You approved that piece eight months ago.
An executive byline. You drafted it, shaped the argument, got the sign-off from legal and leadership. It went live on a trade publication. You moved on.
Then, someone flagged it. A statistic in the third paragraph was off. Not wildly wrong, but wrong enough. The number your CEO cited confidently, in their name, on a site that gets indexed and archived and screenshotted, was slightly off.
The piece is live on a third party website, and now you have to try to issue a correction and minimize the impact to the CEO’s reputation.
If this hasn't happened to you, it's happened to someone you know. And if you've been using AI to move faster on drafts, the odds that something slipped through are higher than they were two years ago.
You're not imagining that pressure. In a 2026 industry survey of 1,400 professionals, PR, media, and communications ranked highest of any sector for misinformation anxiety with 59% citing it as a top concern, against 40% across all industries (LexisNexis, Future of Work 2026). This isn't a niche concern. It's the sector's defining one.
The content lifecycle can be longer than you think
A lot of what we do in communications is built to move fast. Crisis response. Media relations. Social media. Most of it has a short shelf life by design.
Executive bylines don't. Neither do white papers, op-eds, contributed articles, or the LinkedIn essays you ghostwrite for your leadership team. That content goes into the permanent record: indexed, searchable and attached to real names. It doesn't expire when the news cycle moves on.
In some cases, the impact of inaccurate information can be damaging professionally and personally.
Roughly 83% of pages that get indexed are indexed within the first week of publication (Onely research.) Once indexed, content can persist in search results for years, even after deletion, through caching, archiving, and third-party reproduction.
Most of us know this. We feel the weight of it every time something goes out under a C-suite name. What's shifted is how much of that content we're now expected to produce, and how much of the first draft is coming from a tool that generates confident-sounding prose whether or not the underlying facts hold up.
Don’t mistake polish for accuracy
AI-assisted drafts have a particular quality that makes fact-checking harder, not easier. The writing sounds polished. The statistics look specific. The sourcing feels authoritative. Nothing flags as obviously wrong, so you read for tone and clarity and flow (which is exactly what you're trained to do) and the number that's slightly off, or the study that doesn't quite say what the draft implies it says, slips right through.
If this feels familiar, you aren’t alone. More than a third of PR and Communications professionals (36%) say they struggle to verify whether an AI tool's output is based on high-quality sources, and 32% say they can't reliably identify a hallucination or an incorrect output when they see one (LexisNexis, Future of Work 2026). That's not a knock on the profession, it's a resourcing gap. Communicators are being asked to ship more content than ever, with less training on how to use AI tools or to catch what they get wrong.
In 2024, a reporter at a small Wyoming newspaper resigned after his published articles were found to include AI-generated fabricated quotes and stories.
In New York, when two lawyers filed a court brief with fake cases generated by ChatGPT, a federal judge sanctioned and fined them $5,000 each.
The issue is not a failure of attention. It's that the output doesn't look like something that needs the kind of scrutiny it actually needs.
Most comms teams have developed some version of a review process. You read it carefully. You Google the stats that feel off. You loop in a subject matter expert before anything goes under a senior leader's name. That works until you're managing more volume than that process was built for. 62% of PR professionals say they use genAI tools without approval because deadlines won't wait, and of those respondents, 61% say it comes down to needing the efficiency (LexisNexis, Future of Work 2026). The pressure isn't hypothetical. It's the daily condition most teams are already operating under.
Why AI content needs a credibility review, not just an editorial review
If a factual error makes it into a piece under your CMO's byline and someone (a reporter, a regulator, your own leadership) asks what the review process looked like, the answer matters. Not just for the correction conversation, but for what it says about how your function operates.
The professionals carrying the most risk right now usually aren't the ones being careless. They're the ones running a rigorous manual process that's continually outpaced by what they're being asked to produce. The gap isn't effort. It's infrastructure.
Some in-house comms teams are starting to build a more formal credibility review into their workflow. A documented step, not just a careful read. This isn't an argument against AI in your content process. Used well, it makes good communicators more productive.
It's an argument that the output of AI-assisted work needs a different kind of review than the output of human-drafted work - one that specifically evaluates factual accuracy, source credibility, the precision of claims, and whether the content is supported by context that can hold up to scrutiny.
That's not what a spellchecker does. It's not what an editor does when they're reading for voice and flow. It's a different function, and right now most content workflows don't have it. Tellingly, when the same LexisNexis research asked leaders across industries what actually builds trust in AI-assisted content, the top answers were transparent data sources (45%) and human oversight (41%). It’s clear they are looking for a structural layer that makes sourcing visible and keeps a person in the loop. That's the core function of a credibility review.
How to build an effective credibility review layer into your workflow
It doesn't require a new approval chain. It requires a different kind of checkpoint, one that's separate from your editorial review and focused on a specific set of questions.
1. Separate the credibility review from the copy review. Reading for voice, clarity, and flow is not the same as reading for factual integrity. Most review processes conflate them. When you read a polished draft looking for tone, your brain fills in meaning. For example, a number that sounds plausible reads as correct. The key is to treat them as two distinct passes, done by two different sets of eyes if possible. The credibility pass happens after the draft is clean, not during it.
2. When AI is generating the first draft, flag every factual claim before the review. Before the credibility pass begins, go through the draft and mark every number, study, name, title, date, or attributed claim. Not to check them yet, just to make them visible. Flagging first means your reviewer isn't simultaneously hunting for claims and evaluating them. It slows down one step to speed up the actual verification.
3. Verify every claim against a primary source, not the AI's citation. AI-generated drafts often cite sources that exist but don't say what the draft implies they say. The original study may be from a different year, a different population, or a narrower finding than how the statistic was written. If you can't pull up the primary source and confirm the specific number or claim, it doesn't go out under your executive's name. This is the standard. Write it down so it's not subject to time pressure.
4. Build a content register for anything published under a named executive. A simple log: piece title, publication, date, the claims that were verified and against what. Two reasons. First, if a question arises six months later, you have a paper trail for your review process. Second, it creates accountability in the workflow — people fact-check more carefully when they know they're signing off on something documented.
5. Document a repeatable process. Not every piece of content will take the same amount of time to review. A short LinkedIn post and a white paper distributed to a regulated-industry client will take different amounts of time and involve varying levels of complexity. Define the process explicitly for the various types of content that you create and the time it should take to complete the credibility review for each category.
6. Build in a lag between draft completion and publication. The hardest credibility errors to catch are the ones you're staring at when you're tired and behind deadline. A minimum 24-hour buffer between "draft final" and "approved to publish" for high-stakes pieces creates the breathing room to catch what the first three reads missed. It also gives you time to pull a source you meant to verify and didn't.
None of this eliminates the risk entirely. What it does is make the risk manageable and, critically, documented, which matters both for preventing errors and for explaining your process if one makes it through anyway.
The volume of executive content isn't going down. The tools generating first drafts aren't getting less confident-sounding. The permanent record keeps accumulating. A credibility layer in your workflow isn't a hedge against worst-case scenarios. It's standard operating infrastructure for the job communications teams are now doing.
The piece you approved 8 months ago is probably still live somewhere. The question worth sitting with is, if there had been a misquote or error, whether your current process would have caught it, and whether you could demonstrate that it did.
Ready to improve your content workflow? Join our early adopter program and see exactly how a credibility layer can help your team.
