While movies like Terminator and iRobot explored humanity’s fears that AI would bring about the end of the world as we know it, the reality is far more complicated.
Instead of appearing as shapeshifting terminators, generative AI tools often materialize as virtual assistants, creating all types of content for people, from resumes and emails to bedtime stories, blog posts, and jokes that may or may not actually be funny.
For many marketers and content producers, the rise of AI-generated content has been equal parts exciting and infuriating, which is understandable.
AI content has the power to irrevocably change the content landscape, but the question is, “Will it take us with it?”
To better understand the future of AI-generated content, it’s crucial to start by learning what it is, how it’s made, and its pros and cons. With deep understanding, marketers can better incorporate AI into content strategies and become creators who are supercharged by AI, not easily replaced by it.
What Is AI-Generated Content?
AI-generated content is text, images, video, or audio content created by an artificial intelligence tool as a result of human input or prompts. You can produce AI-generated media using tools like OpenAI’s ChatGPT and DALL-E, as well as AI-powered features inside content software like HubSpot and Canva.
How AI Content Differs from Human-Generated Content
Human-generated content includes media created entirely by a person, whereas content generated by AI tools is essentially a remix of other works created by humans. (We’ll dive deeper into this when we look at how AI generates content.)
As such,
AI content usually lacks elements that human-generated content has, including human experience, point of view, creativity, nuance, and human quality control. It’s more paint-by-numbers than an entirely new work.
The Role of AI-Generated Content in Today’s Digital Landscape
Whether it’s B2B webinars, viral TikTok videos, or bingeable TV, demand for content has increased across almost all types of consumers and industries. As content producers and marketers work to keep up with consumer cravings, AI has been instrumental in scaling up content production.
Specifically, a 2023 study by Salesforce found that:
- 76% of marketers use AI for basic content creation
- 76% of marketers use AI for writing copy
- 71% use it to inspire their creative thinking
While AI is often used for basic content creation and copywriting, the study doesn’t clarify whether those AI-generated assets were ready to be published straight from the AI tool.
We’d venture to say that for most businesses, that’s not the case. AI-generated content often requires many human adjustments before getting approval for publishing. Understanding how AI generates content is crucial to understanding why all those edits are needed.
How AI Generates Content
AI content generation is primarily achieved in three ways: machine learning algorithms, natural language processing, and deep learning techniques. These techniques work together and have some overlaps.
Here’s how each breaks down.
Machine Learning (ML) Algorithms
Machine learning algorithms use supervised learning to train computers and apps how to “think” and make decisions based on input. Supervised learning involves asking the computer to do tasks and then telling the program if it is right or wrong so it can learn. It uses a dataset that includes the answers so you can check the program’s work.
For example, say you want to train a computer to identify whether or not social media captions are casual or formal. In supervised learning, you have a training database of captions that you have labeled as either casual or formal.
To train the computer, you provide it with the captions and ask them to label each one as either casual or formal. Then, you give the program the right answers from your database so it can compare its answers to the ones that were accurate. By repeating this process, the program begins to “learn” what makes a caption casual versus formal.
Supervised learning for computers is similar to school tests for kids, just without dreaded red pen marks.
Unsupervised learning, on the other hand, is like when you send students out into the real world for their first job. They may be asked to solve questions similar to those discussed in school, except this time, there’s no answer key, and answers may be conditional based on the circumstances. It’s the same for computers.
In unsupervised learning, computers are given unlabeled datasets and asked to analyze and group them by similarities. If we return to the social media caption example, the computer is fed a dataset filled with captions, but you haven’t gone through and labeled them as formal or casual.
In this case, you’ll use the program to do that based on what it learned with the supervised data, so some (or all) will likely be mislabeled. The accuracy of the machine in an unsupervised environment depends heavily on the data you used during supervised learning. For instance, if you focus on B2C social media captions, the machine might not be as accurate when applied in a B2B setting.
Natural Language Processing (NLP)
Natural language processing (NLP) in AI involves teaching machines how to understand and use human language. It’s the Rosetta Stone or Duolingo for an AI program.
The two main components of NLP are:
- Natural language understanding (NLU): Allows computers to interpret human language correctly.
- Natural language generation (NLG): Allows computers to generate meaningful responses that make sense to human readers.
In content generation, NLP is the interpreter component that allows humans to provide AI chatbots and tools with prompts similar to those we would give another person. Without NLP, you would need to learn the computer’s language or code to get any output.
For instance, you can go to ChatGPT and say, “Write me a sonnet about the sun,” and it will attempt to understand what you want and generate a result using its understanding of the words and phrases used.
Deep Learning Techniques
Deep learning is a type of machine learning that simulates more complex decision-making with the use of neural networks, which are made of connected node layers that are designed to simulate the neurons in a human brain. In contrast with machine learning, deep learning relies on more complex algorithms to mimic human reasoning through processes similar to a series of complex if-then statements.
In practice, deep learning is used to power more advanced AI features, like content personalization and new content generation, such as images, blog posts, translation, and text-to-speech.
