Artificial intelligence has become ubiquitous in modern workplaces and universities, whether we want it to or not. LLM (large language model) and generative AI products are being in nearly every department. Marketing teams are using it to draft campaign copy, sales teams are using it to generate entire proposals, and legal departments are using it to summarize case law. Even research teams are using it for literature reviews, parsing data, and more. Certainly the perceived productivity gains seem real, measurable, and compelling. There’s a huge problem, however: AI hallucinates.

When I say AI hallucinates (lies), I don’t mean occasionally, I mean it lies regularly. Not only that, it lies with complete confidence, generating plausible-sounding information that is entirely fabricated. This includes made-up statistics from Perplexity and other AI tools. Non-existent research paper and fake authors from Claude and ChatGPT. Fake case citations from Gemini and the rest of the publicly available AI models. Product specifications that don’t match reality, garbled text and bizarre imagery from models designed to create images and video. And because this false output often sounds authoritative, it all slips through the cracks and into the public domain.

We don’t necessarily need to abandon AI. For some people, the perceived productivity benefits are too important, and for others AI bridges other gaps they’ve had professionally without requiring them to disclose their past struggles.

The solution? Create an entirely new role in your organization: The AI Fact Checker or The AI Output Auditor.

What are the wake-up calls for AI hallucination being a problem?

The warning signs that AI lies are harmful are already here. In 2023, lawyers submitted legal briefs in Mata vs Avianca that cited completely fabricated cases generated by ChatGPT. This led to sanctions and professional embarrassment. You’d think this would have warned other lawyers not to make the same mistake, but no. It happened again in 2025 in many states, including California, Utah, Wyoming, Alabama, and New York. In fact, this short sighted behavior is occurring in defendants now, as well.

Academic researchers have included citations to papers that don’t exist in their lit reviews. Some have even used AI to conduct peer review, not only submitting colleague’s unpublished work into AI systems without consent, but getting back AI interpretations of the research that are wrong. The problem extends beyond high-profile instances in law and academia. Every day, organizations publish content containing AI-generated “facts” that no one verified: statistics without sources, expert quotes from people who never said any such thing, technical specifications that are actually dangerous, and more. Even the creators of LLM AI systems are getting caught sharing misinformation generated by their own systems.

Why AI Fact Checker or AI Output Auditor roles will pay for themselves

The cost in time or money saved by using AI to augment human work can be negated by the cost of publishing AI-generated misinformation. This AI-hallucination penalty isn’t abstract, it’s measurable and can be severe. Where does this cost hit hardest?

Reputational Damage: If a company publishes or presents false statistics or fake case study results, any correction is simply not going to travel as far as the original misinformation did. You’re competing with short attention spans and an overly full information pipeline. Trust, once lost, takes years to rebuild. Competitors monitor each other’s content more closely than ever before, so a single viral example of AI-generated falsehoods can define your brand narrative for months.

Legal Liability: False product specifications can endanger people and lead to product liability claims, fabricated compliance statements can trigger regulatory investigations, and misleading marketing claims can result in FTC enforcement actions. The legal costs of defending against these issues dwarf the salary of a fact checker.

Lost Productivity: Discovering hallucinated content after publication means pulling materials, notifying customers, retracting statements, and rebuilding whatever was contaminated. Teams will spend days or weeks fixing what could have been caught in hours by a trained fact checker.

Competitive Disadvantage: Companies that publish AI-generated errors train their customers to distrust them. Meanwhile, competitors who implement rigorous verification processes and transparency about how they use (and how they fact check) AI will build reputations for accuracy and reliability.

The math is straightforward: even one mid-level hire dedicated to AI output verification costs significantly less than one major incident of published misinformation. For organizations producing high volumes of AI-assisted content, the ROI of having one or more fact checkers becomes even clearer.

Who Needs AI Fact Checkers Most Urgently?

Not every organization faces the same level of risk from AI hallucinations. This means priority should go to teams where AI-generated errors carry the highest consequences. Legal departments represent perhaps the most urgent case, as the lawyers referenced earlier in this post learned through painful professional consequences. Legal research, contract summaries, and case law analysis require perfect accuracy because a single fabricated citation can result in court sanctions, malpractice claims, and permanent damage to professional reputations.

