"The television and TV advertising industries are being radically reshaped by digitisation and the emergence of video streaming technologies. We take a look at how you can utilise these emerging technologies to maximise the impact of your advertising spend."

1. AI AND THE FUTURE
OF TELEVISION
ADVERTISING
EMEA EDITION - 2019

2. INTRODUCTION
CURRENT STATE OF TV ADVERTISING
WHAT YOU NEED TO KNOW ABOUT AI
CURRENT USE OF AI IN TV ADVERTISING
AI’S IMPACT ON TV ADVERTISING
FUTURE OF AI IN TELEVISION
THE WAY FORWARD
1
4
11
17
24
30
37
TABLE OF CONTENTS

3. INTRODUCTION

4. 2
The television and TV advertising industries are
being radically reshaped by digitisation and the
emergence of video streaming technologies. A
growing number of consumers are shifting to online
streaming services while TV broadcasters and TV
companies are switching to digital technologies to
retain customers.
Artificial intelligence (AI) is still not widely
implemented across the TV industry vertical.
Nonetheless, AI and machine learning (ML)
technologies are witnessing increased use by
marketing and advertising companies while TV
stations look to deliver personalised experience
through AI and ML solutions.
TV advertising is still a huge business, with
organisations spending over US$183 billion a year,
which continues to grow. According to Magna
Global, the fastest growing regions in 2019 are
predicted to be Latin America (+7.5%), Central &
Eastern Europe (+6.4%), with Western Europe
slowing to only 2.8 percent growth.
Marketers and TV stations looking to optimise
marketing strategies, as well as advertisers seeking
the highest returns on their investment, need to tap
into this burgeoning market. One way of doing this
is wider adoption of AI and ML solutions, as well
as automated solutions for programmatic ad buying
and delivery.
For their part, TV viewers will benefit from more
personalised experiences as brands and agencies
adopt AI and machine learning technologies.
TVadvertisingstrategieswillincorporateinteractive
online solutions as more consumers search for news
and entertainment online. The success of such
interactive platforms will depend largely on the
capability of machine learning algorithms to glean
consumer behaviour from user preferences and
online habits.

5. 3
Contrary to popular belief, older audiences are also
searching for news and shows online and should be
considered in the growth and expansion strategy of
any media and entertainment company.
Another trend that should be examined is the
growing use of mobile connected devices to read
news, watch shows and browse the Internet. Mobile
has surpassed desktop browsing in many regions,
and advertisers are adjusting their ad spending and
creative video strategies accordingly.
With so many platforms to choose from – digital,
radio, television, newspapers, magazines, etc. ,
spending ad dollars has never been so prevalent.
Add to these the rise of social media, corporate
and personal blogs, myriads of podcasts and
online forums for consumers to discuss brands and
products and the opportunities become positively
dizzying. Product-review sites are a bankable
industry of their own while video streaming services
such as YouTube and Netflix have disrupted
a TV business model that has been in place for half
a century.
Most of these innovative video services rely on AI
and ML algorithms to deliver content to viewers
and paid subscribers. The same is true of the
distribution of ads over these platforms. To remain
competitive, the traditional TV model will need to
explore solutions that use AI and machine learning
to deliver the same level of personalised experience,
including highly targeted ads.
Although there are more channels than ever for
spending ad dollars, advertisers want to target
specific groups, a goal to which the traditional one-
ad-reach-all model is not well suited. To help reach
their intended audiences, companies now have
tools to track and assess their ads’ performance
using Big Data solutions.

6. CURRENT STATE OF
TV ADVERTISING
DIGITAL CHANNELS ARE REPLACING
TV ADVERTISING IN TOTAL DOLLARS

7. 5
Source: PwC
Annual global TV advertising is projected to reach US$192 billion by 2021-2022. Terrestrial TV
advertising still dominates the market, but multichannel advertising and online ads are gaining
momentum, according to a report by PwC. Nonetheless, terrestrial advertising revenue will still
account for about two-thirds of global TV advertising revenue, or US$128 billion, by 2021.
Online TV
advertising
Multichannel
TV advertising
Terrestrial TV
advertising
Source: Global entertainment and media outlook 2017-2021, PwC, Ovum
Global TV advertising revenue by source (US$bn), 2012-2021
$200
$150
$100
$50
$0
2013 2015 2017 2019 2021
TERRESTRIAL TV ADVERTISING
STILL DOMINATES THE MARKET

