Generative AI and DAM: a new era for creating content

In just two years, Generative AI (Gen-AI) has evolved from a viral experiment into a core driver of business transformation. When OpenAI launched ChatGPT in late 2022, over one million users tested its capabilities within the first week. By 2025, adoption has surged: 71% of organizations now report using Gen-AI in at least one business function, from marketing and customer service to product development.

Global investment in AI reached nearly $34 billion in 2024, and the momentum continues to accelerate in 2025. For marketers and enterprises, the real breakthrough lies in combining Generative AI with Digital Asset Management (DAM) platforms, enabling brands to deliver personalized, omnichannel, and compliant content at unprecedented scale.

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This trend carried on for many years until 2010 when influencers began to create User Generated Content (UGC). Instead of coming directly from the brands, UGC was created by “real people” and more readily available on social media sites than corporate websites.

Now as we embark on a new year and a change in the technology available to us, so begins a new phase of content marketing. Consumers are increasingly demanding a personalised approach to how brands target them and the rise in Gen-AI software is only going to make this more apparent.

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What is Generative AI?

The term AI (Artificial Intelligence) has been around for a while, but what is the difference between AI and Generative AI (Gen-AI)?

This trend continued for decades until the 2010s, when the rise of social media and influencers drove a new wave of User-Generated Content (UGC). For the first time, content created by “real people” on platforms like Instagram, YouTube, and TikTok often carried more influence than brand-owned channels.

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Fast-forward to 2025, and we are entering yet another phase of content marketing. Consumers now expect hyper-personalized, localized, and context-aware experiences across every touchpoint. With Generative AI integrated into Digital Asset Management (DAM) systems, brands can finally deliver this at scale, powering dynamic content personalization, omnichannel content delivery, and brand compliance in real time.

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Examples of Generative AI

AI (Artificial Intelligence) broadly refers to machines that can learn, reason, and perform tasks that usually require human intelligence. Generative AI, however, goes further: it doesn’t just analyze—it creates.

• Images: AI can be trained on thousands of visuals to generate new, unique combinations, such as product mock-ups or campaign concepts.
• Text: From blog posts to personalized product descriptions, tools like Jasper and Writesonic allow marketers to scale content creation.
• Video: Platforms such as Wisecut and Movio enable automated video editing or avatar-based presentations.
• Music: Gen-AI can compose royalty-free tracks or emulate styles to inspire artists.
• Science & medicine: Researchers leverage Gen-AI to generate chemical compounds and accelerate drug discovery.
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Generative AI is a form of artificial intelligence that creates new content—text, images, video, or even chemical compounds—based on patterns learned from existing data.

How Generative AI is Reshaping Creative Work

The ability of Gen-AI to produce convincing, human-like content has redefined how businesses approach creativity. From winning art competitions with Midjourney-generated images to powering hyper-personalized marketing campaigns, AI is blurring the line between inspiration and execution.

Still, Gen-AI remains a partner, not a replacement. While it can accelerate ideation and production, true creativity and emotional resonance continue to depend on human teams.

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The New Phase of Content Marketing in 2025

The evolution of content marketing has unfolded in distinct phases:

1. 1960s mass advertising – bold, one-to-many campaigns.
2. 2010s User-Generated Content (UGC) – influencers and creators shaping consumer trust.
3. 2025 hyper-personalization – fueled by Gen-AI and DAM integration.

Today’s consumers expect localized, context-aware, and personalized experiences across every touchpoint. By integrating Gen-AI into DAM systems, brands can finally deliver this at scale—ensuring brand consistency, compliance, and speed-to-market.

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Why has Generative-AI risen to prominence now?

The ability of Gen-AI to produce convincing images or articles that seem written by the human hand has come about through progressive technological advances over several years.

Indeed, the process of creating an image that doesn’t yet exist has involved computers being able to understand images and what is in the image. This has involved teaching the AI processing many examples certain elements, a flower for example, which then leads the AI to be able to recreate its own lifelike version.

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AI has gone through various stages to get to where it is now, from creating very basic texts or images to now being able to produce content which is convincingly human.

It is this new ability that has meant that investors, stakeholders, companies and consumers are seeing the potential for what AI can do for the world we live in.

Couple this with a new desire to create a personalised content experience for users, whether it be on a brand’s website or within their app, and AI has begun to establish itself as a handy tool for many content producers.

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How is Generative-AI being used?

Along with the example of Open AI’s chatbot, companies and individuals are using Gen-AI to build or inspire many different forms of content. Text, images, music and gaming are all fields which AI is beginning to infiltrate and its success is not going unnoticed.

In August 2022, Jason Allen won the Colorado State Fair’s first prize for the Digital Arts/Digitally Manipulated Photography category. His futuristic submission, which mixes Victorian stye costumes with astronaut suits, made history as it was in fact created with Midjourney, a Gen-AI software. This ability to create high-quality and striking images, that can go as far as winning an art prize, is testament to the new abilities of AI.

