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Artificial IntelligenceAre you AI-ready? How will its adoption transform business and what will the expansion of AI mean for society?
www.disruptionhub.com / 3
ForewordArtificial intelligence (AI) offers organisations more
opportunities to become smarter, faster – to gain
a competitive advantage. But like other disruptive
technologies, it’s not a panacea for underlying business
problems. You need to know why you want to apply AI.
So rather than asking, “What can AI do for me?” you
should focus on where problems and opportunities lie
and use AI to exploit them.
Whether that’s reducing costs through time-saving
automation, improving customer experience with new
insight or predicting which clients will call you next
and what they’ll ask.
While there’s no one way to resolve a problem with AI,
data is vital. Generate it, gather it or buy it, data is the
lifeblood of AI. But using complex algorithms to find
hidden patterns in data isn’t a solution in itself. Real
impact can only come from determining causation
from correlation, then understanding and exploiting it.
To begin on the AI path, you need a flexible vision of
where you want to end up, as the technology evolves
quickly. More important than high-end computers are
eager and enthused people. Finally, you need a desire
to scale up so when you’ve proven a concept, you fully
exploit it.
So think big, start small and scale fast. And realise that
ultimately, people are the key to making AI a success.
Artificial Intelligence isn’t human intelligence, so
human characteristics will become more valued.
In fact, AI could drive us to be more human.
Dr Lee Howells AI and automation expert PA Consulting
ContentsForeward 3
Executive summary 4
What is Artificial Intelligence? 5
Glossary 6
The future has not been written 10
Investment landscape 11
How business is embracing AI 12
Impacting the workplace 16
The race to regulation 18
Is your business AI-ready? 20
What does AI mean for society? 22
Case studies 24
Key takeaways and considerations 27
Sources 28
/Artificial Intelligence
Are you AI-ready? How will AI’s adoption
transform business and what will its
expansion mean for society?
In this special report, D/SRUPTION draws
on industry trends, statistical evidence
and expert observations to explore the
most challenging questions surrounding
Artificial Intelligence today.
But first thing’s first. What does AI mean?
We start with a concise definition of AI and
a glossary of need-to-know terms, then
quickly go through AI’s development in
a visual timeline.
In the main body of this report,
D/SRUPTION tracks the investment
landscape to understand why AI funding
has increased, where this funding is
coming from and how the market might
develop. Through an examination of
different industry sectors and real-world
examples, we discuss AI’s advantages and
potential applications. We also consider the
AI is an umbrella term that spans a range
of techniques, tools and technologies. The
term was first coined by John McCarthy
in 1956 and refers to a machine’s ability to
replicate human intelligence. While there
is no single theory of intelligence, it can be
understood as the acquisition of knowledge
through thoughts, experiences and senses.
Now that AI has become a catch-all phrase
for both current and future capabilities, it is
important to distinguish between narrow
AI and AGI – Artificial General Intelligence.
effect of AI adoption on the world of work,
including what possible actions employees
can take in order to cope with or mitigate
its disruptive impact.
Then there are the regulatory implications
of AI that have added yet another layer of
complexity to AI adoption, contributing to
growing tensions between governments
and corporations. Can big businesses be
trusted to develop AI applications that
benefit society? D/SRUPTION offers six key
considerations for organisations when
applying an AI strategy and looks at the
implications for society generally. While
the end point of AI’s trajectory is still hotly
debated, expert opinions demonstrate a
notable level of agreement.
Three case studies then demonstrate how
industry leaders are already using AI to
their advantage. After looking at them,
we pose a series of questions that every
organisation should ask as it develops its
own AI vision.
Narrow AI refers to artificially intelligent
tools that achieve a single aim. In its
current form, narrow AI is used to augment
human ability.
AGI, on the other hand, can perform any
task that a human can, with comparable or
higher accuracy. AGI is the technology that
is often associated with an uncertain future
in which AI overtakes human intelligence.
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Executive summary
What is Artificial Intelligence?
