Why Your GenAI Tools Keep Getting Things Wrong (And What to Do About It)
How to spot and fix bias before it messes up your projects
Last week, I was helping our team review a new GenAI tool (I won’t say which one, as it was a third-party SaaS platform that integrated an LLM).
When we tested it with different scenarios, something happened.
Every social media campaign it created targeted the same narrow age group. All the "expert" quotes came from people with similar backgrounds. Every product recommendation assumed everyone had the same budget and priorities.
The AI had learned these patterns from its training data and was now reproducing them in every output.
Not exactly the diverse, inclusive content we need.
This got me thinking about how many agencies are rushing to adopt GenAI tools without considering the bias problem. A reminder that we all need to be responsible in our use.
Where bias comes from
AI bias isn't some mysterious technical glitch. It happens for pretty straightforward reasons:
The training data is skewed. If an AI model learns from content that mostly features men in leadership roles, it thinks that's normal. The same goes for any other pattern in the data.
The data isn't representative. Maybe the training included lots of content from one region or industry, but not others. The AI model develops blind spots.
Human assumptions get baked in. The people building these systems bring their own perspectives. Without diverse teams reviewing the work, these assumptions stick around.
Feedback loops make it worse. When biased outputs get used and fed back into the system, the problem grows stronger over time.
What this means for your projects
As a marketer, you're probably thinking, "Great, another thing to worry about."
But here's the thing - catching bias early saves you from bigger problems later. Like having to redo an entire campaign because the AI-generated content doesn't represent your client's diverse audience.
Or worse, having a client's brand associated with biased or inappropriate content.
Practical steps you can take
You don't need a PhD in data science to tackle this. Here's what actually works:
Test with different scenarios
Before rolling out any AI tool, run it through various scenarios. Different demographics, industries, regions. See what patterns emerge.
Get diverse input early
Include people from different backgrounds when reviewing outputs. They'll spot things you might miss.
Set clear guidelines
Define what good looks like for your specific use case. Give the AI explicit instructions about the diversity and tone you want.
Monitor outputs regularly
Don't just set it and forget it. Check outputs periodically, especially when you're working on new types of projects.
Build feedback loops
When you spot problems, document them. Most AI tools can be retrained or adjusted based on feedback.
Train your team
Make sure everyone knows what to look for. Bias can be subtle, and it helps to have multiple people reviewing outputs.
The business case
This isn't just about doing the right thing (though that matters). Biased AI outputs can damage client relationships, hurt brand reputation, and create legal risks.
Plus, diverse and inclusive content often performs better anyway. It reaches wider audiences and resonates with more people.
Making it part of your process
The key is building bias checks into your existing workflows. Don't make it a separate, time-consuming task that people will skip when they're busy.
Add bias review to your content approval process. Include it in your quality checklists. Make it as routine as checking spelling and grammar.
Most importantly, start conversations about this with your team and clients. The more people are aware of the issue, the better equipped they are to spot and address it.
AI tools can be brilliant for speeding up your work and handling repetitive tasks. But like any tool, they need proper handling to get the best results.
Taking bias seriously from the start means you can use AI confidently, knowing it's actually helping your work rather than creating new problems.