The digital era turned the traditional formula for media and entertainment success on its head, ushering in new technologies that have changed how content is produced, distributed, experienced, and monetized. Audiences have more choice, flexibility, and power over what they consume, and today’s media companies have to embrace ongoing transformation or risk falling behind – or becoming irrelevant. 

A new wave of transformation is arriving with generative AI, a type of artificial intelligence that can interact with users in natural language and create novel data, ranging from story outlines, reports, and other text outputs to multimodal content like images, videos, and audio. Media and entertainment are inherently about content creation and creativity—so what does this new technology mean for the industry? 

At Google Cloud, we see tremendous opportunity for creative industries, from more efficient creation methods to improved user experiences. Let’s explore.

AI for media with Google Cloud

Google Cloud has a long history with large language models (LLMs) and other generative AI technologies—from their influence over the years on products like Document AI, to recent announcements like Generative AI support in Vertex AI, which lets businesses access and tune generative AI foundation models, and Generative AI App Builder, which lets developers build chatbots and other generative apps in minutes.  

We’ve helped our global media and entertainment customers with AI for personalization, search and recommendations, predictive analytics, and much more — and with generative AI now on the rise, we have some ideas to help media leaders, technologists, and creators think about and prepare to utilize powerful AI in their work. 

Three lenses on innovation in media

The media and entertainment industry is increasingly diverse and complex, with companies spanning over-the-top (OTT) subscription streaming services, 24-hour linear channels, live broadcasts of sporting events, digital journalism, traditional publishing, short-form user-generated social video, and more. More and more, the boundaries between these segments of the media industry are blurring — but common to them all is the focus on providing compelling content in an engaging audience experience that can be directly or indirectly monetized.

With this in mind, we suggest media and entertainment companies look at the application of innovative technologies like generative AI through the following three lenses:

1. Improving content creation, production, and management
2. Enhancing and personalizing audience experiences
3. Improving monetization

Improving content creation, production, and management

Generative AI democratizes many aspects of content creation, opening new ways to create written material, illustrations, sound effects, special effects, and more. Its recent maturation has been so rapid, some in the media industry have expressed concern that generative AI implies the end of creative professions. We think the opposite is more likely: just as photography, audio recordings, and computer generated images have enabled new modes of creativity, rather than making old ones obsolete, generative AI has the potential to both enable new forms of expression and enhance familiar ones. 

For example, journalists could use generative AI to speed up research by helping them synthesize and analyze large volumes of information, or to help them create initial drafts or summaries of editorial content. Film and television producers could leverage the technology to accelerate the post-production editing process, with new AI-enabled interfaces for rapidly adjusting or enhancing scene details such as lighting and color. Broadcasters could use generative AI to make vast libraries of video footage searchable and accessible for use in telling more compelling stories. The potential use cases go on and on.

Far from undermining incredible creative professions, generative AI is poised to free writers, artists, editors, and many others from the tedious and mundane aspects of their work, empowering them to focus more of their time on creativity.

Enhancing and personalizing audience experiences

Every media organization in the world today faces the reality that for most consumers, switching costs are extremely low. This puts incredible pressure on these companies to invest in delivering low-friction and compelling audience experiences that help mitigate subscribers from churning and viewers from abandoning content experiences for competitive platforms. 

Generative AI can help media companies engage and retain viewers, such as by enabling more powerful search and recommendations on their digital content platforms. With its increasingly multimodal capabilities extending from natural language to both audio and video content, generative AI is well-positioned to power more personalized audience experiences. 

Consumers often complain about “the paradox of choice” or their inability to find something interesting to watch on streaming platforms that have incredibly vast libraries of content available on demand. Imagine a not-too-distant future wherein a consumer can simply ask the content platform they’re using to help them find a specific show to watch based on mood, specific types of scenes, combinations of actors, award nominations, or practically anything they can think to ask. And that’s just the tip of the iceberg — imagine generative AI’s potential to curate, assemble, and even create personalized content for a viewer to consume!

