AI can help operators accelerate broadband deployments, in particular if it's used to design networks, according to new research.
A report by the World Broadband Association (WBBA) argued that the technology could help in the pre-deployment phases of broadband networks by being put to work during network planning and expansion.
The findings of the report were presented at the Broadband Development Congress (BDC) in Barcelona this week.
So far, telecom companies are focused on AI for customer service and network troubleshooting and optimization — all useful applications but by bringing AI into systems earlier, there could be further benefits.
By using AI to help with network development, operators can make sure they roll out infrastructure to the right places at the right time and with the right technologies, the report noted. Similarly, this could help ensure a return on investment (ROI).
"The pre-deployment phases in broadband are periods of decision-making and network planning before infrastructure is rolled out or upgraded," said Martin Creaner, Director General of the WBBA.
"It’s an ideal environment for AI to make an impact, and if its models are supported by the right data and algorithms, then it can be used to help operators predict the return on their investments much more accurately."
Using AI to support broadband deployments
The white paper suggested, for example, that an AI model can identify the most suitable areas for a new broadband deployment, when to make upgrades from copper to fiber, or how to improve penetration in rural areas by considering existing networks, customer data, and geography, as well as costs.
"All of these decisions should be informed by many interdependent factors and moving pieces related to deployment costs, demand dynamics, wholesale versus retail trade-offs, and regulatory constraints," the report noted.
"AI can play a key role in generating and analyzing a huge quantity of high-quality data for some (or all) of these factors to produce outputs for one (or more) of these decisions while optimizing the ultimate objective variable (which, in this case, is assumed to be ROI in broadband investment)."
That information can help operators decide when and where to deploy, which customers to target, and how to price services, the white paper noted.
However, the WBBA warned that this approach requires the right dataset and that AI has limitations. As such, operators should understand that it works best with clear constraints and is used alongside existing analysis for the best results.
"AI is not a magic wand that can solve the entire ROI optimization problem, but it can add significant accuracy to parts of the equation," the paper noted.
AI needs broadband too
The WBBA also noted that more efficient broadband was necessary to power the shift to AI and next-generation internet services.
"There is no single use case that will drive demand for gigabit broadband access," the WBBA said in a separate report about Gigabit broadband.
"It will be driven by a combination of three core trends: a rapid increase in connected devices, the development of advanced video-enabled AI applications, and the shift of computing power to the cloud. This combination will drive the need for high-speed, low-latency, highly reliable, and consistent networks."
As part of its Gigacity Index 2025, the WBBA ranked the broadband sophistication of cities around the world, revealing Singapore, Shanghai, Zurich, Hong Kong, and Dubai as the leaders.
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