For a full glossary of AI techniques and their applications, please see page 26.

Contents
Introduction: 2
Big prize, big impact 4
AI Impact Index 10
Realising the potential 20
Conclusion 22

Business leaders are asking: What impact
will AI have on my organisation, and is our
business model threatened by AI disruption?
And as these leaders look to capitalise on AI
opportunities, they’re asking: Where should we
target investment, and what kind of capabilities
would enable us to perform better? Cutting
across all these considerations is how to build AI
in the responsible and transparent way needed
to maintain the confidence of customers and
wider stakeholders.
These are the strategic questions we’ll be
addressing in a series of reports designed to help
enterprises create a clear and compelling business
case for AI investment and development. While
there’s been a lot of research on the impact of
automation, it’s only part of the story. In this new
series of PwC reports, we want to highlight how
AI can enhance and augment what enterprises
can do, the value potential of which is as large, if
not larger, than automation.
There’s a lot of
expectation
surrounding
artificial
intelligence
(AI). There’s
also a
significant
amount of
wariness.
Introduction:
Getting down to
what really counts
2 Sizing the prize

The analysis carried out for this report gauges
the economic potential for AI between now and
2030, including for regional economies and
eight commercial sectors worldwide. Through
our AI Impact Index, we also look at how
improvements to personalisation/customisation,
quality and functionality could boost value,
choice and demand across nearly 300 use cases
of AI, along with how quickly transformation
and disruption are likely to take hold. Other key
elements of the research include in-depth sector-
by-sector analyses.
In this opening report, we outline the regional
economies that are set to gain the most and
the three business areas with the greatest
AI potential in each of eight sectors. Future
reports will focus on specific sectors, along with
functional areas such as marketing, finance and
talent management. We’ll also be setting out the
detailed economic projections and, in partnership
with Forbes magazine, publishing interviews with
some of the business leaders at the forefront of AI.
Game changer
What comes through strongly from all the
analysis we’ve carried out for this report is just
how big a game changer AI is likely to be, and
how much value potential is up for grabs. AI could
contribute up to $15.7 trillion1
to the global
economy in 2030, more than the current output of
China and India combined. Of this, $6.6 trillion is
likely to come from increased productivity and
$9.1 trillion is likely to come from consumption-
side effects.
While some markets, sectors and individual
businesses are more advanced than others, AI is
still at a very early stage of development overall.
From a macroeconomic point of view, there are
therefore opportunities for emerging markets to
leapfrog more developed counterparts. And within
your business sector, one of today’s start-ups or a
business that hasn’t even been founded yet could
be the market leader in ten years’ time.
$15.7 trillion
1

$ denotes US dollars throughout, estimated values are expressed in real terms at 2016 prices (i.e. excluding the impact of general price inflation when looking
ahead to 2030).
3
What’s the real value of AI for your business and how can you capitalise?

How much is at stake and why should
you take action?
From the personal assistants in our mobile
phones, to the profiling, customisation, and
cyber protection that lie behind more and more
of our commercial interactions, AI touches
almost every aspect of our lives. And it’s only
just getting started.
Big prize,
big impact:
Why AI matters
AI touches almost every
aspect of our lives. And it’s
only just getting started.
According to our analysis, global GDP will
be up to 14% higher in 2030 as a result of the
accelerating development and take-up of AI – the
equivalent of an additional $15.7 trillion. The
economic impact of AI will be driven by:
1. Productivity gains from businesses automating
processes (including use of robots and
autonomous vehicles).
2. Productivity gains from businesses
augmenting their existing labour force with
AI technologies (assisted and augmented
intelligence).
3. Increased consumer demand resulting from
the availability of personalised and/or
higher-quality AI-enhanced products
and services.
4 Sizing the prize

As new technologies
are gradually adopted and
consumers respond to
improved products with
increased demand, the
share of impact from
product innovation
increases over time.
Labour productivity
improvements are
expected to account
for over 55% of all
GDP gains from
AI over the period
2017 – 2030.
58% of all
GDP gains in 2030
will come from
consumption side
impacts.
0
2
4
6
8
10
12
14
16
18000
2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Global GDP impact by effect of AI (£trillion)
Labour Productivity Personalisation Time Saved Quality
$
trillion
How we gauged the impact and
potential of AI
To estimate the impact and potential of AI, our team
conducted an ambitious, dual-phased top-down and
bottom-up analysis. In addition to drawing on input from
our extensive network of clients, and sector and functional
advisors within PwC, we’ve been working with our partners
Fraunhofer, a global leader in emerging technology research
and development and Forbes. Together, we set out to identify
the most compelling examples of potential AI applications
across each sector’s value chain, and designed a framework
to assess the degree and pace of impact of each. In total,
we identified and rated nearly 300 use cases, which are
captured in our AI Impact Index.
Our Econometrics unit then used this bottom-up input as
part of their top-down analysis assessing AI’s impact on,
and the interactions between, key elements of the economy
including labour, productivity, business and government.
The models were informed by global economic datasets,
extensive academic literature, and existing PwC work on
automation. The analysis looked at the total economic
impact of AI, accounting for increased productivity (which
may involve the displacement of some existing jobs), the
creation of new jobs, new products, and other effects. We’ll
be publishing an extended technical read out of these results
later in the year.
For a more detailed methodology see page 27.
Over the past decade, almost all aspects of
how we work and how we live – from retail to
manufacturing to healthcare – have become
increasingly digitised. The internet and mobile
technologies drove the first wave of digital,
known as the Internet of People. However,
analysis carried out by PwC’s AI specialists
anticipates that the data generated from the
Internet of Things (IoT) will outstrip the data
generated by the Internet of People many times
over. This increased data is already resulting
in standardisation, which naturally leads to
automation, and the personalisation of products
and services, which is setting off the next wave of
digital. AI will exploit the digital data from people
and things to automate and assist in what we do
today, as well as find new ways of doing things
that we’ve not imagined before.
Productivity gains
In the near-term, the biggest potential economic
uplift from AI is likely to come from improved
productivity (see Figure 1). This includes
automation of routine tasks, augmenting
employees’ capabilities and freeing them up to
focus on more stimulating and higher value-
adding work. Capital-intensive sectors such as
manufacturing and transport are likely to see
the largest productivity gains from AI, given that
many of their operational processes are highly
susceptible to automation.
The impact on productivity could be competitively transformative – businesses that fail to adapt and adopt could quickly find
themselves undercut on turnaround times as well as costs. They stand to lose a significant amount of their market share as a
result. However, the potential of this initial phase of AI application mainly centres on enhancing what’s already being done,
rather than creating too much that’s new.
Source: PwC analysis
Figure 1: Where will the value gains come from with AI?
5

