A data center company recently enabled autonomous robot dogs to patrol and protect their premises, but soon realized the bots' potential was being stunted by a few key missing features.

Robots are swiftly moving from factories to the real world, working alongside humans to increase productivity, enhance security, and eliminate human error. In fact, the robotics industry is predicted to grow to $165.2 billion by the end of 2029, with a compound annual growth rate (CAGR) of more than 16% over the next five years.

Still, maximizing the ROI of autonomous robots can be extremely challenging. Though people see the potential benefits of robots, they don't fully trust them. Robots are still learning to convey human-like social skills, while humans are still learning how to engage with robots.

Companies can meet this challenge by leveraging the power of generative AI to maximize the impact of enterprise-level autonomous robots, creating a more seamless experience for bots and people alike.

Data security plays a vital role in business operations. When one company invested in an agile mobile robot that could be used to enhance 24/7 on-premise security, the out-of-the-box experience of the four-legged robots lacked the integration needed to align with existing services, systems, and processes.

The company needed to equip robots with more advanced security features to address this challenge while fostering greater trust between robots and employees, customers, and visitors. This challenge demanded careful attention to technical, design, and user experience considerations.

The latest advances in generative AI ease these processes. In this case, generative AI enabled unique AI personalities on different robots while helping detect potential threats. Ultimately, when applied with a human-centric approach, generative AI can improve security both internally and externally, bringing humans and robots together in a more harmonious, secure, and efficient manner.

Establishing trust in autonomous robotics poses a significant challenge in any industry. Companies looking to implement this kind of security change must address ongoing challenges by identifying ways to make the robots more relatable and engaging.

For example, integrating ChatGPT and various voice services can create unique voice personas to make them more relatable. It's more sophisticated, and employee interactions become intuitive. To reinforce trust in advanced robotics, they can also be designed with a custom-branded skin, making them a distinct part of a company's operations.

The release of ChatGPT changed how we're able to train models, unlocking the ability to better integrate with vision capabilities for anomaly detection and alerts. A large language model (LLM) solution can have numerous advantages over the machine learning (ML) route. For one, using an LLM doesn't require the large amounts of data, training, resources, and time that it would take to get similar results through an ML-powered solution.

In a real-world situation with security bots, further tailoring of the instruction set of the LLM can allow for high levels of accuracy in identifying security threats in a complex environment.

Security cameras are essential to any high-security operation, but one downside is that they are static. Yes, higher-end security cameras offer high-definition video streaming with pan, tilt, and zoom capabilities, but sometimes situations require you to get up close and personal. Video streaming and facial recognition technologies can optimize this.

For example, paired with a robot's unique voice personality, a security bot can recognize, catalog, and greet folks it sees on autonomous security patrols, maintaining a friendly environment while keeping it secure. Using generative AI, the robots can also detect anomalies on their routes. When people approach the robot, it can detect familiar faces vs. security threats and alert personnel as needed.

In addition to optimizing facial recognition, advancing technology can also build services that integrate with an existing security platform, such as one that processes license plates. As the robots patrol, they can process license plates captured and generate alerts if unknown vehicles are identified.

Robots can become more relatable, interactive, and engaging with the right approach. Most importantly, they can bolster a company's day-to-day safety and efficiency operations, offering data-driven decision-making and enhanced situational awareness.