AI-generated content now permeates almost every area of corporate communication. Texts, videos, presentations, training materials, and product descriptions are now created at a speed and volume that would have been unthinkable just a few years ago. What at first glance appears to be a huge gain in efficiency turns out to be a double-edged sword upon closer inspection. While production costs decrease and scalability increases, something on the recipient's side diminishes that no algorithm can replace: trust.
A large-scale study by the Melbourne Business School, which surveyed over 48,000 people in 47 countries between November 2024 and January 2025, paints a sobering picture. Although 66 per cent of respondents already use AI regularly, less than half are willing to actually trust it. Compared to an earlier survey before the launch of ChatGPT in 2022, a clear trend emerges: with increasing use, it is not trust that grows, but scepticism (cf. Melbourne Business School, Global AI Trust Study 2025).
When machines explain, but mean nothing
The actual problem is not the quality of AI-generated content. Many are grammatically flawless, structurally coherent, and factually correct. What they lack is something that is difficult to quantify but immediately noticeable: conviction. AI can explain, it can structure and simplify. What it cannot do is mean. It has no personal relationship to the content, no responsibility for the consequences of a statement, and no awareness of the context beyond statistical probability.
For the recipient, this makes all the difference. Human explanation always carries an implicit promise: I stand by this. AI content does not carry this promise. It appears like a smooth surface behind which no one is visible. In sensitive areas such as strategic communication, compliance and change management , or complex technology topics , this is precisely the problem. People do not trust content – they trust senders. Where there is no discernible sender, distance arises.
The AI Authorship Effect: When Authenticity is Lost
How profound this distrust actually is has been documented in a groundbreaking study in the Journal of Business Research . Kirk and Givi (2025) demonstrated in seven pre-registered experiments what they call the "AI-Authorship Effect": as soon as consumers believe that emotional marketing communication was written by an AI, positive word-of-mouth and customer loyalty significantly decrease. The mechanism behind this is remarkable – AI-generated content is perceived as less authentic, which triggers "moral disgust" in recipients and permanently damages brand loyalty (cf. Kirk & Givi, 2025, Journal of Business Research).
Particularly insightful: the effect was significantly stronger for emotional messages than for purely factual information. What does this mean for companies? It means that precisely where communication is intended to have the greatest impact – in establishing an emotional connection with the target audience – AI content reveals its greatest weakness.
Interchangeability undermines credibility
Another core problem of AI-generated content is its interchangeability. Content is similar in tone, structure, and argumentation. It is cleanly formulated but generic. The more such content is consumed, the less weight the individual contribution has. The Nuremberg Institute for Market Decisions (NIM) found in a 2024 study that 100 per cent of the 600 marketing professionals surveyed already use AI in their activities. At the same time, their research shows that merely labelling content as AI-generated leads to a more critical evaluation – even if the content is identical in substance to human-created material. Dr. Fabian Buder, Head of Future & Trends Research at NIM, sums it up: "Transparency alone does not solve the fundamental problem" (cf. NIM, Transparency Without Trust, 2024).
This leads to a paradoxical effect: the more is explained, the less is believed. Explanation loses its value if it is not recognisably born of experience, conviction, or responsibility. Even technically flawless AI content is subject to a kind of "Trust Penalty" – a systematic penalty of trust that sets in as soon as recipients get the impression that there is no human behind a message, but an algorithm. Trust is not built through the repetition of generic messages, but through classification. People want to know why something applies here, is relevant to them, and becomes important now. AI content often remains on a general level. It explains the right thing, but not the crucial thing.
Why explanation is an offer of relationship
Explanation is not a neutral, technical process. It is always an offer of relationship. Whoever explains implicitly says: I have understood and I will help you to understand. This relational level is completely missing in purely automated content.
Human explanation recognises uncertainties, sets priorities, and deliberately omits things. It does not say everything, but what is relevant. This very selection is a sign of responsibility. If companies explain without visibly choosing, weighting, or taking a stand, distrust arises – not because the content is wrong, but because it appears arbitrary.
