Introduction to AI in Content Marketing
In late 2023, Sports Illustrated became embroiled in a scandal involving the publication of dozens of AI-generated articles under fake bylines. The fallout was swift, with the editor-in-chief being fired and the brand’s credibility taking a hit. This incident highlights the risks associated with using AI in content marketing, particularly when it comes to maintaining a brand’s voice and tone.
The Risks of Using AI in Content Marketing
While AI has the potential to revolutionize content marketing, it also poses significant risks. One of the biggest risks is sounding generic or getting facts wrong. Without strong guardrails, AI tends to default to safe but stale phrasing, or worse, confidently fabricates misinformation. This can damage a brand’s reputation and erode reader trust.
Putting Up Guardrails
To avoid these risks, marketers need to put up guardrails before unleashing AI on their content. This involves understanding the basics of training AI on brand-aligned inputs and clear intent signals. A three-layered safety net can be implemented quickly, regardless of technical expertise. This includes:
- Starting with reusable prompts that specify exactly who the AI is speaking to, which tone to use, and which words or topics are off-limits.
- Adding a built-in cheat sheet, such as Retrieval-Augmented Generation (RAG), which lets AI pull relevant facts from a trusted source as it writes.
- Layering in quality control, including automated style checkers and human editors to flag banned words and tone inconsistencies.
Feeding AI Great Examples
Feeding an AI model with great examples is crucial to teaching it to sound like the brand. This involves:
- Building a "gold standard" dataset with content that already works, such as flagship blog posts or genuine thought leadership pieces.
- Giving the AI rich context by tagging each piece with metadata about audience, funnel stage, geographic region, and compliance requirements.
- Being intentional with what is left out, ensuring that only content that reflects the brand’s voice and tone is included in the training set.
Testing, Tuning, and Tossing What Doesn’t Work
Once the guardrails are solid and the content examples are carefully curated, the AI’s output can be adjusted to match the brand’s voice more precisely. This involves:
- Choosing the level of intervention carefully, using techniques such as Low-Rank Adaptation (LoRA) for subtle voice tweaks.
- Testing systematically, using a 70/20/10 ratio to split examples into training, validation, and testing groups.
- Making sure the math works, ensuring that the cost of GPU time and platform fees does not exceed the editing hours saved within six months.
People Powering AI’s Potential
People are the secret sauce that can turn AI from a liability into a differentiator. Today’s content teams need solid talent to fine-tune the tech and enforce editorial standards, including:
- Prompt architects who know how to steer tone and structure through careful A/B testing.
- Model specialists who can evaluate which tools and settings deliver the best results for each content type.
- Journalistically minded editors with strong fact-checking chops to catch red flags before a piece publishes.
Frequently Asked Questions
What’s the Biggest Risk of Using AI in Content Marketing?
The biggest risk is sounding generic or getting facts wrong. Without strong guardrails, AI tends to default to safe but stale phrasing, or worse, confidently fabricates misinformation.
How Much Content Do I Need to Train an AI on My Brand Voice?
Less than you think, as long as it’s the right content. A few dozen examples that clearly reflect your tone, structure, and audience fit are far more valuable than a massive archive of outdated or inconsistent pieces.
How Can I Tell if My AI Training Efforts Are Actually Working?
Treat it like a science experiment: split your sample into training, validation, and test sets, and have human reviewers rate the outputs without knowing which were written by AI and which weren’t.
Conclusion
Using AI in content marketing can be a powerful tool, but it requires careful consideration and planning. By putting up guardrails, feeding AI great examples, testing and tuning, and powering AI’s potential with people, marketers can avoid the risks associated with AI and create high-quality content that resonates with their audience. With the right approach, AI can amplify a brand’s voice and tone, rather than flattening it into forgettable corporate-speak. By following these guidelines and being intentional with AI training, marketers can create content that is both efficient and effective, and that ultimately drives business results.