- Adopt edge AI only where it makes sense (such as inference in low-connectivity environments).
- Continually communicate business value to non-technical leadership.
- Consider a hybrid cloud-edge strategy rather than fully edge or fully cloud deployments.
- Abstract architectural software layers from specific hardware dependencies.
- Choose models optimized for edge constraints.
- Envision the full model life cycle, including updates, monitoring, and maintenance, from the outset.
From centralized to distributed intelligence
Although interest in edge AI is heating up, similar to the shift toward alternative clouds, experts don’t expect local processing to reduce reliance on centralized clouds in a meaningful way. “Edge AI will have a breakout moment, but adoption will lag that of cloud,” says Schleier-Smith.
Rather, we should expect edge AI to complement the public clouds with new edge capabilities. “Instead of replacing existing infrastructure, AI will be deployed at the edge to make it smarter, more efficient, and more responsive,” says Basil. This could equate to augmenting endpoints running legacy operating systems, or optimizing on-premises server operations, he says.
The general consensus is that edge devices will become more empowered in short order. “We will see rapid advancements in hardware, optimized models, and deployment platforms, leading to deeper integration of AI into IoT, mobile devices, and other everyday applications,” says Agrawal.
“Looking ahead, edge AI is poised for massive growth, driving a fundamental shift toward distributed, user-centric intelligence.”