Improving monetization

As consumers’ content consumption further expands from traditional theatrical and linear television programming to include digital offerings across an array of platforms, devices, and content types, media companies face the challenge of maintaining and improving monetization. The conventional economics and approaches to advertising and subscription models are proving, in many cases, not to deliver sufficient ROI. 

Generative AI has the potential to help media companies improve their monetization of audience experiences. As mentioned previously, enhanced personalization can play a role in mitigating churn, which in turn can help sustain and grow subscription and advertising revenues. Going beyond this, generative AI can be leveraged to drive even greater advertising revenues via more targeted, contextual, and personalized advertisements. Imagine both display and video advertisements that are generated on the fly to personalize product specifics, messaging, style, colors, and innumerable other characteristics to drive greater engagement and higher click-through rates (CTR), and thus higher advertising CPMs (cost per thousand impressions).

Coming up next

Generative AI presents a significant opportunity for media companies to fundamentally transform content creation, engagement, and monetization. Compelling services are already on the market — but there is far more to come. 

Google Cloud continues to build on its deep experience and expertise with AI, and we are committed to working with the industry to develop compelling, accessible, trusted, and responsible AI solutions that will drive meaningful business outcomes. We are excited to create the future together with our global media customers and partners across the ecosystem. To learn more about this disruptive topic, read “Debunking five generative AI misconceptions” from Google Cloud vice president of AI & Business Solutions Phil Moyer, or explore our Trusted Tester Program for generative AI.

Financing digital transformation poses a significant problem for many enterprises we work with. It’s not because the C-suite doesn’t support it: the need to become more agile, digital, and data-driven is nearly ubiquitous in executive public pronouncements. But most large organizations look at IT investment in a way that’s ineffective for a successful digital transformation effort.  

A varied combination of  factors affect an organization’s success in the digital ecosystem, and a lack of alignment between IT and business leaders can hamper even the most digitally sophisticated organizations. Empirical research by the Apigee Institute shows clear evidence that a solid digital transformation effort, aligned across business and marketing, whose progress is carefully tied to core enterprise metrics, must be supported by an investment strategy that uses a “real options” portfolio approach.

Let’s explore ways to think about funding digital transformation.

Planning for the Unimaginable

Common IT investment strategies are focused on straight-line net present value (NPV) business case calculations, which emphasize linear solutions and impacts. These methods ignore the value of enabling a range of options and potential future use cases, which an API program, with its reusable, highly consumable digital assets, makes available for the enterprise. In addition, these methods often ignore the value of the new categories of business data generated by such programs, as well as business capabilities that were impossible to imagine before the capabilities came online.

One media company we’re working with was astounded by a project that now makes key sales provisioning available to large distribution partners within two months, when previous estimates predicted two years and many millions of dollars, each.

More importantly, that functionality will now be reused across all of their numerous current partners and sales channels, and is enabling exciting new partnerships that were too costly before. This functionality was an unexpected by-product of their original API effort—it wasn’t even the main focus of the initial project.  

There was no way that the impact of this capability or its implications on new business could have been predicted at the outset, and no way to formally model the outsized impact this functionality will have on the enterprise. Any attempts to do so would have been deemed ludicrous.

Breaking from the Status Quo

So how do you fund an API-driven digital transformation, which by nature emphasizes speed and optionality to unlock new ways of doing business, instead of the business-as-usual single-use, point-to-point integrations that perpetuate status quo thinking? For a world where the latter is the dominant paradigm and you don’t have a clear C-level mandate, here are three suggestions.

1.  Enable your web/e-commerce teams. Many such teams find their responsibilities expanded to include mobile, kiosk, and many other digital initiatives, while being trapped in the traditional web server, do-it-yourself paradigm. These teams are actually quite open to technology, and often harbour weekend hackers who love to explore the new capabilities that are expanding the digital ecosystem at light speed. Tapping this passion by funding new approaches and quickly realizing immediate projects can create first the technical, and then the business proof points that resonate with a strategy team. These capabilities must be nurtured to move out of the lab into production, where real results are created.  

