Many public and private organizations are realizing that a wealth of information is hidden in the data collected from video sources. New advances in AI computer vision can help tap this source of information to drive superior customer and employee experiences and better financial outcomes through revenue increases and cost reductions - not just for the primary organization but for all the closely related business entities.

​Any large public or private space requires a wide range of business applications to operate safely and efficiently. The computer vision and data analytics components must be easy to install and manage with minimal time and cost to realize the benefits of these new AI opportunities. Organizations across the globe will need to quickly evaluate the most cost-effective approach to investing in these new video analytics and artificial intelligence (AI) capabilities to create safer and more engaging experiences by improving the flow of people and objects within the spaces they manage.

Many of the AI software options available today include pre-trained models from independent software vendors (ISVs) that generate operational insights and real-time alerts from video and other sensor data with minimal customization. However, a complete AI solution requires integration of several applications that are specialized for certain use cases, including video data management, AI computer vision, and data analytics tailored to different types of use cases. Meeting these needs requires an easy-to-acquire, easy-to-manage, GPU-enabled hardware platform that can support various applications using virtual machines, containers, and a mixture of operating systems.

Video management is an established use case with a long history of innovation and change. The currently installed base of technologies supporting traditional video management and computer vision applications is largely siloed and aging. IT organizations interested in providing the newest capabilities to their businesses need a new approach to technology acquisition and deployment that can be seamlessly ordered, deployed, maintained, and supported.

There is a significant opportunity to both gain new AI capabilities and reduce the cost of ownership when replacing the old, siloed technologies currently deployed in private data centers and at the edge. A complete lift and shift to the public cloud is not viable. The scale of the data management requirements and the need for low-latency analytics greatly favor a distributed computing approach. As organizations decide on what technologies to invest in next, they will prefer options that can leverage some services of public cloud providers when appropriate. They would also like to simplify ongoing operations by integrating their private data center assets with a globally distributed and feature-rich management ecosystem to create a hybrid cloud ecosystem.