Brands aren’t losing audiences because they’re artificial intelligence (AI); they’re losing them because they blindly trust it too much. Their content teams are using AI to publish content faster than ever before (and more of it), but is it any better? As many brands have learned the hard way, this content shouldn’t have been trusted without a second pair of eyes looking at it before it was published. For many of us readers, this has created a “trust gap” — and that’s exactly why human judgment and human editors of AI content matter. AI can draft content. Human writers protect trust.
Readers are noticing more and more frequently when content feels machine-made. Some care about it, some don’t, some stop reading. But for anyone building a corporate or personal brand today, the real question isn’t whether AI is being used at some point for content creation. The question is whether a human writer or AI content editor has reviewed the content before it was published.
Trust in general has been dropping. The 2026 Edelman Trust Barometer found that 70% of nearly 34,000 respondents across 28 nations are hesitant or unwilling to trust someone who has different values, information sources, approaches to societal problems, or backgrounds than them. This majority holds across income levels, gender, age groups, and developing and developed markets.
Regarding trust and AI, the 2025 Edelman Trust Barometer noted that trust in AI can vary widely. In China, for example, 72% of people trust AI, but in the U.S., that number drops to 32%. This isn’t a difference in policy or regulation, according to the report, but is instead a reflection of how societies perceive risk, control, and opportunity. Some see AI as a force for progress, while others worry about its unintended consequences. But the disparity in trust doesn’t stop at national borders. Older adults, those with a lower-income, and women are less likely to trust AI.
The Roots of Mistrust
The mistrust of AI has its roots in various factors. The one that I consider the most important is factual accuracy.
The problem with AI is that it always sounds right. AI writes with absolute certainty. It writes fiction with the same certainty as it writes fact – and that’s the problem. Every sentence holds the same authority, whether it’s describing the latest game console or the latest Supreme Court case. The sentences flow, the tone seems good, and if no one checks, this AI content gets published.
Time and time again AI has generated factually inaccurate text (“hallucinations”). It can cite studies that don’t exist, make up statistics, or attribute quotes to the wrong person. The problem isn’t just that mistakes happen; it’s that the writing gives the reader no reason to doubt its accuracy. A legal brief might reference a fictitious court case or a health article might claim “75% of doctors recommend” a treatment that a single doctor has never endorsed.
That’s the danger. Readers assume a polished, professional tone equals content accuracy. But one false claim will unravel the credibility of an entire piece and can spell doom for a brand or person’s reputation. And once trust is broken, it’s extremely hard to earn back. This is exactly why a human judgment must be applied to all AI-generated content to ensure it meets the highest standards of accuracy and reliability.
Source Transparency Matters
Another key issue with AI is a lack of transparency and accountability in AI systems. Many of these models operate as “black boxes,” making it difficult to understand their decision-making processes fully, even by those that have designed and work with them. This opacity raises concerns about the potential for unintended consequences, errors, or even malicious exploitation.
Traditional writing leaves a paper trail. You interview someone, cite a study, reference a book, and then generate multiple drafts of your article. AI instead generates text from patterns it learned during training. When it does provide citations, some are real, some are not, but they are plausible enough to slip past casual readers.
Even accurate information can carry a hidden question: Was it pulled from copyrighted material? Scraped from someone’s blog without permission? “Borrowed” from a licensed database the company doesn’t pay for?
This ethical tension is real and a skilled human editor of AI content can navigate it responsibly, ensuring transparency and ethical use.
Readers Notice When Writing Feels Robotic
Read enough AI-generated content, and you start to notice that it sounds the same. There’s a certain cadence and phrasing. Certain words begin to appear again and again.
Good writing has texture to it. It reflects how someone thinks, what they feel, what they value, and how they see the world. AI can imitate tone if given enough examples, but it doesn’t have human instincts (how could it?).
Human editors of AI content can sense when something is off. They can feel if the writing is too stiff, too vague, or is trying too hard. They know how humans are supposed to sound and how to humanize AI content. The result: They eliminate AI sameness.
Privacy, Security, and Bias
Privacy and security risks are also a significant source of mistrust. As AI has become increasingly integrated into various aspects of our lives, there are legitimate concerns about the potential misuse of personal data and the vulnerability of these systems to cyberattacks or unauthorized access.