ArtificialIntelligencetimeline
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Swiss physician, astrologer and alchemist Paracelsus (1493-1541) claims to have created an artificial man using alchemy
Spanish engineer Leonardo Torres y Quevedo (1852-1936) builds a chess automaton able to play a king and rook endgame
Mathematician John von Neumann (1903-1957) publishes a paper that introduces game theory, the mathematical study of conflict and cooperation in rational, intelligent decisions
Alan Turing (1912-1954) publishes Computing Machinery and Intelligence. This paper questions the ability of machines to think and proposes the Imitation Game (the Turing Test) as a testing method
The first working AI program is written at the University of Manchester. It is capable of playing draughts and chess
American computer scientist John McCarthy (1927-2011) coins the term ‘Artificial Intelligence’
The General Problem Solver is demonstrated at Carnegie Mellon University
John McCarthy invents the Lisp programming language
MIT professor Joseph Weizenbaum (1923-2008) builds ELIZA, an interactive program for English dialogue
1500 1915 1944 1950 1951 1956 1957 1958 1965
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Glossary
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The AI winter sees commercial and scientific applications decline due to a lack of government investment. AI research continues under different names, including informatics, and machine learning
The Lighthill report advises British governments against investment in AI, leading in part to the ‘AI winter’ of the 1970s
The Fifth Generation Computer Systems (FGCS) project begins in Japan
Ian Horswill creates Polly at the MIT Artificial Intelligence Laboratory. It is the first robot capable of navigating using vision
Checkers-playing computer program Chinook wins the USA National Tournament
IBM’s Deep Blue beats reigning world chess champion Gary Kasparov
IBM’s Watson defeats Jeopardy! champions Rutter and Jennings
In 2011, Apple develops Siri, a mobile-dwelling AI that uses natural language processing to answer questions, make suggestions and perform tasks. In 2012 Google releases its answer to Siri, Google Now, followed in 2014 by Microsoft’s Cortana
Poker AI Libratus beats four human opponents at the Rivers Casino in Pittsburgh, using perception, reasoning and deception
Google’s AutoML creates its own daughter AI called NASNet that can recognise images in videos with the highest accuracy level to date (82.7 per cent)
1970s1973 1982 1993 1994 1997 2011 2011-2014 2017
Algorithm A set of steps – usually computerised –
taken to solve problems. Algorithms can perform
calculation, data processing and automated reasoning.
Artificial General Intelligence (AGI) AGI learns without
supervision. It can perform tasks to at least the same
accuracy as a human. Although not currently possible,
AGI is expected to eventually outperform human
intelligence in every application.
Artificial Neural Networks (ANN) Artificial replicas of
the biological networks seen within brains. ANNs are
a type of machine learning that takes inspiration from
neuron activity to solve problems that are too complex
for traditional programming. Instead of neurons, ANNs
use interconnected nodes to simulate the nervous
system. In their current state, neural networks are far
less powerful than living brains, although they can still
perform such complicated tasks as playing chess.
Automation The process of performing a task without
human assistance. Automation is often used in
conjunction with AI but, at a basic level, uses sensors
and automatic control systems that aren’t necessarily
artificially intelligent. Alongside AI, automation has
fuelled fears over the future of employment levels.
Capsule networks In 2017, British computer scientist
Geoffrey Hinton introduced a new type of neural
network he called “capsule networks”. These are
capable of gathering more information than traditional
networks by using capsules, and are expected to
improve deep learning capabilities.
Convolutional Neural Networks (CNN) Neural networks
used in image recognition and classification, as well as
Natural Language Processing (NLP). CNNs are made up
of neurons that are trained to make classifications by
extracting features from input data, such as an image.
Deep learning Also known as a ‘deep neural networks’,
deep learning uses algorithms to understand data and
datasets. It’s a subfield of machine learning that has
enabled practical applications in image recognition,
speech recognition, natural language processing
and the environmental awareness necessary for
autonomous vehicles. Deep learning feeds data to a
computer via artificial neural networks, aiming to solve
any problem that requires thought.
Expert system Some of the first successful forms of
AI. Expert systems are computer systems that replicate
human decision-making, using reasoning to solve
complex problems.
Conversational interfaces powered
by AI. They live in apps and mainly
handle customer queries.
Chatbot Autonomous systems are able to function without
human intervention using machine learning
techniques. Current examples include autonomous
robots and self-driving vehicles.
Autonomous
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Machine learning The methods and algorithms used to
improve the performance of data collecting software.
Although the term is sometimes used interchangeably
with Artificial Intelligence, machine learning is actually
a statistical approach to creating AI. It’s a process of
learning from examples which allows machines to
adapt to new data without reprogramming. Machine
learning methods include pattern recognition, natural
language processing and data mining.
Narrow AI In contrast to AGI, narrow AI carries out
a single task. Narrow AI describes the artificially
intelligent tools used by organisations today to augment
the human workforce.
Natural Language Processing (NLP) Through NLP,
machines are able to understand human language.