8. 6
The areas with the highest potential growth for TV
advertising are the APAC (Asia Pacific) region, EMEA
(Europe, Middle East and Africa) and LATAM (Latin
America). According to PwC, the fourth largest TV
advertising market in 2021 will be Indonesia, growing at
CAGR of 10.4 percent by 2021.
Digital channels are gradually replacing TV advertising in
total dollars. Digital advertising sales continued to grow
in 2018, reaching 45% of global advertising revenues.
The same Magna Global report stated that non-digital ad
sales (linear TV, linear radio, print and out-of-home) were
flat in 2018 at $301 billion (U.S).
North America is still the
largest source of TV advertising
revenue, but growth is set to
slow to 2.4 percent in 2019. This
is in comparison with the EMEA
market which is set to
grow by 4.7 percent.
Magna Global

9. 7
DIGITAL
KILLED THE TV STAR
Worldwide digital and TV ad spending (in billion U.S. dollars)
‘99
0
100
200
300
400
‘00 ‘01 ‘02 ‘03 ‘04
TV Digital
‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 ‘17 ‘18 ‘19 ‘20 ‘21 ‘22
4.8
347.7
183.4
94.5
Projections for 2018 to 2022
Source: Magna Global

10. 8
The report from Magna Global estimates digital ad
spending will skyrocket to almost US$350 billion by 2022
while the TV advertising market will remain flat at just over
US$180 billion a year.
Video marketing is increasingly popular. Social networks
such as LinkedIn have introduced free video embedding
features to satisfy the growing demand for marketing
space in a video format. According to eMarketer, in 2019
UK companies will spend £14.27 billion on digital ads,
accounting for 74% of total media spending.
Moreimportantly,marketersplantoincreasetheirbudgets
for business videos. According to the report, 84 percent
of those surveyed say they plan to create more business
videos in the coming years. This looks to be a lasting trend,
especially in the US with some 60 percent of businesses
spending over a quarter of their marketing budgets on
business videos, with business videos interpreted to be
any video content produced by a company to market a
service or a product.
Trend Spotting: According to the annual In-House
Creative Services Industry Report, businesses are
increasingly developing videos and other marketing
materials internally, as opposed to using outside
agencies. Though the trend is growing, big-budget
global and enterprise creative campaigns are still
handled by agencies.

11. 9
SPENDING SENTIMENT FOR VIDEO
AND TV CONTENT TYPES
In the next 12 months, would you expect the spend on the following to increase, decrease or
maintain the same?
Mobile Video 62%
61%
48%
36%
36%
37%
49%
48%
2%
3%
3%
16%
Digital /
Online Video
Advance TV
Broadcast
Cable TV
Increase Maintain Decrease Net Optimisim
(Increase Minus Decrease)
60%
68%
45%
20%
Source: Interactive Advertising Bureau (IAB)
Online video (including TV videos and commercials) will continue growing, according to a report
by global agency Dentsu Aegis. The report highlights similar trends in programmatic ad buying for
which a growth of 25.4 percent year-over-year is predicted. Programmatic ads are using AI and
machine learning algorithms to deliver the best possible return on investment (ROI) for advertisers.

12. 10
4.8
3.8
4.3 5
2016
Global North America Western Europe Central and
Eastern Europe
Asia Pacific Latin America
2017 2018
3.6 4 4 3.5 3.6
7.6
6.6
6
4.7
4.3 4.6
11.9
7
8.9
GROWTH IN ADVERTISING
EXPENDITURE 2016-2018
(% Growth at current prices)
Overall the share of total digital ads is forecast to have reached 37.6 percent in 2018 (up from 34.8
percent in 2017) vs. 35.9 percent for TV advertising (down from 37.1 percent in 2017).
Source: Dentsu Aegis
Programmatic will increasingly
intermix with advanced
technologies such as virtual
reality and augmented reality to
deliver exceptional experiences.
Voice activation is another
disruptive technology.

13. WHAT YOU NEED TO
KNOW ABOUT AI
“THE CREATION OF INTELLIGENT MACHINES
THAT WORK & REACT LIKE HUMANS” -
TECHNOPEDIA

14. Having a computing machine to assist or
replace humans in performing tasks is a
concept that dates back to the dawn of
civilization and the rudimentary calculations
of the abacus. Before the 1940s and
1950s, technology was unable to produce
a computing device capable of outdoing
its creators in anything besides simple
computing operations.
Sincethen,wehavewitnessedtheemergence
ofpowerfulmicroprocessorsanddatastorage
devices that enable execution of complex
algorithms. Programming languages have
evolved to allow creation of sophisticated
software systems, which inevitably have
resulted in a push for the creation of “smart”
devices.
A definition of artificial intelligence by
Technopedia reads:
This is a very broad definition that invites
speculation and various interpretations. A simple
robot able to perform one or two tasks on a
production line is hardly an AI machine.
The Technopedia article continues:
“Artificial intelligence (AI) is an area
of computer science that emphasises
the creation of intelligent machines
that work and react like humans.”
The core problems of artificial
intelligence include programming
computers for certain traits such as:
12
KNOWLEDGE
REASONING
PROBLEM SOLVING
PERCEPTION
LEARNING
PLANNING
ABILITY TO MANIPULATE
AND MOVE OBJECTS

