Revolutionizing Healthcare: The Rise of Generative AI in Clinical Decision Making


The advertising industry is seeing a surge in the use of generative artificial intelligence (AI). With just basic text prompts, generative AI—powered by sophisticated deep learning models—can produce unique images, videos, text, and more. To develop these AI systems and make them commercially available, major tech companies such as Google, Microsoft, and startup partners are investing heavily in their development.

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Proponents contend that by automating creative development and enabling countless customized variations, generative AI can completely transform the advertising industry. Without requiring lengthy photo shoots or video production, advertisers could describe the exact image or video they want to use to promote their product, and AI would create it instantly. Additionally, the technology allows for iteration at never-before-seen speeds. For example, advertisers can modify the prompt’s wording to generate dozens of variations before settling on the most successful creative.

Nearly 60% of advertisers are already experimenting with generative AI, even in these early stages, according to a recent Advertiser Perceptions survey.

But there are also issues with morality, laws, and the effects on the economy. Questions about ownership, attribution, and copyright are brought up by machine-generated content. If generative models are not properly monitored, experts caution that they may reinforce societal biases. This is especially sensitive when it comes to advertisements. Additionally, by automating certain creative jobs, technology poses a threat to industries like graphic design, photography, and videography.

However, generative AI advertising is here to stay, as demonstrated by the capabilities of systems like DALL-E 2, Midjourney, and Stability AI. Technology may drastically alter the creation and targeting of advertising creatives as it advances and becomes more widely available. Personalized and dynamic ads can provide advertisers with a significant competitive advantage. This can be achieved by strategically implementing generative AI now. But as this technology continues to advance quickly, it is imperative that we carefully consider the ethical implications.

Here is some ways generative AI is being applied in advertising:

Personalized Creative: Countless personalized ad images, videos, and copy that are catered to particular target audiences can be automatically generated by generative AI. Companies such as Lexus have experimented with using AI to generate personalized digital ads for specific customers according to their interests and demographics.

Dynamic Product Rendering: Instead of spending money on pricey photoshoots, retail brands can use generative AI to create product renderings by entering product descriptions. To showcase products, for instance, brands can adjust elements like color, lighting, and orientation using apps like ElevenLabs.

Automated Campaign Testing: Since generative AI can quickly produce high volumes of creatives, it enables brands to A/B test a wide range of ad variations to optimize campaign performance. Brands can experiment with different text prompts and select the top performing assets the AI generates.

Copywriting: AI copywriting tools like Jasper and Phrasee can generate endless catchy slogans, social posts and text/audio ads to capture consumer attention. Brands use these tools to brainstorm ideas or produce initial drafts for human refinement.

Logo & Design Creation: Businesses can give generative AI a text brief to generate a large selection of logo, packaging, and other design concepts. Instead of relying too much on agencies, this enables marketing teams to independently brainstorm designs.

As the technology continues advancing, even more applications in personalized retargeting, AR/VR content and voice advertising are likely to emerge. But generative AI’s ability to automate idea generation and iteration is already driving transformation across the advertising sector today.

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