Increased consumer demand
Eventually, the GDP uplift from product
enhancements and subsequent shifts in consumer
demand, behaviour and consumption emanating
from AI will overtake the productivity gains,
potentially delivering more than $9 trillion
of additional GDP in 2030. Consumers will be
mostly attracted to higher quality and more
personalised products and services, but will
also have the chance to make better use of their
time – think of what you could do if you no longer
had to drive yourself to work, for example. In
turn, increased consumption creates a virtuous
cycle of more data touchpoints and hence more
data, better insights, better products and hence
more consumption.
The consumer revolution set off by AI opens the
way for massive disruption as both established
businesses and new entrants drive innovation
and develop new business models. A key part
of the impact of AI will come from its ability to
make the most of parallel developments such as
IoT connectivity2
.
AI front-runners will have the advantage of
superior customer insight. The immediate
competitive benefits include an improved ability
to tap into consumer preferences, tailor their
output to match these individual demands and,
in doing so, capture an ever bigger slice of the
market. And the front-runners’ ability to shape
product developments around this rich supply of
customer data will make it harder and harder for
slower moving competitors to keep pace and could
eventually make their advantage unassailable.
We can already see this data-driven innovation
and differentiation in the way books, music, video
and entertainment are produced, distributed and
consumed, resulting in new business models, new
market leaders and the elimination of traditional
players that fail to adapt quickly enough.
Healthcare, automotive and financial services are
the sectors with the greatest potential for product
enhancement and disruption due to AI according
to our analysis. However, there is also significant
potential for competitive advantage in particular
areas of other sectors, ranging from on-demand
manufacturing to sharper content targeting
within entertainment we set out the business
areas with most AI potential in each sector in the
next section.
Some job displacement – but also new
employment opportunities
The adoption of ‘no-human-in-the-loop’
technologies will mean that some posts will
inevitably become redundant, but others will be
created by the shifts in productivity and consumer
demand emanating from AI, and through the
value chain of AI itself. In addition to new types
of workers who will focus on thinking creatively
about how AI can be developed and applied, a
new set of personnel will be required to build,
maintain, operate, and regulate these emerging
technologies. For example, we will need the
equivalent of air traffic controllers to control
the autonomous vehicles on the road. Same day
delivery and robotic packaging and warehousing
are also resulting in more jobs for robots and for
humans. All of this will facilitate the creation of
new jobs that would not have existed in a world
without AI.
Impact on different regions
As Figure 2 highlights, some economies have
the potential to gain more than others in both
absolute and relative terms. China and North
America are likely to see the biggest impact,
though all economies should benefit.
2

AI is the key to realising the promise of IoT as AI becomes an indispensable element of IoT solutions and the convergence of AI and IoT
spur the development of ‘smart’ machines. We explore this further in ‘Leveraging the upcoming disruptions from AI and IoT’ (https://
www.pwc.com/gx/en/industries/communications/assets/pwc-ai-and-iot.pdf)
6 Sizing the prize

