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Artificial intelligence (AI) has been steadily influencing business processes, automating repetitive and mundane tasks even for complex industries like construction and medicine.
While AI applications often work beneath the surface, AI-based content generators are front and center as businesses try to keep up with the increased demand for original content. However, creating content takes time, and producing high-quality material regularly can be difficult. For that reason, AI continues to find its way into creative business processes like content marketing to alleviate such problems.
AI can effectively personalize content marketing to the audience it is aimed at, according to David Schubmehl, research vice president for conversational AI and intelligent knowledge discovery at IDC.
“Using pre-existing data, AI algorithms are used to make sure that the content fits the interests and desires of the person it is being targeted to,” Schubmehl said. “Such AI can also be used to provide recommendations on what the person might be most interested in engaging with, whether it is a product, information or experience.”
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AI can not only aid in responding to your audience’s questions but can also help connect with consumers, generate leads, build connections and, in turn, gain consumer trust. These advantages are now being made possible, in part, with the use of AI content generator tools.
“AI-supported and AI-augmented content creation capabilities have begun to blossom over the past 18 months and are approaching an inflection point where they are transforming content creation and content-scaling,” said Rowan Curran, an analyst at Forrester.
How AI content generators work
AI content generators work by generating text through natural language processing (NLP) and natural language generation (NLG) methods. This form of content generation is beneficial in supplying enterprise data, customizing material to user behavior and delivering personalized product descriptions.
Algorithms organize and create NLG-based content. Such text generation models are generally trained through unsupervised pre-training, where a language transformer model learns and captures myriads of valuable information from massive datasets. Training on such vast amounts of data allows the language model to dynamically generate more accurate vector representations and probabilities of words, phrases, sentences and paragraphs with contextual information.
Transformers are rapidly becoming the dominant architecture for NLG. Traditional recurrent neural network (RNN) deep learning models struggle with long-term modeling contexts due to the vanishing gradient issue. The issue occurs when vanishing gradient occurs when a deep multilayer feed-forward network or recurrent neural network cannot propagate information from the model’s output end back to the layers near the model’s input end. The outcome is a general failure of models with multiple layers to train on a given dataset or to prematurely settle for a suboptimal solution.
Transformers overcome this issue as the language model expands with data and architecture size, transformers enable parallel training and capture longer sequence features, making way for much more comprehensive and effective language models.
Today, AI systems like GPT-3 are designed to generate text similar to human creativity and writing style that most humans cannot generally distinguish. Such AI models are also known as generative artificial intelligence, i.e., algorithms that can create novel digital media content and synthetic data for a wide range of use cases. Generative AI works by generating many variations of an object and screening results to select the ones that have helpful target features.
AI content generation use cases
There are various ways AI is assisting enterprises in creating great content, some of which are the following:
- Voice Assistants: With the assistance of NLG, AI content generation tools can be used to build voice assistants ready to answer our queries. Alexa and Siri are examples of how companies can use the technology in real-life applications.
- User-based personalization: AI is adept at targeting each client by leveraging customer data to develop customized content. This is currently being improved by obtaining data from multiple sources, such as social media platforms and smart gadgets in the home, to learn further about the customer’s requirements and desires.
- Chatbots: Chatbots are one of the most used services in the market since they can answer most requests in a few seconds. These AI-powered bots employ a speech generator to generate pre-programmed information based on realistic human conversations.
- Extensive content creation: Currently, content generation is mainly confined to short to medium copy, such as newsletter subject lines, marketing copy and product descriptions. However, in the future, AI content production is expected to write lengthy chapters, if not whole novels.
Top content generation tools
The following is a list of widely used content generators — compiled with information from reviews by Search Engine Journal, G2, Marketing AI Institute and others:
- Writesonic: Writesonic is built on GPT-3 and claims the machine is trained on the content that the brands using the tool produce. The generator is based on facilitating marketing copy, blog articles and product descriptions. The generator can also provide content ideas and outlines and has a full suite of templates for different types of content.
- MarketMuse: MarketMuse assists in developing content marketing strategies by using AI and ML. The tool shows you which keywords to target to compete in specific topic areas. It also highlights themes one may need to target if you wish to own particular topics. AI-powered SEO tips and insights of this caliber can guide your whole content development team throughout the entire process.
- Copy AI: Contains over 70 AI templates for various purposes. Its AI creates high-quality material and provides limitless usage alternatives. Copy AI offers templates for various content categories, including blogs, advertisements, sales, websites and social media. The generator can also translate into 25 different languages.
- Frase IO: Frase builds outline briefings on various search queries using AI and ML. It also includes an AI-powered response chatbot that uses material from your website to answer user inquiries. The chatbot understands user inquiries using natural language processing (NLP) and then brings up content on your site that provides suitable replies. The outlines can help you speed up content development by automatically summarizing articles and gathering relevant statistics. One may also utilize the user questions compiled by the response bot to help you decide what to write about next.
- Jasper AI: Jasper is an AI writing assistant that can write high-quality content, blog articles, social media posts, marketing emails and more. Jasper knows more than 25 languages, the content is built word-by-word from scratch. Jasper has been taught over 50 skills based on real-world examples and frameworks to aid writing tasks such as writing email subject lines to fictional stories.
Pros and cons of AI content generation
Businesses can establish an effective content marketing strategy using AI content generator tools. A study by Fortune Business Insights predicts that the AI-based content technology market to reach $267 billion by 2027. According to the data, organizations that use these systems receive more traffic and have more excellent conversion rates than those that do not.
AI content technologies have shown to be far more valuable to businesses than human resources because they are far less expensive and time-consuming to invest in. AI content generation is significantly faster because computers can handle enormous volumes of data in much less time than humans can. These AI content generators can also generate infinite pieces with little input, making them ideal for enterprises that require consistent, new material.
Curran noted that the industry is just beginning to see what these tools and techniques can do in terms of content creation, but fundamentally it’s still going to be about humans being enhanced by AI.
“Over the next few years, we’ll likely see a Cambrian explosion of different applications, use-cases and approaches for AI-supported content generation as the technology gets into the hands of a wider array of enthusiastic users,” Curran said.
However, there are also some drawbacks associated with using an AI content generator. First, setting the generator to hit the right tone for your content can be challenging. The generator may produce AI text that is not particularly well-written or appropriate, as AI sometimes lacks the judgment to give an opinion and cannot provide a definitive answer. While AI is smart, writing depends on the context and triggering the correct emotions, and humans are still superior at both.
“AI can be a powerful tool for generating large quantities of text, but the output can sometimes lack emotion and common sense,” Schubmehl said. “This happens because an AI writer cannot read between the lines like human writers and may use words that are not necessarily what was meant by the author.”
Schubmehl also noted that AI-based content generators (NLG programs) do not really understand the text that is being generated, as the created text is only based on a series of algorithms.
“While natural language-generated text can provide increasingly accurate summaries, there are still areas of preference such as brand voice, tone, empathy, etc. that are difficult to program into AI algorithms and will continue to require human intervention in the content creation process,” he said. “Over time, we expect that large language models, based on billions of lines of text, will use unsupervised machine learning to do a better job of creating AI-based content.”
Machine-generated content cannot be subjective, no matter how great the ML training using structured data is. Human writing reflects our richness of topic knowledge and has an expressive aspect that a machine cannot equal.
Only a human content expert can address such gray areas. Therefore, developing an AI tool that can completely replace a person while matching human authors will take time.
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