15. 13
SMART ASSISTANTS USE NARROW AI
AND MACHINE LEARNING
This broader definition is a closer approximation of how scholars and researchers envision AI. Yet
even this one is not an all-encompassing accurate definition. Why?
First, there are two basic types or categories of artificial intelligence. One is the so-called “narrow
AI”, the other is “general AI.”
Narrow AI enables machines and their core algorithms to perform specific tasks and accomplish
particular goals. For example, a highly sophisticated device such as a Mars rover uses narrow AI to
find its path on the Martian surface, take samples and recognise promising spots and routes while
exploring the planet.
Most people mistakenly call a device “smart” even when it is capable only of automating certain
tasks and performing jobs faster and more accurately than a human. Voice assistants such as Siri and
Cortana or home automation devices like Alexa or Google Home are perfect examples of narrow AI
that many consider intelligent when in fact they are not.
Mathematician Alan Turing developed a test in the 1950s with the goal of determining whether a
machine could convince a jury of humans that it has human intelligence in thoughts, words and
actions.
Much more recently, in 2014, a computer chatbot called Eugene Goostman impersonating a 13-year-
old boy passed the Turing test by convincing 33 percent of a panel of judges that “he” was human.
Many AI experts contend this was no more than a brilliant demonstration of narrow AI in which an
algorithm is capable of deceiving people about being human.

16. 14
We have no clear definition of intelligence and thinking, and this further complicates the successful
passing of a Turing test. IBM pitted its AI supercomputer Watson against human geniuses on the
game show Jeopardy!, and it swiftly defeated them. Watson is now being utilised behind the scenes
at IBM to solve issues for major brands with a focus on research and development projects in the
pharmaceutical industry, publishing and biotechnology.
Scientific disputes aside, we are now in an age where narrow AI is finding a place in fields as varied
as manufacturing, marketing and advertising, and space exploration.
The next step will be the development of general AI that will pass the Turing test by comprehending
its environment as a human would. Such a machine or device would think abstractly while planning
for and solving problems at a general level. More critically, general AI would innovate and create
items and concepts that have no precedents, just as human inventors have done over the centuries.

17. 15
PROCESSES INVOLVED IN
CURRENT AI SYSTEMS
Deep Learning
Machine Learning (ML)
Natural Language
Processing (NLP)
Expert System
Vision
Speech
Planning
Robotics
Supervised
Unsupervised
Content Extraction
Classification
Machine Translation
Question Answering
Text Generation
Image Recognition
Machine Vision
Speech to Text
Text to Speech
ARTIFICIAL
INTELLIGENCE
(AI)

18. 16
In theory, artificial super intelligence is much
smarter than human beings and possesses far
greater scientific creativity, general wisdom
and skills. Researchers disagree how super-
intelligent AI will evolve. Some believe it will
happeninstantlyandthenexpanditsknowledge
at the speed of light.
On the other hand, current AI systems need
to be supplied with basic information and
require algorithms to operate. A feasible AI-
powered system also features machine learning
capabilities enabling it to learn from experience
or through intentional information input.
Machine learning is as compromised as it is
powerful because AI systems accumulate
experience based on actions taken by humans.
Thus, there is a danger of creating a biased
system. As any intelligent or computing system
relies on data to make decisions or produce
outputs, entering biased primary data into an
AI algorithm can produce undesired and even
harmful results.
AI and machine learning technologies are still
in the early stages of development, with more
advancedsolutionssuchasneuralnetworksand
quantum computing emerging and developing
alongside AI and ML. Several issues need to
be resolved before we have trustworthy and
intelligent systems that produce unbiased and
ethical outputs.
Take for example, the “black-box phenomena.”
This relates to a fundamental problem of a
computing system’s creators knowing its input
andoutputbutnothowthemachinedetermines
the results that lead to the output. Acting on a
decision made by an AI system in this kind of
“black box” is problematic because it ignores
the reasoning capabilities of the machine and
therefore erodes trustworthiness. Researchers
are formulating solutions to this problem, but
we have more complicated issues to solve
concerning AI.

19. CURRENT USE OF AI
IN TV ADVERTISING
ADVANCED TECHNOLOGY TO DELIVER THE
RIGHT ADS TO THE RIGHT VIEWERS

20. 18
Artificial intelligence is witnessing wider
use across all creative and entertainment
industries where the retention of consumers’
attention is of critical business importance.
Research by TV Technology shows that about
35 percent of broadcast TV networks, 30
percent of cable TV networks and 17 percent
of corporate government educational TV
networks in the U.S. use some sort of AI
technology. The main fields of application
include advertising, transcribing and enriching
content in real time.
Most TV networks and stations strive for AI
solutions that help them identify successful
shows and programs and deliver customised
content in real time. In the U.S., networks such
as NBCU have adopted AI solutions which
enable them to scan TV show transcripts
and deliver relevant ads to the viewer. Such
technologies increase ROI for advertisers
and reduce total advertising times by 10
percent. They also decrease the total number
of commercials per show by as much as 20
percent, further boosting ROI and increasing
the likelihood of an ad being viewed by
consumers.
Consumershavelittlepatienceforcommercials
bombarding them on every communication
channel.Asaresult,TVstationsneedadvanced
technology to deliver the right ads to the right
viewers to retain their consumer base.