The way people communicate is typically full of
nuances and colloquialisms that are hard for software
to comprehend, yet tech giants are already heavily
invested in improving voice search. Google, for
example, aims to reach human level accuracy in the
NLP systems used in Google Home devices.
Pattern recognition A branch of machine learning
that assigns a label to an input value. Systems can
be trained with labelled data (supervised learning) or
discover patterns on their own without ready-made
labels (unsupervised learning). Image recognition is an
example of pattern recognition, in which an algorithm
identifies features in an image.
Predictive analytics This uncovers patterns in
structured and unstructured data to discern the
likelihood of future events. The technique uses a
combination of methods that include machine learning,
data mining and predictive modelling, to make
predictions based on current and historical information.
Prescriptive analytics In the same way that predictive
analytics predicts what might happen through
analysing data, prescriptive analytics uses this
information to offer a relevant course of action. For
example, if a supply chain is likely to handle higher
demand for a certain item, prescriptive analytics would
advise an increase in production.
Technological Singularity The Technological
Singularity is the prediction made by American writer,
inventor and futurist Ray Kurzweil (below) that AI will
eventually outpace human intelligence.
Turing Test Also known as the Imitation Game, the
Turing Test was devised by Alan Turing (above) in
the early 1950s as a way to determine whether any AI
developed could ever pass as human. The most simple
iteration of the test involves three players. If Player C,
the human interrogator, is unable to work out whether
Players A or B is the machine, then the machine has
passed the test. As of yet, no AI has ever passed the
Turing Test.
Glossary
Neural NetworksInformation processing systems modelled
on human and animal biology. They
imitate neurons in the brain via connected
nodes, enabling computers to learn from
observational data.
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The future hasnot been written
Investment landscape
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All eyes are currently on AI and it can
never be watched too closely, if the
concerns of sceptics like technology
billionaire Elon Musk are any indication.
The business tycoon has repeatedly warned
of the dangers of AI, even going so far as to
suggest that the creation and installation
of a safety “kill switch” for all AI-powered
machines would only further antagonise
any super-powered system.
On the other side of the AI debate sits
another billionaire – Jeff Bezos. The
Amazon CEO wants the field of AI to
keep expanding and believes there is
no institution that can’t be made better
through machine learning technology.
Before either Musk’s gloomy vision or
Bezos’ optimistic predictions are realised,
AI has a lot of growing up to do. We are
still far from the development of a super
powerful Artificial General Intelligence
(AGI). However, AI that can learn and
complete specific tasks to the same skill
level as humans is already shaping many
parts of industry and government. How we
all approach the next generation of AI will
play a major part in shaping how humans
work and live in the future.
The market for AI is in an important
transitional phase. Corporations and
venture capitalists certainly view AI
technology as a top spending priority,
and 2017 saw venture capital in AI
double[1]. It would seem that companies
are overcoming their initial wariness
surrounding the technology and are now
vying to implement the most effective
solutions. AI has become a buzzword in
business, so using (or attempting to use)
AI has become increasingly important to
remaining competitive.
Regardless of the industrial sector,
businesses are currently locked in an
AI arms race. Governing bodies are
also competing to boost AI adoption in
everything from research to commercial
applications. The battleground is currently
dominated by China and the US and,
according to KPMG, China accounted for
five of the world’s biggest venture capital
investments in the fourth quarter of 2017.
CB Insights research also shows that China
has surpassed the US in equity funding by
10 per cent. In other words, the US lead in
AI is no longer so certain.
As of April 2018, the European Commission
has announced plans to invest a further
€1.5bn[2] in AI to catch up with the US and
Asia. This increased funding is likely to
fuel the development of AI-associated
solutions and strategies, building trust
and confidence in the technology. Statista
research, published in September 2017,
shows that worldwide market revenue has
grown exponentially and suggests that this
will continue.
Although investment has grown, market
forecasts vary widely. According to
Tractica, predictions for 2025 range from
$644m to $126bn. Statista’s research sits
somewhere in the middle at $60bn.
