AI-generated advertising: speed, creativity... and the risk of mistrust?

The rise of generative AI in advertising

Generative artificial intelligence (GenAI) has rapidly evolved from a testing ground to a widely adopted advertising production method. It is no longer just major brands that are experimenting with it: the entire market is beginning to integrate it, changing the way campaigns are created, produced, and rolled out.

What recent data shows

• According to the IAB 2025 Digital Video Ad Spend & Strategy Full Report, 86% of advertisers (“buyers”) say they already use or intend to use GenAI to create advertising videos.

• According to the same report, it is predicted that by 2026, approximately 40% of video advertisements will be produced or enhanced with GenAI.

• In 2024, approximately 30% of digital video creations were already using GenAI to be developed or “enhanced.” This figure is expected to rise to ~39% by 2026.

The levers: what AI brings to the table

1. Reduced cost

   One of the major attractions of generative AI is that it dramatically reduces production costs. Producing multiple versions (linguistic, visual) of a commercial or visual no longer requires large studios or large teams.

2. Execution speed

   Production times have been reduced from weeks (pre-production, filming, editing) to a few hours or days for variations. This increase in speed is crucial in a world where responsiveness (just-in-time campaigns, news, social buzz) is very important.

3. Mass customization

   Thanks to AI, it is possible to generate hundreds or even thousands of versions of an advertisement tailored to local audiences, user profiles, languages, and formats. For example, the IAB report notes that:

   • 42% of buyers use or plan to use AI to create different versions depending on the audience;

   • 38% to modify the visual style (tone, aesthetics);

   • 36% for contextual relevance (adaptation to distribution environments).

Nuances and limitations to note

• Adoption does not mean homogeneity: small and medium-sized brands (SMBs) are often ahead of well-established brands in integrating GenAI into their workflow, due to their need to do more with less.

• Creative quality may suffer if the focus is solely on speed or volume. There is a risk that the ads generated will appear too “standardized,” less emotional, and less distinctiv

• There are also governance challenges: managing copyright, the risks of “hallucinatory” content, bias in generated images or scenarios, and compliance. The IAB's AI Adoption Is Surging... study reports that more than 70% of marketers have already experienced an AI-related incident (off-brand content, erroneous statements, etc.) and less than 35% plan to invest more in AI governance in the coming year.

Examples of recent advertising campaigns using generative AI

Case 1 — Coca-Cola and Generative AI (2023–2024)

• Coca-Cola was one of the first major brands to use DALL·E and GPT-4 to create personalized visuals and videos for its “Create Real Magic” campaign.

• Consumers could generate their own visuals around the brand, some of which were used in DOOH and digital campaigns.

• Education: AI creates a creative “wow” effect, but Coca-Cola had to invest heavily in human moderation and selection to avoid abuses and ensure the brand's credibility.

• Source: LinkedIn

Case 2 — Nestlé / KitKat (Australia, 2024)

• Nestlé launched a KitKat campaign featuring humorous video ads co-created with generative AI, testing multiple scripts and visual

• The goal: to produce localized versions tailored to different target audiences (students, young professionals) more quickly.

• Lesson learned: The speed and cost savings were real, but qualitative feedback showed that some videos seemed “too artificial,” highlighting the risk of rejection.

• Source: Campaign Brief

Case 3 — Heinz and AI (2023, extended to 2024)

• Heinz asked AI (e.g., DALL·E) to “draw ketchup” → all the images produced depicted a Heinz bottle.

• This campaign generated enormous buzz, proving the symbolic power of the brand.

• Lesson: Generative AI can boost brand awareness when it illustrates cultural norms, but it alone is not enough to generate trust in criteria such as quality or taste.

• Source: Campaigns of The World

Case 4 — Local campaigns in Asia (2024)

• Several local FMCG brands in Korea and Japan have used generative AI to produce videos customized by region and dialect.

• Lesson: AI enables effective hyper-localization, but Nielsen studies show that consumers remain more convinced by real testimonials than by entirely generated messages.

