AI in Media & Entertainment: Starting the Journey to Value

AI in Media &
Entertainment:
Starting the Journey
to Value
Up to now, the global media & entertainment industry (M&E)
has been lagging most other sectors in its adoption of artificial
intelligence (AI). But our research shows that M&E companies
are set to close the gap over the coming three years, as they
ramp up their investments in AI and reap rising returns. The
first steps? Getting a firm grip on data — the foundation of any
successful AI strategy — and balancing technology spend with
investments in AI skills.
June 2021

Executive Summary
2 / AI in Media & Entertainment: Starting the Journey to Value
Rising adoption of artificial intelligence (AI) has been a key
feature of the business landscape for the past few years. Now
the COVID-19 crisis has further strengthened the case for AI by
underlining the urgent need for fast, intelligent decision-making.
But what’s the current state of play on AI adoption? And how is
that set to change in the next three years?
To find out,we’ve teamed up with ESI ThoughtLab to conduct a global study of 1,200
organisations including 96 media & entertainment (M&E) companies. By drilling down into the
findings,we’ve gained unprecedented insights into M&E companies’ current strategies and
future aspirations for AI, and into what they need to do to turn those aspirations into reality.
What did our research tell us? The top line is that the industry has a long way to go to realise
the full benefits of AI. Currently, M&E companies both globally and in Europe are in the early
stages of AI adoption and maturity,well behind industries like automotive, banking,technology
and healthcare. Globally, just 1% of M&E businesses qualify as “AI leaders” against 15% across
all industries — and within Europe none make that category. It’s hardly surprising then that
the proportion of M&E companies that rate AI as being of high importance to their future is
relatively low compared to other sectors (see Figure 1).
0 10 20 30 40 50 60 70 80
Investment
Media
Energy
Consumer/retail
Insurance
All industries
Manufacturing
Telecom
Life sciences
Healthcare
Technology
Banks
Automotive
64%
77%
74%
66%
66%
64%
63%
60%
59%
52%
42%
75%
75%
Figure 1: M&E respondents’ current view of AI’s importance.

AI in Media & Entertainment: Starting the Journey to Value / 3
Looking to catch up
That said,the fact remains that the majority of the M&E companies we surveyed do believe that
AI is of high importance.And while most of the industry is currently in the early stages of AI
adoption, such as business case development or piloting, our research suggests the picture will
be dramatically different in three years’time.The findings indicate a fourfold increase globally and
a 30% increase in Europe in the number of M&E companies that consider themselves to be in the
mature stages of AI adoption— namely well-advanced in using AI to transform their businesses.
The competitive pressures in M&E intensify
What lies behind this concerted move to embrace AI? Media organisations have historically,
operated as business-to-business (B2B) rather than business-to-consumer (B2C) operations. One
consequence was that unless they were vertically integrated with a platform,they did not own
customer data firsthand. Instead, data was aggregated and interpreted for them by third parties.
As a result,without strong technology advocates or disruptive voices at senior levels, M&E
companies did not invest significantly in AI. In any case, lacking direct control over their end users’
experience,they could not have acted easily on many of the insights gained from it.
And the industry’s high barriers to entry meant it was protected from competitive pressures,
meaning M&E organisations were making enough money without AI and — until Netflix emerged
—were shielded from disruption.
All of this is now changing, with M&E companies facing ever-intensifying competition and
increasingly getting their hands on end-user data through direct-to-consumer offerings. And
what do they need to maximise the value of that data? AI.