Figure 2: Which regions will gain the most from AI?
Figure 1: Which regions gain the most from AI?
North
America
Total impact:
14.5% of GDP
Latin
America
Total impact:
5.4% of GDP
($3.7trillion)
($0.5trillion)
Total impact:
5.6% of GDP
($1.2trillion)
Total impact:
11.5% of GDP
($0.7trillion)
Southern
Europe
Africa, Oceania and
other Asian markets
Total impact:
10.4% of GDP
($0.9trillion)
Developed
Asia
Total impact:
9.9% of GDP
($1.8trillion)
Northern
Europe
China
Total impact:
26.1% of GDP
($7.0trillion)
Developing
countries will
experience more
modest increases
due the much lower
rates of adoption of
AI technologies
expected.
26.1%
14.5% 2030
North America
and China stand to
see the biggest
economic gains
with AI enhancing
GDP by
70%
of the global
economic
impact
All regions of the
global economy will
experience benefits
from artificial
intelligence.
Total
$10.7
trillion
9.9%
10.4% 2030
Europe and
Developed Asia
will also experience
significant economic
gains from AI
enhancing
GDP by
11.5%
All GDP figures are reported in market exchange rate terms
All GDP figures are reported in real 2016 prices, GDP baseline based on Market Exchange Rate Basis
Net effect of AI, not growth prediction
Our results are generated using a large scale
dynamic economic model of the global economy.
The model is built on the Global Trade Analysis
Project (GTAP) database. GTAP provides detail on
the size of different economic sectors (57 in total)
and how they trade with each other through their
supply chains. It gives this detail on a consistent
basis for 140 different countries.
When considering the results, there are two
important factors that you should take into account:
1. Our results show the economic impact of AI
only – our results may not show up directly
into future economic growth figures, as there
will be many positive or negative forces that
either amplify or cancel out the potential
effects of AI (e.g. shifts in global trade policy,
financial booms and busts, major commodity
price changes, geopolitical shocks etc).
2. Our economic model results are compared to a
baseline of long-term steady state economic
growth. The baseline is constructed from
three key elements: population growth,
growth in the capital stock and technological
change. The assumed baseline rate of
technological change is based on average
historical trends. It’s very difficult to separate
out how far AI will just help economies to
achieve long-term average growth rates
(implying the contribution from existing
technologies phase out over time) or simply be
additional to historical average growth rates
(given that these will have factored in major
technological advances of earlier periods).
These two factors mean that our results should be
interpreted as the potential ‘size of the economic
prize’ associated with AI, as opposed to direct
estimates of future economic growth.
Source: PwC analysis
7

North America
In North America, the potential uplift to GDP from
AI will be amplified by the huge opportunities to
introduce more productive technologies, many
of which are ready to be applied. And the gains
will be accelerated by the advanced technological
and consumer readiness for AI, along with the
impact of rapid accumulation of assets – not just
technology, but data touchpoints and the flows
of information and customer insight that come
with them.
North America is likely to see the fastest boost in
the next few years. While the impact will still be
strong from the middle of the 2020s, it probably
won’t be quite as high as in the earlier years.
One of the main reasons is that as productivity
in China begins to catch up with North America,
this will stimulate exports of AI-enabled products
from China to North America.
China
The high proportion of Chinese GDP that comes
from manufacturing heightens the potential
uplift from introducing more productive
technologies. It is likely to take some time to
build up the technology and expertise needed
to implement these capabilities and therefore
the GDP boost won’t be as rapid as the US. But in
around ten years’ time, the productivity gains in
China could begin to pull ahead.
A key part of the value potential comes from the
higher rate of capital re-investment within the
Chinese economy compared to Europe and North
America, as profits from Chinese businesses are
fed into increasing AI capabilities and returns.
AI will also play an important part in the shift to
a more consumer-oriented economy on the one
hand and the move up the value chain into more
sophisticated and high tech-driven manufacturing
and commerce on the other. The focus and
investment are amply demonstrated by the surge
in AI patents filed in China3
. An acceleration in
talent development in areas such as analytics will
be crucial in realising the potential gains from AI
within the Chinese economy.
North America
is likely to see
the fastest
boost in the
next few years.
3

China is now second behind the US in AI patent filings, a key indicator of long-term trends in technology. Source: ‘The Global Race for
Artificial Intelligence – Comparison of Patenting Trends’, Wilson Center, 1 March 2017 (https://www.wilsoncenter.org/blog-post/the-
global-race-for-artificial-intelligence-comparison-patenting-trends)
Critical assumptions
Our estimates reflect certain assumptions, which we will stress-test
in our forthcoming detailed economic assessment. What happens if
the pace of AI adoption is faster/slower in particular countries, for
example? How does that affect the distribution of global growth? What
happens if estimated changes in product quality do not materialise? A
slowdown in the pace of AI uptake would delay the benefits that feed
through to labour productivity. We see this as a key driver to both the
timing and the overall impact of AI on GDP.
We’re currently exploring the quantitative effect of several key
scenarios. This includes examining alternate combinations of input
parameters, as well as the timing of AI uptake. These sensitivity tests
are designed to help better understand the risks around our results,
while providing more insight into the parameters that drive the
relationship between AI and economic growth. We plan to present the
results of several scenarios in our detailed economic assessment.
8 Sizing the prize

Automation in action
An online insurer has leveraged an AI bot to automate the claims process
from beginning to end. Instead of the days or even months it traditionally
took to settle a claim, the bot is able to complete the entire pipeline from
claims receipt, policy reference, fraud detection, payout and notification
to customers in just three seconds. When rolled out at scale, this solution is
poised to have a huge impact on the insurance industry.
How to respond?
If your business is operating in one of the sectors
or economies that is gearing up for fast adoption
of AI, you’ll have to move quickly if you want
to capitalise on the openings, and ensure your
business doesn’t lose out to faster-moving and more
cost-efficient competitors.
If you’re in one of the sectors or economies where
the disruptive potential is lower and adoption
likely to be slower, there is still a significant
challenge ahead – no sector or business is in any
way immune from the impact of AI. In fact, the
potential for innovation and differentiation could
be all the greater because fewer market players are
currently focusing on AI. The big question is how to
secure the talent, technology and access to data to
make the most of this opportunity.
Doing nothing is not a feasible option. It’s easy to
dismissalotofwhat’ssaidaboutAIashype.Yetasour
analysis underlines, without decisive response, many
well established enterprises and even whole business
models are at risk of being rendered obsolete.
In the short-term, many of the opportunities
and threats are likely to focus on productivity,
efficiency and cost – the transformative phase.
If you’re the CEO of a transport and logistics
company, for example, you’re already seeing the
impact of robots within packing and fulfilment
operations. The bigger disruption will emerge
when the sector switches to autonomous trucking.
Are you in a position to move ahead of your
competitors? What are the openings for vehicle
manufacturers, technology companies and other
potential new entrants to make inroads in your
market? Could your business be at risk of becoming
obsolete if you don’t move quickly enough?
Source: PwC AI specialists
Figure 3: How quickly will AI impact productivity?
North America and China are
expected to witness the
greatest GDP gains from AI
increasing productivity, but the
trajectory of the impact for the
two countries differs.
China will likely
uptake AI technology
more slowly but could
see a large impact on
GDP by 2030.
North America
is expected to
realise the majority
of AI benefits faster.
9