2. Focus on partnerships. Opening and accelerating  your relationships with partners via APIs (read more about partner API initiatives here) is high-impact and relatively easy to model. This business area can prove out an API platform, and in our experience creates clear business value that can support future justifications for other business units. What’s best about these wins is that sales operations groups are typically very tactical and demanding, so when they see the value, these groups will send a message to the rest of the organization that APIs have business value. Further, your enterprise’s core partnerships have the focus of executive attention. Adding value here will elevate the stature of the API program in the C-suite.

3. Work on your CFO/financial organization. Using your successes in e-commerce and partnerships, you can propose a more robust investment using a real options approach that leads to treatment of APIs as products. This path is predicated on what research has proven to work: the adoption of a digital KPI target, coupled with a portfolio of programs whose net result is to achieve the target KPI.  

Building Toward the Big Vision

But what about the visions of ground-breaking business models, ubiquitous connectivity, and enablement of an ever-shifting set of opportunities on big platforms and with unlimited partnership possibilities? Yes, these are still the endpoints to work toward, and fundamentally comprise the strategic reasons to embark on a digital transformation. If your enterprise has the C-Level leadership (in the form of a CDO or CDO-like figure), supported by a well-funded, sophisticated investment approach, by all means, build that new world now.

However, should that vision be shared only within your business unit, a forward-thinking IT group, or a dedicated innovation team, the most important thing to do is to build your team’s capability—and your program’s credibility—while delivering value along the way. The approach we suggest here has been proven in many of the organizations we work with; once your organization begins working faster and at lower costs by orders of magnitude, you’ll have created enough leverage to change the way your organization invests in its API program.

Data-savvy businesses increasingly rely on images and videos for critical functions, and yet are challenged by the sheer mass of information—more than 3.2 billion images and 720,000 hours of video are created daily. This explosion in visual data has paved the way for the growth of computer vision, a form of artificial intelligence (AI) that enables computers to “see” the world similarly to the way people do, but with unblinking consistency, and greater accuracy. 

The transformational impact and value of computer vision solutions are significant and has been a guiding objective for companies and AI developers. And yet, even as the applications for computer vision increase dramatically, architecting and implementing vision AI solutions remain highly complex. Visual data, such as images and video, are made up of thousands of pixels of information that represent millions of different patterns and meanings, which can make interpreting even a single image overwhelming from a computational perspective.

Many organizations struggle with deployments and fail to operationalize vision AI solutions due to development delays, machine learning and data science hiring challenges, inaccurate output, a lack of integration with existing infrastructure, difficulty of use, and high cost. Plainsight, with the power of Google Cloud resources, is addressing all these challenges and helping businesses by enabling the deployment of vision AI within enterprise private networks that can be managed easily and scaled economically.

Plainsight has announced availability of its vision AI platform on Google Cloud Marketplace. Businesses can now easily deploy end-to-end vision AI to private clouds to realize the full value of their video and other visual data for accurate, actionable insights across diverse use cases.

Delivering on the Promise of AI: Seeing What’s Hiding In Plain Sight

For organizations to integrate AI and machine learning into their businesses successfully, the technology must be powerful enough to solve real challenges, yet fast, easy, and accessible enough to ensure the innovation potential is realized. Plainsight on Google Cloud delivers the power of enterprise vision AI that’s quick and easy to use with Google Cloud resources that enable global scale, increased security, bolstered privacy, unified billing, and cost savings. 

To streamline vision AI workflows, Plainsight facilitates the entire pipeline, from visual data ingestion and annotation, through continuous model training, deployment, and monitoring for easier innovation and faster time-to-production. Our platform accelerates vision AI development in a manner that is complete, accurate, and accessible to non-technical business leaders. We believe that AI should be available and accessible to anyone and everyone—so that teams across entire organizations can reap the benefits. 

By integrating Plainsight into their private networks, companies worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. These use cases include: social distancing monitoring, medical imaging, drug compound screening, defect detection in manufacturing processes, identifying gas leaks, or even livestock counting and crop health monitoring for agriculture, to name a few.https://www.youtube.com/embed/A7U_0UkjvEg?enablejsapi=1&

We enable customers so they can create successful solutions that enable them to clearly see their business from all angles and to take advantage of the knowledge visual data can reveal by simply and quickly operationalizing practical vision AI applications.

