This article originally appeared in the August 2018 issue of CCO.

In its widely talked about 2017 State of Marketing Report, Salesforce reported that just over half (51%) of marketers were using AI in one form or another, while another quarter planned to test it by 2019.

Fast-forward to today and you’ll find newer research saying that top-performing companies are now twice as likely as their peers to be using AI for marketing (28% vs. 12%).

The question is, is your brand ready to join their ranks?

One reason that businesses may be reluctant to incorporate AI tools into their content marketing efforts is because they are uncertain about which technologies are genuinely AI-powered and which simply rely on advanced algorithms and analytics.

As Luis Perez-Breva, head of MIT’s Innovation Teams Program and research scientist at MIT School of Engineering, explains, “Most of what the retail industry refers to as artificial intelligence isn’t AI.” He says many “confuse analyzing large amounts of data and profiling customers for artificial intelligence. Throwing data at machines doesn’t make machines (or anyone) smarter.”

Rather, AI’s promise is what is often called relevance at scale. It’s the ability of machines to crunch massive datasets and data lakes – structured and unstructured – and optimize decision-making in a way that algorithm-enabled humans cannot achieve. Perhaps most importantly, in an AI-enabled system the machine learns and improves without human input.

If you can relate to this state of confusion, it’s time to examine some of the ways your marketing peers are using AI-led initiatives to make the most of the technology’s promise – and get ideas so you can do the same.

Using AI for personalization

Marketers have long practiced personalization in content marketing, developing over time more sophisticated ways of personalizing the customer journey – whether through marketing automation and progressive profiling or by using programmatic advertising to support a content path. The idea is that as we learn more about our customers or prospects and fill in information about their needs, budgets, and interests, we can create unique, personalized experiences that educate and delight them.

Now we are entering the era of hyper-personalization: the ability to personalize not just by persona, profile, or the trail of breadcrumbs people leave on your site, but by a massive set of user details and signals that get analyzed and made actionable by machines.

The retail industry is the most talked about application of AI-led personalization, but most examples you read about don’t really fit the definition of AI … they’re just really good personalization.

The examples that seem to cross over – from algorithm-driven personalization to AI-driven personalization – are those in which the AI sifts through data from multiple channels and sources, learning which signals matter in which circumstances and evolving its approach over time. The key variables that influence how one customer interacts with your brand may be completely different from the variables that define another, multiplied millions of times across each person, each channel, and each step of the process – and changing constantly.

Using AI for voice-searchable entertainment and education

A less common but exciting application for AI-enriched content? Virtual assistants. For example, Amazon’s Alexa devices offer marketers the chance to build skills on its platform, which can be used to help customers answer questions, gather information, and even control other internet-enabled devices and appliances. (To be fair, there’s disagreement about whether Alexa is an AI technology or just an advanced natural language technology – another nod to the problem of assessing AI adoption.)

amazon alexa skills screenshot

Companies far and wide are racing to launch Alexa Skills – both to inform and delight customers as well as to test out the channel’s promise. Here are just a few examples of the fascinating applications content marketers have created:

  • Entertainment: Content-rich brands can deliver entertainment and information, as Disney’s done with its Character of the Day Skill, which introduces Alexa users to one of its beloved characters from the Disney, Pixar, Marvel, and Star Wars universes.
  • Real-time news: Media companies have been among the first to offer content snippets via Alexa Skills. If you enable the NPR News Now Skill, for example, you’ll have access to a five-minute news summary, refreshed every hour.
  • Customer service and engagement: Global consumer brands are enabling e-commerce, customer service, and analytics using Alexa Skills. For instance, Capital One’s skill lets you ask Alexa, “How much did I spend at Target last month?” or “When is my mortgage payment due?”

Using AI to put email on steroids

For marketers, AI-enabled decision-making for customizing and delivering email (i.e., dynamic emails) could be a game changer.

Once upon a time, marketers would ask, “What’s the best time of day to send out our email newsletter?” Through trial and error, marketers discovered that certain days and times yielded higher open rates on average.

AI, however, allows marketers to send emails based on the open histories of individual users (or people like them in the absence of better data). And no longer will marketers need to send promotions to huge swaths of their audience. Instead, promotions can be designed uniquely for prospects based on a wide range of signals, from cart abandonment in retail to which times of day an individual is most likely to sign up for a conference. Finally, AI will enable much more customized and nuanced customer journeys. That leads to our next AI application – one that is too often misunderstood.

Using AI to write

Long decried as evidence that AI will herald in a new soulless age, machine-made content is one of the most controversial applications of AI … but, under the right circumstances, it may be the most pro-creative. Let me explain.
Under the right circumstances, AI might be the most pro-creative of all content creators says @Clare_mcd via @CMIContent Click To Tweet

As machine-made content becomes better at approximating human language, there’s a clear case for its use in content marketing. Not all content generated by marketing needs to be highly creative and witty after all. Many organizations are already using machine-generated content, such as Edmunds generating vehicle profiles based on manufacturer data and Homesnap publishing community profiles based on publicly available data. The best applications are those in which there’s a need to publish at scale and the content is somewhat modular or easily put together from pieces and parts.

And, if you’re not convinced, perhaps this will change your tune: Even The Washington Post uses machine-generated content. According to Digiday, as of September 2017, the paper’s robot writer (a solution from Heliograph) had published 850 articles and tweets like this one:

The key is in how you pair the robot to the writing. For The Washington Post, Heliograph-generated articles about local political races when the paper didn’t have the resources to assign reporters but had data to fill in the story. It also published short summaries about the Olympics in Rio via machine. (The paper reports that four employees previously took 25 hours to collect, analyze, and report on a small portion of local election results. Using Heliograph, The Washington Post created more than 500 articles generating 500,000 views.)

And therein lies the most powerful promise of AI: to release marketers from the mundane to focus on more creative and fulfilling efforts. Marvin Chow, vice president of global marketing at Google, wrote that artificial intelligence and machine learning “will spark new ideas and push the boundaries of creativity. With new tools, what will makers, artists, and musicians design? And how will that affect the marketing world we work in?” The full vision is still out of reach, but early signs point to a machine-led period of creative efficiency.

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