AI workloads place demands on networks that go beyond what traditional applications require. They need very low latency, massive bandwidth, robust security and the ability to prioritise different types of traffic. They also require integration with computing resources at the network edge so data can be processed as close as possible to where it is generated.
Consider an industrial safety system that uses machine vision and AI to detect pedestrians in the path of heavy machinery. Cameras and sensors capture images, AI processes them in real time, and instant alerts are sent to drivers or automated systems. For this to work, vast amounts of data need to be transferred and analysed in fractions of a second.
Other examples include predictive maintenance in manufacturing, where sensors track the condition of equipment and AI identifies potential faults before they cause downtime; precision agriculture, where environmental sensors and drones feed live data into AI models to guide irrigation, fertilisation and harvesting; and healthcare, where remote diagnostics and telehealth deliver specialist advice to patients far from major hospitals.
What these have in common is that they all rely on network infrastructure that can move and process data instantly and without interruption. The smarter the AI, the more demanding it is of the network.
The regional opportunity
One of the most compelling opportunities for AI lies in its ability to close the gap between metropolitan and regional Australia. Industries such as agriculture, mining and energy – many of them based far from city centres – stand to benefit enormously from AI-enabled systems.
But those benefits will only materialise if the connectivity is there. Without fast, reliable networks, regional businesses risk being left out of the AI economy. That makes investment in regional infrastructure not just a matter of fairness, but one of national economic competitiveness.
When remote operations can feed real-time data into AI systems, those models become richer, more accurate and more globally competitive. Better connectivity also enables regional businesses to access AI-driven services that can improve productivity, safety and sustainability.
Building an AI-ready network is a task that will require collaboration between government, industry and technology providers. Public investment can fill the gaps that private capital alone cannot justify, particularly in remote areas. Private sector innovation can then focus on developing the services and applications that ride on top of that infrastructure.
AI is evolving faster than many expected. Network infrastructure has to be designed not only to handle current applications but also to be ready for those we can’t yet imagine. That means building in flexibility, scalability and energy efficiency from the outset.
Technologies such as 5G and emerging network-as-a-service models can help by providing more control, reliability and responsiveness. The integration of edge computing – bringing processing power closer to where data is generated – will further reduce latency and improve performance.
One example is Optus’s work with partners to pilot AI-driven safety systems in industrial settings, combining intelligent networks with machine vision to prevent accidents in real time. It’s a glimpse of what becomes possible when the right infrastructure meets the right application.
The bottom line
AI has the potential to boost productivity, create new industries and improve quality of life. But it will only succeed if the networks behind it are built to meet its demands.
By investing in robust, high-capacity and intelligent network infrastructure today, we can lay the foundations for an AI-enabled economy that benefits the whole country – from farms and factories to hospitals and schools.
This is not just a technology upgrade. It’s an investment in Australia’s future competitiveness, resilience and prosperity. And it’s one we can’t afford to get wrong.
Tony Baird is CTO, Optus.