AI videos vs. real videos: a creative opportunity or a risk for brands?

In 2026, producing a video has never been easier. Just a few prompts are now enough to generate visually impressive content : no camera, no shoot required. The promise is appealing: speed, cost reduction, and large-scale production.

But this ease hides a major strategic question: what do we really gain? And what do we risk by replacing real video with AI-generated video?

Since 2024, more and more brands have been experimenting with AI-generated videos. Some see them as a powerful efficiency lever. Others have found that the absence of humans on screen can undermine authenticity, trust, and brand perception. The largely AI-generated 2025 holiday campaigns from Coca-Cola and McDonald’s are particularly telling examples.

So should real video truly be replaced by AI, or should we instead rethink AI’s role as a tool in service of creativity and marketing performance?

In this article, we analyze the opportunities, risks, and strategic choices involved in producing effective and credible videos in 2026.

What does AI bring to video production?

Artificial intelligence has profoundly transformed the video production pipeline. But contrary to some marketing promises, its true potential does not lie in replacing human creativity. In 2026, AI is above all a tool for optimization and assistance, particularly effective when integrated at the right stages of the process.

AI in pre-production: speeding up creative thinking

It is upstream of the shoot that AI proves most naturally useful. It does not replace strategic vision, but it reduces friction, speeds up decision-making, and improves the clarity of the creative brief.

In practical terms, AI can support ideation and scriptwriting by quickly exploring different narrative approaches based on marketing objectives. It can also generate simplified storyboards to visualize a concept before a single frame is shot, or produce mood boards (visual styles, atmospheres, color palettes) to align creative teams and clients from the outset.

The main benefit is a gain in time: the time usually spent structuring ideas can be reinvested in capture and the creation of higher–value content.

In post-production: streamlining without interfering

Post-production is where AI is most relevant and integrates most effectively. Its strength does not lie in writing the story, but in its ability to accelerate the finalization, circulation, and distribution of that story, once the creative intent has been clearly established.

Editing assistance is probably the most misunderstood, and most overestimated, use case. AI does not edit in the creative sense of the term. It analyzes technical signals such as speech clarity, pauses, repetitions, and topic changes. From there, it can suggest relevant segments, identify sections suitable for short-form versions, or prepare initial assemblies. In concrete terms, this allows editors to spend less time navigating through hours of footage and more time working on rhythm, emotion, and storytelling. Final decisions remain human.

It can also contribute to the technical execution of certain visual effects or creative transitions. When an effect has been planned in advance, for example, a specific transition, background extension, image cleanup, or a particular visual treatment, AI can significantly accelerate its production. Once again, it does not determine the artistic intent; it facilitates its implementation. This type of use is especially effective when anticipated from the concept development stage.

Automatic subtitle generation is often the first step after editing. From a validated version, AI can generate time-coded subtitles directly from the audio, with a level of accuracy that significantly reduces manual correction time. Because subtitles are a derivative layer rather than a creative one, they do not alter rhythm, framing, or editorial intent. They improve accessibility and comprehension without affecting the edit itself.

Multilingual translation naturally follows this step. Once the dialogue is transcribed, it can be translated into multiple languages while preserving timing and structure. Brands can therefore adapt the same content for multiple markets without having to re-edit or re-record each version. Cultural adaptation remains supervised by humans; AI handles the logistical infrastructure.

Format adaptation naturally closes the pipeline. Once the master edit is approved, AI can automatically generate the necessary variations: vertical formats for mobile, square formats for social media, short versions for advertising. The creative intent is already locked in; AI manages repetition and scaling without compromising brand consistency.

The Coca-Cola and McDonald’s cases: when AI is poorly received

Coca-Cola, November 2025: when nostalgia turns cold

Coca-Cola’s Christmas ads have been iconic for many years and are eagerly awaited at the start of the holiday season. The silhouette of the red truck, the warmth of families gathered together, the instantly recognizable music, all of this is part of an emotional capital built over decades. In November 2025, the brand decided to recreate this universe using exclusively AI-generated imagery.

The result triggered an immediate wave of criticism. Comments described the ad as “soulless,” “cold,” and “disconnected” from what Coca-Cola usually produces. What is striking is that the backlash was not aimed at the technical quality of the video, but at the absence of human presence behind the images.

What makes this case particularly instructive is the production context. According to several reports, the campaign was not a quick project generated in a few hours. Coca-Cola reportedly mobilized a small team of specialists who generated nearly 70,000 clips to compose the final commercial. This team spent weeks selecting, sorting, and assembling sequences. The human effort was significant, but invisible on screen. And that is precisely what the audience perceived.

McDonald’s Netherlands, December 2025: pulled under pressure

A few weeks later, McDonald’s Netherlands released its own fully AI-generated holiday commercial, produced by the agency TBWA\Neboko. The 45-second spot, titled It’s the Most Terrible Time of the Year, played on a humorous, offbeat take on holiday chaos.

The public reaction was so negative that McDonald’s first disabled comments on YouTube before removing the video entirely. Criticism focused on unstable visuals, characters with strange proportions, and an aesthetic many found unsettling rather than amusing.

Once again, the production context tells a different story from what one might assume. Sweetshop, the production company involved, stated that the project mobilized up to ten in-house AI specialists over seven weeks, generating thousands of takes that were selected and edited like any traditional production. Sweetshop’s CEO even went so far as to say: “This wasn’t an AI thing. It was a film.”

The audience did not share that view.

