Artificial intelligence (AI) is eating the world, one boring, routine task at a time. 

From navigation apps using AI to crunch a bunch of data at a super-fast speed to determine the best and fastest route from A to B, or automatic spam filters and categorizations that make email more manageable, AI is truly ubiquitous.

It was only a matter of time before AI applications in the content management space arose. And when it comes to content ops, the combination of content management and artificial intelligence is a great tool for giving workers back the time they need to perform more complex tasks that still require a human brain.

The state of play – how content management and AI are already impacting Content Ops 

AI excels at ‘understanding’ vast pools of data and automating routine tasks. This is typically orientated towards objectives of improving consumer experiences, saving the time and money invested in routine processes, and even exposing patterns that can uncover new revenue opportunities. 

These are typically seen in a content operations workflow in four areas:

1. Smart Content Analysis

AI can analyze a piece of content to identify its sentiment and overall tone, very quickly. This is important for helping content managers determine whether a piece of content is right for their audience or if it needs tweaking before it will truly engage the intended consumer. IBM Watson, for example, uses AI to automate content categorization, text labeling, sentiment analysis, keyword extraction, and more.

2. Automatic Image Tagging

A picture still tells a thousand words.  Images enhance content increase engagement. Unfortunately, there is almost nothing less engaging for workers than manually tagging image after image for search and SEO purposes. But it is still a supremely important task. And that is what makes it a great job for AI.

AI-powered automated image recognition is now smart enough to tag images in a matter of seconds—letting content workers get back to deeper work instead of routine classification.

3. Scalable Personalisation and Predictions

AI also brings scalability to another important but nearly impossible task for human staff: tracking and making use of individual user behavior. 

AI can automate the process of watching what each user on a website or app is doing simultaneously. Then, it can compile this data to look for patterns that will help it predict, based on past behavior, what each user might want next.

This information can dramatically improve personalization efforts, from serving dynamic content to making product recommendations and more. And improving personalization has never been more important. In the words of the management consulting firm McKinsey, “Personalization will be the prime driver of marketing success within five years.” In fact, they found that leaders in personalization were already able to increase revenue by 5 to 15 percent and improve efficiency on marketing spend by 10 to 30 percent. Achieving that kind of improvement automatically is a massive competitive advantage.

4. Time-Saving Content Creation Assistance

Controversially, AI can also be a big help when it comes to creating content. 

Whilst artificial intelligence still is not great at coming up with original ideas or creating nuanced pieces of content, it is catching up fast. A well-trained AI tool should be able to contribute to straightforward writing projects such as news articles, factual reports, translations, transcriptions, and editing for accuracy. 

At present, in the content creation workflow, AI is basically a tool for improving the ROI on content marketing, which can often be resource-intensive. Simply put, AI can do the legwork when it comes to research and data while human writers can take this material and do the deep work required to create high-value, relevant content for each target customer.

The shape of things to come 

Based on these areas in which content management and artificial intelligence are already coming together to improve content operations, AI may improve marketing even more in the future.

Interactions Between AI Tools

AI interactions already abound in the consumer space.  A voice-activated smart speaker to controlling house lights or audio is an everyday example. Similar interactions are in store for the future of AI-powered tools in the content operations space. It is only a matter of time before AI-enabled content management systems (CMSs) and other content platforms and tools will be able to interact with each other in smart, automatic ways to provide faster functionality and better experiences for consumers and marketers alike.

On-the-Spot SEO Improvements

Taking the idea of sentiment analysis one step further, soon AI-enabled CMSs may be able to identify opportunities for SEO improvements in real-time. This capability would empower marketing professionals to create more effective content in less time, which will outperform competitors and rank better in search engines.

Content Gap Identification

Whilst AI may not create great content on its own, it can spot a lack of it – especially if it has access to huge pools of data on customer behavior preferences.   This can then alert a business to where its content may be lacking (or where competitors’ content may be lacking). 

Both situations are a huge opportunity to fill those “gaps” and capture more traffic. AI is becoming smart enough to flag gaps and make recommendations so businesses can create fresh content that adds value and generates new leads.

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Customer service is another expensive yet necessary element of business. Chatbots have already come to the for as a way of reducing both the time and money needed to deliver customer service excellence. 

