How AI Is Turning Network Cameras Into Real-Time Intelligence

Exclusive interview with Bashar Al Daoud Team Leader UAE and Oman Axis Communications

1. How is AI transforming network cameras into real-time intelligence tools?

AI is moving cameras from just recording events to actually understanding what’s happening in real time. Instead of reviewing footage after the fact, the camera can now detect, classify, and trigger actions instantly — whether it’s identifying a person, detecting unusual behaviour, or flagging a safety risk. So the system becomes proactive, not reactive, and that’s a big shift in how organisations use video.

2. What role does edge AI play in reducing reliance on the cloud?

Edge AI means the intelligence sits inside the camera itself, not in the cloud. This reduces latency, lowers bandwidth usage, and allows decisions to be made immediately. It also strengthens data privacy, since sensitive information can stay on-site. The cloud still has a role, but edge processing is what really makes large-scale deployments more efficient and practical.

3. How are AI-powered cameras helping businesses cut costs and improve efficiency?

They reduce the need for constant manual monitoring — the system only alerts you when something actually matters.

They also help prevent incidents early, which can reduce losses and disruptions. And beyond security, they improve operations — things like queue management, traffic flow, or site efficiency. So it’s both cost-saving and performance improvement at the same time.

4. How does AI help future-proof network camera investments?

With AI-enabled cameras, especially on an open platform, you can add new applications over time without replacing the hardware. So today it’s security, tomorrow it could be analytics or operational insights. That flexibility is what future-proofs the investment — the system grows with the business instead of becoming outdated.

5. How is AI expanding network cameras beyond security into business intelligence?

We’re seeing cameras becoming data sources, not just security devices. They can provide insights on people movement, occupancy, and behaviour patterns, which helps organisations make better decisions. At ISNR Abu Dhabi, this is a big part of the conversation — how to use video data not just for protection, but to improve overall operations and planning.

6. How is Axis upscaling its game with AI security surveillance?

At Axis, we’ve been investing in AI at the edge for years, especially through our own chip technology. Our focus is on making sure the AI is reliable and works in real-world conditions, not just in theory. We also prioritise cybersecurity and open integration, so customers can build complete solutions with partners — not just standalone systems.

7. What are your plans for the next three years to up AI into your products?

The focus is on making AI more accurate, scalable, and easier to deploy. We’ll continue pushing more advanced analytics to the edge, while improving how video, audio, and other systems work together. At the same time, there’s a strong focus on responsible use — making sure AI is secure, transparent, and trusted by customers.