The explosive growth of generative AI over the last year has been truly phenomenal. Kick-started by the public release of ChatGPT (was it really only a year ago?), it’s now everywhere. Keen to ride the wave, every app from Microsoft Office to eBay has been adding generative AI capabilities, and growing numbers of us are finding uses for it in our everyday and professional lives.
It’s not surprising that content creators, in particular, have found generative AI a powerful addition to their toolset. Marketing agencies, advertising creatives, news organisations and social media influencers have been among the most enthusiastic early adopters.
While it brings great opportunities for improving efficiency and automating some of the manual, repetitive elements of creative work, it also throws up significant challenges. Issues around copyright, spam content, hallucination, the formulaic nature of algorithmic creation and bias all need to be considered by professionals planning on adopting it into their workflow.
With that in mind, here’s an overview of the impact that generative AI is already having in this field, as well as some thoughts on what we can expect as the technology becomes even more powerful and society adapts to an AI-augmented world.
How is AI Used in Content Creation?
Generative AI learns how to create by studying data and working out how to make similar data. Some of the most common generative AI models in use today are large language models like the one that powers ChatGPT, which is capable of creating language and text, and diffusion models, which create images and video.
Tech-focused corporations have quickly assimilated it into their creative processes. For example:
Netflix is using generative AI to create more engaging movie trailers and is working on creating personalised trailers that will appeal to individual members.
Buzzfeed is using it to create personalised content, including quizzes tailored to individual interests. It’s also created a generative recipe creator that suggests meals based on the ingredients on hand.
Google is showcasing a service designed for news organisations to allow them to automate the creation of news reports. Outlets including the New York Times, Guardian, BBC, Bloomberg and Reuters are among the many that have published stories written by generative AI.
And a new breed of toolmaker is emerging. There’s Synthesis, which lets businesses create videos from text without needing to book actors or studios, and Writesonic, which automates written content tailored to a brand’s voice, as well as the creation of AI chatbots. And there are many more.
Augmenting Creative Workflows
If you’re a creative wondering how you should be building generative AI into your own day-to-day work, the first rule is to remember that it’s there to augment your capabilities rather than replace them.
As Writesonic CEO and founder Sam Garg explains, generative content by itself is often very generic, formulaic, and not primed to do the one thing digital content usually has to do, which is to grab our attention.
This means that while generative AI tools are perfectly capable of, for example, writing an article like this one, a script for an ad, or generating an entire promotional video, this isn’t usually the best way to use it.
Garg says, “The idea is to augment humans rather than replace them. And basically, to see AI as a tool that enhances the productivity of humans and works hand in hand, rather than something that replaces humans.”
This human-in-the-loop approach ensures that content doesn’t become robotic and formulaic.
You can find uses for generative AI at just about every step of the creative process – from ideation to planning, storyboarding, drafting, fact-checking, and distributing to the audience.
But great content usually works for two reasons – it introduces something new, like a new idea, and builds an emotional connection with the audience.
These are two areas where generative AI often falls flat on its face. It can’t really have new ideas in the same way a human does; it simply regurgitates what it knows from its training data. And it doesn’t really understand emotions in the same way we do.
Pitfalls of Generative AI in Content Creation
Other than the dangers of bland and uninspiring content, there are other issues that need to be considered. High on the list are issues around copyright, which are two-pronged.
Firstly, the jury is still out on who owns AI-generated content. Is it the person who uses AI to create the content? Is it the creator of the tools that generated it? Or is it the owner or creator of the original data that was used to train the AI in the first place?
Secondly, if you avoid being sued by a creator who says that your AI copied their work, there’s the question of whether you’ll be able to enforce copyright on your own creations. This could obviously be a problem for businesses using it to create proprietary assets and materials.
There’s also the fact that AI, as things stand today, has a tendency to get things wrong. This is known as hallucination because it often seems like they’re simply making things up. Obviously, no business wants to look stupid by putting out factually incorrect information. And there’s certainly enough disinformation online already without letting AI run wild and create even more!
Where to Next?
Although it’s already had a huge impact, we’re clearly only just getting started with generative AI. In the near future, we’ll see even more powerful tools and, just as importantly, ones that are even simpler to operate.
It’s very possible that most of the challenges we’ve spoken about here – bland content, a lack of emotional resonance, and factual inaccuracies – will be overcome. As language models become more powerful and complex, we may well see generative AI tools that can equal humans when it comes to creating content that inspires and engages us.
This will make issues like identifying deepfakes – very realistic AI creations designed to fool humans – and mitigating the spread of AI-generated disinformation even more critical.
But it’s also likely that the technology will become increasingly accessible, meaning its powers can be put to use by a more diverse user base. This will result in generative AI tools and creations that are informed by a richer tapestry of human stories and experiences.
Most people believe there will always be a need for humans in the content creation process. After all, we’re storytellers and creators by nature. But those of us who learn to use generative AI tools to boost our creative potential will have a distinct advantage over those who don’t as we develop new ways of creatively expressing our thoughts and ideas.