Photo: Steve Jurvetson, Flickr
Jeff Bezos, CEO, Amazon
“There is no institution that can’t be made better
through the application of machine learning”
2016
1,378.19 2,420.364,065.99
6,629.4410,529
16,241.52
24,161.77
34,381.76
46,519.61
59,748.54
10,000
20,000
30,000
40,000
50,000
Mar
ket
in U
S do
llar
s (m
illion
s) 60,000
70,000
2017 2018 2019 2020 2021 2022 2023 2024 2025
[1] https://bit.ly/2DBg5I8 [2] https://reut.rs/2r3UD6G
Source: Statista
Worldwide AI market revenue & predicted growth
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[3] https://bit.ly/2IWKDUd [4] http://www.croptix.solutions/
Retail
Major corporations such as Amazon and Ocado offer
clear examples of scenarios in which AI is the right
solution for consumer-facing companies. Amazon has
pursued a number of AI-powered projects, including
deep learning for recommendations, smart robotics
and, of course, its flagship AI venture, Alexa. Through
Amazon Web Services, the ecommerce leader sells
its machine learning platform to external parties,
generating revenue as well as more data. Data is
paramount to a successful AI vision, which largely
explains why big tech companies, with their keen
interest in consumer data, dominate the market.
Ocado, for example, uses AI to categorise customer
enquiries and connect its warehouse robots.
Aerospace
Lockheed Martin has applied AI to real-time,
autonomous systems that track engine health and
connect unmanned vehicles. The company’s Artificial
Intelligence Laboratory[3] develops AI solutions that
monitor and manage both piloted and pilotless aircraft,
automatically detecting system failures or threats
before assessing their impact.
Lock
heed
Mar
tin
Am
azon
Despite the rush to invest, no one really knows just
how transformative AI will be. According to Andrew
Burgess, AI advisor and strategist for Symphony
Ventures, AI’s business impact will go far beyond
the cost-saving scenarios envisaged by the more
conservative voices.
“AI is about providing greater insights and more
informed decisions,” says Burgess. “AI extracts value
from the data within your business. If you look at
Google, they used AI on their own data centres to
predict heating demand and they were able to reduce
the cooling requirements of those centres by 40 per
cent. If you expand that to tens of thousands of data
centres, not only are you saving money but you’ve got
the environmental benefits as well.”
AI looks set to reimagine central operations and has
disrupted supply chains and end-to-end processes
across many sectors. This has been encouraged by
convergence and the open source movement and
been augmented by collaboration with other new
technologies such as Big Data and the IoT. By fuelling
AI with appropriate data, it is altering many sectors…
How business is embracing AI
/Artificial Intelligence
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Healthcare
Sometimes, data can be as much of a hindrance as
a help. This is especially the case for industries that
handle sensitive information, such as healthcare.
“Healthcare is certainly interested in AI and how they
can use it to improve services,” explains Lee Howells,
Artificial Intelligence and automation expert at PA
Consulting. “The problem with healthcare adopting
any new technology is that they have to be very, very
careful. It’s not that they might lose money, it’s that
somebody might die. That’s a very different problem
to deal with.”
In one innovative project, healthtech startup Woebot
Labs[5] has developed an AI-powered chatbot that uses
clinical techniques such as CBT to monitor patients’
mental health[6].
[5] https://woebot.io/ [6] https://bit.ly/2sife8t
Manufacturing
Deep learning and machine learning enable the
interrogation of data flows from machinery in order
to examine history and performance. This way,
the algorithm can predict how likely the machine
is to break down and when that might happen. By
recognising such patterns, AI provides insight as well
as foresight, in that the manufacturer is able to carry
out maintenance and repairs on machinery before
faults occur, yet also evaluate the specific reasons for
each failure.
Financial services
The financial services sector has been one of the
quickest off the mark when it comes to adopting AI.
Deutsche Bank uses AI to monitor dealers’ calls with
clients in order to identify fraud or noncompliance.
The AI listens to every call, mitigating huge amounts
of risk. AI is also being trialled to help combat money
laundering, especially in the important but time
intensive area of Know Your Customer (KYC).
Technology
AI is enabling developments in computing by bringing
far greater capabilities to the cloud. To meet growing
demand for cheap computing power and data
storage, the cloud has become artificially intelligent.
This has given rise to a range of cloud computing
services provided by the likes of Amazon, Google, and
Microsoft, the tech giants at the top of the AI pack.
Through these vendors, AI itself has become a service.
Agriculture
Machine learning is being utilised to provide remote
monitoring and alerts, to recommend optimal levels
of water and pesticides, and improve crop yields
with data forecasts. In emerging economies, AI is
equipping smallholders with the predictive tools to
navigate volatile natural environments. CROPTIX[4], a
Pennsylvania State University spin-off, uses predictive
analytics to detect potential disease in plants,
preventing crop failures.