AI is set to be the key source of transformation,
disruption and competitive advantage in today’s
fast changing economy. Drawing on the findings
of our AI Impact Index, we look at how quickly
change is coming and where your business can
expect the greatest return.
In the research carried out for this report, we’ve
drilled down to the sector-by-sector and product-
by-product impact of AI to enable your business to
target the opportunities, pinpoint the threats and
judge how to address them.
AI Impact Index:
Targeting and
timing your
investment
The unique analysis within PwC’s AI Impact
Index includes a rating for the potential to free
up time and enhance quality and personalisation.
We’ve used this analysis to create nearly 300 use
cases setting out the openings for innovation, the
drivers, timings and current feasibility of market
adoption, what could hold this up and how these
barriers could be overcome.
The areas with the biggest potential and
associated timelines we outline at a high level
here are designed to help your business target
investment in the short to medium term. Some
aspects of change, such as robotic doctors, could
be even more revolutionary, but are further off.
10 Sizing the prize

Potential AI
Consumption Impact
Sector Subsector
Healthcare 3.7
Providers/Health Services
Pharma/Life Sciences
Insurance
Consumer Health
Automotive 3.7
Aftermarket Repair
Component suppliers
Personal Mobility as a Service
OEM
Financing
Financial Services 3.3
Asset Wealth Management
Banking and Capital
Insurance
Transportation and Logistics 3.2
Transportation
Logistics
Technology, Communications and Entertainment 3.1
Technology
Entertainment, Media and
Communication
Retail 3.0
Consumer Products
Retail
Energy 2.2
Oil Gas
Power Utilities
Manufacturing 2.2
Industrial manufacturing
Industrial Products/Raw Materials
Grand Total 3.1
Figure 4: What’s the potential impact for your sector?
Scores based on PwC’s AI impact index evaluation. Potential scores range from 1-5, with 5 indicating the highest potential impact due to AI, and 1 being the lowest.
Healthcare: 3.7
37% 23% 40%
Automotive: 3.7
35% 47% 18%
Financial Services: 3.3
41% 59% 0%
Transportation and Logistics: 3.2
41% 41% 17%
Retail: 3.0
54% 38% 8%
Energy: 2.2
39% 44% 17%
Manufacturing: 2.2
14% 83% 3%
Technology, Communications and Entertainment: 3.1
47% 36% 17%
% Adoption maturity – Near term (0-3 yr)
% Adoption maturity – Mid term (3-7 yr)
% Adoption maturity – Long term (7+ yr)
11

Healthcare
Three areas with the biggest AI potential
• Supporting diagnosis in areas such as
detecting small variations from the baseline in
patients’ health data or comparison with
similar patients.
• Early identification of potential pandemics and
tracking incidence of the disease to help
prevent and contain its spread.
• Imaging diagnostics (radiology, pathology).
Consumer benefits
Faster and more accurate diagnoses and more
personalised treatment in the short and medium-
term, which would pave the way for longer-
term breakthroughs in areas such as intelligent
implants. Ultimate benefits are improved health
and lives saved.
Time saved
More effective prevention helps reduce the risk
of illness and hospitalisation. In turn, faster
detection and diagnosis would allow for earlier
intervention.
Timing
Ready to go: Medical insurance and smarter
scheduling (e.g. appointments and operations).
Medium-term potential: Data-driven
diagnostics and virtual drug development.
Longer-term potential: Robot doctors carrying
out diagnosis and treatment.
Barriers to overcome
It would be necessary to address concerns over
the privacy and protection of sensitive health
data. The complexity of human biology and the
need for further technological development
also mean than some of the more advanced
applications may take time to reach their
potential and gain acceptance from patients,
healthcare providers and regulators.
High potential use case: Data-based
diagnostic support
AI-powered diagnostics use the patient’s unique
history as a baseline against which small
deviations flag a possible health condition in
need of further investigation and treatment. AI
is initially likely to be adopted as an aid, rather
than replacement, for human physicians. It will
augment physicians’ diagnoses, but in the process
also provide valuable insights for the AI to learn
continuously and improve. This continuous
interaction between human physicians and the
AI-powered diagnostics will enhance the accuracy
of the systems and, over time, provide enough
confidence for humans to delegate the task
entirely to the AI system to operate autonomously.
Supporting
diagnosis in
one of the areas
in healthcare
with the biggest
potential
12 Sizing the prize

