The Impact of #AI on Content Creation and Content Marketing

Natural Language Generation is been around for a while. Some reports indicate that since 2015 Forbes and the Associated Press were producing machine-generated content

Financial Reports and sports reports like results from the last Olympics we're generated by machines using Natural Language Generation (NLG) which is a data to text algorithm. 

AI tools such as predictive analytics, natural language processing, and generation algorithms, Markova says, “allow us to get smarter not only about content production, but also about making it work more effectively. We expect the most impactful changes in the ways organizations use content for their business objectives to come precisely from such collaboration between human talent and AI.”

Currently, Markova says, machine-generated content is being widely used by blogs and news aggregators. But, she notes, it only works in some very specific instances. “First of all, it requires a structured dataset and a clever, detailed template—created by a human—to automate content production. Second, the type of content this approach typically works for is limited to pieces that intend to be informative and accurate, rather than creative or empathetic.” A lot depends on the subject matter and type of content being created, she notes. “For example, machine-generated content is often used in business, financial, or sports reporting.”

While content creation is a key use case for AI. Content distribution is where marketers can benefit fast now and learn a lot about what, why and who they are writing for.

Simon A. Thalmann, a digital marketer and writer with Kellogg Community College, sees one of the most important applications of AI and machine learning in content marketing in the area of content distribution. “What AI and machine learning have been getting better and better at over the years—particularly online within search and social platforms—is recognizing what specific individuals want to see and will engage with.” He points to Google as an example of this, with its semantic search algorithms that go beyond simply reading the words searchers are entering into their browser—and extends to interpreting the intent behind those words “by considering the potential relationships between the terms in their query, learning as it goes.”

This presents opportunity for some and obstacles for others. As algorithms become smarter, says Thalmann, “it becomes harder and harder for content creators to ‘game the system’ via social or SEO tricks that, in the past, might have gotten their content to go viral via social or hit the top of search results.” For content marketers, that means those who create quality, high-value content will be rewarded.

Read More