R E G I O N A L C H E C K- U P
seeking comprehensive AI solutions that can process and analyse 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 edgedeployed LLMs( 32 %) but higher adoption of multimodal AI( 68 %).
Security: Both a 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 optimising 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 %).
Hybrid approach dominates strategy
ZEDEDA edge AI survey – key findings
Most organisations( 54 %) report that edge AI complements their cloud AI strategy for a hybrid approach. Nearly half( 48 %) are exploring edge AI specifically to reduce cloud
• 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 organisations 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 number one reason( 53 %) for edge AI investments, security risks and data protection concerns( 42 %) represent the most significant challenge for implementations.
computing costs, while 44 % consider edge AI critical for real-time processing and lowlatency requirements.
“ Rather than viewing edge and cloud AI as competing approaches, organisations increasingly recognise them as complementary parts of a unified strategy. Edge AI spans a continuum – from embedded systems within factories and other locations to edge data centres that are closer to users and devices – enabling realtime, localised intelligence,” Ouissal said.
“ By combining this with the cloud’ s strength in large-scale analytics and model training, businesses can unlock faster, more efficient and context-aware AI capabilities.”
Budget increases reflect strategic importance
Edge AI’ s strategic importance is reflected in rising budgets, with 90 % of CIOs reporting increases for 2025. Three in ten( 30 %) organisations are significantly increasing edge AI budgets by 25 % or more, while 60 % report moderate increases of up to 25 %.
Larger businesses( 500 + employees) are more aggressive in their investments, with 39 % reporting significant budget increases compared to 23 % of mid-sized organisations( 250-500 employees).
This survey, conducted by Censuswide between 26 February 2025 and 4 March 2025, interviewed 301 US CIOs aged 25 years and older and excluded sole traders. �
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