To overcome these hurdles, the author suggests a software-defined approach that embraces existing data and infrastructure, a 'hybrid AI' approach that utilizes both edge and cloud AI, making AI accessible to non-technical experts, and ensuring security across the device ecosystem. This approach can help manufacturers adapt to changing market conditions, improve efficiency, and stay competitive.
Key takeaways:
- Edge AI can enable manufacturers to make near-real-time, informed decisions, increasing operational speed and efficiency, better cost control, and more comprehensive data security.
- Despite the benefits, over half of all AI projects fail due to complex, siloed, or unavailable data and absent talent, legacy and fixed-function infrastructure and cost, and the need for more secure and efficient operations.
- A software-defined approach can address the challenges of implementing edge AI in factories, allowing manufacturers to work in time-critical environments and address issues caused by diverse data types, power, performance, latency and cost.
- Organizations willing to embrace AI at the edge better position themselves to reap the business advantages of intelligent industrial systems.