In Part 9 of our Convergence Or Collision? AI and Content Marketing series, we’ll explore the evolutionary landscape of artificial intelligence.

Despite what we might have told you in previous posts, AI isn’t limited just to content marketing, of course. It could be argued that in this day and age, everyone has some idea of what the phrase “artificial intelligence” stands for.

While AI may span a variety of fields and specialties, let’s take a deeper look at what specific roles are currently adopting AI and where it may progress in the next five to ten years.

Continue to create and refine AI for data

As we’ve looked at previously, Automated Insights is a fantastic example of a company that has used specific sets of data, such as sports statistics, and combined them with artificial intelligence to produce human-readable content.

As Natalia Markova, senior web content strategist at Jellyfish, pointed out, AI-generated content is commonly used by blogs and news aggregators at the moment. This is because it requires structured datasets as well as a human-created template to automate content creation.

The technology is evolved enough that some of these articles are hard to distinguish from those written by human journalists.

However, this is limited to specific content pieces that are informative, or more standardized by nature, such as business or financial reporting. Machine-generated content is not evolved enough to include creative, empathetic pieces.

Along the same lines, this AI technology may be used to create all of the content around a particular product line in the near future. In fact, according to Emerj, product descriptions and related articles can be based on product information and produced entirely by machines, leading to an increase in AI-generated web descriptions for ecommerce sites and other such platforms.

Moving into the creative sphere

While it may not be the typical application of artificial intelligence currently, certain brands are experimenting with and interested in using AI for creative work, including:

  • advertisements
  • music composition and creation
  • moviemaking
  • and even painting


As this article by Adweek illustrates, Coca-Cola is exploring how brands can use AI and bots to produce advertisements.

Mariano Bosaz, Coca Cola’s senior digital director explains, “In content, what I want to start experimenting with is automated narratives.”

Essentially, Coke is looking into using AI to make the creative process more efficient – and what does that mean? It means Bosaz believes that AI can be used to create music for their advertisements, write the scripts, and even buy media.

Bots are the first step, in his view, of moving towards this fully automated creative vision.

Music to our ears

Bosaz isn’t the only one to see this future for AI. The first computer-generated score dates back to the 1950s, when Lejaren Hiller used a computer to compose Illiac Suite, a string quartet.

Fast forward to today and Jukedeck, an app that was (reportedly) acquired by TikTok’s owner ByteDance, is just one example of companies using artificial intelligence to create music.

Jukedeck is also a startup out of the U.K. that built AI technology to create music, interpret videos and automatically set music to those videos. They aimed to sell tracks to companies that needed background music for ads, games, or videos. At a fraction of the cost of hiring a musician, it made for a harmonious business decision!

There are several other tech companies that have permeated the AI-music industry:

  • Magenta, owned by Google Brain, explores “the role of machine learning as a tool in the creative process.”
  • Watson Beat by IBM, “composes music by ‘listening’ to at least 20 seconds of music, and then creates new tracks of melodies, ambient sounds, and beats based on what it learned from the original sample.”
  • Flow Machines project by the Sony Computer Science Laboratory in Paris “aims to expand the creativity of creators in music.”

Movie trail(blaz)ers

IBM Watson has made some strides in the movie business too.

While it may not be on the Hollywood Walk of Fame just yet, IBM Watson produced the first AI-generated movie trailer for 20th Century Fox’s horror movie, Morgan, in 2016.

To do this, Watson analyzed hundreds of horror movie trailers, then analyzed the completed movie and selected scenes for the editors to put together, resulting in one pretty spooky trailer. This process, done by Watson in just one day, would usually take weeks to complete.


As we consider the strides that we’ve made in terms of robotic brains and creativity, it’s no surprise that artificial intelligence neural networks are now capable of mimicking the style of a painting and applying it to another image.

As described by one of the researchers, this artificial system is “based on a Deep Neural Network that…uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimized artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.”

One layer of the neural network analyzes the content of the image in question, while the other layer analyzes its style. The result? AI-produced art that mimics the style of famous artists such as Picasso or van Gogh.

Content creation

Part of the IBM AlphaZone Accelerator program, Articoloo also helps create content by simulating a human writer.

Rather than replace the humans, Articoloo aids writers in creating high-quality, unique content. The content creators can choose the topic and length of the piece, and the algorithm will do the initial work for them. This helps to save both time and money. Articoloo also offers other content-related tools to help professional writers, and plug-ins to make blogging on API and WordPress easier.

Pretty incredible, eh? And these are just a few of the exciting new developments for AI and creative work.

In our next post, we’ll continue to explore the evolutionary landscape of AI.

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