Automotive
• Autonomous fleets for ride sharing.
• Semi-autonomous features such as driver assist.
• Engine monitoring and predictive,
autonomous maintenance.
Consumer benefit
A machine to drive you around and ‘on-demand’
flexibility – for example a small model to get you
through a city or a bigger and more powerful
vehicle to go away in for the weekend.
Time saved
The average American spends nearly 300 hours
a year driving4
– think what you could with
that time if you didn’t have to spend it behind
the wheel.
Timing
Ready to go: Automated driver assistance
systems (e.g. parking assist, lane centring,
adaptive cruise control etc.).
Medium-term potential: On-demand parts
manufacturing and maintenance.
Longer-term potential: Engine monitoring and
predictive, autonomous maintenance.
Technology still needs development – having an
autonomous vehicle perform safely under extreme
weather conditions might prove more challenging.
Even if the technology is in place, it would need to
gain consumer trust and regulatory acceptance.
High potential use case: Autonomous
fleets for ride sharing
Autonomous fleets would enable travellers to
access the vehicle they need at that point, rather
than having to make do with what they have or
pay for insurance and maintenance on a car that
sits in the drive for much of the time. Most of
the necessary data is available and technology is
advancing. However, businesses still need to win
consumer trust.
Predictive engine
monitoring and
maintenance
technology is
advancing.
4

American Automobile Association media release 8 September 2016 (http://newsroom.aaa.com/2016/09/americans-spend-average-17600-min-
utes-driving-year/)
13

Financial services
• Personalised financial planning.
• Fraud detection and anti-money laundering.
• Process automation – not just back office
functions, but customer facing operations
as well.
Consumer benefit
More customised and holistic (e.g. health, wealth
and retirement) solutions, which make money
work harder (e.g. channelling surplus funds into
investment plans) and adapt as consumer needs
change (e.g. change in income or new baby).
Timing
Ready to go: Robo-advice, automated insurance
underwriting and robotic process automation in
areas such as finance and compliance.
Medium-term potential: Optimised product
design based on consumer sentiment and
preferences.
Longer-term potential: Moving from
anticipating what will happen and when in areas
such as an insurable loss (predictive analytics)
to proactively shaping the outcome (prescriptive
analytics) in areas such as reduced accident rates
or improved consumer outcomes.
Time saved
The information customers need to fully
understand financial position and plan for the
future is at their fingertips and adapts to changing
circumstances. Businesses can support this by
developing customised solutions rather than
expecting consumers to sift through multiple
options to find the one that’s appropriate.
Consumer trust and regulatory acceptance.
High potential use case: Personalised
financial planning
While human financial advice is costly and time-
consuming, AI developments such as robo-advice
have made it possible to develop customised
investment solutions for mass market consumers
in ways that would, until recently, only have been
available to high net worth clients. Finances are
managed dynamically to match goals (e.g. saving
for a mortgage) and optimise client’s available
funds, as asset managers become augmented and,
in some cases, replaced by AI. The technology and
data is in place, though customer acceptance would
still need to increase to realise the full potential.
Businesses can
develop
customised
solutions
rather than
expecting
consumers to
sift through
multiple
options to find
the one that’s
appropriate.
Assisted intelligence in action
A financial services organisation used machine learning
to develop time-series clusters of their policyholder
transactions. The machine learning solution helped
the company to identify common customer transaction
patterns and better understand the key triggers driving
variances. Combining policyholder data with external
data on customer preferences, financial literacy, and
other behavioural dimensions allowed the firm to better
predict which patterns would occur for each customer
persona. The organisation designed interventions around
these insights, which opened the way for improved
outcomes for both the customer and the company.
14 Sizing the prize

Retail
• Personalised design and production.
• Anticipating customer demand – for example,
retailers are beginning to use deep learning to
predict customers’ orders in advance.
• Inventory and delivery management.
Consumer benefit
On-demand customisation as the norm and
greater availability of what you want, when and
how you want it.
Timing
Ready to go: Product recommendation base on
preferences.
Medium-term potential: Fully customised
products.
Longer-term potential: Products that anticipate
demand from market signals.
Time saved
Less time exploring shelves, catalogues and
websites to find the product that you want.
Adapting design and production to this more agile
and tailored approach. Businesses also need to
strengthen trust over data usage and protection.
High potential use case: Personalised
design and production
Instead of being produced uniformly, apparels
and consumables can be tailored on demand. If
we look at fashion and clothing as an example,
we could eventually move to fully interactive and
customised design and supply in which AI created
mock-ups of garments are sold online, made in
small batches using automated production, and
subsequent changes are made to design based on
user feedback.
Retailers are
beginning to
use deep
learning to
predict
customers’
orders in
advance.
15

Technology, communications and entertainment
• Media archiving and search – bringing
together diffuse content for recommendation.
• Customised content creation (marketing, film,
music, etc.).
• Personalised marketing and advertising.
Consumer benefit
Increasingly personalised content generation,
recommendation and supply.
Timing
Ready to go: Content recommendation for
consumers.
Medium-term potential: Automated
telemarketing capable of holding a real
conversation with the customer.
Longer-term potential: Use-case specific and
individualised AI-created content.
Time saved
Quicker and easier for consumers to choose what
they want, reflecting their preferences and mood
at the time.
Cutting through the noise when there is so much
data, much of it unstructured.
High potential use case: Media archiving
and search
We already have personalised content
recommendation within the entertainment sector.
Yet there is now so much existing and newly
generated (e.g. online video) content that it can
be difficult to tag, recommend and monetise. AI
offers more efficient options for classification and
archiving of this huge vault of assets, paving the
way for more precise targeting and increased
revenue generation.
16 Sizing the prize

