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Pre-pandemic, the idea of using cloud services for content production was on a slow burn, with broadcasters preferring a cautious approach

to adoption due to security and reliability concerns. These were quickly overcome as the increased need for elasticity (both up and down),

scale, performance, collaboration, and evolving consumption patterns have turbocharged cloud adoption across the content supply chain.

Cloud-based virtual production, distribution, and monetization can address both economic and competitive challenges, including higher

demand for original and live content, high inflation, and lack of highly skilled resources. Plus, it lays the foundation to use artificial intelligence

(AI), machine learning (ML), and data analytics to enhance workflows and improve ROI.

The media and entertainment (M&E) business cycle that was once all facility-based now needs to include cloud services components. This

new digital business model means all internal workflow orchestration must also be on-demand. The need for collaboration across a distributed

workforce, a global footprint, and distribution to web, mobile, and connected TV (CTV) are all driving cloud services. The challenge is unifying

and testing facility-based systems with cloud services to facilitate hybrid environment applications.

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Leveraging the power of cloud in the media content supply chain

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Industry challenges and opportunities

As M&E business models adapt to a direct-to-consumer (DTC) world, it's become increasingly necessary to reorient all intermediate workflows

so they're orchestrated with customer experience in mind. This has resulted in multiple challenges to revenues and the cost of operations.

Pay-TV cord-cutting. The pay-TV market continues to shrink. As cord-cutting continues, over-the-top (OTT) distribution will grow,

including new OTT services, DTC video services, virtual pay-TV services, and existing OTT offerings. The industry understands the future is

based on OTT services rather than legacy pay-TV, and consumers are willing to pay for multiple OTT subscriptions, which will drive market

growth, support multiple winners, and keep video spending strong

Delivering high-speed connectivity. Continued cord-cutting in pay-TV has turned the attention of the industry to broadband internet

services to provide consumers high-bandwidth connectivity to support their appetite for OTT video streaming services

Increased content licensing fees. As spending on streaming content skyrockets in the media and entertainment industry, a new formula

is needed to drive a sustainable return on investment. The surge in consumer demand for streaming video content is yielding blockbuster

deals as well as a steep increase in the cost of sports rights – set to reach $15.4 billion in 2023, according to Bloomberg. To overcome these

high costs for programming, the industry must think differently about everything, from more cost-efficient ways to produce content to

improved distribution and monetization processes. This includes migrating to SaaS-based cloud services combined with AI/ML and data

analytics to facilitate workflows and create more value to offset higher programming costs.

Content creation and production cost containment vs. cloud services and content delivery fees. Remote workflow concepts are

increasing in popularity as higher demand for original and live content, compressed timelines for content creation and delivery, high

inflation, and lack of highly skilled resources have blown up budgets. These software-defined workflows (SDWs) span the media

ecosystem, allowing assets, systems, and processes to be joined and run together. They handle repetitive tasks and can optimize pipelines

by allowing content to be processed frequently and quickly. The major cloud service providers, including Amazon AWS, Google Cloud,

Microsoft Azure, and others, are investing heavily in infrastructure as a service (IaaS), platform as a service (PaaS), and (software as a

service) SaaS at all levels to address increased demand while also addressing security, latency, and workflow efficiencies using advanced

AI/ML and data analytics to create smarter solutions. But not all vendors provide an end-to-end cloud-based solution that covers all

aspects of work, meaning production teams often must move from service to service

The future of streaming. The future of streaming will not be limited to subscription business models. It will include a scaled approach with

tiers for subscription-only, hybrid, and ad-supported models that allow for an increase in the overall total addressable market. It will

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Applying AI, ML, and data analytics to the business cycle (cloud

production, distribution, and monetization)

According to the International Trade Association for Broadcast & Media Technology (IABM), about a quarter of the M&E industry had already

deployed AI/ML technology by the end of 2021, and most companies with media content business models are planning to deploy AI/ML over

the next few years.

The primary drivers of the adoption of AI/ML include the opportunity to automate routine workflows and gain increasing insights into

audiences, which are vital for DTC models. On the front end, AI/ML is helping content creators make decisions that maximize ROI, whereas

most use cases of AI/ML in content creation and production are to automate production and post-production processes. The same applies to

content management, where AI/ML automates routine tasks such as metadata tagging, image recognition, audio/video recognition, and

speech to text.

Use cases include:

include revenue coming from subscriptions, advertising, and ecommerce with the introduction of contextual actionable advertising where

an immediate transaction can take place on the connected device

Media companies equipping camera systems with more advanced AI/ML edge features to capture more context and intelligence from the

content that they are creating

End users leveraging AI/ML to automate operations to produce higher quality sports and entertainment content with fewer resources as

the demand for live content increases

Syncing and grouping video clips and automating mundane, time-consuming tasks in post-production to save time and avoid bottlenecks

Automating matching content delivery to destination profiles to improve reliability, quality of service (QoS), and quality of experience

(QoE)

Metadata tagging in content management to improve the level and detail of metadata and the value of content through search, discovery,

personalization, and content performance

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From content creation, production, and publishing to distribution and monetization, AI/ML is still in its infancy regarding the complete

orchestration of the entire business cycle. The importance of laying the breadcrumbs everywhere along the content and consumer journey

and then using them collectively to synchronize and optimize the demand and supply cannot be understated.

A media factory in the cloud

The M&E industry is at an inflection point in the transition to live and on-demand cloud services. The ultimate goal is to have a complete 100%

media factory of the future, with lean and real-time characteristics across the value chains that comprise the entire business cycle, including

production, distribution, and monetization. Meanwhile, the industry is taking a cautious approach by adopting a hybrid model where some

processes remain facility-based while others are transitioned to the cloud. The transition to cloud services allows the industry to rethink its

current workflows and innovate by leveraging advancements in AI/ML and data analytics.

Reimagine business models and operations in media, publishing, and

entertainment START

NOW

Augmenting rights management, content planning/scheduling, subscriptions, and data analytics to support advertising in content

monetization

Predicting viewer behavior and turnover, analyzing renewal history, and providing valuable insights for QoS, QoE, and proactive viewer

retention and acquisition strategy, decisioning, and management

AI script analysis can make decisions on the best content genre for a particular script

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