Ask the AI Help Desk: Fact Check Follies

Question: I’ve been trying to help my content marketing manager move faster on our trend reports, and she’s started leaning heavily on AI for early research. She is asking AI to summarize statistics from blog posts and PDFs, identify patterns, and pull out “key insights” for our drafts.

At first, this seemed efficient, but now I’m getting worried because some of the numbers she’s surfacing don’t match sources I’m familiar with, and the AI occasionally cites links that don’t exist or mixes older data with more recent reports. Since our leadership team relies on these reports to inform strategy, I want to make sure our research remains accurate and credible, and I need a clear way to explain how to use AI as a helpful research assistant without treating it as a fact-checker or authoritative source. 

Answer: AI can be a helpful tool when you’re trying to find, summarize, and incorporate data into larger projects. But that’s all it is — a tool. It’s not a statistician who can compare methodologies or interpret patterns across multiple datasets. It’s not an industry veteran who understands the operational realities behind the numbers. Like a calculator or a pen, AI can accelerate your work, but you’re still the one responsible for accuracy, interpretation, and final judgment. When we forget this foundational principle, that’s when problems happen. 

It’s tempting to ask AI to hand you a list of relevant stats and datapoints, but doing so can introduce unnecessary risks. AI may provide outdated statistics, hallucinated or fabricated numbers, invented citations, or broken links. It may also mix data from different years, summarize findings out of context, or confuse correlation with causation. And because AI can’t truly evaluate methodology, sample size, geographic scope, or publication date, it’s easy for inaccurate — or irrelevant — information to slip into your drafts. 

So, should we avoid AI entirely when it comes to gathering research? Of course not.

But you do need to understand the strengths and limitations of the tool.

You should always verify that a report or study is real, reputable, and relevant to your audience. You can ask AI to suggest recent reports related to your topic, but it’s up to you to confirm that: 

• The report actually exists and isn’t a hallucination
• It’s the most recent version of a recurring study
• It came from a reliable organization or data provider
• The methodology and scope align with what your project needs
• The information genuinely supports your angle 

Once you’ve confirmed those things, AI becomes a valuable assistant. You can use it to: 

• Identify themes or patterns across the data
• Summarize long reports into key takeaways
• Brainstorm angles, questions, or follow-up ideas
• Draft outlines rooted in verified information
• Generate comparisons across multiple reports to identify alignment or contradictions
• Turn verified data into simple explanations, analogies, or definitions for your audience
• Suggest visual or narrative structures for presenting the findings
• Reframe the same information for different audiences or roles
• Highlight gaps or missing information that could strengthen your argument 

And you can make your fact-checking process easier by adding small guardrails during the prompt stage. For example: 

• Provide the source when pasting data into AI so it has more context
• Ask AI to flag uncertainties by adding a line such as, “Show me any claims that seem unclear or need further verification”
• Use AI to compare definitions and scope, especially for metrics like revenue, output, utilization, or units
• Ask the AI to provide confidence levels for each claim and explain why
• Request publication dates for any sources it references
• Tell the AI to separate verified facts from interpretations or speculation
• Instruct the model to analyze only the text you provide and not introduce outside information
• Ask the AI to list any assumptions it made while summarizing the content
• Require consistency when citing sources, and direct it to label any unclear origins
• Ask the AI to flag contradictions in the data or note when two sources conflict 

Next Steps

If your company wants to utilize AI to speed up the data gathering process, I suggest implementing a three-step workflow: 

1. Gather: Use AI to surface themes and potential data sources, not final facts. 
2. Verify: Go to the original source and confirm accuracy, date, methodology, and context. 
3. Synthesize: Use AI to summarize, organize, or draft text after facts are confirmed. 

By using this method, you’ll improve the speed and efficiency of your project without compromising accuracy or credibility. 

Your AI challenges deserve answers. Send in your questions and see them solved in the next edition of Ask the AI Help Desk.