Generative AI is a type of artificial intelligence that creates new content, such as text, images, video, audio and code, from instructions called prompts, based on patterns it learned from large amounts of existing data. In advertising it now powers ad copy, product and lifestyle imagery, short video, voiceovers, virtual presenters and AI influencers, personalised creative for different audiences and rapid testing of many variations. It changes the economics of advertising production, because creating one more version of an ad costs a fraction of what it used to, which lets brands test more, localise more and respond faster.
The short version
- Generative AI makes new content. Text, images, video, audio and code, created from prompts
- Advertising is a natural fit. Copy, visuals, video and variations are exactly what ads need at volume
- Testing becomes cheap. Brands can try many creative angles and keep what performs
- Humans still direct it. Strategy, brand judgement, accuracy and ethics remain human responsibilities
- Transparency matters. Realistic AI people and scenes should be disclosed where rules require it
How generative AI works, in plain English
Generative AI models learn patterns from very large collections of examples, such as text, images or video. A language model learns how words and ideas usually follow each other, so it can write new sentences that fit a request. An image model learns how visual features relate to descriptions, so it can create a new image from a written prompt. Video models extend this to motion over time, and audio models to speech and music.
The models do not copy a single source. They produce new outputs that follow learned patterns. That makes them powerful for producing variations quickly, and it also explains their weaknesses. They can produce confident mistakes, struggle with exact details like small text or logos, and default to generic results when the prompt is vague.
The main types of generative AI used in advertising
| Type | What it creates | Advertising uses |
|---|---|---|
| Language models | Text | Headlines, ad copy, captions, scripts, product descriptions, emails |
| Image models | Pictures | Lifestyle scenes, product placements, backgrounds, concepts, AI influencer images |
| Video models | Moving images | Short ads, Reels, product showcases, talking-head clips |
| Voice and audio models | Speech and sound | Voiceovers, localised narration, music beds, sound effects |
| Multimodal systems | Several formats together | Complete ad concepts from a single brief |
What generative AI changed about advertising
The cost of variation collapsed
In traditional production, the first version of an ad is expensive and every additional version adds significant cost. Generative AI makes additional versions cheap. A brand can produce ten hooks, five settings and three calls to action for the same budget that used to buy one or two finished ads.
Testing became the strategy
Because variations are cheap, the winning approach is to test many ideas and let performance data choose. Social and search ad platforms already reward fresh creative and automated testing, and generative AI supplies the creative they need.
Speed became an advantage
Campaigns can respond to trends, news and competitor moves in days. Seasonal and event-driven creative no longer needs to be planned months ahead.
Localisation became practical
The same campaign can be adapted for different languages, cultures and markets quickly, with native review, instead of separate productions for each country.
Personalisation scaled
Ads can be tailored to different audience segments, such as different products for different interests, different settings for different regions and different messages for new versus returning customers.
Generative AI in action
- Social ads. Dozens of image and video variations testing different hooks, scenes and offers.
- E-commerce. Product images placed in lifestyle scenes for each season and market. See what AI can do for product photography.
- Search ads. Headline and description variations that match different search intents.
- Video. Short ads, Reels and talking-head clips produced in days. See what AI video can do.
- AI influencers. Consistent digital characters who promote products across social platforms. See what an AI influencer is.
- Audio. Voiceovers in many languages for video and radio-style ads.
Meet the SI influencers who could front your campaign
Examples by industry
- Retail and fashion. Seasonal lookbooks, outfit variations and lifestyle scenes for every product line, refreshed for each sale event and market.
- Beauty and skincare. Routine videos, texture close-ups and ingredient explainers, with careful review of any claim about results.
- Travel and hospitality. Destination scenes, room and dining imagery, and localised video for each source market.
- Food and beverage. Product moments in different settings and occasions, with real packaging composited for accuracy.
- Technology and apps. Explainer videos, feature demos and ad variations for different user needs.
- Financial services. Clear explainer content and illustrations, with strict compliance review of every word.