Benefits of AI-Generated Content
Efficiency and Speed
Compared to humans, AI can generate more content and variations in less time than humans. That said, it’s important to remember that speed does not equal quality. In fact, it’s oftentimes the opposite.
Opting for speed almost always requires a sacrifice in quality, and vice versa. The key always lies in finding a balance that allows for consistent production while maintaining high-quality content standards.
An AI tool can generate a blog post in minutes, but the content still needs human editing and review before it’s ready to publish.
Still, AI-generated content can save time, as 71% of marketers expect generative AI to help eliminate busy work and help them focus on strategic tasks. In the same Salesforce survey, marketers predict generative AI will save them five hours of weekly preliminary work.
For example, some effective ways to use AI to save time in the content creation process involve brainstorming, topic generation, outline generation, and generating product descriptions for item variants.
And as many content marketers will tell you, using AI for ideation to shake you out of writer’s block when a deadline is looming can save your sanity. Tools like Wordtune offer quick rephrasing suggestions so your brain doesn’t get stuck in a loop looking for that elusive perfect word.
Cost-Effectiveness
Supporting your content writing strategy with AI tools enables you to scale more cost-effectively. Free tools such as ChatGPT can help you increase the productivity and efficiency of your content team, allowing you to produce more content in less time.
AI can also help you quickly reformat content. For example, you can give ChatGPT a piece of content you wrote and ask it to generate a social copy summary, such as text for a LinkedIn carousel. Then, you can focus more on fine-tuning social media posts instead of getting completely stuck in brainstorming, generating, or editing.
That said, you don’t want to sacrifice quality just to save money. It’s crucial to ensure that your content remains authentic and resonates with your audience.
If the bulk of your content doesn’t engage your audience, you’ll lose more in sales and decreased brand visibility than you saved on the content creation.
Personalization Capabilities
AI can enhance the personalization and delivery of content. This has many applications in marketing, sales, and customer service, as it provides an opportunity for more personalized customer experiences throughout the entire buying journey.
For example, AI personalization use cases include:
- Personalized chatbot conversations using data from your CRM.
- Providing tailored product recommendations on e-commerce websites and in email campaigns.
- Automatically sending email campaign messages based on the times and days they will most likely engage.
With the help of AI automation, marketers can achieve personalization at scale in ways that would be almost impossible if people on your team had to tailor each individual interaction.
Challenges and Limitations of AI-Generated Content
Quality Control
Ensuring factual accuracy is one of the biggest concerns with AI content generation tools. Users often don’t know what data was used to train AIs to help them judge quality and accuracy. More training transparency and human quality control are needed because AI tools can hallucinate and confidently deliver false or misleading information.
In a McKinsey survey, 63% of organizations list inaccuracy as a top concern with generative AI, and 23% say their organization has experienced a negative impact due to inaccuracies in generative AI tools.
It turns out AI doesn’t dream of electric sheep as much as it can’t tell daydreams from reality
AI hallucinations, when generative AI tools produce inaccurate, illogical, or nonsensical output, are a major concern. Without proper fact-checking or research, these hallucinations can end up in published work and wreak havoc on someone’s credibility.
For example, when two Manhattan lawyers used ChatGPT to research cases to find legal precedents for an argument, the tool made up court cases that never happened and even mentioned airlines that didn’t exist.
At the end of the day, AI writing tools are trying to fulfill their programming demands to be helpful, even if that means making up information like a toddler who doesn’t know the answer to your question.
The main difference?
It’s cute when a kid invents an airline, less so when ChatGPT does it during your legal research.
Creativity and Originality
For all its speed and efficiency, AI can’t match human creativity and originality. That human touch, a nuanced point of view based on each person’s experiences and knowledge, is missing in AI content.
When you look closely at the methods used to train AI models, it’s clear that these tools are only remixing what’s already out there. They’re not creating anything new.
The U.S. Copyright Office has issued guidelines supporting this idea, ruling that AI-generated content cannot be protected by copyright unless significant human modification or input exists in the final product. Even then, guidelines state that only the human-generated parts of the final piece are protected by copyright.
Ultimately, while AI can do many things well, it’s not a replacement for an individual’s creativity and point of view. AI-written content also fails to replicate the results produced by creative collaboration, where multiple unique individuals work together to create something that blends each person’s experiences.
For content marketers specifically, it’s crucial to note that if you are using the same AI generation tools as other brands, you may end up with assets that are quite similar (or identical). Any time-saving gains you earn can be overshadowed if your content can easily disappear into a sea of similarly written posts.
Bias and Discrimination
Using AI to generate content for your brand can introduce biases into your content without your intent or knowledge. The same can be said with human copywriting, but if we approach AI content tools with the belief they are “neutral,” that’s not accurate.
Developers use training data to teach AI models how to “think.” The problem is that training data can reflect long-standing societal biases and effectively infect the AI tool with those biases.
For instance, a team at MIT found that one language model identified flight attendant, secretary, and physician’s assistant as feminine jobs, while categorizing fisherman, lawyer, and judge as masculine occupations. The model also thought that emotional states such as anxiety, depression, and devastation were feminine.