Manufacturing
• Enhanced monitoring and auto-correction
of manufacturing processes.
• Supply chain and production optimisation.
• On-demand production.
Consumer benefit
Indirect benefits from more flexible, responsive
and custom-made manufacturing of goods,
with fewer delays, fewer defects and faster
delivery.
Timing
Ready to go: Greater automation of a large
number of production processes.
Medium-term potential: Intelligent
automation in areas ranging from supply chain
optimisation to more predictive scheduling.
Longer-term potential: Using prescriptive
analytics in product design – solving problems
and shaping outcomes, rather than simply
predicting and responding to demand in
product design.
AI will facilitate
more seamless
integration of
supply chain
data, enabling
anticipatory
production and
more efficient
delivery of
products to
customers.
Time saved
Faster response and fewer delays.
Making the most of supply chain and production
opportunities requires all parties to have the
necessary technology and be ready to collaborate.
Only the biggest and best-resourced suppliers and
manufacturers are up to speed at present.
High potential use case: Enhanced
monitoring and auto-correction
Self-learning monitoring makes the
manufacturing process more predictable and
controllable, reducing costly delays, defects or
deviation from product specifications. There
is huge amount of data available right through
the manufacturing process, which allows for
intelligent monitoring.
17

Energy
• Smart metering – real-time information on
energy usage, helping to reduce bills.
• More efficient grid operation and storage.
• Predictive infrastructure maintenance.
Consumer benefit
More efficient and cost-effective supply and usage
of energy.
Timing
Ready to go: Smart metering.
Medium-term potential: Optimised power
management.
Longer-term potential: More efficient and
consistent renewable energy supply in areas
such as improved prediction and optimisation of
wind power.
Time saved
More secure supply and fewer outages.
Technological development and high investment
requirements in some of the more advanced areas.
High potential use case: Smart meters
Smart meters help customers tailor their energy
consumption and reduce costs. Greater usage
would also open up a massive source of data,
which could pave the way for more customised
tariffs and more efficient supply.
18 Sizing the prize

Transport and logistics
• Autonomous trucking and delivery.
• Traffic control and reduced congestion.
• Enhanced security.
Consumer benefit
Greater flexibility, customisation and choice in
how goods and people move around and the
ability to get from A to B faster and more reliably.
Timing
Ready to go: Automated picking in warehouses.
Medium-term potential: Traffic control.
Longer-term potential: Autonomous trucking
and delivery.
Time saved
Smart scheduling, fewer traffic jams and real-time
route adjustment to speed up transport.
Technology for autonomous fleets is still in
development.
High potential use case: Traffic control
and reduced congestion
Autonomous trucking reduces costs by allowing
for increased asset utilisation as 24/7 runtimes
are possible. Moreover, the whole business
model of transport logistics (TL) might be
disrupted by new market entrants such as truck
manufacturers offering TL and large online
retailers vertically integrating their TL.
Augmented intelligence in action
An automotive company developed a dynamic agent-based
model to simulate thousands of strategic scenarios for entering
the ridesharing market. The model allowed key decision makers
to test a variety of policy configurations in a virtual, risk-free
simulated environment to help them understand the ultimate
impact on market share and revenue over time, before actually
making any decisions. Flight simulators allow pilots to test the
impact of their decisions in a virtual environment to better
prepare them for making decisions in flight, so why shouldn’t
business executives do the same?
19

What do you want from AI? Where do you begin?
How do you keep pace with change?
1/Work out what AI means for your
business
The starting point for strategic evaluation is a scan
of the technological developments and competitive
pressures coming up within your sector, how
quickly they will arrive, and how you will respond.
You can then identify the operational pain points
that automation and other AI techniques could
address, what disruptive opportunities are opened
up by the AI that’s available now, and what’s
coming up on the horizon.
2/Prioritise your response
In determining your strategic response, key
questions include how can different AI options
help you to deliver your business goals and what
is your appetite and readiness for change. Do
you want to be an early adopter, fast follower
or follower? Is your strategic objective for AI to
transform your business or to disrupt your sector?
AI provides the potential to enhance quality,
personalisation, consistency and time saved, but
it’s also important to consider the technological
feasiblity of AI and the availability of the data
needed to support AI. How are you planning to
overcome barriers and accelerate innovation?
Realising
the potential:
To prioritise your response, it’s important to
map the key process flows to be automated and
decision flows to be augmented. What functions
contain high potential processes that could drive
near-term savings, for example? As data becomes
the primary asset and source of intellectual
property, what investments and changes would
enable you to capture more data and use it
more productively? With this map in place, you
can then develop the cost-benefit analysis for
automation and augmentation.
AI is applicable across all elements of the
value chain, which can lead to multiple silos of
initiatives or confusion in finding a good starting
point. Developing the insight, governance and
organisational collaboration to pick your spot and
drive initiatives forward are therefore critical.
To prioritise
your response,
it’s important
to map the key
process flows
to be
automated and
decision flows
to be
augmented.
20 Sizing the prize