Where the real creative leverage sits
Generative AI does not make advertising good on its own. Everyone has access to similar tools, so the advantage comes from what you bring to them. A clear brand identity and positioning gives the tools a direction. A deep understanding of the customer means the right problems get addressed. Strong creative ideas and judgement decide which concepts are worth producing. Rigorous testing and measurement reveal what works. And consistent characters and visual systems make content recognisable as yours. Brands that invest in these see generative AI multiply their impact. Brands that skip them get more of the same generic content everyone else produces.
The limits and risks
- Accuracy. Models can invent facts, misstate product features or render logos and text incorrectly. Every asset needs checking.
- Generic output. Vague prompts produce interchangeable content.
- Consistency. Characters, products and styles drift without a system and references.
- Rights and originality. Avoid generating content that imitates real people, copies distinctive protected works or uses brand assets you do not own.
- Bias. Models can reflect biases in their training data, so representation needs deliberate attention.
- Disclosure. Realistic AI people and scenes should be labelled where platforms and advertising rules require it.
How to use generative AI responsibly in advertising
- Start from strategy. Define the audience, message and goal before generating anything.
- Build a brand system of voice, visual identity, characters and product references.
- Keep humans in the loop for creative direction, approval and accuracy checks.
- Test and measure to let results guide the creative.
- Disclose clearly where required, and never deceive audiences about what is real.
- Respect rights of people, creators and brands.
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Website, app, SEO, ASO, social and SI influencers under one roof, with one plan and one team accountable for growth
Measuring generative AI advertising
Judge AI-produced ads by the same standards as any creative. Compare click-through rate, cost per click and cost per acquisition against your existing ads, along with hook rate and watch time for video, conversion rate on landing pages and the speed of producing each new round of creative. Track creative fatigue too, meaning how quickly performance drops as audiences see an ad repeatedly, because the ability to refresh creative often is one of the biggest advantages generative AI brings.
Questions to answer before you adopt it
- Which part of your advertising is held back by production time or cost?
- Do you have clear brand guidelines that AI output can follow?
- Who will review and approve AI-generated assets?
- How will you check product accuracy and claims?
- How will you disclose realistic AI content where required?
- How will you measure whether it works better than what you do now?
How SI builds on generative AI
At Proklisi, SI, our Super Intelligence approach, connects generative AI across the whole marketing loop. Research informs the brief, generation produces on-brand content, people approve it, platforms distribute it, and results feed straight back into the next round. That connection is what turns individual generative tools into a system that keeps improving. Read what SI marketing is for more.
What it means for advertising teams
Generative AI is shifting where advertising teams spend their time. Less time goes into production logistics, and more goes into ideas, direction, testing and analysis. Copywriters become editors and concept leads. Designers become art directors of systems. Media buyers work with far more creative to test. The teams that thrive treat AI as a production partner and keep their judgement, taste and understanding of customers at the centre.
A short history of how we got here
Automation has been part of advertising for years, from programmatic media buying to dynamic product ads that swap images and prices for each viewer. What changed in the early 2020s is that machines learned to create the creative itself. Text models became capable of fluent copy, image models moved from abstract experiments to photorealistic results, and by the middle of the decade short video and natural voice had reached a quality suitable for many social and performance ads. Each step lowered the cost of producing a new idea, and advertising, which has always been hungry for fresh creative, adopted the tools faster than almost any other industry.
Getting started
Start with one part of your advertising where production is the bottleneck, such as social ad variations, product imagery or short video. Define your brand system, produce a test batch, run it against your existing creative with clean tracking, and expand to the next area once the results are clear. Or work with a partner who already has the pipeline and the review process in place.
A glossary of terms you will hear
- Prompt. The instruction you give a generative model.
- Model. The trained AI system that generates content.
- Fine-tuning. Further training a model on specific examples, such as a brand's visual style or a character's face.
- Reference image. An image used to guide style, composition or identity in new generations.
- Upscaling. Increasing an image's resolution and detail after generation.
- Hallucination. A confident but false output, such as an invented fact.
- Multimodal. A system that works with several formats, such as text and images together.
Proklisi builds websites and apps, runs SEO, ASO and social media, and creates SI influencers for brands. Everything here comes from work we do for clients every week. About us or talk to the team