Inside Brinks Home’s AI Strategy to Reduce Call Volume and Modernize Customer Support

How are you defining “modernization” in the context of customer support and monitoring operations?

For me, modernization should improve the customer experience while also reducing operational cost. If our technology doesn’t accomplish both of those things, it’s probably not the right investment.

Modernization means giving customers the tools they need to manage their systems on their own terms through digital channels while equipping agents and technicians with better tools when human support is needed. At Brinks Home, that includes a digital help center, AI-powered chat experiences, improved self-service capabilities through our customer portal and mobile app, and intelligent agent assistance tools that help our support teams resolve issues quickly and consistently. It also means simplifying the employee experience. When agents have the right information at the right moment, they can focus on solving the customer’s problem instead of navigating multiple systems.

Advances in AI at the device level are also transforming how security systems interpret activity around the home; cameras and sensors can distinguish between routine activity and potential threats to identify alarm events. Combined with capabilities like video verification via remote video monitoring, this gives both customers and monitoring teams clearer context about what’s happening in real time — resulting in reduced false alarms while improving overall safety and response.

Ultimately, modernization is about integrating intelligent devices, digital experiences, and AI-enabled support to create a security experience that is faster, more accurate, and easier for customers to manage.

Where generative AI is delivering measurable impact

Where is generative AI delivering the most measurable impact today?

We’re seeing meaningful impact across several parts of the customer support ecosystem.

One area is customer self-help. In our help center we deployed a ChatGPT-based experience using a retrieval-augmented generation model. When a customer asks a question, the system retrieves the most relevant support articles from our knowledge base and uses that information to generate a clear response. That approach dramatically improves how quickly customers can diagnose and solve issues.

Another major impact is in the contact center. We deployed an AI agent assistance platform called Cresta that transcribes calls in real time and provides agents with guidance during the interaction. The system highlights key moments in the conversation and suggests best practices or workflows that help the agent resolve the issue.

From an operational standpoint, Cresta also transformed quality assurance. Historically, a QA team might review a small sample of calls. With AI we can now evaluate essentially 100% of customer interactions and identify coaching opportunities much more effectively.

We’ve also applied AI to retention, marketing, and how we engage with our customers. These systems allow us to continuously test and refine messaging and how we deliver communications, helping us better understand customer needs over time and personalize engagement to deliver more relevant experiences at scale.

Across all of these areas, the outcome has been the same: higher customer satisfaction delivered more efficiently.

How are you balancing automation with the human element, particularly in a security environment where trust and accuracy are critical?

In our view, AI should augment people rather than replace them.

The most successful implementations we’ve deployed help employees perform at a higher level. For example, Cresta provides agents with real-time coaching during calls, helping them navigate complex situations and deliver more consistent service.

We’ve also deployed virtual technician capabilities powered by smartphone video that allow technicians to diagnose issues remotely in real time. This doesn’t remove the human expertise from the process — it allows technicians to help customers faster while often avoiding a truck roll, saving customers the cost and inconvenience of an in-person visit.

A capability growing within the industry is remote video monitoring, which adds another layer of support. Emergency operators can verify alerts using a live camera view and relay critical information to first responders while remaining on the phone with the customer until help arrives.

Automation works well for routine questions and basic troubleshooting, but human support remains essential when customers encounter complex issues or need reassurance in a critical moment.

Governance and organizational change

What governance structures or safeguards have you put in place to manage risk around accuracy, data privacy, and potential AI errors?

Accuracy is extremely important in our industry, so we’re careful about how generative AI systems are implemented.

For example, our help center implementation uses retrieval-augmented generation and other techniques to ensure accuracy. That means responses are grounded in verified support documentation rather than relying solely on an open model’s training data. By anchoring AI responses to our curated knowledge base, we significantly improve reliability.

AI also improves oversight in some areas. With tools like Cresta, we have complete visibility into customer interactions and can analyze performance trends across the entire call center. Instead of reviewing a small sample of interactions, we can evaluate nearly every call.

Ultimately, the key governance principle is aligning AI deployments with clear business outcomes and operational accountability.

What organizational changes were required to support AI adoption?

Digital transformation only works when technology teams and business operations are tightly aligned.

At Brinks Home we emphasize communication and shared ownership of outcomes. Our teams operate using agile practices with quarterly program planning sessions where business leaders, stakeholders, and IT align on priorities. Once priorities are established, IT teams hold daily standups to ensure progress against goals, and project owners meet with business stakeholders regularly to provide updates and maintain alignment.

Another key element is helping employees understand the “why” behind transformation initiatives. When teams understand how technology investments improve the business and the customer experience, they become strong advocates for change.

We also invest heavily in data infrastructure. AI systems depend on reliable access to high-quality data, so building the pipelines and governance around that data is foundational.

Lessons for security dealers and monitoring providers

For security dealers or monitoring centers considering generative AI, what early lessons would you share?

First, start with clear business objectives. It’s easy to get excited about AI tools, but technology alone won’t create value unless it solves a real operational problem.

Second, focus on people. The best technology implementations succeed because the teams using them are experienced, collaborative, and aligned with the business.

Third, invest in data. Data is the foundation for both AI and broader digital transformation. If your data environment is fragmented or incomplete, your ability to leverage AI will be limited.

Finally, deliver value quickly and iterate. Some of our early digital transformation efforts involved improving existing platforms rather than replacing them. That allowed us to move fast and generate measurable results while continuing to evolve the technology stack over time.