The essentials
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AI improves DAM. Generative AI transforms digital asset management by automating content creation and improving asset organization for faster marketing execution.
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DAM as a story engine. DAM systems are evolving into story engines, in which AI curates and creates marketing assets based on historical data and brand guidelines.
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The future of marketing. With AI-powered DAM, marketers can streamline their campaigns, create content from scratch, and improve their effectiveness.
For two years, everyone in the marketing world has been trying to understand what generative AI can and cannot do. At first, it seemed odd to outsource work driven by the human need for content, connection, and community to an algorithm incapable of experiencing or appreciating any of those things.
However, once marketers understood what AI could generate for pennies, in seconds, ROI calculations overcame any remaining resistance. How could marketers not charge a digital asset management (DAM) with at least some AI-generated content?
DAM today is a library-like system for organizing, searching, and distributing content. He will continue to fulfill this function. We think it could also become a “story engine” for marketing campaigns. And that could have consequences for everyone who works in marketing.
How AI is reshaping marketing jobs and roles
When ChatGPT debuted in November 2022, its impact on employment was questionable. It is now clear that generative AI shifts or modifies tasks that, until recently, only skilled creators could accomplish.
In the Harvard Business Review (HBR), researchers recently reported that on freelancing platforms, weekly postings for “jobs subject to automation” dropped 21% in the eight months since ChatGPT launched. Writing contracts declined by about 30%, while software, app and website development fell by 20%.
In the year since the introduction of AI image generators, like DALL-E and OpenAI’s Midjourney, weekly graphic design and 3D modeling assignments fell by 17%. During the same period, job postings began listing “ChatGPT” as a required skill, indicating that “the ability to integrate AI tools into the job is increasingly valued.”
In other words, AI competes with the people who populate a DAM system with assets. As AI becomes more sophisticated, it could move from creating one-off assets to becoming more involved in campaign orchestration. And we found a good model for how this could work.
Related article: Understanding the impact of AI on digital asset management jobs
The great American robot novel
To see a potential future for digital asset management, take a look at AIStoryBuilders, a long-form writing tool. Developed by Michael Washington, AIStoryBuilders creates a database of characters, locations, timelines, and plots so ChatGPT can assist with writing. Essentially, it learns from an initial set of human-generated assets to create new ones.
On the Hanselminutes podcast, Washington describes the limitations and potential of his software.
“I found out that it’s just about writing one paragraph at a time, and I have to keep editing that paragraph,” Washington says. This paragraph is not easy either, he adds. “I have to build these characters. I have to say the locations. I have to set deadlines. There is a lot to build.
It describes the work of “grounding” an AI in the reality of a story, so that it does not hallucinate. Once this information is organized in a database, the AI can learn and relearn this information through retrieval augmented generation, or RAG, before spitting out a paragraph. This way, basic facts are accurate, like a character’s hair color, setting, year, etc. ChatGPT can even fine-tune a model to imitate someone’s writing style. It only takes about three chapters to get there, Washington says.
Will Generative write the next great American novel? Not alone. “(AI) has no soul, it has no desires, it has no creativity,” Washington says. “But if you organize things a certain way and power the AI through grounding, it can actually be useful.”
How Digital Asset Management Powers AI-Driven Campaigns
A marketing campaign is a kind of story. It is a product (what) created for an audience (who) who needs or wants it in a certain context (when/where), and the function of the product (how) helps deliver a value proposition (Why).
If AIStoryBuilders can organize characters, locations, and timelines in a database, then surely an AI-powered DAM could organize the elements of a marketing campaign and build on them. The database would include product information, personas, historical assets and perhaps performance data from past campaigns. These could be communicated to the AI via RAG and used to refine a model that adheres to the brand’s voice and guidelines.
Initially, the DAM story engine would work best on finite tasks, like AIStoryBuilders. Create a 15-second YouTube ad, Instagram post, or TikTok video. Develop an email template. Write the first email in a drip campaign to people who have shared their email address for a chance to win a free product. It could also run email, television commercials and radio commercials.
Could generative AI run a marketing campaign from start to finish? Maybe one day. The new version of Anthropic, Claude, can “use computers like people do: looking at a screen, moving a cursor, clicking buttons, and typing text.” It could surely exploit advertising platforms and marketing automation platforms.
Additionally, with access to influencers and AI models, the DAM story engine could generate campaign assets without waiting for humans to photograph models or ship products to TikTok stars. Like a driver in the Tesla seat on autopilot, the human marketer should be alert and ready to take the wheel, if necessary.
Related article: Why Customer Experience Professionals Should Care About DAM
Overcoming Barriers to Integrating AI into DAM Systems
Digital asset management is an ideal system for an AI story engine because it contains content and metadata – rich information about that content and how marketers expect it to work. it be used.
However, there are still some important obstacles to overcome:
- The first is cost. Generative AI companies incinerate cash and sell tokens at or below price. Unless generative AI companies develop more resource-efficient models (or cheaper chips and power sources), there’s a good chance that costs will rise to the point that a story engine DAM is no longer economical.
- A second obstacle is the pace of progress in Generative AI. Until recently, generative AI reliably improved with scale; give a model more data and processing power, and you’ll get more intelligence out of it. But today, OpenAI employees admit that progress has slowed.
- Third, the concern about image quality. DALL-E, for example, still produces cartoonish, airbrushed images and has difficulty rendering text in images. While the result is good enough for a Slack joke or a greeting card, it’s a far cry from what professional designers do. Then again, OpenAI’s Sora model for video generation seems to produce realistic images, so perhaps DALL-E has hope.
Regardless of these obstacles, we expect creative workflow to take place more in digital asset management systems, which will increasingly become a database for organizing stories and an engine for creating content. We also expect marketers and creatives to become skilled users of generative AI.
Generative AI may not understand and experience content, connections, and community like humans, but we do. And that’s perhaps the most important qualification we’ll have in the age of DAM story engines.
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