Generative AI in advertising is no longer a futuristic concept — it’s happening now, and it’s shaking up the marketing and ad industry as we know it.
From creating content to hyper-personalizing offers, marketers using generative AI have a massive canvas to work with. We’re still very much in Wild West territory, where every new discovery leads to more uncharted territory.
Like any new frontier, generative AI in advertising raises many questions: How can you use generative AI tools for ad campaigns? Is using generative AI in advertising ethical? How will gen AI impact the future of advertising?
Let’s explore these and other details of using generative AI to create smarter, more effective ad campaigns.
Contents:
Can AI Be Used in Advertising?
How Is Generative AI Used in Advertising?
Key Advantages of Generative AI in Advertising
Real-World Applications of Generative AI in Advertising
Challenges of Implementing Generative in Advertising
Choosing the Right Generative AI Advertising Tools
Generative AI is Redefining Advertising
Can AI Be Used in Advertising?
AI-generated advertising refers to using artificial intelligence tools to handle some or all of an ad campaign. AI can apply to any stage of the campaign process, from ideation and creative development to launching and tracking performance.
It’s no longer a question of “if” AI can be used in advertising. Major brands like Coca-Cola, Heinz, and Sephora have already said yes to gen AI tools, and they’re certainly not the only ones.
Today, it’s a matter of “how” to use generative AI across the campaign process. Marketers are still figuring out all the nuts and bolts and where AI fits, but every campaign creates new learning opportunities.
How Is Generative AI Used in Advertising?
For many brands, gen AI isn’t just a tool; it’s becoming a creative partner. Generative AI can handle everything from developing creative elements to defining customer personas to analyzing campaign performance and audience data.
Because generative AI tools do much of the heavy lifting, marketers can spend more time on ad strategy and focused work.
It’s estimated that 45% of brands are already using generative AI in their marketing, and another third have plans to do so in the future. So far, opportunities include:
- Writing ad headlines and calls to action
- Creating images, videos, and podcasts
- Personalizing ads for different segments
- Aligning messaging with brand voice
- Choosing the right communication channels and timing for launching ads
- Finding trends and topics to capitalize on via trendjacking strategies
- Optimizing content for search engines
- Generating summaries of content
- Developing product descriptions
And the list keeps growing — new tools and use cases are developed every day. AI is like a giant sandbox where brands can play around with its potential and find more ways to augment their creative processes.
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Key Advantages of Generative AI in Advertising
Generative AI for ads offers attractive benefits to marketers, particularly as marketing budgets continue to shrink. AI tools can assume some of the responsibilities that busy marketing teams have historically managed hands-on, allowing them to roll out complex campaigns in a matter of days or weeks, not months.
Here’s why more marketers are exploring AI technologies for advertising.
Get unlimited campaign ideas quickly
Generative AI acts like a brainstorming partner who can come up with lists of ideas in minutes. Having fresh perspectives can empower your human creatives to think in different directions and turn AI-generated ideas into inspired campaigns.
Save time by creating first drafts in seconds
Generative AI’s core advantage is in its name. It can “generate” written or visual content based on what you tell it to do. For instance, it can write anything from email subject lines to multi-paragraph articles in seconds. Tools like ChatGPT are conversational, so you can refine its outputs without having to start over each time.
There are caveats to using AI-generated content, though. Keep in mind that AI tools can’t “think” of new content; it generates content based on its training data, so originality is not its strong suit.
Outputs require editing to make them unique and add your brand flavor. Otherwise, you risk unintentional plagiarism that can damage your brand image.
Develop on-brand content
Maintaining a consistent voice and tone poses challenges, especially among large teams where in-house creators, influencers, and freelancers are all creating content. Brands can train generative AI tools to develop content according to their brand guidelines. Companies can prioritize brand integrity while scaling their marketing efforts.
Produce ad campaigns at a lower cost
Generative AI can handle a wide range of tasks, from copywriting to graphic design. Like the Swiss Army knife of content creation, these tools can help brands produce their own assets faster and at a lower cost.
Level the playing field for smaller businesses and brands
Smaller teams (with smaller budgets) don’t have the tools or expertise to compete with larger competitors. AI tools can help level the playing field by allowing teams to do more with fewer resources.
Users can gain an advantage at every phase of the advertising process, from creating content to analyzing data — and everything in between. This may allow teams to do more of their advertising in-house rather than relying on pricey ad agencies.
Get in-depth data analysis from campaign performance
Analyzing campaigns allow brands to adjust future ads. Once a campaign concludes, generative AI can review the data and create reports and insights based on numbers, not feelings.
AI tools may be able to find hidden insights or patterns and share in plain language your biggest wins and opportunities for improvements.