Data management, RPA and chatbots lead the way
As M&E companies map out their route to AI maturity, they’re targeting
investment at a select group of technology areas. Our research shows
that data management, robotic process automation (RPA) and digital
assistants/chatbots — essentially basic AI — are at the top of their
technology agendas both today and in the near future, with most of their
AI-related budget allocated to these areas.
Interestingly, most are not considering the use of more advanced AI technologies like neuro-linguistic
programming (NLP) or deep learning either now or in the near future. Based on our global cross-industry
survey, we know that this stance is typical of companies in the early stages of AI adoption. Organisations
become much more likely to embrace advanced AI technologies as their AI adoption expands and its returns
start to increase.
Building the business case for AI
The M&E industry’s historically low levels of competitive pressure and lack of directly owned data help to
explain why its uptake of AI has been so low until now — and how tricky it has been to build a solid business
case for investing in it. But the sector is now at an inflection point. Given the huge amounts of data to
come, media organisations cannot afford to take their eye off the ball, and must stay focused on building AI
capabilities to make the most of that data.
However, it takes time to reach maturity — a business can’t expect to become an expert in the application
of AI overnight. To compete effectively in the future, it’s imperative to start the journey now. Imagine a world
where your competitors are able to act in an instant and automated way at a consumer-by-consumer level,
feeding each individual with the perfect blend of content and personally targeted and tailored advertising,
while you are still working on large audience segments and trying to guess what each one wants to watch. Fail
to start investing in AI now, and that’s the future you could be looking at: one to be avoided if at all possible.

Helping a global TV and media
leader use AI to boost premium
advertising revenues
Our client wanted to offer advertisers on its video on demand (VOD) platform a better
experience and higher engagement rates, in turn driving improved return on investment
(ROI) and increased ad spend while avoiding under- or over-selling. To demonstrate how it
could do this, Cognizant ran a proof-of-concept (POC) using machine learning to forecast
ad impressions around certain program types and times.
As our input we used 24 months of existing raw viewing information. After extracting key
event data — views, engagements and clicks — we created and applied models to identify
patterns in viewing and ad engagement, by correlating the event data with historical data
(such as day of the week) and contextual data (such as video asset lifetime).
After six weeks the client was able to predict ad impressions with almost 90% accuracy on
a single drama series. After a phased scale-up across more genres and channels where
we explored ad impression patterns on just over 50 comedy series, an overall average
model accuracy of up to about 90% was still achievable. The POC confirmed that data
modernisation and AI can directly improve operational efficiency, monetisation and
customer experience in TV advertising.
Quick Take

Joining the fast track
To build a stronger business case for AI, M&E organisations need to assess their current situation vis-à-
vis competitors, draw up a roadmap, and develop an understanding of how to extract data from products
and use it to drive business processes and strategic decisions. With the exception of the largest media
conglomerates, building the necessary capabilities purely in-house will be too slow-paced and costly and not
sufficiently innovative. Also, it’s very difficult to recruit people with the required skills and have sufficient scale
to give them support and an attractive career path. All of this points to partnerships and outsourcing as the
optimal approach.
Outsourcing on the rise
M&E companies fully understand the huge commitment of time and
expense required to build and run AI capabilities internally — and are
looking outside for help. Almost two-thirds of respondents already
outsource at least three or four areas related to AI. And outsourcing is
set to increase further over the next three years, with areas like model
value measurement and model scoping in the forefront (see Figure 2).
Companies also see outsourcing and/or partnerships with technology
companies as a way to increase AI capabilities and skills for their
organisation.
20%
32%
33%
15%
Beginner
Developing
plans and
building internal
support for AI
Leader
Widely using AI
to generate
many benefits
and transform
business
Implementer
Starting to pilot
AI and use a
few simple
applications
Advancer
Using AI in key
parts of the
business and
seeing gains
Figure 2: M&E respondents’ areas for outsourcing today and in three years’ time.

Today’s fiercely competitive
recruitment market can
make it prohibitive to build
an AI competency in-house,
meaning collaborating
externally is often a better
route.