AI-Powered Dataset Creation, Automated Model Training & Easy Deployment Without A Single Line Of Code 

For vision AI applications, success is inextricably dependent on the quality and quantity of the datasets required to train the relevant models. To aid enterprises in this vital stage, the Plainsight platform provides built-in data annotation for the fast and easy creation of datasets. This includes AI-powered features that accelerate the speed and quality of labeling such as SmartPoly, for the automated polygon masking of objects, TrackForward, to predict and automatically label objects from frame to frame in video annotations, and AutoLabel for automated object recognition and labeling based on pre-trained machine learning models, to highlight a few.

In addition, to ensure the success of AI integration, we significantly reduce time-intensive processes with Plainsight vision AI’s automated machine learning with continuous model training and easy deployment capabilities. In just a few clicks, users can leverage optimizations for the most reliable model training without endless experimentation cycles. And, models are easily deployed at scale all within one, easy-to-manage model operationalization process for the business.

Growing With Google Cloud

Plainsight is a vision AI innovation leader, developing solutions that address unmet needs for challenger brands and Fortune 500s across vertical markets. As a team recognized for succeeding where others have failed, our expanding partnership with Google Cloud provides a powerful combination that helps customers see and activate the value of their visual data with a suite of services in a secure and private manner.

Our vision AI Platform simplifies building and operationalizing AI to solve business problems enterprises are facing every day—and the demand is increasing. To accelerate our journey to faster, more accessible AI for enterprises, we knew we needed strong support to grow Plainsight and scale our backend tools to match our vision. 

Google Cloud delivered everything, and more, in one program. The Startup Program by Google Cloud provided the technology and services for scale and the support we needed to maximize the value the Program provided us. The Startup Program has been a springboard for architecting Plainsight vision AI in the cloud, accelerating our goals and optimizing innovation, efficiency, and growth. The team also helped us optimize Google Ads campaigns, fueling adoption of Plainsight. 

After launching the SaaS version of Plainsight Data Annotation in November 2020, we grew our user base by nearly 110x in just three short months. Google Ads has also dramatically increased website traffic, growing new users by nearly 5.75X and page views by over 5X. The Google team helped us identify where Google Cloud offerings could be leveraged instead of developing in-house solutions and offered best practices that enabled us to deliver faster on our initiatives. 

Kubernetes was already the underlying component of our platform and leveraging Google Kubernetes Engine (GKE) as a managed service removed a layer of complexity. By combining GKE and Anthos, we were able to standardize our deployments, aligning to how our customers leverage Anthos for enterprise applications in their own organizations. In addition, as a fast-moving, customer-centric company we use Google Workspace to help us centralize and manage our day-to-day work internally. By leveraging multiple products across Google’s ecosystem, we take advantage of a holistic partnership that has helped our business tremendously as we scale. 

Leveraging Google’s Partners for Strategic Consultation

To facilitate this expansion of our partnership with Google and to maximize our use of Google Cloud services, we are working with DoiT International, a Google Managed Services Provider and 2020 Global Reseller Partner of the Year. DoiT provides us with ongoing technical consultation for cloud-native architecture, Google Cloud Marketplace integration, production-grade Kubernetes support, Google Cloud cost optimization, and technical support. The DoiT team has been invaluable in compiling best practices, tips, and strategies from their vast experience with various cloud customers to ease our Marketplace integration and is providing input for infrastructure strategy to support our continued rapid growth.

Plainsight Delivers Enterprise Vision AI Through The Google Cloud Platform Marketplace

Plainsight vision AI is now available to Google Cloud Customers on Google Cloud Marketplace enabling organizations across industries to deploy private Plainsight instances within their own environments. Marketplace customers will benefit from Google Cloud privacy, security, scalability and unified billing through their Google Cloud account. 

Combining the powerful benefits provided by Google Cloud resources with Plainsight’s vision AI Platform into private networks, enterprises worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. 

Through our Google partnership, we’re able to leverage a powerful foundation that allows us to rapidly innovate, scale and accelerate delivery on our vision AI platform capabilities. By executing on our vision to make AI easier, faster and more accessible for all users across entire enterprises, we’re helping businesses see more and by seeing more, they’ll have the power to solve more. 

