While there are plenty of dire-sounding discussions taking place these days around artificial intelligence (AI) and machine learning—and their potential to disrupt the world as we know it—this isn’t technology of the future. It’s already gaining traction across multiple industries and professions, including with content creators. New technologies are promising to upend the traditional ways in which content is conceived, produced, and disseminated. Examples already exist. 20th Century Fox used IBM Watson to create a trailer for Morgan. A Copyblogger article from as far back as 2015 noted that both Forbes and the Associated Press were producing machine-generated content.
These examples are likely to both thrill and chill content marketers, depending on where they’re perched along the content creation continuum—including the need to generate an increasing volume of content and to make a living from creating that content. For now, though, there is fortunately less to fear than there is to cheer, says Natalia Markova, senior web content strategist with Jellyfish, a global digital agency.
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.”
Cost also impacts use. To justify an investment in this type of technology, companies have to regularly and frequently produce similar types of content, she says. AI and machine learning, at least for now, represent more of an aid for, rather than a replacement of, human inputs.
Paul Barba is chief scientist at Lexalytics, a text analytics software firm. He points to four areas of impact for AI in content marketing:
- Reducing production cost—At this point, Barba says, AI is too cost-prohibitive for the actual generation of pure content—although that may come. For now, though, he points to opportunities in the post-production of content, making high-quality content less costly to produce. “Although completely novel generation of content is in its early days, computers are getting better and better at editing content from a rough first draft into a version suitable for sharing,” says Barba.
- Microtargeting—The ability to finely target market segments with messages most likely to resonate with them at a certain point in time is one of the big benefits of AI. “Simple variables like the gender of example people in an article, or the soundtrack to a clip, or which sport is mentioned in passing could be varied automatically to connect more directly with readers based on information about them and their interests,” says Barba. Since machine learning makes it easier for content marketers to evaluate the potential reception of content, he adds, “the bar is likely to be raised for the quality of content marketing needed to get picked up by interesting channels.”
- Research—The ability to automate understanding of content to distill the main ideas is right in the wheelhouse of AI technology. “The step of getting your pencil onto a sheet of blank paper can be sped up with AI, providing promising avenues for content creation,” Barba says.
- Outcome tracking—The ability to accurately measure the bottom-line impact of content marketing activities is becoming increasingly important. The combination of Big Data and sophisticated machine learning can aid content marketers with this analysis, says Barba. “By providing direct evidence about the impact of various content topics, or lengths, or voices, the [content] creators can learn directly what the market is looking for.”
These impacts are already being felt and, of course, as AI and machine learning technologies are being put to use in various ways as Barba describes, people are certainly being impacted.
AI Implications for Real People
Nancy A. Shenker is the founder and CEO of theONswitch, a marketing firm. She’s a content strategist, blogger, and author of the upcoming book Embrace the Machine: 111 Ways AI Will Change Your Marketing Job. Shenker predicts that at least 25% of marketing jobs will be eliminated by AI over the next 5 years and says that content creation is no exception. In fact, she states, “My estimate is that 50% of all content will be developed by machines, with oversight and editing by humans.” Intelligent machines, she says, “will recommend topics based on trends, gather facts—and validate them—and assemble very tight posts and suggested graphics based on those combinations.” The role for humans will become adding “soul and humor as needed,” Shenker says.
“Human writers will still need to contribute the humanity behind a story,” she says. “For example, if you were to write a profile of me, your machine would only know what can be found in public records, my online profile, and—scarily—my consumer and business behaviors. The machine would not know what keeps me up at night or what I am doing tomorrow—although it might be able to make a prediction.”
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.
Dealing With the Portended Disruption
Rather than panic at the prospect of being replaced by machines, content marketers, says Shenker, should “embrace the machine.” Instead of wasting time and energy focused on why they can’t be replaced, they should think about how technology could streamline their day-to-day research tasks, editing, and other more routine activities, she says. “For example, you have to sift through Google, ProfNet, and HARO daily to find expert sources. Wouldn’t your life be easier if an intelligent machine was doing that work and consolidating it?”
Larger, enterprise-level content organizations are already using a variety of content-generation tools, Shenker says. Those tools, she says, “will ultimately—and rapidly—find their way down to smaller businesses.”
Markova agrees but adds, “Use AI tools wisely—make sure to keep the human element in the equation. No matter how advanced the technology gets, creativity, empathy, tone, and interpretation are still a human prerogative.” At least, for now.
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