Artificial intelligence and generative AI are everywhere, and their impact on every industrial sector is becoming more and more significant. Digital communications is no exception. AI is expected to have major effects on every part of the network.
AI in networking has seemingly unlimited potential
AI’s potential to deliver greater business value for network owners seems unlimited. AI can streamline complex network operations, improve performance, reduce costs, and speed service delivery, just to name a few.
I’m Rich Colter, Head of Marketing at the Fujitsu Network Business. Let’s do a quick flyover and see why AI for networks is now a pivotal force driving network transformation, starting with network software and operations.
AI network analytics & automation
The market for AI-powered network automation software is already approaching one billion dollars, according to Appledore Research.
Let’s look at some of the ways AI is being used in networks today:
- Decision-making
- Network optimization
- Sustainability and energy savings
- Data analytics and intelligence.
AI support for decision-making
A digital twin is a dynamic, virtual copy of the physical network. AI digital twins can be used to assist and inform decision-making by modeling “what if” scenarios; predicting future behavior, anticipating the effects of changes and external events; and even suggesting enhancements.
This strengthens disaster preparedness and resilience, and it helps anticipate service-affecting events or plan needed upgrades or expansions.
AI network optimization
It’s no surprise that network optimization is also a leading use case. AI-powered applications can provide many practical network optimization benefits, including:
- Automated lifecycle service orchestration
- Accelerated root cause analysis,
- Automated network traffic steering
These all help to improve network performance and reliability, minimize service disruption, reduce congestion, and improve latency.
AI and network energy consumption management
A recent survey by telecoms.com names energy consumption management among the top five use cases for AI in 5G networks. AI-powered applications help reduce environmental impact and support sustainability goals. For example, AI-powered estimates for user equipment data and traffic can be used to switch capacity on or off as needed while maintaining service continuity. Overall, AI enables fuller use of resources with less waste.
AI network information management
Operating a network successfully depends on effective use and management of information. AI can be a powerful tool here; for example, by using Large-Language Modeling and AI-generated datasets to mine specialized network knowledge, support evidence-based analysis, and improve business forecasts.
AI analytics can also help operations teams model the relationship between user experience and quality of service, encapsulating measurements such as throughput, delay and loss.
These advanced capabilities empower operators to change the competitive landscape with differentiated service offerings, based on more realistic QoS and QoE predictions.
New capabilities are constantly emerging in AI networks
AI is evolving fast. New capabilities are constantly emerging on the path to light-touch operations. Watch for upcoming videos where we take a deeper dive into the applications I’ve summarized here, and as always, reach out to Fujitsu. Let’s keep the open dialogue going.