Key Takeaways
- 16.3% compound annual growth rate (CAGR) expected for the global digital asset management market from 2024 to 2030
- The global digital asset management (DAM) market was valued at $6.0 billion in 2023
- The global digital asset management market is forecast to reach $17.3 billion by 2030
- 73% of marketing professionals say they use digital asset management tools to manage creative assets (survey statistic)
- 43% of organizations have a digital asset management system in place (survey-based adoption level)
- 54% of marketers say they cannot find the content they need fast enough (DAM adoption motivation)
- The global market share of DAM vendors: Canto (ranked #1 by G2) among digital asset management products in the G2 category (leading vendor position)
- As of the 2024 G2 category pages, Bynder, Frontify, and Canto are among top-ranked DAM products in the category (market trend: vendor consolidation)
- OpenAI/ChatGPT usage in organizations increased rapidly, with 41% of surveyed organizations using or planning to use generative AI in 2023 (drives AI-assisted tagging/search over DAM content)
- 46% of marketing teams report fewer outdated assets being used after implementing DAM (version control metric)
- 37% of organizations say DAM reduces compliance risk by keeping audit trails and controlled distribution (risk-control metric)
- Up to 20% cost savings from reduced rework is reported in DAM business cases (cost-performance linkage)
- Reduced asset search time can translate into $1.2 million annual labor savings for a mid-market company in a published business case (labor cost metric)
- DAM can reduce marketing production costs by 15% in organizations with high asset duplication (cost metric)
- Data center energy cost: US data centers consumed about 17,000 megawatt-hours (MWh) in 2022 for IT load (context for storage/compute efficiency)
DAM adoption is accelerating with 16.3% forecast CAGR, driven by better governance, faster search, and significant cost savings.
Market Size
Market Size Interpretation
User Adoption
User Adoption Interpretation
Industry Trends
Industry Trends Interpretation
Performance Metrics
Performance Metrics Interpretation
Cost Analysis
Cost Analysis Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Samuel Norberg. (2026, February 13). Digital Asset Management Statistics. Gitnux. https://gitnux.org/digital-asset-management-statistics
Samuel Norberg. "Digital Asset Management Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/digital-asset-management-statistics.
Samuel Norberg. 2026. "Digital Asset Management Statistics." Gitnux. https://gitnux.org/digital-asset-management-statistics.
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