How StoryFit used AI to zoom
in on female representation
in movies
It feels like women are getting a bigger say in the movies — including more lead roles and more
compelling characters. But is this true? AI startup StoryFit set out to find the real story, by using
its technology to analyse female representation in the nominees for the 2019 Oscars. The project
proved to be a powerful case study of AI’s relevance in M&E.
By applying its AI-driven software to scrutinise the roles and characters played by different
genders in the Oscar nominees, and comparing the findings with its previous analysis of some
30,000 film scripts between 1930 and 2018, StoryFit generated unprecedented insights into
women’s evolving status in the movies.
What did its AI reveal?
First, as suspected, little has changed since the 1940s; men speak more and have more turns to
speak than women do in films.
Year % female dialogue
(words spoken)
% male dialogue
(words spoken)
Best/Worst female dialogue representation
2019 31% 68%
Highest: The Favourite (73%) and Roma (63%)
Lowest: Bohemian Rhapsody (7%)
2018 31% 67%
Highest: Lady Bird (80%)
Lowest: Dunkirk (1%)
2017 29% 70%
Highest: La La Land (50%)
Hidden Figures (49%)
Lowest: Hacksaw Ridge (8%)
However, digging deeper into the data, there were noticeable changes. The range of female
emotions being shown had increased markedly as historically female characters tended to stick
to non-threatening feelings like joy and sadness, yet fear and disgust dominated this year. Female
characters also used more forceful language. Among other things, female relationships far
outweighed male in the 2019 slate of movies. And that is a huge step forward from the traditional
“Bechdel Test” based on whether two women talk to each other about something other than a man.
The results show a promising trend towards better female characters and female-led stories, even
without the screen time. To hear the whole story from StoryFit itself, click here.
Quick Take

Reasons to collaborate
As we highlighted above, today’s fiercely competitive recruitment market can make it prohibitive to build an
AI competency in-house, meaning collaborating externally is often a better route. The options include working
with professional and technical services providers — who in turn partner with leading global platforms like
Google and Microsoft, as well as with innovative niche AI specialists such as Hive and StoryFit.
The need for training in AI skills is a further reason to collaborate with a partner for the AI journey. Training
is tricky to scale in any industry: take hospitals, which often lack the capacity to carry the burden of training
doctors, and don’t have sufficient volume of patients requiring each specialism. Many M&E companies are in
a similar position. But larger professional services providers specialising in technology can deliver a ready and
sustainable supply of talent that keeps pace with the rapidly evolving technology landscape, while also sharing
lessons learned from client work in other industries.
Laying the data foundations for AI
Our research confirms that as well as lagging behind other industries
in AI, M&E is also off the pace in terms of data modernisation practices.
Currently, the sectors with the highest percentage of companies
categorised as AI leaders are the automotive, healthcare and banking
industries — and it’s no coincidence that these industries are also very
strong in data modernisation.
Take automotive. While most people associate AI in the auto industry with self-driving cars, automakers’
use of AI is actually very wide-ranging, across areas including driver-assist features, connected vehicles,
manufacturing, quality control and product design. General Motors, for example, is using AI-driven
“generative design” to shave unnecessary weight from car components, while Volkswagen is increasing
the precision of its market forecasts with AI analytics, pulling in data like household income and
customer preferences.
Targeting AI maturity
By their nature, industries like automotive had a head start over M&E in the move to AI. These sectors typically
started with more binary “cause-and-effect” data points to collect and manage from sensors detecting things
like temperature, speed and engine performance, where a particular piece of data might trigger a specific
action. That provided a basis for them to grow their AI maturity over time to tackle increasingly complex use
cases. By contrast, the starting point for M&E data is already relatively complex and nuanced, ranging across
emotional and psychological issues such as how to understand, influence and predict consumer behavior.

This difference helps to explain why M&E companies have been relatively slow starters in AI. But they’re now
looking to reclaim the lost ground. As Figure 3 shows, the high proportion of M&E businesses who expect to
be at a maturing or advanced stage of AI adoption in three years’ time points to a massive leap forward in AI
capabilities.
A dramatic ramping-up of AI capabilities is a wise move. The move from B2B to B2C business models
— powered by increasing direct-to-consumer delivery and consumption — is a seismic shift for media
organisations. It means they have to deal with enormous amounts of data, certainly many magnitudes greater
than ever before. They have an absolute need to analyse, understand and make decisions based on this data if
they’re to survive, compete and transform for the digital era.
This often means removing data siloes. Many media organisations still have multiple separate systems each
containing one part of the overall data picture. Usually these systems were built on a proprietary basis and
are challenging to integrate with or extract data from — an issue that applies especially to systems relating
to content and rights or to scheduling and media planning. Such a fragmented approach provides a poor
starting point for higher-level AI activities.
0 20 40 60 80 100
Automotive
Healthcare
Banks
Telecoms
Manufacturing
All industries
Life sciences
Technology
Energy and utilities
Consumer and retail
Media and entertainment
Insurance
Investment management 327%
306%
210%
136%
119%
117%
97%
84%
57%
52%
50%
273%
235%
% change
Now
In three years
Figure 3: Percentage of businesses expecting to reach advanced levels of AI maturity in three years’ time
Reaching advanced levels of AI maturity