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Impacting the workplace
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In time, there will be AI for everything. This
is both exciting and unsettling because
once AI can do anything, what will be the
point of having human workers?
Until a few years ago, certain jobs were
considered to be ‘safe’ from automation
and it was the blue collar workers carrying
out repetitive manual labour that were
expected to be replaced by automation and
robots. Yet AI is starting to prove that it’s
every bit as good at carrying out repetitive
tasks in the white collar world as it is on
the factory floor. AI can already write news
articles, settle legal disputes and diagnose
diseases. Any job can and will be impacted
as AI becomes more mature and prevalent
in business.
However, walk into any corporation’s
headquarters and the most obvious thing
you will notice is still… people. So many
industries (hospitality, healthcare, and
retail, to name a few) rely heavily on the
face-to-face connections between humans.
AI may be able to replace certain roles but
there is likely to be an even greater need
for human employees who can collaborate
successfully with the machine.
Fewer roles or newer roles?
Without doubt, many roles within the
workforce are set to change over the
next few years, so businesses will have
to navigate this period carefully through
reskilling and recruitment. Any employee
able to delegate certain tasks to an
algorithm can then move beyond their
traditional role, becoming more creative,
and making more valuable contributions.
Employers who recognise the value of
reskilling will foster a collaborative
relationship between human workers and [7] http://www.jfgagne.ai/talent/
AI. Instead of replacing people, AI will
provide them with invaluable tools.
Employees, much like the supply chains
they work on, are becoming increasingly
connected. Robotic Process Automation –
RPA – is a class of software that mimics
human operators to carry out rules-based,
repetitive actions. RPA is often confused
with AI and while they are different, they
can be used in conjunction to great effect.
Humanyze offers ‘people analytics’, a term
coined by company CEO Ben Waber, to
track employees via biometric ID badges.
People analytics enables employers to
understand their workers by recognising
patterns in data. Whether or not
‘understand’ is synonymous with ‘control’
is another discussion entirely.
The scramble for talent
Perhaps the most important of the new
generation of jobs created will be those that
create AI, run the programs and determine
whether or not it is appropriate to use AI
in the first place. The difficulty, of course,
is recruiting and retaining these talented
individuals. Estimates of the number
of people with the expertise to create
machine learning systems vary wildly,
from thousands to hundreds of thousands.
Recent research by Jean-François
Gagné, based on LinkedIn and academic
conferences, suggests that there are 22,000
PhD-educated researchers[7] worldwide
capable of working in AI, with just over
3,000 actively looking for work.
Regardless of which estimates are most
accurate, demand for AI skills clearly
exceeds supply. Organisations are therefore
locked in an ongoing battle for AI talent,
often poaching experts from competitors
or academic institutions. After finding that
attracting individuals was not their strong
suit, the AI enthusiasts at Amazon adopted
a strategy of acquisition. In 2013, the
company bought Ivona, a text-to-speech
startup that literally gave Echo a voice.
Creating a successful and harmonious
human and AI workforce will take more
than smart programmers, however. It will
involve a wholesale review of corporate
culture and governance so that both
humans and machines have trust in work
being produced, in the way they are being
managed and in the way that the company
values their input.
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The raceto regulation
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Across the world, governments are
demonstrating an interest in AI. In many
ways, this reflects a growing realisation
of just how disruptive AI technologies will
be, along with a recognition of regulatory
needs. In a recent report[8] by the UK House
of Lords’ Select Committee on AI, members
considered the economical, financial
and social implications of AI. The report
concluded that the UK was in a strong
position to become a global AI leader,
providing it could continue to uphold
a healthy startup ecosystem, dynamic
academic research and to design unbiased
AI. The report’s recommendations include
a growth fund for SMEs working on AI
and greater efforts to shape data culture.
Regulators, however, are generally viewed
as hindering rather than enabling
this development.
This is especially the case when it comes
to taxation. Business leaders certainly
think that it is a hindrance to growth but
how to measure impact. I would say that
we should never ever solely rely on private
companies to self regulate, or trust them to
always consider the wider impacts of what
they do.”
Too much power?
If we look at who controls AI, we are drawn
back to major vendors. What can be done to
ensure that these companies continue their
development in a way that benefits wider
society? Taxation applied equally and fairly
would be one such way. Another would be
the compilation of universal standards.