Real-World Applications of Generative AI in Advertising
World-class brands are using generative AI in all phases of the advertising process, including full-fledged commercials and campaigns that promote AI’s new (and likely permanent) role in advertising.
Let’s look at a few examples of brands that are finding the balance between generative AI and advertising — and promoting the potential of AI in the process.
Coca-Cola
Coca-Cola’s Unexpected Santa ad is 100% AI-generated and underscores the power and curiosity of this technology. The brand collaborated with multiple studios to create a series of AI-generated ads for Christmas, featuring iconic brand elements like the script-style logo, red trucks, a polar bear, and Santa Claus.
The brand did face some backlash from customers, with some calling the ads “soulless” and lacking human creativity. Comments like these are common and expected when introducing new ideas and technologies.
This ad series can serve as a learning opportunity for other brands as they explore how to use AI and maintain authentic human connections.
Heinz
Condiment powerhouse Heinz asked an AI tool to draw ketchup. The brand has crafted a powerful image that its customers can recognize even without logos or words.
AI proved the brand’s legacy by recreating images of ketchup that resembled a bottle of Heinz, including colors, bottle shapes, and labels. It used these AI-generated images in an ad campaign to show that Heinz is what ketchup looks like, making it a clear choice for consumers.
The company took the campaign a step further by asking its followers to do the same. Users could share their images with the brand, giving them a wealth of user-generated content that they could post on social media or repurpose in other ways.
Mint Mobile
The Mint Mobile x Ryan Reynolds collab was one of the first of its kind. Reynolds openly shared that he asked ChatGPT to write an advertising script, creating a layer of transparency about using AI.
The results were equal parts humorous and original. This tongue-in-cheek approach to AI represents a literal use of AI in advertising. It introduces AI’s potential in creative copywriting while subtly underscoring the need for human creatives.
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Challenges of Implementing Generative in Advertising
Choosing to bring generative AI and advertising into the same sphere isn’t a decision to take lightly. Brands need to understand safety and ethics in AI and train human teams to work with and not against AI tools.
Common challenges of using generative AI for advertising include but are not limited to:
- Fact-checking AI-produced content before it goes live
- Not over-relying on AI tools to produce ad content or insights
- Struggling with originality or creativity
- Maintaining emotional, human-driven connections
- Eliminating bias in AI outputs
- Choosing AI tools and implementing them into workflows and processes
- Keeping pace with rapidly changing technologies
- Calculating the total cost of campaigns
- Producing unpredictable outcomes or off-brand content
As the old saying goes, If it were easy, everyone would be doing it.
The reality is that while generative AI for advertising is growing in practice, it’s still an art form that needs to be mastered. Acknowledging and overcoming these and other challenges will be key to creating AI campaigns that resonate.
Choosing the Right Generative AI Advertising Tools
A quick Google search for generative AI tools gives marketers plenty of food for thought. It’s almost overwhelming.
Popular examples of generative AI tools for advertising and marketing include:
- ChatGPT (for writing copy)
- DALL-E (for generating images)
- Synthesia (for AI video creation)
- Jasper (tailored for marketing content)
- Claude AI (useful for analyzing data or reports)
- Meltwater (for consumer insights and trend analysis)
Marketers are in various stages of implementing AI into the advertising mix. McKinsey breaks it down into three distinct tiers of sophistication:
| Getting started with gen AI | Customized gen AI solutions | Transformative gen AI | |
|---|---|---|---|
| Approach to marketing | Integrate mostly off-the-shelf generative AI (gen AI) software into existing workflows to drive incremental changes in efficiency and effectiveness. | Lightly tailor foundation models for use cases. Use both gen AI and nongenerative AI solutions in tandem to accelerate chains of action. | Use a full suite of digital and AI capabilities, including customized gen AI solutions. Humans define objectives and monitor performance but are otherwise not "in the loop." |
| Examples | A retailer increases efficiency in creating copy for email communications by using ChatGPT for first drafts. | A telco increases personalized communications to its customer base by tapping gen AI to generate creative copy and imagery. Humans review and approve content, and machine learning decides which customer gets what communications. | A home improvement retailer trains a gen AI model on its corpus of information about home improvement projects. Gen AI is embedded into an app, which is used to provide project guidance to customers during in-store experiences. |
Source: McKinsey
Every company starts somewhere, even if it’s just creating a few lines of copy for a one-off campaign. Choosing when or whether to move to the next stage is up to each brand or business, but it’s clear there’s potential for making AI a natural part of the advertising ecosystem.
Generative AI is Redefining Advertising
Big brands like Coca-Cola and Heinz are leading the way in using generative AI in advertising, but these tools are fair game for companies of all industries and sizes. Gen AI is changing advertising not just in terms of what we see but also how ads are developed, monitored, optimized, and analyzed.
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