Generative AI has emerged as a game-changing force in the tech world, fundamentally altering how we generate, interact with, and manage digital content. Artificial intelligence has gone from an intriguing innovation to a powerful disruptor. According to Gartner, by 2025, 30% of outbound messages from large organizations will be synthetically generated—an astounding leap from less than 2% in 2022. Furthermore, generative AI will drive a tenfold increase in data production, accounting for 10% of all data by 2025, compared to less than 1% in 2021.
While the potential of generative AI is undeniably exciting, it also introduces new challenges—chief among them, managing the overwhelming influx of content this technology will produce. For businesses already navigating the complexities of content management, from scattered repositories and overburdened content calendars to scaling creative operations efficiently, the need for robust digital asset management (DAM) systems is more critical than ever.
As generative AI continues to fuel an explosion of content, DAM solutions will bridge today’s content scarcity issues with tomorrow’s content abundance. Let’s explore how DAM can help organizations manage the content chaos and prepare for the content-rich future generative AI is ushering in. Integrating generative AI with DAM can empower teams with advanced content discovery, streamlined prompt management, and intelligent workflows that enable scalable, personalized omnichannel experiences.
What is generative AI?
Generative AI is a type of machine learning that uses neural networks to create new content. Unlike other types of machine learning that are trained on existing data, generative AI uses a generative model to create new data from scratch based on a wide variety of data, including Common Crawl, webtexts, books, and Wikipedia. Additionally, generative AI can be trained to produce better output.
Generative AI has many applications, including the creation of text, images, and videos. In the film industry, generative AI is used to create special effects and virtual backgrounds, making it easier and cheaper to produce films. Marketing teams across industries are starting to use generative AI to create emails, social copy, collateral, and a variety of other digital content.
The Evolution of Content Creation: From Manual to Machine-Assisted
Content creation has undergone a remarkable transformation over the decades, moving from labor-intensive manual processes to the highly automated, AI-driven capabilities we see today. This evolution has reshaped not only how content is produced but also the scale, speed, and quality with which it can be delivered.
The Manual Era: Creativity with Constraints
In the early days, content creation relied entirely on human effort. Writers, designers, and marketers worked independently or in small teams, manually crafting everything from copy to visuals. While this approach fostered creativity, it was time-intensive and prone to inconsistency. Studies from this period show that teams spent up to 70% of their time on repetitive tasks like reformatting assets or repurposing content, leaving limited bandwidth for strategic innovation.
The Automation Era: Streamlining Repetition
The introduction of early automation tools marked the first major leap in productivity. Content management systems (CMS), templates, and workflow automation allowed teams to streamline repetitive tasks. Automation also improved consistency, helping brands maintain a cohesive identity across channels. However, creativity still hinged on human ideation, with technology serving more as an enabler than a driver.
The AI Era: Unleashing Machine-Assisted Creativity
By leveraging advanced algorithms and machine learning, organizations can now ideate, draft, and refine content with unprecedented speed and precision. AI-driven tools can reduce content creation time by 30-50% while enhancing quality through improved grammar, tone, and personalization. These capabilities enable marketers to produce content at scale without sacrificing creativity, paving the way for hyper-personalized campaigns and richer customer experiences.
The importance of DAM for the rise of AI-generated content
Organizations are under enormous pressure to create and deliver on-demand, personalized omnichannel content across customer segments and verticals. Despite constrained resources and this demand for higher volume and faster delivery, teams have achieved new economies of scale, often without making changes to people, processes, or technology.
They’ve grown accustomed to the “we’ve always done it this way” thinking, which prevents them from being strategic. The challenge for most organizations today is content scarcity. New tools like ChatGPT, Dall-E, and Runway are rapidly moving organizations from content scarcity to a world of content abundance.
The pace at which all this is happening is not without risk. It presents a slippery slope with potentially dire situations for organizations, which already face challenges in how their human-generated content is organized, stored, accessed, updated, and delivered.
That’s where DAM comes in. DAM software is a foundational technology that is core to mature content operations. It acts as a system of record to help teams manage their rapidly multiplying digital assets. Streamlining the processes of asset creation, storage, and retrieval saves time and money while providing deeper insights into their content usage and performance.