3/Make sure you have the right talent
and culture, as well as technology
While investment in AI may seem expensive now,
PwC subject matter specialists anticipate that the
costs will decline over the next ten years as the
software becomes more commoditised. Eventually,
we’ll move towards a free (or ‘freemium’ model)
for simple activities, and a premium model for
business-differentiating services. While the
enabling technology is likely to be increasingly
commoditised, the supply of data and how it’s used
are set to become the primary asset.
To make the most effective use of this technology,
it’s important to instil a data-driven culture that
blends intuition and analytical insights with a focus
on practical and actionable decisions across all levels.
Demand for data scientists, robotics engineers and
other tech specialists is clearly growing. These
are in short supply, especially in less developed
markets according to the interviews we carried
out with PwC’s data and analytics’ regional
leaders, so it will be important to gear long-term
training and development to these emerging
needs. As adoption of AI gathers pace, the value of
skills that can’t be replicated by machines is also
increasing. These include creativity, leadership and
emotional intelligence5
.
It’s important to prepare for a hybrid workforce
in which AI and human beings work side-by-side.
The challenge for your business isn’t just ensuring
you have the right systems in place, but judging
what role your people will play in this new model.
People will need to be responsible for determining
the strategic application of AI and providing
challenge and oversight to decisions.
4/Build in appropriate governance
and control
Trust and transparency are critical. In relation
to autonomous vehicles, for example, AI requires
people to trust their lives to a machine – that’s a
huge leap of faith for both passengers and public
policymakers. Anything that goes wrong, be it a
malfunction or a crash, is headline news. And this
reputational risk applies to all forms of AI, not
just autonomous vehicles. Customer engagement
robots have been known to acquire biases through
training or even manipulation, for example.
AI should therefore be managed with the same
discipline as any other technology enabled
transformation. Key questions to ask while
building AI include:
• Have you considered the societal and ethical
implications?
• How can you build stakeholder trust in the
solution?
• How can you build AI that can explain its logic
so that a lay person can understand?
• How can you build AI that is unbiased and
transparent?
It’s important to put in place mechanisms to
source, cleanse and control key data inputs and
ensure data and AI management are integrated.
Transparency is not only important in guarding
against biases within the AI, but also helping to
increase human understanding of what the AI can
do and how to use it most effectively.
We further explore business strategies for an
AI world in ‘A strategist’s guide to artificial
intelligence’ (https://www.strategy-business.
com/article/A-Strategists-Guide-to-Artificial-
Intelligence?gko=0abb5).
It’s important
to prepare for
a hybrid
workforce in
which AI and
human beings
work side-by-
side.
Autonomous intelligence in action
Entertainment industry consumers now have an unprecedented
choice of movies, television, music and games. While this
provides consumers with more opportunity to enjoy content
specific to their unique tastes, they can experience ‘choice
overload’ during their search. And worse, sometimes they make
no choice at all! Video and music streaming companies have
begun using autonomous recommendation engines that combine
segment trends, ratings and content similarity to personalise
suggestions and engage customers. Engaging customers not only
increases retention, but also allows companies to collect more
data on individuals and improve the personalisation of offerings
– creating a virtuous feedback loop that provides a significant
competitive advantage.
5

We explore AI’s role in this creative process in ‘AI is already entertaining you’, strategy+business, 1 May 2017 (https://www.strategy-business.com/
article/AI-Is-Already-Entertaining-You?gko=dc252)
21

Conclusion:
The options for
survival and success
22 Sizing the prize

Picture your market in five years’ time. How
can you create the capabilities to compete?
As our analysis underlines, AI has the potential
to fundamentally disrupt your market through
the creation of innovative new services and
entirely new business models. We’ve already
seen the creative destruction of the first wave
of digitisation. With the eruption of AI, some of
the market leaders in ten, even five years’ time
may be companies you’ve never heard of. In turn,
some of today’s biggest commercial names could
be struggling to sustain relevance or have even
disappeared altogether, if their response has been
too little or too late.
Tomorrow’s market leaders are likely to be
exploring the possibilities and setting their
strategies today. We believe there are four key
questions your business should address if it wants
to keep pace and capitalise on the opportunities:
• How vulnerable is your business model to AI
disruption? How soon will the change arrive?
• Are there game-changing openings within
your market and, if so, how can you take
advantage?
• Do you have the right talent, data and
technology to help you understand and
execute on the AI opportunities?
• How can you build trust and transparency into
your AI platforms and applications?
Get this right and creativity, collaboration and
decision making within your organisation can
all be empowered. You’ll have the potential to
understand customer behaviour and anticipate
and respond to their individual needs with a
precision and foresight that have never been
possible before.
The ultimate commercial potential of AI is doing
things that have never been done before, rather
than simply automating or accelerating existing
capabilities. Some of the strategic options that
emerge won’t match past experience or gut
feelings. As a business leader, you may therefore
have to take a leap of faith. The prize is being far
more capable, in a far more relevant way, than
your business could ever be without the infinite
possibilities of AI.
The prize is
being far more
capable, in a
far more
relevant way,
than your
business could
ever be without
the infinite
possibilities
of AI.
23

Helping your
business to make
the most of AI
PwC is already working with companies across
each of the different sectors highlighted in this
report, to help them plan for and take advantage
of AI to support their business strategy and
improve performance.
If you would be interested in a consultation about
the potential within your business, please feel
free to get in touch.
Gerard Verweij
Global US Data Analytics Leader, PwC US
T: +1 (617) 530 7015
E: gerard.verweij@pwc.com
Twitter: @gverweij
Euan Cameron
Artificial Intelligence Leader, PwC UK
T: +44 (0)20 7804 3554
E: euan.cameron@pwc.com
Twitter: @euancameron55
Anand Rao
Global Leader of Artificial Intelligence, PwC
T: +1 (617) 530 4691
E: anand.s.rao@pwc.com
Twitter: @AnandSRao
24 Sizing the prize