The Getty Images study "Building Trust in the Age of AI", which surveyed over 30,000 adults in 25 countries, confirms this finding from a visual perspective: 98 per cent of respondents consider authentic images and videos crucial for building trust. Almost 90 per cent demand transparency in the use of AI images (cf. Getty Images, Building Trust in the Age of AI, 2024). The message is clear: where visual communication is intended to build trust, a human touch is not a luxury – it is a necessity.
Illustration and explainer video: Trust through visible decisions
This is precisely where illustration and the explainer video format can play a crucial role. Not because they are inherently more emotional than text, but because they make conviction visible. A well-designed explainer video shows what is important, how connections are conceived, and where boundaries lie. Every illustration is the result of a conscious decision: What do we show? What do we omit? In what order? With what focus?
These design decisions are anything but trivial. They require a deep understanding of the topic, the target audience, and the communicative intent. This is precisely why they are a human signal – they show that someone has taken responsibility for structure and meaning. Animation and motion design are not used as decorative effects, but as a means of orientation.
When an illustrator translates a complex process chain into a clear visual dramaturgy, something happens that no AI can achieve: they make a selection based on experience, empathy, and contextual knowledge. They know where the viewer stumbles, where confusion arises, and where a visual anchor point is needed. A character guiding the viewer through an onboarding video, an isometric infographic condensing a production process to its essence, or a metaphorical visual world making an abstract concept tangible – these are all creative achievements that go far beyond what a prompt can generate.
This form of conscious reduction – omitting the unimportant in favour of the essential – is the true core of human explanation. And it is what makes the difference between information and a message. Those who know their target audience and consciously tailor the visual language to themcreate something that AI content cannot structurally achieve: relevance through perspective.
Humanity lies not in tone, but in responsibility
Often, attempts are made to make AI content "more human" by making the language more casual, friendly, or emotional. This falls fundamentally short. Humanity does not arise from the right tone, but from taking responsibility. An explanation appears human when it is limited, clear, and conscious. When it shows that someone has understood what is difficult – and addresses it precisely there.
Research impressively confirms this: AI content with human strategic oversight performs significantly better than fully automated content. Hybrid approaches, where AI serves as a tool but the creative and strategic process remains in human hands, prove to be the superior solution (cf. Deloitte, Connected Consumer Survey 2024). This is an important insight for anyone considering the use of AI in corporate communication.
Especially in the field of explainer videos, this hybrid strategy proves particularly effective. AI can support research, accelerate script drafts, or generate animation templates. But the content condensation, the visual narrative structure, and the illustrative design – all of this remains the task of experienced creatives who not only know what is technically possible, but what is communicatively effective. A professional explainer video process combines precisely these levels: strategic consulting, editorial depth, and design expertise.
Trust as a communicative task
The current loss of trust due to AI content is not a technical problem. It is a communicative one. Companies have automated explanation too much and taken too little responsibility. Explanation is not a secondary service that can be delegated to an algorithm. It is a task that demands conviction, makes decisions visible, and assumes responsibility. The numbers speak a clear language: four out of five people worldwide are concerned about the risks of AI, and two out of five already report negative impacts – primarily the loss of human interaction and the spread of misinformation.
Illustration and explainer videos offer a way back to the essentials here. They force reduction, prioritisation, and creative positioning. An illustrated character guiding the viewer through a complex process is more than just a pretty animation – it is a promise: We have engaged with your topic, we have understood what is important, and we take responsibility for making it understandable.
AI can support this. It can prepare, structure, and accelerate. But it must not explain instead of humans, but for them. The companies that understand this will not be those that use AI most extensively – but those that use it most intelligently. Where the human touch becomes visible again in communication, trust also returns.
Conclusion: Explanation needs a sender
The loss of trust due to AI content does not arise because AI explains poorly. It arises because explanation has lost its human sender. Trust grows where content is not only correct but intended – where it becomes visible that someone has understood, prioritised, and taken responsibility.
Illustration and the explainer video format make exactly this possible: they visualise not only information, but also the conviction behind it. Every design decision, every conscious reduction, every visual focus is a signal to the viewer – a human is speaking here, not a machine.
You can find an overview of all current developments in our article on Explainer Video Trends 2026.
Explanation must therefore become human again. Not slower. Not more emotional. But clearer, more conscious, and more responsible.