While many of today’s chatbots can address very simple questions with answers pulled from a knowledge base, the future will see a large percentage of queries – if not the majority – that do not have to be routed back to human agents. After all, it is the instantaneous and round-the-clock support that consumers are truly looking for when interacting with brand chatbots.

Bridging the gap – going headless 

At the heart of this evolution from where AI and content management is today to where it could be tomorrow, is the need to integrate content management systems with an array of new technologies driven by AI.  

In practical terms that means developing headless architectures that will enable content operations teams to explore and exploit everything from automated content analysis to smart content creation. Adopting this strategy will leave a business ready to capitalize on the next wave of AI-based innovation with minimal disruption, driving return on investment.

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While many organizations and sectors are deploying AI solutions for the first time, one industry has been gobbling up algorithms and artificial intelligence solutions – Media. Big players in media have been successfully using personalization and intelligent algorithms for quite some time. Highlighting the media industry’s large appetite for AI, Ted Sarandos, Head of Content at Netflix, said, “Our shows intentionally don’t have a unifying brand. Our brand is content personalization.”

But the applications of AI in media are not just limited to content personalization. Media teams have to deal with manual processes for everything – from tagging the media to creating multilingual subtitles. But recent advances in AI are automating many of these tasks. Developments in computer vision, speech to text and natural language processing algorithms are changing the face of media creation, distribution and most importantly, media consumption.

Voice Search

Voice is the most natural way for people to communicate. That’s why the use of voice search in media is skyrocketing. Even in India, the voice is becoming a dominant way to interact with computers – Google reported that Hindi Voice Queries are growing 400% YoY. Media companies are scrambling to meet this demand – YouTube has a voice search and recently, Gaana also introduced a voice assistant in their mobile apps.

Voice queries are very different than traditional search. People ask long questions like “Show me when Kohli scored a century” and expect to see the exact time when Kohli scored. Traditional media is not really equipped to do that – the match recording will be a huge, 4 hour plus monolithic file. The part where Kohli actually scores a century is just a 2-minute clip in this long file. This means a clip will have to be created manually, which cannot scale for all the different queries people will ask.
AI can change all that. AI can analyze the video frame by frame and the commentary word for word. Using face detection, AI can identify Kohli and the NLP algorithms can detect the time when commentators are announcing the century. This defines a short section of the video when it is most likely that Kohli has scored a century. This is a dynamically created section, without any manual editing, and can be played when the user makes the search query.

Conversant Media

Once the media can understand voice interactions, it can start to interact with you. This creates exciting new avenues for storytelling. Look at a company like Novel Effect, which has created a new experience of reading storybooks with children. As you read the book aloud, the smart voice recognition stays in sync and the AI automatically plays custom-created music like thunder, violins or songs at the right time to keep the child engaged in the story.

The media is now talking with you, understanding you and then creating something completely new. Is this still a book or something more? Is it a story or an experience? AI is unshackling media from the traditional silos of audio, video and text and fusing them together into something exciting.

Adaptive Media

Content personalization is all about the recommendation. While it suggests a video or audio that you will like, the media itself does not change for you. In the future, the media will change itself and adapt for you. With AI, production teams can give more context to media – the location, the user profile, metadata of the file and based on that the media can dynamically change.

Consider the simple example – There is a movie that has spoken profanity in it. Traditionally, editors will bleep out the profanity manually to make it suitable for younger audiences. Deep learning algorithms, in combination with the speech to text algorithms, can detect profanity and the time where it was spoken in the media and automatically bleep it with very high accuracy. This type of media accepts user inputs (the app can pop the question “What is your birth date?” to the user) and based on the user inputs, the profanity is dynamically bleeped if the user is below 18, else it is not. The key word here is dynamical – it happens on the fly, without any human intervention.

The Rise of Intelligent Content

Traditional media infrastructure cannot support all these use cases and that is why making content intelligent is a top priority of all media houses. Instead of storing media as unintelligent, monolithic files, companies can have AI take a pass at the files for intelligent labelling, transcribing and classifying. This makes the media itself intelligent and ready for the future of voice search and other exciting, new experiences.

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