AI-assisted Solution for Hybrid Workspace Optimization

Problem:

The future of hybrid work culture is increasingly complex. With remote workers, distributed teams, flexible multi-tenant office space, and how workers interact with each other and their physical space is dynamically changing everyday.
Yet, decisions around the workplace design and strategy are all too often anecdotal, static, and incomplete. Companies need better data in real-time about how people are using spaces, so that they can build more purposefully.

COVID-19 has changed how we work and where we work, forcing different and flexible forms of the workplace. With more employees choosing to work from home on a hybrid basis, it has become essential to perform workspace optimization. This factor will inevitably change the role of current physical working environments and will increase the demand for technological developments that can help us track and manage resources and reduce capital and operational costs.

We are proposing an approach in AI, data, and analytics to ensure we deliver the most appropriate real-time useful insights about workspace usage and workspace allocation in hybrid situations.

High-Level Solution:

We propose AI in the Hybrid Workspace to estimate office space utilization.
By leveraging the cameras in the office, we can count the number of people working using AI & ML techniques and hence the total workplace space utilization metrics. The more cameras, the more accurately this solution gets the space utilization.

Now having more cameras will create problems like overlapping visual fields that can result in duplicate counting. The USP of our solution is a smart algorithm which will deduplicate the number count from multiple camera feeds by using AI image processing techniques by separating the “Region of Interest” on each camera’s visual field.

Once deployed, our solution will need the initial effort of setting up cameras and identifying the “Region of Interest” as a one-time activity with the help of our accelerator. After that our data pipeline consisting of AI Models will be able to find the utilization count from a continuous feed of video streams and capture the utilization statistics in real-time. Once we identify the utilization count, we can use this data to decide how much space optimization is needed and whether we can reduce operational costs by removing redundancy.

Please watch the video on “AI-assisted Hybrid workspace optimization” to know more

Thought Leadership