Key Findings
97% of CIOs have Edge AI either already deployed or on their roadmap.
Risk management is planned for 73% of future edge AI implementations.
Most organizations (54%) report that edge AI complements their cloud AI strategy for a hybrid approach
Multimodal AI is the most commonly deployed AI model at the edge (60%).
High operational and maintenance costs of Edge AI are a challenge for 40%.
77% of CIOs with deployed edge AI solutions focus on risk management applications.
30% are reporting significant budget increases of 25% or more in Edge AI.
34% of organizations are testing with plans to deploy AI at the edge within the next 24 months.
In retail, CIOs reported higher adoption of multimodal AI (68%).
Finding the right technology vendors and partners of Edge AI is a challenge for 37%.
Larger businesses (500+ employees) are more aggressive, with 39% reporting significant budget increases in Edge AI compared to 23% of mid-sized organizations (250-500 employees).
In retail, CIOs reported lower interest in edge-deployed LLMs (32%).
Security risks and data protection concerns represent the biggest Edge AI implementation challenge (42%).
22% of organizations are actively in production with limited deployment of AI at the edge.
Large Language Models (LLMs) are widely deployed in the cloud (59%).
Improving security and data privacy is the No. 1 reason (53%) for Edge AI investment.
48% are exploring edge AI specifically to reduce cloud computing costs.
A shortage of talent with edge AI expertise is a challenge for 37%.
High operational and maintenance costs of Edge AI are a challenge for 40%.
60% report moderate budget increases of up to 25% in Edge AI.