21. 19
A SURVEY BY TV TECHNOLOGY SHOWS THAT TV NETWORKS ARE
INVESTING IN THE FOLLOWING AI-POWERED ACTIONS:
AUTOMATED METADATA CREATION: 47%
AUTOMATED CLIP GENERATION/DISTRIBUTION: 36%
CONTENT QUALITY ASSURANCE/MEASUREMENT: 36%
AUTOMATED CAPTIONING: 33%
OTHER (INCLUDING IMAGE RECOGNITION): 14%
TESTED AI BUT DO NOT USE: 33%
Based on the above data, one can conclude that TV stations invest primarily in AI technology that
enables them to generate and distribute clips automatically.
These applications of AI mostly concern end-user experience. AI and machine learning already
see wide implementations in marketing TV products and video search.
Video streaming services such as Netflix do not limit their recommendations of films and shows
to only those enjoying the highest popularity. Their machine learning algorithms take into
account indicators such as multiple times viewing, rewound and fast-forwarded scenes and other
elements to determine which content is best performing. The same algorithms can be applied to
promotional videos and commercials. For example, an AI algorithm can determine which viewers
fast-forward through a particular commercial and then deliver a variation that they won’t skip.

22. 20
PRIORITIES OF PURCHASERS OF
BROADCASTING TECHNOLOGY
AI is also used to enhance the technology that
delivers videos directly to viewers, both in
compression and encoding. Usually, online video is
compressed uniformly for a particular connection
speed. This results in better compression for
simple video content like cartoons, but bigger file-
sizes for videos that are more complex, like live-
action dramas. Netflix’s Dynamic Optimizer uses
the fact that less-complex video content allows
for higher compression to decide on the amount
of compression shot-by-shot. As even the most
visually complex TV-shows feature quieter scenes,
this allows greatly increased compression without a
perceptible loss in quality. With no perceivable loss
in quality, Netflix’s Dynamic Optimizer can reduce
a 555kbps stream to 227kbps. Cloud-based video
delivery pipelines offer advantages over traditional
infrastructure; elasticity and scalability that simply
isn’t economically feasible when you’re working
with physical hardware. Even though most cloud-
services offer an impressive amount of flexibility,
there’s often still a small delay as resources are
increased. Today, AI is being used to predict these
increases in resource requirements, to reduce
delays to zero.
Unsurprisingly, broadcast technology buyers point
to multi-platform content delivery as a priority in
their media technology purchasing strategy (see
below), according to a survey by the International
Association of Broadcasting and Media. By
comparison, big data analytics and AI as well as
programmatic advertising rank low on the list of
priorities, at below 5 percent.
Source: Statista

23. 21
However, the survey figures reflect the early-
adoption stage of AI and machine learning across
themediaindustryvertical.ManyC-levelexecutives
still need to grasp that top priorities such as media
assetmanagement,cloud-basedservicesandsocial
TV will require implementation of machine learning
and AI tools in one form or another.
It’s no mystery why TV broadcasting and TV
advertising executives have placed AI on the lower
endoftheirstrategicpriorities,asubstantialnumber
remain unaware of the potential uses of AI and
ML. Many existing automations, for example, use
narrow AI without top industry managers realising
thattheirrevenue-optimisingsolutionsarepowered
by machine learning.
As mentioned, people tend to view AI systems as
either automated spreadsheets or a sort of “brain in
a box.” This is not the case. Every TV software that
automatically distributes content among viewers
or finds patterns in viewers’ behaviour rests on AI
algorithms and uses machine learning methods to
make more accurate recommendations over time.
That said, AI and ML can be utilised across a variety
of use cases ranging from content creation to idea
generation, personalisation of user experience (UX)
to search optimisation.
Companies such as IBM and 20th Century-Fox
have created movies and video using AI and ML.
In 2016, these two companies partnered to create
a “cognitive movie trailer” for the horror film,
Morgan. The basic idea behind this AI application
involves feeding a machine learning algorithm with
thousands of scenes and settings from horror films
so the software can produce a suspenseful trailer
that converts.
The same method can be used to create
an AI-generated video plot. Visuals that
touch the viewer and scenes guaranteed
to evoke emotional reactions can also
be engineered in this manner.
Creative and marketing agencies experiment with
AI differently and with varying degrees of success.
Whether algorithms can possess creativity is
debatable.

