What happens when an article about risk gets a risk expert’s own work wrong? For organisations using AI in research and publishing, the question is a practical one: who checks the finished work, and what happens when an error passes that check?
In an August 2026 episode of Boomer Has It!, risk expert and author Michele Wucker discussed errors in a Fortunearticle about her “Gray Rhino” concept. Her experience offers a useful starting point for leaders considering how to use AI while protecting the accuracy of what they publish.
What is a Gray Rhino?
Wucker coined “Gray Rhino” to describe a high-impact, highly probable danger that is visible in advance but often neglected. The point is that people have an opportunity to recognise the threat and respond before it strikes.
That is why the distinction matters. The Fortune article described a gray rhino as “obvious in retrospect, ignored in the moment”. Wucker explained on the podcast that looking back misses the heart of her idea: the danger is already in front of us. The article also misspelled her first name.
What does the article tell us about AI and fact checking?
Fortune disclosed that its journalists used generative AI as a research tool for the article and said an editor verified the information before publication. The disclosure does not establish that AI caused the errors Wucker identified. It does show that an article can contain errors even when it has passed through a stated review process.
Wucker does not dismiss every use of AI in journalism. In the podcast, she suggested it could help with routine material, provided a person checks the result. She also raised a wider concern: if people rely on AI to do too much of their research and writing, how will they develop the judgement needed to spot mistakes?
That question applies beyond newsrooms. Communications teams, researchers and executives may use AI to organise notes or prepare a first draft. The resulting text can read confidently while getting a name, definition or source wrong. Fluency alone gives a reviewer little assurance that a claim is accurate.
Why is this a leadership issue?
Wucker described AI as a “crash of gray rhinos”: a collection of visible risks that is easier to address when broken into specific parts. For a publishing team, one such risk is allowing unchecked claims into material issued under the organisation’s name.
The practical response is to make responsibility clear. Before publication, a named person should check consequential claims against their original sources, confirm names and quotations, and distinguish a source’s words from the writer’s interpretation. The level of review should reflect the potential impact of an error. Teams also need a clear way to correct published material when a mistake is found.
These are lessons we draw from Wucker’s discussion, rather than a checklist she presented in the episode. Her Gray Rhino framework helps make the underlying decision plain: when a risk is visible, what will we do about it while there is still time to act?
For conferences, leadership meetings and risk or AI strategy programmes, Wucker brings that question to life. Her work gives audiences a way to discuss foreseeable risks, the decisions that allow them to grow, and the judgement needed to respond.
Contact us at info@speakersconnect.com to engage Michele Wucker for your next conference.