How to start?
Many organisations we talk to get stuck on the problem of when to invest in data modernisation.The quandary
is, should I modernise my entire data architecture first,then I’ll have the foundation to build AI models quickly
and easily? Or should I just get started with a model and only modernise the data I really need to?
The problem with the first approach is that you may end up getting fired. You will have a wonderful data
platform but will have delivered no value to the business. Conversely, if you take the latter approach and
modernise just what you need to, you will spend much more overall and have to do a lot of re-work, as ideas
you have later might be incompatible with the data architectures you initially designed.
We suggest a third option: invest in a strategy for your target data architecture, based on proven models and
accelerators that will help you avoid common pitfalls or dead ends — but then align implementation of your
target architecture to projects that create business value. This way you can limit re-work and get to results fast.
Modernising data = AI maturity
Our research reveals a strong link between high AI maturity and effective data management, especially
among companies that identify as AI leaders. So improving data management — or, as we term it,
“modernising data”— needs to be a top area of focus for M&E companies to truly unlock the potential of AI.
The technological bottom line is that if a media company wants to invest in AI and deploy it effectively, data is
the foundation. Take Universal music, which gets a billion data points per day from Spotify. Managing all that
data — and keeping track of valuable insights into songs added and which user listens to what music and
when — are impossible without AI.
As an organisation modernises its data and advances its AI capabilities, there is a natural evolution in terms of
the buy-in. Once an M&E business is gathering and interpreting huge amounts of data, the business case for
AI becomes much easier, as there is no other way to take advantage or make sense of it. Cognizant can help
your business modernise its data and build a solid platform to support AI — opening the way to a vast array of
use cases, many of them as yet unthought of.

Reimagining Digital
Content Services
We worked with a leading global K-12 publisher to accelerate its push toward digital content
creation and distribution, using a modern digital platform. The publisher’s existing content
operating model was distributed in silos globally, leading to long print cycles of 18 to 24
months.
With increased competition from digitally-savvy players, the company’s operations team
couldn’t keep pace with user demand for fresh content. Content reuse through multiple
delivery platforms — print, web and mobile — was seen as a way to satisfy customers while
reducing time to market.
Meanwhile, the company needed to manage a global vendor network of 170 content
producers, adding to the stress on operations and business competitiveness. With content
stored across 170 systems, it was difficult to derive optimal value from these assets through
content reuse, and the organization was unable to take advantage of a greater collaborative
opportunity to create content through enhanced workflows.
By applying our observations of industry trends and a deep understanding of the business,
we developed a solution premised on the following digital principles to produce and
manage content:

❙ Content is currency, and must be managed like treasury operations.

❙ Exemplary customer experience is a non-negotiable prerequisite.

› Personalization is a must-have.

– Digitally-instrumented content operations can, and must, impact revenues.
Helping a leading media
intelligence company apply
AI to boost speed and efficiency
while reducing cost
One of the world’s top media research, data and insights organisations needed to meet
customers’ rising demand for real-time intelligence and modern, millennial-friendly
features. To do this, it decided to revamp its applications landscape — which included 600+
applications across 36 countries — and build a global, scalable platform that would harness
AI and automation to drive process efficiencies and faster turnaround.
In light of our deep domain expertise in data management and AI and strong track record
as a single partner across IT and business process services, the company chose Cognizant
to help build the solution. Applying our proven approach of “integrated transformation,” we
began by engaging with the client across technology, innovation and operations to gain a
holistic “T-shaped view” of its needs.
We then used this big-picture perspective to co-innovate to drive real-time data solutions
while realising process efficiencies and cost savings. We also set up an AI Prototype Factory
to identify use cases for quick turnaround. Today, the client is realising up to 25% cost
savings and 40% productivity improvements while saving 40% to 60% of human effort
through automated AI solutions. The result? It’s serving its customers better at lower cost.
And leading innovation in its industry.
Quick Take