In another example, researchers find a significant bias in ChatGPT toward the Democratic Party in the United States and the Labour Party in the U.K.
It’s easy to think of computers as non-emotional and objective beings, but when it comes to AI we have to remember that these tools are a reflection of the data and instructions they receive from humans, much like pet parrots will pick up their human’s habit of cursing.
Reduced Engagement
The possibility of misinformation, biases, and lack of originality in AI-generated content can lead to a lower level of audience engagement, resonance, and trust. One survey found that 62% of consumers are less likely to engage with and trust content if they know it’s created by an AI application.
This is especially relevant for marketers creating content for search engine optimization (SEO) as Google’s 2024 update included penalties for unoriginal and low-quality copy. In many ways an extension of the helpful content update released in 2022, actions taken in 2024 included the manual deindexing of hundreds of domains containing AI-generated content.
Moving forward, it’s up to content marketers to understand the best ways to harness human and AI collaboration so we can keep engagement high and maintain trust with readers.
Ethical Considerations
Since generative AI exploded on the scene, content creators like the New York Times, and Getty Images have sued generative AI companies over copyright issues.
Currently, issues such as plagiarism and copyright infringement by AI are still being decided in courts across the country.
Until there’s a clear answer on what’s usable in an AI training model or who is held liable for AI-generated content that infringes upon another creator’s rights, content creators using AI to generate finished content pieces are introducing risk into their process.
Issues around the legality of image and likeness use rise to a new level with AI-generated deep fakes, where people can create hyper-realistic images, video, and audio of people without their knowledge or permission. Deep fakes range from innocuous videos replacing one Spider-Man actor with another to AI-generated images of war scenes or videos of politicians that have the potential to sway public opinion.
As these types of content become increasingly realistic, consumers and creators will have to be more vigilant about spotting and identifying computer-generated media and fact-checking sources, especially if you’re creating or reporting on this content.
It should also be noted that many generative AI platforms aren’t open about their models and the data they use. Not only that, but research by Stanford found that in one test, closed AI models outperformed their open counterparts, providing a performance advantage that was 24.2% higher on average. As a result, people may be more likely to use AI tools that don’t disclose their data or methodology, giving users fewer insights into examining the potential for copyright infringement or bias.
Potential Job Displacement
When companies decide to use AI-generated content instead of hiring internal staff or working with independent content creators, that leads to job displacement. However, companies experimenting with replacing staff with bots often find themselves without the flexibility needed to manage more complex content creation.
The top marketing roles concerned about losing their jobs to AI are:
- Content writers 81.6%
- Email marketers 42.7%
- Social media managers 33.9%
That said, 60% of marketers in the same survey felt positive about the overall expansion of AI in the industry.
Like anything else, AI implementation can be taken to the extreme, and if it is, the potential for job displacement becomes high. If we, as creators, take a more balanced approach and focus on leveraging AI to improve the productivity and output quality of human writers, then it can become an empowering tool.
At The Blogsmith, we know that human-to-human connections are the most impactful, and we have no plans to replace the foundation of our creative work with AI.
As such, we do not use AI-generated content at any stage of our process. That said, we are excited about the potential for AI tools to support content marketing teams.
The Future of AI in Content Creation
Here’s what we see on the horizon in terms of AI-generated content.
- AI empowers more efficient content workflows: By eliminating time-consuming prep work tasks like brainstorming, competitive research, and finding supplemental keywords, AI tools will help continue to augment human work and empower content teams to streamline and scale content production.
- The need for transparency when using AI: Providing transparency as to how AI is used during content creation will likely become more standard in conversations when working with internal teams, contractors, influencers, and agencies. However, in an article referenced by Cornell’s guide to AI and academic integrity, we do caution against over-reliance on AI content detectors, as they tend to be unreliable.
- Supercharged personalization and segmentation: AI can speed up the personalization process, empowering marketing teams to create campaign variants that suit their different audience segments and improve user experiences.
We’re also looking forward to seeing how AI advances in the following areas:
- Multilingual functionality: Increased performance in effective translation and the ability to generate content in multiple languages.
- Real-time content adaptation and optimization: The use of AI to tailor content in the moment based on factors such as time, location, and user behavior.
- AI-powered collaboration: Tools like Zoom’s AI Assistant can leverage AI to take teamwork to the next level with features like post-call summaries, task extraction, and conversation summaries. These types of tools empower more streamlined relationships between agencies and clients or brands collaborating on a content strategy.
Final Thoughts: An In-Depth Look at AI-Generated Content
AI-generated content has arrived on the scene, and it’s not leaving anytime soon. As content producers, it’s crucial to understand how AI content generation works, along with its advantages and disadvantages.
While it poses potential threats to jobs, it also offers avenues to increase productivity and eliminate menial tasks.
All said, the core of marketing is creating meaningful human connections with your audience.
While AI can enhance content creation, we believe this only happens when the process is driven by human creativity and guided by a dedication to ethical and responsible technology use.
If you want to work with a partner with experience creating full-funnel content that resonates with your target audience, contact The Blogsmith today.
