Generative AI is ushering in dramatic changes in content marketing. The potential to save time and resources, no doubt, is a major part of its appeal. But this technology comes with complexities and challenges, including data quality and bias, to ethical considerations.
To successfully embrace generative AI, it’s essential to understand both the capabilities and limitations of this technology. Human involvement and oversight is critical to its responsible implementation. With the right approach, content marketers can make the most of generative AI’s potential, ensuring their content is accurate, relevant, and aligned with brand values.
Chances are that you’ve already experienced generative AI. Maybe you’ve read something created using AI, whether you knew it or not. Or you’ve experimented with generating your own content using ChatGPT or another large language model (LLM).
In the context of content marketing, generative AI can generate new content such as text, images, and even video. It can do this because it’s been trained on an enormous amount of data. GPT-3 was trained on 45 TB of data, which is equivalent to:
That collection of data (corpus) comes from a variety of sources including the Common Crawl (raw web page data), Reddit, two internet-based books corpora, and Wikipedia pages.
Through training in this manner, generative AI can generate content that seems familiar and plausible. As a result, marketers are looking at generative AI to save time and resources by generating new content automatically in the style and tone of their brand.
But, as many are discovering, generative AI isn't an easy button—you can’t just let it loose and expect to receive quality output.
There’s no doubt that generative AI is a powerful tool, and with that power comes responsibility. Human involvement and oversight is crucial to making sure the generated content is accurate, relevant, and aligned with the brand's values.
It’s humans that are ultimately responsible for what’s contained in the generated content. The challenge is some of the training data may be biased, discriminatory, not-safe-for-work, unethical, and promote stereotypes. Ideally, these AI systems should be transparent, explainable, and fair.
Unfortunately, as end-users, we can’t tell. It’s because AI systems can be complex, opaque, and unpredictable that human accountability in AI is critical.
Ignorance of AI is no excuse.
Even if an AI system is designed to make autonomous decisions, humans are still responsible for guaranteeing it operates within ethical and legal boundaries. So humans will need to provide oversight, monitoring, and intervention as required.
Of course, you don’t want generative AI to negatively reflect on your brand. But in the bigger scheme of things, we need to ensure that AI systems benefit humanity and don’t harm it.
That requires setting clear boundaries for AI systems, defining their intended use cases, and ensuring that they don't act beyond their intended scope. It also means that you’ll need to introduce effective governance mechanisms to oversee the development and deployment of AI systems, including regulations, standards, and guidelines.
ChatGPT is likely the first thing that comes to mind when discussing generative AI. Having reached 100 million users two months after launch, that comes as no surprise. But what may shock you is that AI-written articles first appeared in public over a decade ago!
One of the earliest examples is Narrative Science using its software to write articles for select news outlets. Their platform analyzed data and generated news stories that read like they were written by human reporters. Another early example of AI-generated articles happened in 2012, with the Associated Press (AP) announcing its use of Automated Insights Wordsmith to turn raw financial data into corporate earnings reports.
These early use cases centered around automating routine tasks associated with low-value content, freeing up human journalists and writers to focus on more complex and creative work. But the introduction of ChatGPT has changed the dynamics.
Content marketers face many issues such as data quality, bias, and privacy, in addition to ethical considerations such as transparency and fairness. But there are other challenges, including:
Maybe the biggest challenge is learning how to interact with something that appears to be human but isn't. ChatGPT can generate some very convincing output that's completely inaccurate. And unlike a human, it’s just as likely to do it next time, than not!
Despite all the challenges, the potential of generative AI is too considerable to ignore. Simultaneously, the risks are so great that we need to be cautious. Here are some issues to consider when incorporating generative AI into content marketing:
This last point deserves extra attention. Despite your best efforts, you'll make mistakes integrating generative AI into your content marketing strategy. Starting with a small pilot project will at least minimize the impact of those mistakes.
The question isn’t whether you should use generative AI—it's how you’re going to do it. Embrace AI while prioritizing human involvement and accountability. Keep them in the loop while establishing clear guidelines and processes for using AI in content creation.