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AI in Media & Entertainment: Starting the Journey to Value

Cognizant
Cognizant

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.

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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.
4 / AI in Media & Entertainment: Starting the Journey to Value
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
AI in Media & Entertainment: Starting the Journey to Value / 5
6 / AI in Media & Entertainment: Starting the Journey to Value
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.
Ad

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AI in Media & Entertainment: Starting the Journey to Value

  • 1. 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
  • 2. 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.
  • 3. 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.
  • 4. 4 / AI in Media & Entertainment: Starting the Journey to Value 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.
  • 5. 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 AI in Media & Entertainment: Starting the Journey to Value / 5
  • 6. 6 / AI in Media & Entertainment: Starting the Journey to Value 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.
  • 7. AI in Media & Entertainment: Starting the Journey to Value / 7 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.
  • 8. 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. 8 / AI in Media & Entertainment: Starting the Journey to Value Quick Take
  • 9. AI in Media & Entertainment: Starting the Journey to Value / 9 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.
  • 10. 10 / AI in Media & Entertainment: Starting the Journey to Value 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
  • 11. AI in Media & Entertainment: Starting the Journey to Value / 11 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.
  • 12. 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. 12 / AI in Media & Entertainment: Starting the Journey to Value 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
  • 13. AI in Media & Entertainment: Starting the Journey to Value / 13 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.
  • 14. 14 / AI in Media & Entertainment: Starting the Journey to Value 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.
  • 15. AI in Media & Entertainment: Starting the Journey to Value / 15 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.
  • 16. 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 16 / AI in Media & Entertainment: Starting the Journey to Value
  • 17. 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. World Headquarters 300 Frank W. Burr Blvd., Suite 600 Teaneck, NJ 07666 USA Phone: +1 201 801 0233 Fax: +1 201 801 0243 Toll Free: +1 888 937 3277 European Headquarters 1 Kingdom Street Paddington Central London W2 6BD England Phone: +44 (0) 20 7297 7600 Fax: +44 (0) 20 7121 0102 India Operations Headquarters #5/535 Old Mahabalipuram Road Okkiyam Pettai, Thoraipakkam Chennai, 600 096 India Phone: +91 (0) 44 4209 6000 Fax: +91 (0) 44 4209 6060 APAC Headquarters 1 Changi Business Park Crescent, Plaza 8@CBP # 07-04/05/06, Tower A, Singapore 486025 Phone: + 65 6812 4051 Fax: + 65 6324 4051 © Copyright 2021, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners. Codex 6604