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Edge Computing And AI: The New Backbone Of Smart Retail Operations

Nov 06, 2024 - forbes.com
The article discusses the increasing importance of edge computing and artificial intelligence (AI) in the retail industry. These technologies are enhancing efficiency by processing data in real time, enabling faster decision-making and personalized interactions. Two types of edge technology are highlighted: in-store edge and on-device edge computing. The latter is more transformative, capturing and processing data directly on devices like cameras or sensors, enabling full offline functionality. The article also mentions that global spending on edge computing is expected to reach $350 billion by 2027.

The future of retail is predicted to be a fully integrated, self-regulating ecosystem that learns and improves with every interaction. To implement edge computing and AI successfully, retailers need to choose the right technology partner, prioritize security and compliance, be agile and ready for experimentation, ensure efficient deployment and management, balance cost and value, and repurpose data for multiple use cases. The article concludes by stating that those who invest in these technologies today will thrive in tomorrow's retail landscape.

Key takeaways:

  • Edge computing and AI are transforming the retail industry by improving efficiency, enabling real-time data processing, and personalizing customer interactions.
  • On-device edge computing, where data is processed directly on devices like cameras or sensors, is particularly transformative, offering high levels of privacy and security, reducing latency, and enhancing customer experience.
  • Edge computing and AI are expected to become foundational in retail, with the future of the industry envisioned as a fully integrated, self-regulating ecosystem that learns and improves with every interaction.
  • Successful implementation of edge computing and AI in retail requires a thoughtful strategy, including choosing the right technology partner, prioritizing security and compliance, being agile and ready for experimentation, balancing cost and value, and repurposing data for multiple use cases.
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