The technology has itself become so advanced that humans can teach it to replicate something which doesn’t yet exist, based on its understanding of hundreds and thousands of existing images. From works of art to song lyrics, Gen-AI is opening up new possibilities for many industries.

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The different types of Generative-AI

With the power of Gen-AI being increasingly understood by many different companies and brands, how can it be harnessed by different industries?

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• Mixing to create: In the architecture industry, AI can be used to influence new shapes, forms or even styles of buildings. For example, architects can mix AI which has learnt about buildings with an AI that emulates what it has been taught about nature. In this way, the results that the AI generate could provoke new types of buildings which draw inspiration from nature
• Building off an existing structure: In fashion, some examples of Gen-AI have been seen that take an existing structure, i.e., the body and build an outfit around it. The AI is therefore able to create garments that perfectly suit the shape of the human body and adapt to different sizes and shapes
• Enhancing the existing:  American artist August Kamp was about to use OpenAI’s DALL-E software to imagine what’s outside of the frame of Johannes Vermee’s famous painting, ‘Girl with a pearl earring’. By taking into account the visual cues inside the original painting including textures, shadows and reflections, the AI was able to build a picture of the background which the girl with a pearl earring might have stood in front of
• Replicating the existing: Based on what is has been taught Gen-AI is able to replicate existing content and accurately create new versions of it. For example, teaching the AI what a Louis Vuitton campaign is, means that it will then be able to produce near perfect examples of imagined campaigns

The power of the DAM and Generative-AI

As a way of storing, tagging and distributing different media assets, a Digital Asset Management (DAM) platform is a powerful tool that companies use to manage their content. This content could be used for advertising campaigns, social media posts or for e-commerce websites.

Thanks to the integration of a Gen-AI into a DAM, the ways of managing and creating content are maximised.

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For example, for a company like Michelin the identification of its different tyres is crucial for its business as they need to be integrated into their different campaigns. A Gen-AI can identify what a tyre is but being able to understand what the specific references are is a harder task. Teaching an AI that works with your DAM what the various models of tyres are, is therefore a way of enhancing how your DAM can be used and integrating very specific products details into marketing campaigns.

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Building content from within the DAM

Gen-AI is a way of creating content that doesn’t exist yet. This content can then be used to brief creative teams, create moodboards which could inspire campaigns or easily and effectively produce images for social media.

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With an integrated Gen-AI solution, Wedia.ai, Wedia’s DAM is able to power the creation of such images.

Take for example a new campaign that a car company wants to carry out. They have just released a new model and the campaign is going to be a key way of promoting its launch. The team isn’t sure whether to shoot the advert with the car in a rainforest, beside a volcano or even in a futuristic, invented environment. Thanks to the DAM, the car company can mock up images with the car in these different scenarios in order to present them to stakeholders or the creative team.

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Wedia’s DAM is able to inspire new ideas for images that don’t yet exist. For example, a drinks company is looking to promote a new soda and wants to add a new social media post with the drinks can in space. The image doesn’t exist yet but through refined language prompts within the DAM, the image can be generated and added to the brand’s social media campaign.

What are the limitations of Generative-AI?

AI is at present a way to inspire the creation of content, not to replace it. Whilst AI can imitate human behaviour and expressions, it is not capable of truly mastering human interactions or creativity.

When generating images, AI software still struggles to perfectly replicate certain elements, for example the text on a bottle of sun cream gets mixed up when the AI tries to replicate it and certain proportions of the human body can become distorted.

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The technology is certainly not perfect enough to take over the work of humans and requires the nuanced human language to prompt it into generating its creations.

Gen-AI is a powerful tool when it comes to how it can be used by brands to enhance their campaigns and streamline the process of content creation.

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Integrated into a DAM, Gen-AI becomes a way of enhancing a company’s media library and stimulating the creativity of the marketing team.

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Want to try it yourself? Book a Demo Today

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FAQs: Generative AI and DAM in 2025

Q1. What makes Generative AI different from traditional AI?

Traditional AI analyzes and predicts outcomes, while Generative AI creates new content (text, images, video, etc.) based on what it has learned.

Q2. How does Generative AI improve DAM platforms?

Gen-AI enhances DAMs by automating content generation, enriching metadata, and enabling personalized omnichannel delivery at scale.

Q3. Can Generative AI replace human creativity?

No. While Gen-AI accelerates production and ideation, true creativity, empathy, and storytelling remain uniquely human strengths.

Q4. What industries benefit most from Generative AI in 2025?

Marketing, e-commerce, media, fashion, architecture, and healthcare are leading adopters, using Gen-AI to create new products, campaigns, and even drug compounds.

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