With constant advances in technology, performing basic business tasks has become increasingly complex and time consuming, paradoxically forcing business owners to increasingly rely on software and technology to keep up with their new technology.
The goal of much of the technology being levered today is to increase efficiency and streamline business processes through automation.
The most powerful and useful tools being leveraged in the business world today are most often powered by some form of artificial intelligence (AI) or machine learning-driven algorithms. While there are significant fears that artificial intelligence will supplant human labor, research shows that by 2030 AI will boost the global GDP by 14% over current values, and that there will be more AI assistants in use than there are people in the world.
While machine learning is useful in teaching systems how to identify patterns, AI is increasingly being used as the foundation for customer engagement tools such as chatbots and email marketing platforms.
In the digital marketing and creative spheres, artificial intelligence-powered content generators have risen to the forefront of the discussion on how AI can be best used to support marketing business processes.
Despite misgivings around the AI-powered content creation process, more than 80% of marketing industry experts are leveraging AI technology in some way in their marketing strategies, and for better or worse, this technology seems like it’s here to stay.
In this article, UpCity explores some of the most popular AI-powered content generation tools, the role marketing experts feel AI-generated content should play in future marketing strategies, the benefits and drawbacks of using content generated by software, and how AI-powered content generation tools can properly be used by responsible marketing teams.
What is AI generated content?
At a high level, AI-powered software tools are programmed to automatically create different types of content. Developers have created tools capable of automatically generating media ranging from text and videos to images, music, and even code that can be used to create functioning software solutions.
The artificial intelligence behind these tools is trained using neural networks and deep learning algorithms fueled by massive amounts of existing content. Content can be fueled by any number of sources that are processed by the tool.
While powerful and extremely versatile, these AI-content generators are designed so that they only create content based on user input. Users can craft music through the interface, or describe to the tool the type of written content they need, or even describe in detail what types of images they want generated down to the size and resolution.
Popular AI content creators
The number of unique and API-linked versions of existing AI content generators is growing by the day. They can generally be categorized according to the type of content they generate, and each category has one or more very well known examples currently live and accessible to users.
Text and natural language generation
The category of AI-powered content generators causing the most stir in the media and drawing the most attention are referred to as large language models (LLM), which form the framework for natural language processing (NLP) systems.
Used in speech-to-text and chatbot software, these tools can–with the proper prompting and input–support your copywriting output for both short-form content and long-form content, field questions from customers, and act as a support tool for your marketing team.
This can be especially useful to keep up with the ever-growing demand for original content and customer engagement on social media platforms such as Facebook and LinkedIn.
There are several LLMs leading the pack and showing true longevity in play currently.
- OpenAI’s ChatGPT is an open-source chatbot, designed for conversational tasks. Underpinning ChatGPT is the GPT general purpose model, which can be used to create other LLMs and power other language related tasks. Currently, there are two major iterations of the tool in play: GPT-3 and the latest iteration, GPT-4. The GPT-4 is anticipated to cross over into the purview of other AI-content creation tools, accepting other types of input in order to generate text.
- Google’s array of LLMs include BARD, PaLM, mT5, and LaMDA. LaMDA is a highly adaptable chatbot that provides the foundation of the BARD chatbot tool. BARD has a potential to be a game-changer, as it’s been trained on up-to-date information, while other LLMs are several years behind.
- NVIDIA’s Megatron-Turning NLG model is one of the most powerful and accurate text generators of its kind, as a result of the platform’s massive 530 billion parameters that form the basis of its ability to provide answers to user queries.
AI-Powered music composer
As software developers improve the learning models and training models for AI, they become capable of handling more complex automation tasks. Content generators powered by AI that are focused on music require a massive amount of training on curated samples of existing music, but once properly trained can deliver results that match user inquiries.
- Google’s MusicLM allows users to create snippets of music in specific styles and genres, as well as entire compositions. “Story mode” is a unique application of the tool that allows the user to create a single piece that changes style and genre throughout the piece.
- Tone Transfer allows the user to upload audio files or even hum a tune that can then be used as a template to generate unique music samples using acoustic instruments.
Art and Image AI content generation
In the case of art and AI image generators, the interface allows users to describe the types of image they are looking for, the dimensions and resolution, and other stylistic choices. These tools are trained on massive amounts of artwork culled from the Internet and other sources, allowing the tools to create extremely specific and stylistically diverse results.
- Midjourney was created by a small self-funded team working out of an independent research lab. The platform uses the Discord messaging platform as its main interface and has emerged as one of the most popular image generation tools. Midjourney has a very large and active community.
- OpenAI’s DALL-E 2 combines natural language processing with an AI system capable of generating realistic images and art based on the user input. Because it’s part of the OpenAI open source platform, DALL-E 2 provides the framework for another popular image generator, Bing’s Image Creator. Both platforms are able to create extremely unique artwork with the right prompts.
Video generator
AI tools capable of generating video content based off of provided scripts or provided prompts are recent additions to the field of AI content generation tools. Some animate simple avatars while others convey the video content via a specific animation style. Many of these tools lean into the fact that you can use them to convert blog posts and other written content into effective and engaging videos.
- Synthesia allows you to provide scripts for very realistic avatars to read and act out in a presentation format.
- InVideo provides users with templates, music, sound effects, and other methods that allow users to personalize generated video content to make it more unique and engaging.
Programming language generation
Perhaps one of the most interesting applications of AI to generate content, AI code generators are a natural extension of integrated development environments (IDEs) that assist coders while they code. It’s important when choosing an AI generator for coding that the platform supports the languages and frameworks that you’re working with already.
- GitHub Copilot integrates with popular IDE platforms and provides autocomplete suggestions in real-time to developers. The tool also assesses the code being written and suggests relevant code snippets, helping to streamline code and make it more efficient. The tool is constantly updated, ensuring the suggested code always falls within best practices.