If you want to learn more about how Google Cloud can help your startup, visit our page here where you can apply for our Startup Program, and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.

Our customers tell us they don’t just want to migrate their applications from point A to point B, they want to modernize their applications with cloud-native technologies and techniques, wherever those applications may be. 

Today, we’re excited to tell you about a variety of new customers that are using Anthos to transform their application portfolio, as well as new cloud migration, API management, and application development offerings:

• New customers leveraging Anthos for a variety of on-prem, cloud and edge use cases
• The general availability of Migrate for Anthos
• Apigee hybrid in general availability
• The general availability of Cloud Code

Accelerating app modernization with Anthos

Anthos was the first open app modernization platform to offer a unified control plane and service delivery across diverse cloud environments—managed cloud, on-premises and edge. Since it became generally available in the spring, organizations across a variety of industries and geographies have turned to Anthos to bring the benefits of cloud, containers and microservices to their applications. 

According to the findings from Forrester’s Total Economic Impact study, customers adopting Anthos have seen up to 5x return on investment based on the savings from ongoing license and support costs, and the incremental savings from operations and developer productivity. For one customer in the financial services industry, rolling out new features and updates to their core banking application used to take at least a quarter. Now with Anthos, they were able to eliminate months long development and release cycles, and roll out on a weekly basis. That’s a 13x improvement on time to market. 

This week, several new European Anthos customers will take the stage at Next UK to talk about how they’re using Anthos to transform their IT operations. 

Kaeser Kompressoren SE of Coburg, Germany, is a provider of compressed air products and services. The company needed a consistent platform to deploy and manage existing on-prem SAP workloads, like SAP Data Hub, and also wanted to be able to tap into other services running in Google Cloud to get more value from those environments. 

“Application modernization is enabling business innovation for Kaeser,” said Falko Lameter, CIO. “To gain better insights from data, we knew we needed to incorporate advanced machine learning and data analytics in all our applications. We chose Google Cloud’s Anthos because it offered the flexibility to incrementally modernize our legacy application on-premises without business disruption, while allowing us to run other applications on Anthos in Google Cloud and take advantage of its managed data analytics and ML/AI services.”

Then there’s Denizbank. Based in Turkey, Denizbank provides a variety of commercial banking services, and established the first Digital Banking Department in Turkey in 2012. Denizbank turned to Anthos for an open application modernization platform to help it develop its next-generation mobile banking applications.

“We operate in 11 different countries and have to comply with various regulatory requirements like data locality and sovereignty, which mandates some or all applications to reside on premises in certain countries, while the rest of the apps can move to the cloud in other countries,” said Dilek Duman, COO of DenizBank. “We chose Google Cloud’s Anthos for its flexibility to modernize our existing application investments with ease, and to deliver AI/ML powered software faster while improving operational security and governance. Anthos gives us the ability to have a unified management view of our hybrid deployments, giving us a consistent platform to run our banking workloads across environments.” 

Anthos is even starting to be deployed to edge locations, where, thanks to its 100% software-based design, it can run on any number of hardware form factors. We’re in advanced discussions with customers in telecommunications, retail, manufacturing and entertainment about using Anthos for edge use cases, as well as with global hardware OEMs.

Move and modernize with Migrate for Anthos

In addition to leveraging cloud technology for their on-premises environments with Anthos, customers also want to simultaneously migrate to the cloud and modernize with containers. That’s why we’re happy to announce the general availability of Migrate for Anthos, which provides a fast, low-friction path to convert physical servers or virtual machines from a variety of sources (on-prem, Amazon AWS, Microsoft Azure, or Google Compute Engine) directly into containers in Anthos GKE.

Migrate for Anthos makes it easy to modernize your applications without a lot of manual effort or specialized training. After upgrading your on-prem systems to containers with Migrate for Anthos, you’ll benefit from a reduction in OS-level management and maintenance, more efficient resource utilization, and easy integration with Google Cloud services for data analytics, AI and ML, and more. 