24. 22
AI-generated content is created using past
experiences of people and concepts developed by
human beings. Nonetheless, an increasing volume
of video and other content created or aided by AI
and ML is sure to come.
In TV advertising, AI is applied most intensely
in content distribution and recommendation
(i.e., content marketing). Video streaming
services such as Netflix invest in applications
utilising machine learning for scheduling and
workflow management. UX personalisation
requires complex algorithms that collect data
on consumer behaviour and preferences,
identify patterns and trends, and then validate
actionable insights concerning TV programs’
scheduling and distribution of ads.
Recommending the best content and delivering
themostappropriateadstoindividualviewersis
quite different from old-school mass marketing.
The latter delivers the same experience to the
largest possible audience simultaneously while
the former requires personalised experience
at the level of content timing as well as
brand message customisation in the form of
personalised offers and content.
An example of this application of AI and ML
learning is a recommendation chatbot used by
Sky TV (U.S). The bot utilises sentiment analysis
by IBM and recommends TV shows based on a
combination of group chats and an archive of
viewer data. The algorithm is able to process
and understand natural language and take into
account viewers’ preferred times for watching
shows and other data that accumulates as the
group-chat conversation progresses.
This is a passive example of cross-platform
use of AI to recommend TV shows. The viewer
needs to install and activate the bot before
they get any recommendations. Furthermore,
the trend toward programmatic ads is more
effective on digital channels than traditional
linear TV.
Although a worthy goal, integrated screen
planning has a long way to go before it becomes
viableforTVadvertisingdespitepersonalisation
gaining momentum across all channels.

25. CONSUMERS EXPECT A TAILORED
EXPERIENCE AND PREFER IT IN REAL-TIME
AI’S IMPACT ON
TV ADVERTISING

26. 24Source: Statista
HowAIwillimpactTVadvertisingandthedeliveryoftailoredconsumerexperiencesisafieldinitself.
Although the shift toward customised experience is impacting all major industry verticals, media
and entertainment businesses experience greater pressure. Digitisation has already reshuffled the
media and publishing industries, redirecting advertising-money flows in ways that have caused the
need for segments to diversify in order to survive.
Money Follows Eyeballs - Mobile Ad Boom Continues
Estimated Change in Annual Worldwide Advertising Spending Between 2017 and 2020
ADVERTISERS REDIRECT
BUDGETS TO MOBILE AND TV
Mobile Internet $72.6b
$6.9b
$3.0b
$2.1b
$1.1b
Desktop Internet
Magazines
Newspapers
Television
Outdoor
Cinema
Radio
-$2.4b
-$4.6b
-$7.0b

27. 25
Digital online mediums and interactive content
platforms will grow in importance as mobile
access becomes the new normal for service
providers. This cross-platform and tailored
mobile approach poses challenges, however.
On the one hand, advertisers and mediums
have access to an unprecedented amount of
data on their target audiences and viewers.
On the other, this massive data needs to be
analysed to the smallest detail in order to yield
valuable insights. Only powerful computing
devices and feasible algorithms can do the job
of analysing billions or even trillions of records
about viewers’ past behavior and preferences
while assessing consumer behavior in real
time.
Consumers expect a tailored experience on
every channel and prefer it in real time. This
experience cannot be delivered without the
help of machine learning and AI algorithms
that in turn require access to Big Data to
produce the desired results.
We are moving toward an interactive TV
experience where algorithms will take care
of multiple details concerning scheduling,
content format and even video scenes
delivered to an individual consumer.
Technology that delivers variations of a
TV commercial to different audiences or
individual viewers is already is available. An
AI system can tailor a branding message and
video content depending on factors such as
age, education, TV viewing preferences and
habits. AI can even tweak properties such as
the color scheme of a video commercial to
get the estimated best results depending on
gender, location, time of day or brand. This is
otherwise known as Advanced TV Targeting.
Achieving this on the level of programming
code is not difficult. All that is necessary is
access to large amounts of historical data as
well as real-time data about who is online and
watching in any given moment. The harder task
is to identify changing patterns and emerging
trends in consumer behavior and have AI