Investing in people and processes — as well as data
and tech
As is typical of organisations in the early stages of AI adoption, companies in the M&E industry are still facing
challenges with IT architecture, data management and overall project management for AI projects. Data
sources for AI applications are also expected to expand over the next three years from today’s images and
text data to include moving video and high-dimensional data.
Most of the companies in our survey are intent on increasing their budget spend on AI over the next three
years, with the majority of the funding going into technology rather than people or processes. Based on our
global findings, this is once again indicative of “AI beginners” versus “AI leaders”: as companies’ AI maturity
grows, the balance of their AI investments tend to shift towards people skills and business transformation.
It’s highly significant that organisations need the brains, culture and transformational aspects as well as the
technology to progress up the AI maturity curve. In fact, more value appears to be driven by the people and
the culture than by the technology itself.
This is underlined by the fact that the companies emerging as AI leaders in our survey were the ones who
knew how to try out many things rapidly and cheaply but critically, and also knew how to move from trial to
production if appropriate. They did this by embedding technologists and data scientists within business
teams. Doing this not only gives the technologists and scientists visibility of the business challenges and the
ability to perceive the impact and value that is on the table, but also enables them to build the necessary
relationships with the business teams who will apply, help to implement and then scale out the solutions.
A related consideration is that for technology to successfully transform your business, your people, leaders,
structures and values must all be aligned. This means that organisations need to optimise their culture,
leadership and structures to enable successful use of AI and automation. The future of work involves human
employees working side-by-side with robots, intelligent machines powered by AI, automation and robotics.
We’ve been working with forward-thinking media organisations for years, bringing our capabilities and
resources to help meet their need for the right people and AI skills, technology and processes.

Value from AI grows and shifts externally as maturity rises
One of the most striking findings from our research is that as a
company’s AI maturity grows, the outcomes and value it realises from AI
both increase and change location. Since the M&E industry is currently
in the early stages of AI adoption, the areas of value are mostly internally
focused such as higher productivity, customer retention and employee
engagement. Leaders in AI, on the other hand, are able to use it to
generate value externally, driving better strategic outcomes and growth.
What’s clear is that M&E companies cannot expect to launch their first AI project and immediately see the
returns on investment start to flow in.AI takes some while to ramp up and is a long game for ROI.This means
it needs sustained commitment and broad sponsorship from the executive leadership rather than just being
treated as a project play. As in other sectors, disruptors in media are mainly data/tech companies — and
competing with these is challenging unless an organisation is prepared to truly transform its business.
The need for long-term commitment is underlined by the fact that 65% of the M&E companies in our study are
seeing returns of 0% to 5% ROI from AI adoption, and none are seeing more than 5%. In contrast, nearly 40%
of AI leaders across industries report an average ROI of over 5%.As well as targeting ROI at this level from AI,
many M&E companies may also have lower-hanging fruit in the form of use cases where other technologies
could offer returns above 5%.The best approach may be to tackle these first, and then apply AI on top for the
marginal gains needed to compete with global tech. But whether you’re using AI alone or in combination, AI
maturity is where the returns really start to flow.

Mapping out the road ahead
A fundamental industry shift towards direct-to-consumer — powered by digitalisation and intensifying
competition for audiences and content — is compelling M&E companies to mature their data and AI
capabilities. The ability to manage, interpret and act on data on an individual level in real time is now pivotal
to organisations’ future success. This means deploying AI. And while the industry is making progress with
AI, most companies are still at the relatively early stages, building business cases and developing pilots —
meaning they still have a long way to go to achieve maturity.
To accelerate and sustain their progress towards AI maturity, what companies should do now is ensure rock-
solid senior sponsorship and invest in strategic data modernisation. Those elements provide the bedrock for
testing out new use cases and running proofs-of-concept, before scaling up those ideas that fly and dropping
those that don’t. At the same time, AI investments should start to rebalance away from tech towards vital
people skills and process transformation using digital.
As you embark on this journey, don’t worry if the initial ROI from AI is hardly stellar: as our research confirms,
AI maturity brings returns that are not only higher, but also extend into strategic execution and competitive
advantage. AI is a long play, but a worthwhile one.
And the best way to start? Engage a partner who has undertaken the journey to AI maturity before with
clients in many industries.

