What is AI in content marketing?
And how do you actually get started with this technology?
These are questions we get daily at Marketing AI Institute from forward-thinking marketers who are exploring AI's ability to increase revenue and reduce costs.
This post is designed to answer some of those basic questions about AI that content marketers might have about the technology and its potential to impact their companies and careers.
If you've only heard about AI in passing, you can read through this post sequentially for a basic definition of AI, followed by some ways it's used in content marketing.
If you already know what AI is, you may want to skip to the section titled How to Use AI in Content Marketing and/or Recommended Reads on AI for Content Marketing.
What Is Artificial Intelligence?
Ask 10 different experts what artificial intelligence is, and you'll get 10 different answers. But one definition we like comes from Demis Hassabis, CEO of DeepMind, an AI startup acquired by Google.
Hassabis calls AI the "science of making machines smart."
Basically, we can teach machines to be like humans. We can give them the ability to see, hear, speak, move, and write.
You use AI every day, no matter where you work or what you do.
Your smartphone has dozens of native capabilities powered by AI, such as voice assistants and real-time navigation.
Your favorite services, like Amazon and Netflix, use AI to offer product recommendations.
And email clients like Gmail even use AI to automatically write parts of emails for you.
Many of AI's most impressive capabilities are powered by machine learning, a subset of AI that enables machine systems to make accurate predictions based on large sets of data. The smartest AI tools then actually improve the accuracy of their predictions over time.
It's this last part that makes AI and machine learning different from traditional software or technology platforms. Your typical non-AI software is coded by humans, then follows the instructions humans have given it. These systems only get better when humans manually make them better.
AI tools, on the other hand, can improve on their own, based on both their own historical performance and new data given to the system—unlocking potentially unlimited performance gains.
That means every piece of marketing software you use today, from ad buying to analytics to automation to content strategy to social, can be made more intelligent using AI and machine learning.
These tools can then be trained to leverage individual behaviors, preferences, beliefs, and interests to personalize experiences. They can even know where you've been, where you're going, what you've written in emails, what you've asked your voice assistants, what groups you belong to, what stores you shop at, and more.
All of this data becomes fuel for artificially intelligent systems, which use this information to make increasingly relevant and accurate predictions about everything from what product you want to buy next to which ad campaign to run to which content topics to cover on your blog.
Fundamentally, AI-powered marketing tools are prediction machines. And their predictive abilities can be used by marketers to both increase revenues and reduce costs.
How Is AI Used in Content Marketing?
Make no mistake: AI is already being used today by brands looking to develop a competitive advantage.
Content marketing is one of the top areas in the industry where we're seeing AI make an impact.
We surveyed 200+ marketers using our AI Score for Marketers assessment tool, about how valuable it would be to use AI technology to intelligently automate more than 60 common AI use cases in marketing. Content marketing use cases dominated the list of highest-rated use cases for AI in marketing.
Some of the top content marketing use cases identified include:
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Analyze existing online content for gaps and opportunities.
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Choose keywords and topic clusters for content optimization.
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Create data-driven content.
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Discover insights into top-performing content and campaigns.
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Optimize website content for search engines.
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Recommend highly targeted content to users in real-time.
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Assess and evolve creative (e.g. landing pages, email, CTAs) with A/B testing.
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Deliver individualized content experiences across channels.
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Define topics and titles for content marketing editorial calendars.
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Predict content performance before deployment.
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Draft social media updates with copy, hashtags, links, and images.
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Analyze and edit content for grammar, sentiment, tone, and style.
Today, AI can partially or fully tackle all of these use cases—and commercially available tools exist to do so.
That's because of AI's ability to make predictions using large datasets. The systems that do the use cases above all take data you own from websites and databases or data from across the internet to make accurate predictions. These predictions enable a range of use cases across content marketing.
And AI's capabilities in content marketing are improving rapidly.
For instance, when Marketing AI Institute launched in late 2016, Gmail hadn't even released its Smart Compose functionality, which auto-completes sentences for you as you write emails.
Shortly after Smart Compose was released, the ability of machines to write text improved even further. In early 2019, OpenAI, a non-profit AI research company backed by the likes of Elon Musk, Peter Thiel, and Reid Hoffman, announced they built an AI model that essentially writes coherent paragraphs of text at scale.
That doesn't necessarily mean AI will start writing all your content for you tomorrow.
But it does mean that AI can help you build a competitive advantage in your content marketing starting today. That means it's critical for content marketers to get started now, not later, understanding AI.
To accelerate your education, check out our other articles on AI for content marketing below.
Recommended Reads on AI for Content Marketing
Mike Kaput
As Chief Content Officer, Mike Kaput uses content marketing, marketing strategy, and marketing technology to grow and scale traffic, leads, and revenue for Marketing AI Institute. Mike is the co-author of Marketing Artificial Intelligence: AI, Marketing and the Future of Business (Matt Holt Books, 2022). See Mike's full bio.