When you cannot trust what you see, what happens? For many people, that is becoming their reality. Whenever they go online, watch videos, look at reviews, like photos, or even read comments from other users on a social media platform, there are seeds of doubt. “Is this even real?” AI has taken those seeds and blown them up. Even a few months ago, it was relatively easy to spot AI generated content. Now, even as someone who works with AI everyday, I can tell you it is even difficult for me to identify whether or not a photo or video has been AI-generated.
Where does that leave us; when we can no longer identify what is real from what has been generated by LLMs, LVMs, or other AI systems? For organizations, it means that consumer trust, which is already extremely hard to build up, and very easy to dismantle, will be even harder to attain. In some ways, it means that word of mouth recommendations will matter more so than ever before. As people continue to seek connections that are grounded in reality, it will mean that the importance of digital content will mean nothing if trust is not the focal point of every organization’s mission.
As information is harder to identify, and as AI images and content continues to disseminate, companies will need to adapt as trust becomes even harder to win.
Looking at the numbers
90% of people reported to be concerned about AI spreading misinformation, according to an August 2024 survey from the Pew Research Center. Of that number, 34% cited that they were extremely concerned. In parallel, in May 2026, 5W identified a 99-point favorability gap between daily AI users (+57) and everyone else (−42), citing the widest behavioral divide in American public opinion.These numbers are important. They show that for one, more people are worried about AI spreading misinformation than ever before. For another, as some people continue to use AI and have it become a regular part of their day, while others do not, it creates gaps. These are gaps in information, trust, and credibility. These gaps will increase the distrust across the board.
This lack of trust is not limited to politics and societal sentiment. It spans across overall trust in institutions and organizations. In years previous, “seeing is believing” was the status quo online. You could actually get evidence that things did or did not happen when you saw a video, photo, or post online. Now, that is not the case, and organizations need to step up where the gap is forming.
Why AI Is Making False Information Harder to Spot
Part of what makes this moment in time different comes down to accessibility. Previously, producing convincing fake content used to require real skill, time, and resources to stage something believable. GenAI tools have quietly erased those barriers and have made it incredibly easy for anyone to create what they want, in just a few minutes. And as time has gone on, and as AI programs are being constantly trained on new data and trained on how to improve their outputs, the false content is often good enough to pass a casual glance, or even a fairly careful one.
Just as important is scale. The output online is not a single, perfectly executed fake slipping through the cracks. Instead we are seeing massive amounts of data that is flooding in all at once. The sheer volume of "good enough" content is making our ability to spot misinformation harder. A fabricated review doesn't need to be flawless if there are a thousand similar ones surrounding it, each reinforcing the others' credibility. One deepfaked spokesperson video doesn't need to survive frame-by-frame analysis if it is shared and reshared without verification.
At the end of the day, volume, not precision, is what erodes trust at scale, because it overwhelms the normal human instinct to verify before believing.
This not only decreases trust, but it decreases tolerance. We will see people just stop going on a website, platform, or engaging in content altogether. Organizations need customers to be engaged in their product or service, so when they go offline, companies will run into serious issues.
What's Actually Driving the Erosion of Trust
As increased exposure of AI-curated content dismantles peoples’ trust and tolerance, it is going to have consequences. One of those is that people will default to suspicion. This will make it even more difficult for organizations to prove their value.Platform incentives that reward engagement over accuracy will continue to erode trust. As I stated before, trust is very hard to earn and all too easy to destroy in one fell swoop. Organizations and platforms that make it a chore for people to fact-check will be the ones that face the most consequences and loss of business.
Scams, clickbait, and manipulated content, especially at the volume we are seeing, is training us all to be cynical and distrustful of one another and especially of larger institutions and organizations, unless there is a concerted effort to build a bridge of trust before it can crumble. It will be a lot smoother for that bridge to be built now than in the future, because when that trust is eroded, it likely will not be coming back.
How Companies Can Adapt
The starting point is transparency as a baseline; the bridge needs to be built before the trust is gone. You cannot assume an audience will take a claim at face value, brands need to show their work: where information came from, how it was verified, and what process stands behind a given claim. Sourcing and provenance are necessities for organizations to establish that branch of trust.That transparency, though, only goes so far. Where skepticism is the default, self-attestation carries far less weight than it used to. A company insisting "trust us" is making a claim about itself, not offering evidence. It also shows that you think audiences will accept your claims at face value, which will backfire. Independent validation via third-party audits, outside reviews, verified data, or credible external sources, will shift the burden of proof from "believe our claim" to "here's who checked."
This trust cannot be achieved in one smart campaign. It takes consistency; repeated, verifiable accuracy over time, delivered even when no one is watching closely or asking for it. Only with a proven, dated track record can organizations maintain a level of trust with their customers. But that effort and dedication will pay off when organizations that have invested in maintaining trust continue to thrive, while others lose credibility and business.
User experience and content strategy should start from the premise that the user is skeptical.
This means surfacing verification points naturally, making sourcing visible without requiring extra effort or paywalls to find it, and avoiding design patterns that rely on an audience's willingness to simply believe. In a post-trust internet, the companies that adapt and survive will be the ones that have been maintaining and reinforcing a bridge of trust between themselves and their customers.
Author Bio: Frank Palermo is the Chief Operating Officer of NewRocket, where he helps guide the company’s growth strategy and strengthens its position as a leading advisor in digital workflows, AI, and enterprise transformation. He brings decades of experience building and scaling technology and consulting organizations, with a career that spans software engineering, enterprise platforms, cloud, data, and AI-driven services. Frank is known for combining deep technical fluency with clear operational vision, and for helping clients translate modern technologies into meaningful business outcomes.
Reviewed by by Irfan Ahmad.
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by Guest Contributor via Digital Information World
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