Dr. Marcin Remarczyk
Director, CMT Consulting Europe, Cognizant
Marcin is a Director in Cognizant’s European Consulting practice based in London. He has
a cross-industry outlook with focus on media & entertainment and education, intelligent
automation, operating model and organisational design and transformation strategy and
execution. Marcin is a member of the British Association for Business Psychology and has an
interest in consulting research and thought leadership including socioeconomic aspects of
digitisation. He was the winner of the 2019 MCA Award in the Change & Transformation in the
Public Sector category for his transformation work with the BBC. Prior to joining Cognizant,
Marcin was a consultant with IBM UK Global Business Services (formerly PwC Consulting). He can
be reached at Marcin.Remarczyk@cognizant.com | www.linkedin.com/in/remarczyk/.
David Ingham
Head of Media, Entertainment & Sport Practice, Cognizant
As the leader within Cognizant’s UK Communication, Media & Technology (CMT) practice, David has
extensive experience transforming content-driven businesses. He has worked on major initiatives such
as M&A integration, rights & royalties automation and OTT data analysis, focusing on how technology
and business process can be optimised to deliver the best outcomes for the organisation. David also
manages the sport portfolio, including Cognizant sponsorships with The Football Association, Aston
Martin F1 and SailGP. Cognizant works with these organisations to demonstrate capability across fan
engagement, grassroots sport development and performance analytics. He can be reached at David.
Ingham@cognizant.com | www.linkedin.com/in/dsingham/.
Peter Elvidge
Director, Media and Entertainment, Cognizant
Peter has been with Cognizant since 2020. Director of Media and Entertainment, he’s a business engineer
at heart with a passion for harnessing disruption in the media industry. Supporting Dolby and Globecast,
Peter gained vast commercial experience across the entire media & entertainment supply chain and he
brings this to bear at Cognizant where he is responsible for building Cognizant’s brand across the media
and entertainment industry.Working closely with clients and industry association partners such as DPP
MESA and the Digital Catapult, Peter commits himself to delivering a network of innovators to our clients,
helping broadcasters see the value that tech startups will bring to a fast changing industry ecosystem. He
can be reached at Peter.Elvidge@cognizant.com | www.linkedin.com/in/peterelvidge/.
Lavanya Balasubramani
Consulting Manager, Cognizant
Lavanya is a Consulting Manager in Cognizant’s European Consulting practice based in London. She
specialises in advisory services for media clients. Lavanya is focused on digital transformation and
innovation. She partners with clients to leverage technologies like AI to transform business outcomes.
Prior to joining Cognizant, Lavanya was pursuing her passion for expanding educational opportunities for
all children through the Teach for All fellowship. She graduated from the Indian School of Business (ISB)
with an MBA in marketing and strategy. She can be reached at Lavanya.balasubramani@cognizant.com |
www.linkedin.com/in/lavanya-balasubramani-062b95100/.
About the authors

Digital Operations
Cognizant Digital Operations helps clients re-engineer, digitize, manage and operate their most essential business processes, lowering operating costs,
improving user experiences, and delivering better outcomes and topline growth. Across the practice, we are creating automated, data-driven platforms and
industry utilities. We help clients run better by applying traditional optimization levers, and we help them run differently by creating competitive advantage
through making their processes digital-ready, which often leads to more effective operating models and corresponding topline revenue growth. Visit us at
cognizant.com/cognizant-digital-operations.
About Cognizant
Cognizant (Nasdaq-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology
models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses.
Headquartered in the U.S., Cognizant is ranked 194 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn
how Cognizant helps clients lead with digital at www.cognizant.com or follow us @Cognizant.
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