Healthcare organizations face similarly high stakes, though the consequences are measured in patient safety rather than professional sanctions. Patient education materials, clinical summaries, and treatment protocols should not be allowed to contain errors. A fabricated statistic about medication effectiveness or a hallucinated contraindication could lead directly to patient harm, making fact-checking in healthcare settings a matter of life and death rather than merely professional reputation. Financial services and academic research teams occupy a middle ground where the consequences are severe but not immediately life-threatening. Regulatory compliance documents, investment research, and client communications in financial services operate under strict accuracy requirements, and financial regulators have consistently demonstrated that they take a dim view of explanations that amount to blaming the AI for misleading statements. Academic research teams are already publishing literature reviews with non-existent citations, which undermines the entire foundation of scholarly communication. University research integrity offices should be among the first to hire dedicated AI fact checkers, given how rapidly this problem is spreading through academic publishing.

Sales and marketing teams often produce the highest volume of AI-assisted content, which creates its own category of risk through sheer scale. Product claims, competitive comparisons, ROI statistics, and customer testimonials all need verification before publication, and the Federal Trade Commission has made explicitly clear that companies remain fully responsible for AI-generated marketing claims regardless of the technology used to produce them. Journalism and media companies face similar volume challenges but with the added burden of centuries-old fact-checking traditions that must now be adapted to AI-assisted reporting, where the stakes are existential in an era of already-declining public trust in media institutions. Government agencies round out the high-priority list because public-facing communications, policy documents, and regulatory guidance require accuracy not just for legal compliance but for democratic legitimacy. When citizens cannot trust that government communications contain accurate information, the social contract itself begins to fray.

What Does an AI Fact Checker Actually Do?

The role of an AI fact checker differs significantly from traditional fact-checking because it requires understanding not just whether information is accurate, but how AI systems generate misinformation in the first place. This position sits at the intersection of research skills, domain expertise, and technical literacy about how large language models function and fail.

The daily work involves reviewing AI-generated content before it reaches publication, which means the fact checker must move quickly enough to avoid becoming a bottleneck in content workflows while maintaining rigorous standards. This requires developing efficient verification protocols that focus resources where they matter most. High-stakes content like legal briefs, regulatory filings, or patient-facing medical information demands line-by-line verification of every factual claim, while lower-stakes internal communications might only require spot-checking or verification of specific high-risk elements like statistics and citations.

Domain expertise is essential. AI fact checkers need to recognize when something sounds plausible but falls outside the boundaries of what’s actually possible in their field. For example, a fact checker working with a pharmaceutical company needs enough scientific background to recognize when AI generates a drug interaction that doesn’t make physiological sense, even if it sounds medically sophisticated. A fact checker in financial services needs to spot when AI fabricates SEC filing numbers or mischaracterizes regulatory requirements in ways that would be obvious to someone with industry experience but might fool a general audience.

The role also requires strong research skills and comfort with verification tools. AI fact checkers need to know how to trace citations back to primary sources, verify that academic papers actually exist and say what the AI claims they say, confirm that statistics come from legitimate sources and are being used in proper context, and validate that URLs, DOIs, and other identifiers actually lead where they’re supposed to lead. They need access to academic databases, legal research platforms, industry-specific resources, and the judgment to know which sources are authoritative for different types of claims.

AI fact checkers also need a mindset of productive skepticism. They cannot assume that confident-sounding output is accurate, but they also cannot slow down workflows by questioning every single word. The skill lies in recognizing patterns that suggest hallucination, knowing which types of claims are most likely to be fabricated, and developing an intuition for when to dig deeper. This means the role requires ongoing training as AI systems evolve and develop new failure modes.

The Time to Act Is Now

AI is already being used throughout your organization. The question isn’t whether to adopt it, but whether you have the safeguards in place to catch the inevitable errors before they become public problems. Creating an AI Fact Checker or AI Output Auditor role addresses a present reality, not a future concern. The lawyers, researchers, and companies referenced throughout this post all thought their existing processes were sufficient. They learned otherwise through sanctions, retractions, and reputational damage.

Start by identifying your highest-risk AI use cases. Legal departments, healthcare communications, regulatory filings, academic research, and customer-facing marketing should top the list. Even one dedicated fact checker reviewing this content will substantially reduce your exposure. We’re developing additional resources to support organizations building these capabilities. Our upcoming one-sheet guide will help your team quickly identify AI hallucinations. We’re also creating implementation advice for building an AI fact-checking function, including hiring criteria and workflow integration. Look for that this week.

The organizations implementing rigorous AI verification now will build reputations for reliability while their competitors deal with the fallout from published misinformation.
Don’t wait for your organization’s AI incident to make headlines.

Additional third party resource: Master AI Hallucination Tracker