What I've Learned Using AI for Content Creation

A confession: for the past few months, I've been testing AI language models to create content. From medical copy to patient education materials and my own website, I've been taking a look at how they can generate copy for different briefs.

Here's what I've discovered:

There is no point in denying it. AI language models have become an immense support for handling the nitty-gritty tasks of content creation. Having this weight off my shoulders allowed me to explore diverse writing styles and approaches to content without going over the client’s budget. More importantly, it freed up valuable time to focus on strategy for things like audience engagement, tone of voice, and channel optimization.

Human Expertise Remains Crucial

However, a tool remains a tool. After using it for a while, it became more or less clear that it is unlikely to replace human judgment and reasoning, critical thinking, and originality. While AI is unbeatable at processing larges ammount of information quickly, human skills will remain irreplaceable when it comes to content creation. If anything, originality is not AI's forte.

What Is Left For Us Humans To Do?

AI language models have shown they can handle the legwork. But creativity, ideation, and critical thinking remain uniquely human. While AI is great at churning out first drafts, humans need to add the emotions, subtext, and empathy that resonate with audiences. AI is the tool, but we are the creatives behind the brief.

A few things that only humans can do:

• Crafting Effective Prompts: Guiding the AI language models in the right direction to get workable drafts is crucial. The quality of the output is heavily dependent on how well a problem is described and what are the expectations for results.

• Ensuring Fairness and Balance: AI-generated content typically presents a single perspective on any given question. No matter how solid the first draft seems, it still requires fact-checking to confirm accuracy and credibility. Just as importantly, humans must identify potential bias, explore nuances, and incorporate additional research.

• Going Beyond the Algorithm: AI model languages often rely on popular sources, leading to generic content with shallow platitudes. This first draft must be complemented with additional information from diverse, less-known sources. Additionally, despite being trained on massive datasets, AI language models may not always understand the deeper context or relationships between ideas. Making sense of data requires abstract thinking, which remains a uniquely human skill.

After almost a year of using AI language models, I depend on it as much as I depend on my calculator. It makes repetitive tasks less burdening, allowing me to focus on core content creation. But truth be said, while it has improved my workflow, it certainly was not the content creation revolution that some anticipated.