28. 26
respond accordingly. A tailored general AI
system is unnecessary as an advanced narrow
AI algorithm is capable of delivering a tailored
content experience.
Another area that will experience noticeable
change is the removal of language barriers in
real time. We are closer to a working Babel fish
than you might imagine. Predictive algorithms
are becoming more effective in processing
natural language expressions while others
are growing more advanced in understanding
natural language.
AlthoughAIcapableofunderstandingcomplex
content such as scientific articles is still a few
years away, the average comedy or horror
movie will be a no-brainer for an AI-powered
translation tool. Companies are getting access
to both TV content sources and TV audiences
that would have been impossible only a couple
of years ago. Thinking within the framework, or
limits, of the English-speaking market is quite
narrow, as two-thirds of the world speaks a
language other than English. Machine learning
tools that translate in real time open a whole
new world of video-content possibilities and
opportunities for TV advertising.
The future of TV advertising depends on other
factors as well. AI and machine learning can
improve video experience, but how effectively
and how long viewers engage with ads on
different channels and mediums still need to
be considered.
According to a survey by Kantar Millward
Brown, premium mediums such as magazines
and TV networks are still perceived as the
most appropriate and trustworthy to distribute
ads. Ads in magazines and on TV are “well-
received” by 53 percent and 52 percent of
respondents, respectively. Ads on desktops
and laptops, tablets and phones are embraced
by only 30 to 33 percent of consumers.

29. 27
Source: Kantar Millward Brown
Money Follows Eyeballs - Mobile Ad Boom Continues
Estimated Change in Annual Worldwide Advertising Spending Between 2017 and 2020
ADVERTISERS REDIRECT
BUDGETS TO MOBILE AND TV
53%
60%
50%
40%
30%
20%
10%
0%
M
agazine
O
utdoor
TV
N
ew
spaper
C
inem
a
Radio
O
nline
display
(PC
orLaptop)
O
nline
Search
Video
(PC
orLaptop)
O
nline
D
isplay
(Phone
orTablet)
Video
(Phone
orTablet)
53% 52%
51% 51% 44%
34% 34% 33% 30%
30%

30. 28
Obviously, any ad-optimising AI software
should take into account the above consumer
preferences in the context of a multichannel
experience. Machine learning algorithms are
used to reduce the number of commercials a
TV station is delivering to individual viewers.
Theoverallnumberofadscanstillbeincreased,
but they need to be delivered in a targeted way,
thus effectively reducing the number of ads
any single viewer receives. A well-designed
ML algorithm will increase commercials’ ROI
by delivering mostly targeted ads and reducing
unwanted ads.
Brands have been collecting information
about their customers for hundreds of years.
However, the age of Big Data opens the gates
to gathering information on a scale limited only
by privacy regulations and users’ willingness
to exchange private data for free services or
other perks.
Big Data will continue to be vital as it already
greatly impacts every aspect of marketing.
With OTT and Addressable TV, networks will
havemoreinformationaboutthedemographics
of customers as well as more precise data
about their viewing history and TV-watching
habits. This will facilitate the delivery of more
effectively tailored content and ads.
As previously pointed out, the industry cannot
trackandassessallthisdatausingspreadsheets.
Modern TV technology and smart sets allow
collection of unstructured data about details
such as how often a household switches a TV
on and off, when they start recording a show
for later viewing, which ads are unwanted
based on channel switching, and so on.
AI-powered systems can currently can provide
immediate analysis of vast datasets while
machine learning technology enables TV
stations to optimise schedules and delivery
of spots based on consumer sentiment and
behavioral patterns. Furthermore, although it
can take months to discern a trend in viewers’
sentiment, AI-enabled software can produce
insights in mere minutes based on the slightest
deviations from past consumer behavior. As
noted in the article: How AI is Driving a New
Era of TV Advertising from Advanced TV News,
“With Artificial Intelligence (AI) in their corner,
marketers will be able to optimise target sets in
a matter of milliseconds based on both online
and offline behaviours…”

31. FUTURE OF AI
IN TELEVISION
START THINKING IN A NON-LINEAR CONTEXT

32. 30
MARKETERS USING ADDRESSABLE
TV (U.S)
The future comes down to customised content and personalised experience. Certainly, there will
be automation of certain processes and increasing use of algorithms that collect information
with the goal of profiling consumers, but AI and ML will play a major role in the customisation
of advertising content and the interactive experience provided by all varieties of video delivery.
Although the number of non-addressable commercials may gradually decrease due to the
adoption of AI and ML algorithms that determine where each commercial should end, the number
of viewers and outlets will not.
How knowledgeable are you about addressable TV?
Source: Forrester
Not at all aware
6%
15%
35%
28%
18%
Regularly include
addressable in TV plans
Aware of, but don’t know
enough to use it
Knowledgeable but haven’t
bought addressable ads yet
Experimented,
but need to learn more

33. 31
About half of marketers and members of
the Association of National Advertisers (U.S)
use or experiment with addressable ads
on TV. This intuitive technology is gaining
traction. Forrester analyst Jim Nail admits
there are business-model limitations before
the adoption of addressable TV ads. Not all
ads can be delivered to very narrow target
groups; nonetheless, demand for addressable
commercials is growing.
AI has helped deliver addressable ads
from large global advertisers in big-budget
industries such as automotive, travel and
financial services. Other industries will
inevitably follow this path once the technology
matures and proves its efficiency and ROI.
Only 6 percent of the advertisers surveyed
were totally unaware of addressable TV
technology, so we can say with a high degree
of certainty that most advertisers are at least
aware of, or are planning for, personalised ads
in their long-term marketing strategies.
The pressure is on TV networks to adapt to
the new realities of tailored content as video-
streaming services such as Netflix already
delivernearly100percentpersonalisedlanding
pages for their customers. TV stations may
need to start thinking in a nonlinear context
if they are to be successful in delivering the
same level of personalised experience to their
viewers.

