Energy Crunches, The "AI Gap," and Emerging Challenges
Is your organization ready for AI? Explore key challenges in applying AI to Digital Asset Management (DAM), from rising energy demands to foundational readiness.
The momentum around Artificial Intelligence in Digital Asset Management (DAM) is undeniable, but it does not come without risks. Before diving headfirst into the hype, organizations should be aware of the potential challenges associated with the emerging technology.
Trap 1: Energy Demand and Infrastructure Constraints
One growing concern around AI is the increasing demand for electricity. AI processing requires significant computing power and electricity, and existing infrastructure may struggle to keep pace with long-term growth in AI workloads. This strain is already visible; even premium, paid LLM subscriptions like Claude frequently enforce usage limits and pause user sessions, a limitation often tied to infrastructure capacity and operational costs.
In many cases, current AI pricing models may not yet fully reflect the long-term infrastructure costs required to support large-scale deployment. Eventually, these energy expenses will have to be paid for properly, and integration costs will inevitably be passed down to DAM vendors and their users. As highlighted in the book Empire of AI by Karen Hao, the reliance on massive server farms creates significant energy demands that raise questions about long-term sustainability. Data centers already account for about 4% of U.S. electricity and may reach 6.7–12% by 2028. Supporting future AI demand could require substantial expansion of power generation capacity.
Trap 2: The AI Gap: Foundational Unreadiness
The industry is facing what is known as the “AI gap”. This gap represents a disconnect between the AI tools that organizations are selling and the actual readiness of businesses to adopt them. Despite the widespread eagerness to adopt AI, many organizations may not yet have the data governance, asset structure, or workflows needed to fully benefit from AI.
In DAM environments, AI performance depends heavily on structured metadata, consistent taxonomy, and well-organized asset libraries. Without these foundations, automated tagging, search, and generative workflows often struggle to produce reliable outputs. As a result, the real challenge for many organizations is not adopting AI itself, but preparing their content infrastructure to support it.
Where Modern DAM Can Help: Driving Sustainability
Fortunately, a modern DAM strategy can help reduce unnecessary storage and processing demands. Historically, uploading a single file and creating multiple renditions would consume about 25% more storage space. Today, through Dynamic Asset Transformation (DAT), a DAM system can hold a single, super-large master file (like a TIFF) and generate hundreds of specific renditions “on the fly” without permanently storing them.
Furthermore, deep integrations between DAM, PIM, and CMS platforms allow a single master file to automatically populate to thousands of websites or e-commerce marketplaces like Amazon. By utilizing efficient CDN links rather than continuously sending heavy image files across the web, modern DAM systems help reduce digital waste and energy consumption.
Ultimately, organizations adopting AI should look beyond the initial hype and focus on practical readiness. By auditing your foundational readiness and leveraging dynamic, modern DAM practices, you can embrace the future of digital asset management without falling victim to unsustainable costs.