This is not necessarily something that
can or should be done by governments
alone. Collaboration between the private,
public and non-governmental sectors will
be crucial in developing acceptable and
useful AI standards. The Partnership on
AI,[10] for example, is a non-governmental
organisation set up to study and formulate
the best practices on AI technology for
everyone’s benefit. The Partnership
combines the expertise of developers,
regulators and representatives from various
industry sectors to create an open platform
for discussion.
As AI is gradually distributed throughout
society, such need for controls will become
even greater. But a serious obstacle for
globally focused regulators is the existence
of cultural differences between countries.
This is as much a problem on an individual
as an organisational level. To encourage
‘ethical’ or ‘moral’ AI, regulators are faced
with the challenge of creating a universal
code of conduct. Perhaps this is where
corporations will lead governments, by
adopting collaborative strategies and
supporting the Open Data movement.
recognise that it is unavoidable in order to
fairly distribute wealth. Friction between
governments and corporations[9] is hardly
anything new but the wider application
of AI could bring a plethora of underlying
tensions to the fore. Taxation is just one
point of contention but governing bodies
have also conflicted with tech companies
over data privacy, and undoubtedly view
their power as a threat.
The ultimate aim of AI regulators is to
encourage the benevolent development and
application of AI. In order to function in the
real world, highly capable AI also needs to
be ethical.
According to Harry Armstrong, Head of
Technology Futures at Nesta, AI’s ethical
value is determined by how it is used.
“There is definitely a willingness and
a keenness by big companies to use AI
ethically,” he says. “But there is a certain
lack of knowledge about ethics, and about
[8] https://bit.ly/2vhDmfr [9] https://bit.ly/2smCkKB [10] https://bit.ly/2IXO90F
The ultimate aim of AI regulators is to encourage the benevolent development and application of
AI. In order to function in the real world, highly capable AI also needs to be ethical.
Is your business AI-ready?Businesses are beginning to recognise the
extensive benefits that AI can offer but
taking full advantage of them requires a
deliberate reorganisation of infrastructure,
processes and corporate culture. Incumbent
businesses, especially those from
traditional industries, are particularly risk
averse. Getting over this initial hurdle
requires research, as well as a clear vision
of what problems need to be solved.
Dr Tariq Khatri, managing director of
machinable (www.machinable.com), works
with senior management teams across
sectors to identify and realise machine
learning opportunities. While his clients
are keen to solve an array of problems
within their operations using AI, he finds
himself repeatedly pointing out that AI
may not always be the best solution.
“Businesses that haven’t used analytics
much are thinking about how to use AI,”
says Khatri. “Part one of the answer is
that, more often than not, you don’t need
advanced models to solve the question.
Part two is that the problem can’t always
be solved with analytics only. I would say
for most of our clients that the answer is
20 per cent machine learning, 30 per cent
yesterday’s data science and 50 per cent
just good business management practices.”
How then can businesses make themselves
AI-ready if technology is just one part
of the equation? Here are five key
considerations:
1. Create a healthy data culture
At the most basic level, organisations
must make a conscious effort to create
a healthy data culture. Employees need
to understand the importance of data,
how it should be stored and the insights
that it can bring. The better the data,
the better the decisions. Companies can
achieve this by training their workforces
to think differently about data. Given the
availability of Massive Open Online Courses
from sites such as Coursera, this can be
done with minimal resource expenditure.
2. Choose the right vendor
The accessibility of AI through cloud
computing services has widened the scope
for business applications but choosing
the right vendor is still crucial. External
providers will compete to offer ease of
use, algorithmic ability and user-friendly
interfaces. Companies should prioritise
choosing an appropriate vendor, perhaps
even working with multiple vendors to cut
time-to-market.
3. Match overall objectives to AI vision
Before an organisation can realise a
successful AI strategy, it has to build
a clear vision of how AI is going to fit
within operations. Deciding if AI is the
right technology to invest in depends on
what problems the organisation wants
to solve. Technology should not be used
for technology’s sake, only to inform
important decisions.
4. Get the balance right
Businesses should consider the complex
interplay between humans and machines.
Once AI has been implemented,
management teams will need to maximise
the potential of working relationships
between existing employees and AI
systems. This might require the creation
of new roles, including a new generation
of Chief AI Officers.
5. Build AI trust in the organisation
As AI becomes more proficient in
understanding business functions, its
algorithms and thinking will become
more opaque and, in many cases, develop
beyond human understanding. Such a
level of machine learning will raise serious
questions around trust in AI – especially
when crucial decisions are being made
from data processes that human employees
don’t fully understand. For executives and
employees to have faith in their own AI
systems, companies will need to develop
governance structures that considers the
actions of machines in the same way that it
does its human workforce.