There are many benefits of digital asset management that can address today’s content challenges:
- Governance: Provides a single source of truth across global locations, internal businesses, and platforms. Capabilities like metadata, tagging, uploading, permissions for different user groups, and managing digital rights, facilitate consistent and efficient management.
- Content Discovery: Allows users to quickly and easily search, find, access, collaborate, share, and deliver assets.
- Improved Collaboration: Enables more effective collaboration by sharing assets and working on them together, leading to better communication and more efficient workflows.
- Increased Efficiency: Streamline and automate workflows, reducing the time it takes to create, review, and approve creative assets, which increases productivity and cost savings.
- Enhanced Security: Ensure that assets are stored securely and can only be accessed by authorized users, which helps to protect sensitive information and intellectual property.
- Better Asset Quality: Ensure that assets are consistent and of high quality, leading to better customer experiences and improved brand reputation.
- Improved Decision-Making: Get data analytics and insights that can be used to make better decisions about asset creation, distribution, and usage.
- Composable Content Ecosystem: Encourages integration with a broad partner ecosystem to build composable content stacks and increase the value of your existing martech investments.
As AI-generated content becomes a driving force in modern marketing, the benefits of DAM equip organizations to manage the growing volume of assets while maintaining quality and security. As generative AI accelerates content creation, integrating DAM ensures businesses can harness this innovation without succumbing to content chaos, enabling smarter workflows and delivering seamless, personalized customer experiences at scale.
Integrating generative AI with digital asset management software
Combining generative AI with the right digital asset management platform unlocks transformative potential for content creation and management. Together, these technologies can streamline workflows, enhance collaboration, and optimize the entire content lifecycle.
Generative AI’s ability to produce personalized, on-brand content at scale pairs seamlessly with DAM’s organizational capabilities, ensuring that even the most complex libraries of assets remain easily accessible and secure.
Auditing, Traceability, and Transparency
By leveraging AI, organizations can automatically track asset usage, monitor content history, and maintain detailed records of every edit, approval, and distribution. This level of traceability ensures compliance with brand guidelines and regulatory standards while enabling teams to quickly identify inefficiencies or inconsistencies in the content lifecycle. Transparency features provide clear insights into asset performance and user interactions, empowering data-driven decisions to optimize workflows and content strategies.
Prompting
Getting good generative output requires good prompts. When you hand a task to your copywriter, all sorts of campaign planning information can help feed a good prompt. For example, who is it targeting? What’s the aesthetic? What is the objective of the campaign?
Asking a generative AI, “Can you write me a blog covering how great this toothpaste is?” can be greatly impacted with prompt modifiers, such as “Can you write me a blog covering how great this toothpaste is, focusing on the fact that 9/10 dentists recommend it, it leaves you with a minty fresh feeling, and the % chance it has to reduce cavities?”
A DAM that also holds your campaign planning data can streamline a creative’s ability to produce generative AI output that’s consistently on brand and on message across your channels.
Brand Safety
A DAM platform is essential for managing the diverse and intricate metadata associated with AI-generated assets. By organizing metadata effectively, DAM ensures consistency and accuracy across all content, reducing errors and maintaining brand integrity. Additionally, it supports responsible use policies by providing tools to monitor and enforce brand safety, ensuring all assets align with organizational standards and guidelines.
Advanced Search
Traditional search methods, such as keyword searches, may not be effective in finding the desired assets in a generative AI context. Advanced search capabilities, such as computer vision for image recognition and computer hearing for auto-subtitling and content analysis, are needed to search and filter assets effectively, as well as using generative AI to automatically set metadata for better user experience on content uploads.
Responsible Use
Once generative AI is integrated into a DAM, it’s essential that all instances of use are flagged as such. The more content generated by AI, the higher your risk for situations like copyright infringement because it was trained on non-public domain content. Much like OpenAI’s code of conduct, organizations must implement meaningful human oversight, set limitations to reduce misuse beyond an intended purpose, and mitigate certain undesirable or scenario-specific behaviors.