34. 32
TV CONTENT VIA THE
INTERNET AND CABLE
While some age groups access TV content over the Internet less regularly than others do, more than half
of the current population is watching TV online. More importantly, a growing number of consumers of
all ages switch to online to watch TV from time to time. Three quarters of the British population watch
TV with a second screen in hand (mobile device, tablet or laptop), jumping to 93 percent in the under 25
age group.
Now, more than ever, TV networks and TV advertisers have access to accurate consumer data. Cross-
platform, cross-device information is connected to timings and habits.
Collectingandanalysingconsumerdataisnolongeraproblem.TheseAIalgorithmsarealreadyinplace.
The real issue concerns real-time customisation of TV experiences in which there is a mix of linear and
nonlinear video experience. The trend toward digital, connected and mobile is challenging the linear
experience, and inevitably AI and ML tools will control most of the content delivered to viewers.
The future of video and TV content lies with interactive channels and non-linear technologies.
They are another way to provide customised experiences and make commercials noticeable to
increasingly unaware or distracted consumers who have grown used to ignoring thousands of ads
per month.

35. 33
According to a survey by MediaPost, Fox News
TVnetwork(US)broadcastsanaverageof16.52
minutes of commercials per hour. More than
40 commercials every hour is overwhelming
for the average consumer, especially when
one takes into account that with linear TV the
viewer cannot skip ads or fast-forward. Even
utilising an AI algorithm to deliver the right
ads to the right consumers results in a large
number of ads that most viewers ignore. AI
algorithms need to deliver the correct number
of TV commercials to boost ROI for advertisers
and enhance the TV experience of viewers.
This is where the future of AI in TV lies.
With AI systems already understanding natural
language expressions, interactions between
machines and humans are entering a new
age. Speaking to a device is no longer a sci-fi
scenario, and TV will have to adapt to this kind
of interactivity.
Interactivity will incorporate such early-stage
technologies as conversational AI, which
allows users to control and tweak a service
through natural language. A few TV networks
already have services that make use of devices
such as Alexa and Google Home, and we can
experience real conversational AI in a very
short time.
Additionally, AI algorithms are capable of
predicting viewers’ engagement with video
content and MIT researchers have conducted
successful tests predicting how many
comments a certain movie will generate on
social video platforms. In other words, working
AI algorithms for delivery of engaging video
content are already available.
Depending on specific and regional data
privacy regulations, AI is becoming more
effective in reading and analysing data
from different sources. An AI algorithm can
easily extract the signal from the noise when
connected to multiple data sources, not just a
particular video-streaming channel.

36. THE WAY FORWARD
WITH MORE AND MORE DEVICES BECOMING
INTERCONNECTED, THE POSSIBILITIES ARE ENDLESS.

37. 35
As the industry builds on these technologies,
video streaming services, interactive TV
and addressable TV ads will become more
interactive. Users will get recommendations by
AI-powered software while machine learning
algorithms will customise the video-watching
experience to the utmost.
The maturing of these technologies and the
growing number of Internet of Things (IoT)
devices will affect new methods of delivering
brand messages to consumers. With more
and more devices becoming interconnected
and linked to the Internet, the possibilities are
limitless. IoT devices now in use range from
smart bulbs to fridges and ovens to connected
mirrors.
Marketers can deliver messages via content
channels to a wide array of connected devices
and customise those messages while creating
consumer profiles based on the use of one or
more devices. Of course, the issue of data
privacy and personal data protection are factors
that need to be considered when crafting
strategy.
New content is emerging continuously in a
market dominated by video and in a global
connected ecosystem in which new online
content services emerge daily. More than half
of consumers are looking for new TV shows or
moviestowatch,asurveybyPwCreveals.Using
AIandmachinelearningtodeliversmoothvideo
experiences to this large segment is inevitable.
Investment in ML and AI tech also will be aided
by growing demand for video content and
consumers’ willingness to pay more for custom
video content. At the same time, 62 percent
of respondents struggle to find something to
watch amid increasing options.