Each of these conscious changes will have a
domino effect as AI gradually infiltrates company
infrastructure. Friction points will be removed,
which will streamline supply chains, connect
different branches of the business and allow data-
driven decisions based on in-depth knowledge. The
end result? Insight and foresight will facilitate the
construction of coherent, cooperative systems.
For the most part, these are not the systems that
businesses use today. But by choosing the right
vendor, marrying overall objectives with an AI vision,
addressing data culture, augmenting the workforce
with AI tools and shaping an inclusive system of
governance, operational efficiency can reach an
entirely new level.
www.disruptionhub.com / 21
/Artificial Intelligence
20 / Artificial Intelligence / Summer 2018
What does AI mean for society?
ILLO
The real societal value of AI relies on the
technology being able to answer relevant,
real-world questions. AI integration will
reduce friction and meaningless labour.
It will take passengers from A to B in self
driving vehicles, gather constant data from
connected items to inform predictive and
prescriptive analytics, reallocate certain
job roles, integrate the physical and digital
worlds, and transform computational
ability. Artificially intelligent robots and
services will go from novelty to necessity.
More people will want – and need – to
use AI. However, whether or not wider
adoption will be socially beneficial depends
on governance and regulation. Ironically, it
is not technological capability that presents
the biggest obstacle.
At present, encouraged by Hollywood
blockbusters, society largely fears AI. Many
companies can create a highly sophisticated
AI solution but will reap little reward if
nobody is willing to use it. Addressing
such attitudes is a necessary prerequisite
of accelerated adoption, but how can it be
done and who is responsible?
The education system would be a good
place to start. By changing culture
through education, fear can be replaced
by understanding. Coding, for instance,
has now become part of the British school
curriculum. Open conversation can also
demystify AI technologies. Socio-political
debate will characterise the ongoing
development of AI solutions and services
as cultures clash. Russia and China, for
example, have entirely different standards
when it comes to data protection. Bringing
divided opinions under a single body of
legislation is a huge dilemma faced by AI
enthusiasts everywhere.
Jobs vs technology
Perhaps the biggest hurdle to AI acceptance
lies in its impact on the world of work. As
business gradually transitions towards
automated and tech-enabled professions,
uncertainty over unemployment will only
increase. Some jobs will be entirely lost to
automation and their previous occupiers
may not have the resources or opportunity
to reskill. How governments adapt their
social systems to accommodate and reward
the growing number of citizens who no
longer work in the traditional sense will be
critical to the widespread acceptance of AI.
So to revisit the debate between the
wary Elon Musk and the unashamedly
enthusiastic Jeff Bezos, which Silicon Valley
behemoth do we side with? The answer,
as it often does when weighing up two
extremes, lies somewhere in the middle.
Certainly, AI continues to pose considerable
challenges for every organisation. However,
in the words of David Sharp, Head of
Technology 10x at Ocado, “You should be
more fearful about not implementing AI.”
www.disruptionhub.com / 23
/Artificial Intelligence
22 / Artificial Intelligence / Summer 2018
To revisit the debate between the wary Elon Musk and the enthusiastic Jeff Bezos, which Silicon Valley behemoth do we side with?
www.disruptionhub.com / 2524 / Artificial Intelligence / Summer 2018
[11] https://on.bp.com/2wQrdyX [12] https://bit.ly/2H1FokC
Case study Case study
Beyond Limits is an AI startup that
is commercialising industrial grade
AI technology developed by NASA for
deep space exploration. Following a
$20m investment[11] from BP Ventures,
the company’s cognitive computing
software is now moving to the energy
sector to offer operational insight and
process automation.
Says Paul Stone, Technology Director
at BP, “An important challenge that
Beyond Limits can help BP to solve
is codifying knowledge and to make
systems behave more human-like so
they can adapt to changing situations.
Up until now, our systems have been
quite rigid in terms of their objectives.
If the environment or situation
changes, they become less effective.”
Stone explains that Beyond Limits’
previous work in space exploration
has a number of parallels with the
oil and gas industry, including, for
example, communication difficulties,
time delays, harsh environments and
problems with equipment. Building
systems that behave and make
decisions in a more human-like way
helps to address such challenges by
mimicking the creative problem-
solving capabilities of humans and
combining these with the speed and
accuracy of computers.
