I started using AI tools to draft business articles two years ago. It saved time. It also nearly damaged my credibility twice—once with outdated statistics, once with a tone that sounded like a corporate training manual, not me. Since then, I've built a review system that catches these problems before publication.
AI is a drafting tool, not a publishing tool. That distinction matters when your reputation depends on expertise. Here's how I vet every piece before it goes live.
Check the Facts Against Your Own Experience
AI models hallucinate. They invent statistics, misquote sources, and confidently state things that feel true but aren't. I always read the first draft with skepticism.
For my last article on business networking rituals, the AI cited a study I'd never seen. I searched for it—didn't exist. The AI had synthesized something plausible from fragments of real research. Had I published it, readers who fact-check would've caught the error and questioned everything else.
Now I treat every statistic, case study reference, and research claim as unverified until I verify it myself. I ask:
- Have I encountered this data in my own work?
- Can I find the original source?
- Is the attribution correct?
- When was it published? (AI sometimes dates things wrong.)
If I can't verify it in five minutes, it gets rewritten or removed. Your credibility isn't worth the shortcut.
Read It Aloud and Listen for Your Voice
AI often writes in a safe, generic B2B tone. Sentences are balanced. Vocabulary is predictable. It sounds like nobody.
I read the draft aloud—not skimming, actually reading each sentence out loud. When I hit a paragraph that doesn't sound like me, I stop and rewrite it.
For example, AI wrote: "The facilitation of meaningful professional relationships requires a systematic approach to interpersonal engagement." I'd never say that. I rewrote it: "To build real professional relationships, you need a process—otherwise it's just coffee meetings that go nowhere."
Same idea. Different voice. The second one is mine.
Check for corporate jargon—words like "leverage," "synergy," "optimize," and "holistic." These are fine sometimes. Used carelessly, they dilute your expert voice and make you sound like someone reading a script.
Test the Examples for Relevance
AI loves examples. The problem: they're often generic or slightly off.
An AI draft about personal branding mentioned "a software founder who grew her LinkedIn following to 50k." Good detail. But the story was vague—no context about what she actually did differently. I replaced it with a real case: a B2B sales leader I coached who changed his LinkedIn headline from a job title to a value statement and saw meeting requests increase by 40% in three months.
Specific, concrete, verifiable.
Always ask: Could I defend this example in a conversation? Do I know someone this happened to? Can I cite specifics—numbers, timelines, outcomes?
When examples are vague, generalize them instead. "A founder I worked with" is better than a made-up story. "Most executives I've trained report this challenge" is honest.
Check the Structure Against Your Article Purpose
AI structures content logically, but not always for your goal.
I write about business networking and personal branding. When I ask AI for an article about "building executive presence," it often delivers a generic self-help structure: definition, importance, five steps, conclusion. Useful, but not distinctive.
I reorder it. Lead with a problem I see in my clients—executives who are skilled but invisible. Then show what presence actually means in practice. Then the steps. The reordering puts my perspective first.
Before publishing, I ask:
- What problem does my audience face first?
- What's the insight they need before tactics?
- Where should I place the most actionable section?
- Does this flow match how I'd explain it in a conversation?
If the AI structure feels borrowed from a template, rebuild it.
Trim the Unnecessary Paragraphs
AI is wordy. It adds context paragraphs that sound professional but don't move the article forward.
I cut ruthlessly. Every paragraph should either:
- Introduce a new idea
- Support it with a concrete example
- Explain how to apply it
If a paragraph does none of these, it goes. I find that cutting 15–20% of an AI draft always makes it stronger.
Verify Tone Consistency Across Sections
Long AI articles sometimes shift tone mid-piece. One section sounds informal, the next sounds academic. This happens when AI processes different prompts or contexts.
I read for tone consistency in a separate pass. If tone shifts, I pick the one that sounds most like me and rewrite sections to match.
Do a Final Freshness Check
Before hitting publish, I ask one final question: Would I write this today?
Not "Is it accurate?" That's the earlier check. I mean: Does this reflect my current thinking? Have I grown past this perspective? Does this advice still hold given what I've learned this year?
If yes, publish. If there's hesitation, revise. Your blog is a record of your evolving expertise, not a checklist of ideas AI found online.
Why This Matters
AI tools are efficient. But efficiency without judgment creates liability. I've seen executives publish AI-drafted thought leadership that contradicted their own advice from six months earlier, or cited research that didn't exist, or used language so generic that readers couldn't tell who wrote it.
You can use AI to draft faster. Just don't skip the review. Building your personal brand means standing behind every word you publish. The AI didn't write it. You did.
This review process takes me 20–30 minutes per article. Worth it. Your credibility takes years to build and seconds to lose.
For more on maintaining thought leadership while scaling content, see my guide on content strategy for executives.
Need help building a content and communication system that scales? Check my executive communications consulting.