At the same time, AI can perpetuate biases because AI’s large language models (LLMs) are trained on vast datasets that may have inherent biases. They can absorb, amplify, and perpetuate those biases, leading to text that reinforces harmful stereotypes, propagates inaccurate information, narrows perspectives, and denies exposure to diverse ideas.
Context and Judgment
AI understands language patterns, not the world. A sentence that works in one context can be disastrous in another when it’s considered offensive, misleading, legally risky, or just tone-deaf.
Human writers catch these missteps instinctively. We know when a joke isn’t funny and when a topic requires sensitivity. Our human experience informs our human judgment; AI only produces word sequences based on probability.
This gap can show up subtly in a poorly chosen metaphor in a grief-related article, a flippant tone in a serious thought leadership piece, or a reference that alienates part of your audience. These aren’t grammar issues that get fixed with automated AI solutions that check content; these are judgment calls made by humans to ensure that accurate and contextually appropriate content.
The Serious Implications of Lack of Trust in AI
Based on these factors, it is easy to see how this lack of trust in AI has serious implications for its adoption and utilization in generating content. Here are some of the major areas where the trust gap can rear its ugly head:
Content Credibility: One of the most significant challenges is the perceived lack of credibility of AI-generated content. Many individuals remain skeptical about the accuracy, objectivity, and reliability of content produced by AI systems. This can range from consumers shopping online to industries where factual integrity is paramount, such as healthcare, law, journalism, and academic research.
Creative Industries: Creative industries like writing, design, and multimedia production are concerned about the potential for AI to undermine human creativity and originality. Because it’s actually just a pattern of words based on probability, AI-generated content lacks the authenticity and emotional resonance of human-created works. Writers in particular can face the inaccuracy of AI content checkers, which can flag original content as AI-produced and vice versa. At the same time, authors must be aware that their writings may be in a database that companies use to train their AI programs.
Brand Reputation: Companies that rely heavily on AI-generated content may face reputational risks if their audiences perceive the content as unreliable, biased, or off-brand. This can erode brand trust, damage customer relationships, and impact sales and profitability.
Legal and Ethical Considerations: The use of AI-generated content raises complex legal and ethical questions. These include copyright, intellectual property rights, and the liability for potentially harmful or inaccurate content generated by AI systems.
Human Editors Bridge the Trust Gap
It is crucial to address the underlying trust issues to fully embrace generative AI. Here are some strategies that can help bridge the trust gap:
Human Oversight and Fact-Checking: Implementing robust oversight and fact-checking by highly skilled human writer and AI editors can mitigate the risks of inaccuracies or biases in AI-generated content. At the same time, they can produce clear, concise, and compelling messages that will resonate with their intended audiences. The collaboration between human experts and AI systems should leverage the strengths of both to produce higher-quality content.
Transparency and Explainability: AI developers and companies need to prioritize transparency and explainability in their systems. By providing insights into the decision-making processes, data sources, and potential biases of AI models, human editors can better understand and evaluate the outputs.
Ethical AI Frameworks: The development and adoption of ethical AI frameworks, guidelines, and best practices can help ensure that AI systems are designed and deployed in a responsible and trustworthy manner. These frameworks should address issues such as fairness, accountability, privacy, and transparency. This in turn can help ensure that the editors reviewing AI-generated text are not exposed to any ethical issues or can be on heightened alert to eliminate them.
Continuous Improvement and Monitoring: AI systems need to be continuously monitored and updated to address emerging issues or biases. Regular audits, feedback loops, and improvements can help enhance the reliability and trustworthiness of AI-generated content over time. This will help to ensure higher quality AI-generated text, but it will still need experienced human editors to oversee the content to publication.
The Road Ahead
As generative AI continues to evolve and become more widely adopted, trust will continue to remain of paramount importance. I hope that the technological improvements promised by new and future AI models will help alleviate this distrust in AI. At the same time, it is up to the organizations that are using AI to generate content to make the commitment to more stringently monitor the outputs that their chatbots generate. They need to employ highly skilled human writers and AI content editors to check any and all of their AI-generated content. They have too much at stake to avoid this crucial step in their AI workflows.
“It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you’ll do things differently.” — Warren Buffett
Have something valuable to say but need help shaping it for your audience?
Let’s talk about what you’re working on and what you need to accomplish.