“For instance,” says Stone, “If the
cognitive computing system is looking
at data and decides that some is
missing, it can make a confidence-
based decision in the absence of all
the information, or it can look to use
data from an analogous situation.
Becoming more ‘human-like’ means
that computer systems will be able to
make decisions using imprecise data
and in ambiguous situations.”
www.beyond.ai
As a public video sharing site, YouTube
needs to be constantly vigilant about
the content it publishes. In order to do
this in the face of a constant deluge
of uploads, the website has relied on a
combination of machine learning and
human agents to flag inappropriate
or unsuitable videos. At the start of
2017, eight per cent of videos that were
tagged for extremism were removed
with under 10 views. Once machine
learning capabilities were added in
June, over half of the videos removed
for extremism had received less than
10 views. In other words, machine
learning techniques mean that far
fewer inappropriate videos reach any
viewers at all.
According to Google’s quarterly
Transparency Report, YouTube
removed 8,284,039 videos between
October and December 2017. Of this
number, almost 6,700,000 were
identified by automated tagging
systems. The vast majority (76 per
cent) were removed before anybody
had a chance to view them. The
remaining videos were tagged by the
Trusted Flagger programme and by
YouTube users themselves.
Instead of replacing employees,
machine learning algorithms enhance
the work that humans do to put out
relevant and appropriate content. Now
that more consumers are foregoing
TV[12] in favour of online viewing,
applying AI to entertainment and
content will help to ensure a positive
user experience.
www.youtube.com
6,685,731
1,131,962
402,33563,938 73
IndividualTrustedFlagger
Automated flagging
User NGO Government agency
Source: https://transparencyreport.google.com/youtube-policy/overview
Methods by which videoswere removed from YouTubeOctober - December 2017
Key takeaways and considerations
How can you make sure that your employees understand the importance of data and how it fits into an overall AI vision?
Have you chosen the right vendor to offer ease of use, algorithmic ability and a user-friendly interface?
Does AI complement or confuse your business’ overall objectives?
Do you have an appropriate management team to navigate the complex interplay between humans and machines?
Have you created a governance structure that enables you and your employees to trust AI to make crucial decisions?
www.disruptionhub.com / 2726 / Artificial Intelligence / Summer 2018
[13] https://bit.ly/2kyBjLR
Case study
Online grocery company Ocado has
been quick to adopt AI to enhance
various parts of its business. One of
the ways that the technology has been
applied is as an administrative aid for
responding to customer enquiries. By
‘triaging’ emails – making decisions
about the order of treatment –
machine learning can distinguish
between the most and least urgent
messages. For example, if a customer
changes their delivery slot to be an
hour later on the day of delivery, this
needs to be handled immediately so
that the order can be successfully
processed and received.
“A machine learning algorithm can
learn how to do this by reading lots of
emails that a human has categorised
as being urgent or not urgent,” says
David Sharp, Head of Technology 10x
at Ocado. “Over time, we expect more
and more parts of Ocado to be using
machine learning and AI.”
Ocado also uses AI to flag suspicious
activity or anomalies in transaction
data. In March, the company
announced the development of the
first fraud detection system for online
groceries[13]. The machine learning
algorithm, created using TensorFlow
and Google Cloud, can recognise if an
order has been delivered but not paid
for. Since the system was introduced,
Ocado’s fraud detection precision has
improved by 15 per cent.
www.ocado.com
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www.disruptionhub.com / 2928 / Artificial Intelligence / Summer 2018
CBInsights Report Top AI Trends
to Watch in 2018
https://bit.ly/2L7YuYq
Jean-François Gagné, Global AI
Talent Report 2018
http://www.jfgagne.ai/talent/
House of Lords Select Committee
on AI, April 2018, AI in the UK: Ready
Willing and Able
https://bit.ly/2vhDmfr
KPMG, Venture Pulse Q4 2017
https://bit.ly/2DBg5I8
McKinsey Report, June 2017, Is AI the
next digital frontier?
https://mck.co/2iCPq53
The Economist, Special AI Report,
March 2018, GrAIt Expectations
https://econ.st/2s4ZQvw
University of Washington paper,
December 2006, The History of AI
https://bit.ly/2y1KCgw
Sources
Mental health Woebot https://bit.ly/2sife8t
AI cheat sheet https://bit.ly/2LHBzEw
Government and Big Tech https://bit.ly/2smCkKB
D/SRUPTION resources
/Artificial Intelligence
disruptionhub.com
This report was produced in partnership with D/SRUPTION’s founding partners: