AI: Ready for Primetime?

BURLINGTON, MASS.—In technology circles worldwide, artificial intelligence is a dominant topic of reflection, research and increasingly, implementation. AI is delivering real value in several areas of the media production and distribution chain, but applications are limited presently. Other areas are showing tremendous potential and are now being explored. While it’s only a matter of time before the technology and our collective expertise overcome current limitations, it’s interesting to note where we are now and where we’re heading.


The most obvious application of AI today is around automated metadata extraction or content “indexing.” The process of taking content, applying an algorithm to derive time-based metadata that is then registered in an asset management system is greatly enhancing content discoverability. For example, an algorithm that converts speech to text aligns each word in the text to markers within the content, making it possible to locate where a certain word or phrase is spoken.

It’s also possible to index content to find voice signatures that identify where a certain speaker is speaking. Now you have two layers of time-based metadata that allow fine-grained searching. You can keep adding layers or “strata” that enable even more detailed searching. For example, you may want to search for any time a particular public figure said certain words within a specific time frame. AI algorithms are so advanced they can do more than identify faces—they can infer the mood of each person at any given time. As you apply more AI algorithms to a content library, you add more strata of time-based metadata, enabling increasingly sophisticated searching, while automating a time-intensive task that’s difficult and prohibitively expensive to achieve with humans.

Perhaps even more valuable is the ability to discover valuable content long after it has been archived. Often, we don’t know in advance what content will be significant. AI can help uncover valuable assets that are hiding in plain sight within a content library. With enough metadata strata you can triangulate in to find content you may not even know you have.

Often confused with speech-to-text but operating differently, phonetic search has extraordinary promise. A time-based indexing algorithm can convert text into phonemes that take you right to specific locations within a piece of content. The end result is that you can conduct keyword searches on libraries never before transcribed. The ability to find clips/content phonetically works extremely well and is in products now on the market.


Another significant AI use case is automated quality control. AI-driven QC solutions can churn through a library of clips at one keystroke and analyze a broad range of quality parameters. It can show, for example, whether a program master that is targeted for French audiences actually has French language audio tracks, eliminating the need for someone to watch an entire show track by track to know that it’s the right version all the way through—a tremendous time and resource savings.

AI can also be used to ensure the accuracy of closed captions. Without AI, manual caption checks are required. Sometimes captions are wrong in the program master or aren’t in sync with the audio. AI quality control can confirm not only that captions are there but also that they’re correct. Some QC solutions can even make needed timing corrections.

An application at the nation’s leading over-the-top media services provider shows precisely how this type of QC is implemented. One of their brand differentiators is quality, so the company runs all content through a gauntlet of automated QC services, such as audio levels and visual clarity, identifying whether a host of criteria are met to ensure the highest quality standards.


In addition to enabling better search and quality control capabilities, AI is also being used for operational business intelligence. In the pre-digital days, the media supply chain was extremely disconnected, with all steps of the process separated by physical media such as tapes. Today’s workflows are much more integrated and hold tremendous potential for operational efficiency.

But an understanding of the process is critical. To optimize your efficiency, you must track what people are doing and how they’re doing it in ways that inform the operational view of the media supply chain. With this kind of business intelligence, you can see where your pipeline is bogging down. Where are the choke points? Is it a lack of ingest capacity? Or if 30 percent of time in edit suites is taken up by rendering, it may be time to offload editing to a render farm.

Through an integrated AI-enhanced media production platform, it’s possible to measure all operations. How many people are using the system? How many are working on a specific project? Where are the technical bottle-necks and so on? For the most part, this kind of data has been impossible to collect and analyze—until now, media workflows have largely been a black box. But as media organizations seek to do more with less, more transparency into day-to-day operations is critical to garner operational metrics. AI can provide deep business intelligence to optimize production workflows.


It’s clearly an advantage to have intelligence and analytics about your audience and their viewing habits to inform business decisions. Beyond the production stages in the supply chain, AI has perhaps even more potential for optimizing content distribution, providing insights about consumption patterns that are impossible to glean otherwise.

You can see the potential for news outlets and broadcasts. As news has become a 24/7 web and social phenomenon, news organizations and journalists need to know what’s trending and what news is breaking. Understanding events in the broader world context can influence how stories are assigned and prioritized. Today, specialized cloud-based service providers use algorithms to comb the web and synthesize trends, tracking information that may be relevant to a news topic from different information sources as they unfold. This data can be dynamically updated and available to journalists in a dashboard view, for example. AI that informs the creative process by analyzing downstream consumption patterns presents extremely powerful application possibilities.


The major challenge for more widespread AI adoption is not developing the algorithms, it’s how best to use and integrate them. As we add more and more AI capabilities, how do we harness the power they hold in a way that gives tangible business benefits? How do we make the user experience elegant in light of growing dimensions of data? These are questions that will be addressed as the technology progresses and implementations evolve.

In short, AI is an area that’s ripe for innovation. Business models are still immature as companies assess how best to productize AI so it makes business sense both for users and providers. In the end, the closer we to get to the craft of storytelling the more of a human endeavor it becomes. AI is a tool that assists with storytelling; it doesn’t replace it.

Tim Claman is chief technology officer/vice president of product management for Avid.