DevFactory aims to offload repetitive tasks in software development so that dev teams can focus on coding and productivity. As advocates for optimization through containers, they found Migrate for Anthos a key way to help deliver on their goals:  

“We usually see less than 1% resource utilization in data centers. Migrate for Anthos is a remarkable tool that allows us to migrate data center workloads to the cloud in a few simple steps,” said Rahul Subramaniam, CEO, Devfactory. “By automatically converting servers and virtual machines into containers with Migrate for Anthos, we get better resource utilization and dramatically reduced costs along with managed infrastructure in the end state, which makes this a very exciting and much-needed solution.” 

Migrate for Anthos is available at no additional cost, and can be used with or without an Anthos subscription.

API-first, everywhere, with Apigee hybrid 

To drive modernization and innovation, enterprises are increasingly adopting API-first approaches to connecting services across hybrid and multi-cloud environments. To address the need for hybrid API management, we’re announcing the general availability of Apigee hybrid, giving you the flexibility to deploy your API runtimes in a hybrid environment, while using cloud-based Apigee capabilities such as developer portals, API monitoring, and analytics. Apigee hybrid can be deployed as a workload on Anthos, giving you the benefits of an integrated Google Cloud stack, with Anthos’ automation and security benefits. 

Gap Inc. uses Apigee to publish, secure, and analyze APIs and easily onboard the development teams working with those APIs. Apigee hybrid will help Gap Inc. overcome the traditional tradeoffs between on-premises and cloud, providing the best of both worlds.   

“With Apigee hybrid, we can have an easy to manage, localized runtime for scenarios where latency or data sensitivity require it. At the same time, we can continue to enjoy all the benefits of Apigee such as Apigee’s developer portal and its rich API-lifecycle management capabilities,” said Patrick McMichael, Enterprise Architect at Gap Inc. 

Simplifying the developer experience

Google Cloud application development tools are designed to help you simplify creating apps for containers and Kubernetes, incorporate security and compliance into your pipelines, and scale up or down depending on demand, so you only pay for what you use. 

With these goals in mind, last week, we announced the general availability of Cloud Run and Cloud Run for Anthos. Cloud Run is a managed compute platform on Google Cloud that lets you run serverless containers on on a fully managed environment or on Anthos. With Cloud Run fully managed, you can easily deploy and run stateless containers written in any language, and enjoy serverless benefits such as automatic scale up and scale down and pay-for-use—without having to manage the underlying infrastructure. 

Cloud Run for Anthos, meanwhile, brings those same serverless developer experience to Anthos managed clusters, giving developers access to a modern, serverless compute platform while their organization modernizes its on-prem environment with Kubernetes. 

Easier Kubernetes development with Cloud Code

Today, we’re excited to announce the general availability of another important member of the Google Cloud application development stack: Cloud Code, which lets developers write, debug and deploy code to Google Cloud or any Kubernetes cluster through extensions to popular Integrated Developer Environments (IDEs) such as Visual Studio Code and IntelliJ. 

Developers are most productive while working in their favorite IDE. By embracing developers’ existing workflow and tools, Cloud Code makes working with Kubernetes feel like you are working with a local application, while preserving the investment you’ve made to configure your tools to your own specific needs. Cloud Code dramatically simplifies the creation and maintenance of Kubernetes applications.

In addition, Cloud Code speeds up development against Kubernetes by extending the edit-debug-review “inner loop” to the cloud. You get rapid feedback on your changes, ensuring that they’re of high quality. And when it comes to moving code to the production environment, Cloud Code supports popular continuous integration and delivery (CI/CD) tools like Cloud Build. 

Finally, with Cloud Code, diagnosing issues does not require a deep understanding of Kubernetes, thanks to connected debuggers and cluster-wide logging that help you address issues all from the context of your favorite tool. 

Toward modern, efficient applications

Application modernization means a lot of things to a lot of people. Depending on your environment, it can mean updating VMs to containers and Kubernetes, it can mean moving them to the cloud, or it can mean distributing them to edge locations and unifying workloads with consistent API and service management. For others, application modernization means using cloud-native tools and concepts like serverless and CI/CD. Whatever your definition, we can help you realize your business and modernization goals, achieving greater agility while improving overall governance.