Understanding device targets, integration approaches, and key challenges
Edge devices are becoming an increasingly important way for artificial intelligence (AI) to reach end users, from smartphones and laptops to wearables, industrial machines, and connected vehicles. This report aims to understand how developers are currently integrating AI models into edge devices and where the main opportunities to reduce friction lie. Based on a global survey of professional software developers who reported building or implementing AI functionality in the 30th edition of our global Developer Nation survey, the analysis details the widespread usage of edgeAI among these developers, regional differences, the devices they target, the approaches they use, and the main challenges they face when deploying models on the edge.
Questions this report answers
How widespread is the integration of AI models into edge devices among professional developers who build or implement AI functionality, and how do regional differences in adoption vary?
Which types of edge devices do developers primarily target for AI implementations?
What approaches do developers use to integrate ML/AI models into edge devices?
How do these differ by experience and device type?
What are the main challenges developers face when integrating ML/AI into edge devices? How do they differ by device type and approach?