FEATURE: EDGE solutions that can process and analyze data across multiple formats simultaneously.
While Large Language Models( LLMs) are equally popular in the cloud( 59 %), they see somewhat less adoption at the edge( 47 %), which could reflect computational requirements or use case needs. In retail, CIOs reported lower interest in edge-deployed LLMs( 32 %) but higher adoption of multimodal AI( 68 %).
Security: both driver and challenge
Security considerations play a dual role in edge AI adoption. Improving security and data privacy is the primary motivation for edge AI investments( 53 %), followed by improving customer experience( 42 %) and optimizing operational efficiency( 39 %).
However, security risks and data protection concerns also represent the biggest implementation challenge( 42 %), followed by high operational and maintenance costs( 40 %). Other significant challenges include finding the right technology vendors and partners( 37 %) and a shortage of talent with edge AI expertise( 37 %).
ZEDEDA edge AI survey – key findings
• Customer Experience and Risk Management Lead Current Edge AI Use Cases: 80 % of CIOs with deployed edge AI solutions leverage them for customer experience improvements, while nearly as many( 77 %) focus on risk management applications, including predictive maintenance.
• Edge AI Budgets Increasing Pervasively: 90 % of organizations are increasing edge AI budgets for 2025, with 30 % reporting significant increases of 25 % or more.
• Security Remains Both a Key Driver and Top Challenge: While improving security and data privacy is the No. 1 reason( 53 %) for edge AI investments, security risks and data protection concerns( 42 %) represent the most significant challenge for implementations.
www. intelligentcio. com INTELLIGENTCIO NORTH AMERICA 41