The Human Element in AI Content Operations
Even in an AI-powered content ecosystem, human expertise remains indispensable for ensuring success. Rather than viewing AI as a replacement, organizations should embrace it as a creative collaborator—a powerful tool that helps teams achieve results more quickly and elevate the quality of their work. While AI may not always deliver perfect results on its first attempt, it excels at getting projects 80-90% of the way there, providing a strong foundation that human creativity can refine and perfect.
Strategic oversight is essential to guide AI-generated content toward aligning with brand goals and audience needs, while creative direction ensures that content maintains the emotional resonance and originality that only humans can provide. Additionally, humans play a vital role in quality control, reviewing and refining AI outputs to ensure they meet high standards and avoid potential pitfalls like biases or inaccuracies.
Successful human-AI collaboration models often involve AI handling repetitive tasks, such as generating content variations or tagging assets, while humans focus on refining and curating the final outputs. This synergy enhances efficiency while preserving the creative authenticity that resonates with audiences. By treating AI as a collaborative partner in the creative process, teams can leverage its strengths to accelerate ideation and production while maintaining the human touch that makes content truly compelling.
Trained AI
The importance of training AIs to know what good content looks like will be critical. GPT today is trained on an internet dataset. It knows nothing about your tone, brand voice, or the values you want to speak to. You can influence GPT by prompting, but you can reduce the need for specific and complex prompting by training GPT to already be aware of those values or how to talk to a certain audience. And how do you do this? By training it on content usage and performance data models in the DAM.
Democratizing Creative Skills
Tasks previously reserved for creative roles, like removing objects from images, can be democratized so that a channel marketer can adjust content on the fly in new ways. While AI today offers the ability to transform content on demand (i.e., watermarking, grayscale, height/width adjustments, blurs, etc.), with generative-AI-powered transformations, we could offer many more on-demand adjustments to the channel marketer without them having to tap a creative.
Measuring Success in AI-Powered Content Operations
To evaluate the success of AI-powered content operations, organizations must establish clear frameworks and key performance indicators (KPIs) that align with their business goals.
- Content quality should be a primary focus, measured through metrics like audience engagement, accuracy, brand alignment, and personalization effectiveness.
- Production efficiency can be assessed by tracking reductions in content creation time, workflow bottlenecks, and overall time-to-market improvements.
- Resource utilization highlights how effectively human and technological resources are deployed. Metrics such as the percentage of repetitive tasks automated and the reallocation of creative team efforts toward strategic initiatives can offer insights into operational improvements.
- Business impact KPIs, like increased conversion rates, customer retention, or revenue growth tied to AI-generated campaigns, provide a clear connection between content efforts and organizational objectives.
Best practices for performance tracking include integrating AI-driven analytics tools, conducting regular audits of AI-generated outputs, and fostering a feedback loop between creative teams and AI systems to refine processes. By continuously monitoring these metrics, organizations can optimize their AI content strategies and maximize their return on investment.
Aprimo takes a principled approach to generative AI
Organizations navigating the generative AI era must prioritize digital asset management to handle the growing complexity and scale of content operations. DAM solutions ensure consistency, accuracy, and efficiency by managing the vast array of assets and metadata generated by AI, all while enabling teams to maintain brand integrity and deliver exceptional customer experiences.
By integrating DAM with AI, businesses can streamline workflows, reduce costs, and optimize resource utilization—positioning themselves to thrive in an era defined by content abundance and personalization demands.
Aprimo offers a DAM platform built to meet the challenges of today and tomorrow. With advanced AI capabilities, including metadata extraction, AI starter packs, and business-specific content recognition, Aprimo empowers organizations to establish guardrails and take control of their content ecosystems. Our principled approach ensures that AI is embedded seamlessly into workflows, balancing innovation with brand safety, quality, and ethical practices. Aprimo’s solutions make it easy and affordable for businesses to adopt AI, helping them stay ahead of the curve. Book a demo today to see how our DAM and AI capabilities can help your organization unlock the full potential of generative AI while driving measurable results.