Authors
Dr. Anand S. Rao and Gerard Verweij
The authors would like to acknowledge the
contribution of:
• Alan Morrison
• Barbara Lix
• Cathryn Marsh
• Chengyao Gu
• Chris Curran
• Craig Scalise
• Cristina Ampil
• Edmond Lee
• Hugh Dance
• John Ashworth
• John Hawksworth
• John Sviokla
• Jonathan Gillham
• Katherine Barnard Roberts
• Lucy Rimmington
• Mark Paich
• Michael Schneider
• Pia Ramchandani
• Shivanghi Jain
We would also like to thank our academic
and applied research partners at
Fraunhofer, and especially Fraunhofer Big
Data Alliance, with contributions from the
following institutes:
• Fraunhofer Institute for Intelligent Analysis
Information Systems IAIS
• Fraunhofer Institute for Applied Information
Technology FIT
• Fraunhofer Institute for Open Communication
Systems FOKUS
• Fraunhofer Institute for Digital Media
Technology IDMT
• Fraunhofer Institute for Experimental Software
Engineering IESE
• Fraunhofer Institute for Integrated Circuits IIS
• Fraunhofer Institute for Material Flow and
Logistics IML
• Fraunhofer Institute for Transportation and
Infrastructure Systems IVI
• Fraunhofer Institute for Algorithms and
Scientific Computing SCAI
• Fraunhofer Working Group for Supply Chain
Services IIS-SCS
• Fraunhofer Institute for Solar Energy
Systems ISE
6

Examples include our article, ‘Will robots steal our jobs? The potential impact of automation on the UK and other major economies?
(https://www.pwc.co.uk/economic-services/ukeo/pwcukeo-section-4-automation-march-2017-v2.pdf)
What’s the real value of AI for your business and how can you capitalise? 25

Glossary
Main AI areas Description
Large-scale Machine Learning Design of learning algorithms, as well as scaling existing algorithms, to work with
extremely large data sets.
Deep Learning Model composed of inputs such as image or audio and several hidden layers of
sub-models that serve as input for the next layer and ultimately an output or
activation function.
Natural Language Processing
(NLP)
Algorithms that process human language input and convert it into understandable
representations.
Collaborative Systems Models and algorithms to help develop autonomous systems that can work
collaboratively with other systems and with humans.
Computer Vision (Image
Analytics)
The process of pulling relevant information from an image or sets of images for
advanced classification and analysis.
Algorithmic Game Theory and
Computational Social Choice
Systems that address the economic and social computing dimensions of AI, such as
how systems can handle potentially misaligned incentives, including self-interested
human participants or firms and the automated AI-based agents representing them.
Soft Robotics (Robotic Process
Automation)
Automation of repetitive tasks and common processes such as IT, customer servicing
and sales without the need to transform existing IT system maps.
AI consists of a number of areas, including but not limited to those below:
26 Sizing the prize

The basis for
our analysis
AI Impact Index
Our sector specialists worked with market
participants and our partners at Fraunhofer to
identify and evaluate use cases across five criteria:
• Potential to enhance personalisation.
• Potential to enhance quality (utility value).
• Potential to enhance consistency.
• Potential to save time for consumers.
• Availability of data to make these gains possible.
Specific scoring parameters were derived for
each criterion. We also evaluated technological
feasibility. The results helped us to gauge time
to adoption, potential barriers and how they can
be overcome.
Economic analysis
We used a multi-stage approach to first evaluate
how much AI would proliferate in different
regions of the world and how much this would
affect jobs through automation, and second
to assess how much this would impact the key
elements of the economy.
To do this we used a combination of machine
learning and econometrics techniques.
• We followed a similar approach to the best
practice we’ve used in job automation
research6
. We used a machine learning
approach to assess the likelihood that a job can
be automated based on the specific
tasks involved.
• We used econometric analysis to assess how
much automation would impact productivity.
We use the KLEMS datasets (K-capital,
L-labour, E-energy, M-materials, and
S-purchased services) and quantified the
relationship between the use of AI technologies
and productivity in different regions and
industries.
• We also drew insights from the AI Impact
Index and used academic literature on
personalisation and marginal utility to
quantify the effect that AI will have on
consumers’ utility, choices available to them
and the amount of time they would save.
• We brought all of this together into a global
dynamic computable general equilibrium
(CGE) model, an economic model of
interactions between consumers, businesses
and governments. Through this model, we
analysed the total economic impact of AI
focusing on the ‘net’ impact, accounting for
the creation of new jobs, a boost to demand
from product enhancements and other
secondary effects.
27

PwC | AI Analysis, Sizing the Prize

At PwC, our purpose is to build trust in society and solve important problems. We’re a network of firms in 157 countries with more than 223,000 people who are
committed to delivering quality in assurance, advisory and tax services. Find out more and tell us what matters to you by visiting us at www.pwc.com.
This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the
information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the
accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, PwC does not accept or assume any liability,
responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or
for any decision based on it.
© 2017 PwC. All rights reserved. “PwC” refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see
www.pwc.com/structure for further details.
170905-115740-GK-OS
www.pwc.com/AI

PwC | AI Analysis, Sizing the Prize

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