• Source: How brands in Japan and Korea are using AI for hyper-local advertising

Case 5 — Political advertising and regulation (US, 2024–2025)

• In the United States, several AI-generated political advertisements have sparked controversy and calls for regulation because they mixed realistic images with manipulative messages.

• Lesson learned: reputational risk is significant without safeguards, and consumers are becoming even more distrustful of automated advertising content.

• Source: Reuters

Opportunities, risks... and the Achilles heel of trust

The rise of generative AI in advertising opens up exciting possibilities, but it also raises vulnerabilities that strike at the very heart of the relationship between brands and consumers

• Increased creativity: algorithms can generate ultra-realistic images, videos, and voices in record time. But this abundance risks leading to standardized, recognizable, and even interchangeable content. The danger is that advertising will lose its impact by “all looking the same.”

• Volume versus value: falling costs encourage the proliferation of formats. The more messages are broadcast, the more consumers are exposed to the risk of advertising saturation... and increased rejection.

• Compliance and regulation: who owns the rights to an AI-generated image? How can we ensure that messages comply with legal standards (ARPP in France, ASA in the United Kingdom, FTC in the United States)? Regulation is not advancing at the same pace as technology, leaving a gray area for advertisers and advertising agencies.

But beyond these technical and legal issues, the real Achilles heel is psychological: trust.

• Less than one in two consumers today say they trust advertising messages.

• Younger generations (digital natives) are more tolerant of advertising, but they scrutinize it more closely and are quick to detect any artifice.

• AI-generated advertising risks accentuating the feeling of a “manufactured message,” disconnected from real experience.

Advertising is only credible when it is based on tangible evidence. This explains why peer recommendations and customer reviews remain the most powerful drivers of trust worldwide. In short: while AI is changing the way we create, it is not changing what the public expects—evidence, not just promises.

BuyTryShare: the answer at a critical time for the media

Brands and their agencies can now produce faster, cheaper, and on a larger scale. But emotional impact and credibility remain fragile: mass-produced advertising, however spectacular, is not enough to convince without tangible proof.

It was precisely this breach of trust that led to the creation of BuyTryShare, with a twofold ambition: :

• Restoring credibility to advertising by injecting consumer proof where messages lack credibility.

• Offering advertising sales houses new growth drivers at a time when their traditional revenues are structurally declining.

In concrete terms, BuyTryShare integrates verified consumer reviews (ISO 20488/NF522 standards, French Advertising Standards Authority validation) into TV, cinema, and press ads in an ultra-short, high-impact format (5 seconds with a QR code). The first PoCs in the agri-food sector are already showing significant results: +25% attention and +15% incremental sales.

Adding a BuyTryShare spot brings the best of both worlds:

• the speed and creativity of modern formats (including AI-assisted ones),

• complemented by verified human trust, which acts as a genuine reputation guarantee.

By integrating verified reviews into traditional advertising or AI-generated content, BuyTryShare has become a trusted partner for advertisers and advertising agencies: it preserves audience engagement, increases impact, and reduces the risk of mistrust.

But BuyTryShare is more than just a creative tool. It comes into play at a critical moment:

• Advertisers demand tangible proof of ROI before investing heavily.

• Advertising sales houses must reinvent their model to compensate for the erosion of TV, press, and cinema revenues.

• Consumers demand transparent and authentic messages, otherwise advertising loses its effectiveness and legitimacy.

In this context, BuyTryShare stands out as a credible European alternative. Where GAFAM capitalizes on data and hyper-targeting, BTS relies on verified human proof, bringing together advertisers, media, and audiences.

In other words:

• Advertisers gain the security of a credible and measurable message.

• Advertising sales houses have an additional monetization lever, transforming their inventories into trusted premium spaces.

• Consumers regain a reason to believe in advertising messages.

🚀 BuyTryShare gives new meaning to advertising and provides sustainable revenue for changing media.