Using AI, machine learning and advanced analytics to protect and optimize business

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Supply chain complications, rising costs and modern consumer expectations are leading to increased business demands, generating a greater need for more efficient processes using the same or fewer resources. Gartner reports the post-COVID working world will introduce even greater organizational challenges, including a sprawling hybrid workforce, lack of critical talent, and increased turnover.

In order to do more with less, business leaders must look inward and operationalize existing processes and infrastructure. Security executives, in turn, can prove more value to their investments and their teams by using existing security technologies to increase productivity in other areas of the business.

According to STANLEY Security’s 2022 Industry Trends Report, nearly half (46%) of mid-sized and enterprise businesses have already implemented physical security systems such as access control and video surveillance. By further leveraging these technologies — along with recent innovations like real-time location systems (RTLS), weapon and threat detection systems, and workplace management software — organizations can gain business-critical insights to increase efficiency and overall security and, at the same time, reduce costs. 

The key to unlocking crucial organizational knowledge lies in the successful implementation of security artificial intelligence (AI), machine learning (ML), and advanced analytics via automation, all which leverage existing technology to improve operational efficiencies and help protect a business’s most valuable assets.

When successfully implemented, AI- and ML-driven security technologies use a breadth of latently gathered information, such as asset location and device status, to provide insights into baseline business operations. This information can then be automatically applied to security, operations, and other departments within the business through AI.

The cost-saving implications of AI and ML can be immediate. For example, a security system that surveils and grants door access control for employees can identify when all team members have exited a building and use this information to appropriately adjust ancillary systems such as lighting and temperature control. This daily measure reduces energy costs and creates a more sustainable workplace.

AI-powered insights also drive long-term cost savings. When leveraged alongside physical security systems, AI systems identify emerging patterns and provide preventative troubleshooting suggestions. In a restaurant or retail environment, this could look like a workplace management system that monitors device health and anticipates when equipment may be close to malfunctioning. Preventative detection in this case saves power consumption as well as customer satisfaction and service costs that would otherwise be lost in the event of system failure. Or, in a manufacturing use case, security systems powered by ML could use RTLS to track valuable assets, detect traffic patterns and identify opportunities for optimized equipment use and routing.

Capitalizing on the protection provided by advanced analytics

Insights gained from AI and ML processes drive organizational improvements, while the provision of integrated analytics frees time for employees to revert focus to high-impact projects that drive business value. Dependent upon industry, this could be the difference between an extra conversation with a customer, the closing of a deal, or enhanced care for a high-risk patient.

AI, ML, and advanced analytics can also provide relief for labor shortages by handling and codifying routine processes, enabling human laborers to upskill and work at the top of their abilities. But perhaps even more importantly, AI and ML can also improve safety for employees and other human assets.

Using RTLS, ML-enabled security technology can track assets — non-intrusively for humans — and use collected data to provide crucial health-related information. In healthcare settings, for example, RTLS and AI technologies can be leveraged to help predict and prevent falls. 

When business leaders hesitate to adopt these crucial technologies, their organizations miss out on enhanced protection and vital insights into improved business operations — all while those very insights lie dormant inside existing infrastructure. 

In order to succeed in the coming years, business leaders need to adopt these vital technologies sooner rather than later.