- Durable has made its presence known in the crowded automated web builder space. Using AI analysis to determine the user’s location and nature of the business, the tool creates the framework and imagery for a unique website tailored to the user.
Why is AI generated content important for content marketing?
Content marketing has emerged as a crucial component for brands in attracting leads and building relationships. With 90% of consumers in the United States preferring personalized content, content marketing efforts are more effective when the brand is able to ensure that the content being delivered is dynamic and tailored to the needs of specific visitors.
As more industries consider whether to embrace AI tools and debate the benefits and drawbacks of doing so, it’s become clearer that AI will continue to play an increasingly important role in the years to come.
Benefits of AI generated content
As with any automation tool focused on streamlining operations, leveraging AI content generators will allow businesses to create content faster and spend more time on customer engagement.
In scaling up on the volume of content that can be created and posted, a wider range of customers can be targeted in a shorter amount of time, allowing the brand to boost conversions and revenue.
Paying the costs of licensing for full use of AI content generation tools allows a smaller staff of content writers to produce just as much content as a larger team, but at lower cost.
Similarly, you can create powerful SEO content by asking the tool to focus on optimization so as to boost your position on search engine ranking pages (SERPs) quickly using these tools, which will make your content more impactful and draw more traffic.
Risks of AI generated content
There are massive concerns in how quickly businesses are adopting AI tools into their businesses, and concerns about how the tools themselves function that are driving much of the debate and dialogue around AI content generation.
One of the major concerns is that Google has already adapted its algorithms to protect against the anticipated onslaught of AI-generated content in the coming years. The principle of E-A-T (Expertise, Authoritativeness, and Trustworthiness) as a measure of the value of content considers the expertise, authoritativeness, and trustworthiness content instills to determine if it should be prioritized in search.
Google has since added Experience into the algorithm, so as to ensure that content is written in such a way as to show that the writer actually understands the subject and has presented information that can be trusted. Thus far, content created by AI platforms has not shown the ability to adhere to the new E-E-A-T criteria.
AI generated content has also proven to be highly inconsistent in quality, depending what the machine has been tasked to address. Whether from a lack of current data or having to address topics that require a more subjective approach than factual approach, AI tools tend to struggle to create consistently high-quality material. This requires that human writers review, revise, and enhance the content, which–while faster–still requires time and manpower.
There are also significant copyright and plagiarism concerns with AI-generated content, as developers are using material they don’t have the rights or permissions to use as training data for their tools. The resulting content, whether in text or visual form, doesn’t include citations, and most tools in fact don’t keep track of sources used to generate the final output.
Again, this can be mitigated by having staff who go behind the tools and edit and revise the content in order to make sure that it’s not only factually correct but that it’s also not blatantly copying from other sources or that those sources are referenced before publishing.
Tips for using AI-generated content in marketing
With the pros and cons more clearly laid out, it’s obvious that you should not integrate AI into your content marketing strategy without a clear plan and an understanding of how it might best fit into your business needs.
Just like any other automation tool or software solution, understanding the limitations of the tool and what benefits it brings to the table will help establish best practices and business applications to avoid.
Best practices for using AI-generated content
Using an updated and relevant content generator, you can curate content for your business much faster and more efficiently, and even overcome writer’s block. AI tools can also help to create a framework for relevant content for your target audience by helping to identify trends and parse data.
Assume that the content needs to be proofread and edited just like any other piece of content your staff might have submitted for publication. Consider the content created by the AI platform as a rough first draft. Your editorial team should not only proofread but you should also check for brand editorial standards to ensure the content is consistent with your other content.
This will avoid inaccuracies and potential plagiarism while speeding up your publication cycle and boosting your brand’s reputation for consistent and high quality content.
Take advantage of the built-in advantages and functionality of AI platforms’ NLP technology. Understanding how to properly construct your prompts and knowing what you can task the tool to create will help cut down on the editorial process on the back end, as you’ll be able to integrate tone and style at the start.
Before committing to an AI tool or product, test the output or tool with your community of users and customers to ensure that it resonates and is accepted by users. There’s no point in shifting your entire content management strategy to embrace AI if it’s not right for your brand or isn’t what your customers are looking for out of their experience with your company.
How not to use AI generated content
One of the most important lessons in adopting AI content tools is that, like any other automation software, AI content generators should not be treated as a “set and forget” solution. Almost without exception, AI tools require human input and oversight in order to get the most value from their output.
Similarly, as evidenced by the changes in the Google algorithm, AI content tools are not intended to replace human experience or creativity. While the tools offer ideal solutions for creating content focused on facts and datasets at scale, more subjective topics and more creative articles should be handled by writers on your staff.
In integrating AI content generation tools into your existing technology stack, you have to ensure that your use of the tool adheres to standard business ethics and legal standards that impact your business according to industry and state regulatory requirements.
Furthermore, as most tools are available through third-party developers, it’s crucial that your IT and risk management staff assess the tool and have security measures in place to ensure it’s introduction doesn’t leave your brand open to cyber attacks or your customer data exposed to a breach.
Proceed cautiously with AI-driven content generation tools
Whether you’re comfortable with the direction that technology companies have taken in focusing on and advancing AI content generation tools, the truth is that they should be treated no differently than any other automation tool that your company has folded into your technology stack over the past decade.
In order to best integrate AI tools into your content strategy in 2023 and beyond, it’s important to fully understand the benefits and dangers of embracing such potentially powerful software. Matching your business needs to the right tools is essential to getting the value from AI-driven software platforms, and the first step is to ensure that you have a robust content marketing strategy in place.
Need guidance? Our content marketing experts on the UpCity marketplace can help evaluate your current state and how best to evolve your strategy for the challenges ahead.