Beyond differences in tone and market, Coca-Cola and McDonald’s made the same fundamental mistake: using AI as the author of the narrative rather than as a tool in service of it. In both cases, audiences detected an absence, not of technology, but of a recognizable human creative intent.

This distinction is crucial. AI can produce images. It cannot, on its own, produce meaning.

The risks of AI in video production

The Coca-Cola and McDonald’s case studies are not isolated incidents. They reveal systemic risks that emerge whenever a brand entrusts its storytelling to AI without a clear strategic framework. These risks are not merely technical: they directly affect brand perception, audience relationships, and the credibility of the messages being communicated.

Content uniformity

One of the first visible effects of AI-generated video is content uniformity. Models rely on similar datasets, standardized narrative structures, and recurring visual aesthetics. Using artificial intelligence for video creation often results in content that is clean and well executed, but looks the same.

At scale, this standardization makes it harder for brands to differentiate themselves and gradually dilutes their visual and editorial identity.

Loss of emotion and authenticity

Video is, above all, an emotional medium. And this is where AI reveals its deepest limitations. Despite significant progress, it still struggles to reproduce the spontaneity, imperfections, and sincerity that make a video feel credible: a hesitant glance, a slightly asymmetrical smile, a pause that lasts a second too long. These micro-details, imperceptible in isolation, collectively create the feeling that a real human being is present behind the image. When they are missing, audiences feel it. Not always consciously, not always with precise words, but they feel it.

This emotional distance is particularly costly for brands that rely on closeness and trust in their communication.

Audience distrust and brand rejection

As audiences become more aware of AI-generated content, a form of distrust begins to take hold. When a video is perceived as artificial or misleading, the reaction can be immediate: negative comments, loss of credibility, brand rejection. This effect is even stronger when the use of AI is not clearly disclosed or explained, creating the impression that the brand is trying to hide reality or manipulate emotion.

Reputational and legal risks

Finally, the use of AI in video raises significant legal and reputational issues. Image rights, deepfakes, the use of synthetic voices or faces, lack of clear consent, the gray areas are numerous. A single poor decision can lead to public controversy, campaign withdrawals, or even legal consequences. In a context where transparency is increasingly expected, these risks must be considered from the very earliest stages of strategic planning.

How can you tell if a video is real or AI?

As tools continue to improve, the line between AI-generated video and real footage is becoming harder to spot. Yet certain clues persist, and they are often the same ones that fueled criticism of the campaigns mentioned earlier. These cues tend to appear on three levels: visual, emotional, and narrative.

Visual cues

The first signal is an overly polished aesthetic. Textures may appear too smooth, surfaces too clean, and environments too perfectly arranged, lacking the grain and subtle imperfections that characterize real-life footage. It is true that models have improved significantly in recent months, particularly in rendering human faces and features, making some videos far more convincing at first glance.

However, inconsistencies can still appear in the details: proportions that subtly shift from one frame to another, objects that slightly deform, backgrounds that lack continuity, or physical interactions that feel imperfect. These flaws are rarely obvious, but they can create a subtle sense that something doesn’t feel entirely natural.

Physics and lighting can also betray an artificial origin. Movements lack weight and inertia; interactions with the environment feel wrong. Lighting may be visually pleasing yet devoid of dramatic logic. It illuminates, but it doesn’t tell a story. Finally, camera movements are often too fluid, too stable, seemingly freed from the physical constraints of a real shoot. You sense a virtual camera where you expected a human gaze.

Emotional cues

This is where the real issue lies. As seen with Coca-Cola and McDonald’s, a video can be technically flawless and emotionally empty. Faces are the primary reason.

The human face is a living system. It communicates through an accumulation of micro-signals: slight asymmetries, involuntary muscle movements, irregular blinking, tensions that shift depending on context and interaction. These elements are not isolated details. Together, they create a sense of presence, the feeling that a consciousness inhabits the face.

AI-generated faces, by contrast, are built from statistical patterns. They may look convincing frame by frame, but they lack micro-variation over time. Expressions feel slightly generalized, movements too symmetrical, too controlled. Most importantly, they don’t truly react. They simulate emotion without responding to a moment, a person, a hesitation. Viewers may not always be able to name what they perceive, but they feel the absence of a living inner state.

There is also a lack of emotional build-up and release. The emotional register remains flat, without crescendo or narrative breathing. Emotion feels simulated rather than experienced and that is precisely what audiences detect, often without being able to explain why.

Narrative cues

AI’s limitations also appear in story structure. Narrative arcs are generic, with no real stakes or risk-taking. The tone is universal and decontextualized, lacking specific cultural anchoring, making it easy to understand, but harder to relate to. Ideas accumulate without a clear intention, and the absence of a true authorial voice makes these videos competent, yet easily forgettable.

In the end, this is what distinguishes an effective video from a memorable one: not technique, but point of view.

Conclusion: AI in video is a question of balance, not replacement

In marketing, a video only has value if it leaves a lasting impression. In 2026, AI makes production easier, accelerates workflows, and enables content to be scaled efficiently. Artificial intelligence has become a tool for improving process efficiency.

But when AI takes the place of human storytelling, the risk is immediate: loss of emotion, message uniformity, and a breakdown of trust with audiences. The reactions to Coca-Cola’s and McDonald’s campaigns made this abundantly clear.

The most credible brands are not those that replace real video with AI, but those that use it to strengthen human creativity, not erase it. Because a memorable video does not rely on technology alone, but above all on point of view, intention, and authentic emotion.