38. 36
I AM CONSUMING MORE... THAN I WAS A YEAR AGO
I AM PAYING MORE... THAN I WAS A YEAR AGO
DESIRE TO CONSUME AND
PAY FOR MORE CONTENT
Consuming more content; willing to pay more for it
Nearly two thirds of those surveyed by PwC consume more video content and nearly half don’t object
to paying more for video content. However, most prefer nonlinear on-demand video where they choose
what and how to watch, including the increasing use of mobile devices to stream video. Ninety percent
of consumers under age 30 state that streaming video services play a huge role in their discovery of new
video content.
People can stream video on virtually any device that has a screen. Searching for video content on many
of these is a struggle, though. Search services and TV content creators will invest in technologies such as
conversational AI to promote content and deliver more ads to mobile users.
This investment will boost innovative methods such as streaming analytics, which aims to analyse data
streams in real time to customise an online service. AI-powered technology will grow as increasing number
of devices connect to streaming services.
Source: Dentsu Aegis
The Workforce of the Future report
by PwC finds that only 18 percent of
people in China, Germany, India, the U.K
and the U.S. are worried about a future
dominated by AI and automation. About
36 percent of respondents are confident
they will be successful in the age of
smart machines, and 37 percent see a
world full of opportunities.
VIDEO
VIDEO
72%
46%
MUSIC
MUSIC
64%
33%
READING
READING
57%
39%

39. 37
Source: Statista
Human beings are uncomfortable with fully trusting machines, and so adoption of AI in television
and video streaming will be hindered until researchers develop more advanced AI algorithms. A
survey by the British Science Association reveals that 32 percent of Britons are skeptical about AI
and 26 percent distrust AI and ML tech.
Artificial Intelligence: Blessing or Curse?
% of Adults in Great Britain who Feel the Following Ways About Artificial Intelligence
VIEWS ON ARTIFICIAL
INTELLIGENCE
MISTRUSTFUL
26%
POWERLESS
13%
OPTIMISTIC
22%
ANXIOUS
19%
ACCEPTING
18%
EXCITED
20%
DISBELIEVING
6%
FRIGHTENED
11%
SKEPTICAL
32%
INDIFFERENT
14%
INSPIRED
13%

40. 38
1. Arm your brand and your team with the proper
asset management strategy before you need it. You
may only have a small set of brand relevant assets
right now, but that number will grow. It’s critical for
multiple teams in different geographies to be able to
access, edit, and engage with those assets for multi-
channel distribution. Make sure to assess your team
structure and decide if you need to put software or
a full-time employee in place to help manage this
process. Buttoned up team and asset management
workflows equate to campaign success.
2. Align your distribution – is your social strategy a
bi-product of your TV strategy or is it leading the
charge? In many cases, brands will lead with the
channel where they have the most direct connection
with the audience. AI and ML, as shown here, will
change all of that. Targeted advertising by house-
hold through TV and in some cases by individual
through mobile channels is already possible today.
As AI becomes more and more infused into ad tech,
you will need to have a clear understanding of your
audience and make sure that the message is
consistent across all channels. Versioning and
version control can help with this effort. By having
multiple versions of the same content for different
channels, teams can better align a message to the
intended audience member(s).
3. Analytics – critical to success. Aligning
distribution and audience targeting is only possible
with the help of good analytics. Where did your ad
run? What time? How many times? Is your Digital
Asset Management system tracking this for you and
your team in one central location? For ROI and
targeting, these questions should be easily and
quickly answered. As AI becomes common place
the analytics will only get more robust and more
actionable. It’s key to establish this process now and
onboard team members who know how to interpret
and utilise that data. Analytics drive strategy and
that equals currency in a digital economy.
Because AI and ML are still emerging technologies in the TV space, it’s important to lay the ground work
for success. What does that mean? Here are five ways you can prepare teams for success as AI becomes a
regular part of the technology stack:
THE WAY FORWARD FOR AI LIES IN
THE PREPARATION

41. 39
4. Metadata – Similar to search technology, AI and ML are only as good as the data you feed into them.
Machines cannot start from scratch when it comes to learning and there is still a human element inherent
within data integrity. Adding proper identification to assets such as campaign names, general search terms,
categories, years, and so on, will not only serve your team now, but will serve well when implementing AI
technologies. This effort becomes even more essential when blockchain is introduced into the mix.
5. Target audience – Thoroughly understand your target audience and how they see your product and con-
sume TV and video programming. Understanding your audience’s specific needs, TV viewing and listening
habits will become critical to the advertising and marketing processes. For accessibility, consider having your
visual ads also transcribed into an audio format so that you are ready to air across devices.

42. 40
CONTACT US
As Adstream continues to innovate our current DAM and delivery products, we are incorporating
machine learning and AI strategy built around advertiser needs. We invite you to let us know how we can
better serve our brands and agencies and help them deliver ads that win every time.
If you’d like to tell us what you think, or want any more information, please contact
marketing.EMEA@adstream.com.

43. www.adstream.com