For broadband providers, proactively addressing network issues and maintaining optimal network performance is an ongoing challenge. Recent innovations in artificial intelligence (AI) technology, however, are creating new methods to help service providers manage and optimize their networks and deliver new insights on network health, reliability, and performance.
The Benefits of Predictive AI
Predictive AI uses advanced algorithms and machine learning models to analyze data from sources like network monitoring systems, service assurance solutions, and OSS/BSS platforms. The power of predictive AI has multiple benefits for network monitoring and optimization:
- Real-time network traffic optimization. Learning from historical user patterns, bandwidth consumption patterns, and peak usage intervals, predictive AI can analyze a provider’s network traffic and optimize it for the best experience. For example, AI algorithms can automatically adjust bandwidth allocation based on current demand, ensuring that high-priority applications receive the necessary resources while minimizing congestion from less critical services.
- Fault detection, improved reliability. Predictive AI can identify fiber cable degradation or signal interference quickly, allowing network operators to resolve the problem and minimize downtime for customers. AI systems can identify patterns and correlations that human operators might miss and predict equipment failures, service disruptions, or performance degradation with high accuracy.
- Better security. Predictive AI can track and identify abnormal network usage to notify providers when a potential cyberattack is underway. As the AI systems learn the behavior of network users, AI tools can identify cyberattack threats based on pattern matching. Predictive AI can also help providers analyze huge sets of data — including call records, subscriber information, and usage patterns to detect signs of fraud such as a high number of international calls or automatic hang-ups.
- Network automation and increased efficiency. By automating network monitoring and analysis processes, predictive AI reduces the need for manual monitoring and disorganized troubleshooting. This saves both time and money, as providers shift from a reactive maintenance model — in which issues must be addressed after they arise — to a proactive model, where potential problems are resolved before they impact customers.
- Planning for the future. By forecasting network demands based on current usage trends and demographic data, predictive AI can help providers with capacity planning and future-proofing network infrastructure. With an AI-generated view of the future, providers can make informed decisions about infrastructure investments and upgrades. This approach helps providers scale their networks efficiently and avoid over- or under-provisioning.
The Challenges of Predictive AI
Yes, predictive AI is a powerful new tool, but it also comes with potential challenges. Here are three of the most critical:
- The need for high-quality data. AI has an incredible ability to analyze data, but the results are only as good as the source data. “Garbage in, garbage out,” as the saying goes, and this is especially true of AI. It’s vital for providers to validate the quality and accuracy of the data used to optimize networks.
- The complexity of AI systems. Implementing and maintaining predictive AI may require specialized knowledge. Providers may need to invest in training and/or hiring skilled professionals to manage these systems effectively. The cost of integrating AI technologies can also be substantial.
- Trust and transparency. AI — particularly tools like predictive AI — is so new that it’s hard to know how much to trust it to manage a network. AI can sometimes operate as a “black box,” meaning users can see the output but don’t know how the sausage is made. This lack of transparency may be a concern for network operators.
Like any new tool, predictive AI comes with both benefits and challenges. However, once broadband providers can trust and fully implement predictive AI, networks will have powerful automation and optimization tools, run more efficiently, and be more reliable for the customer. Given all of the benefits, implementing predictive AI isn’t a question of if – but when.
Tony Stout, CTO
CDG
Tony Stout joined PRTC in 2010 and serves as the company’s Chief Technology Officer. In his role as CTO, Tony is responsible for overseeing strategic direction for building and expanding PRTC’s next generation network infrastructure. In 2020, PRTC acquired CDG, a telecom OSS/BSS solutions provider. Tony was appointed CTO for CDG and oversees the company’s technology, software engineering, IT and technical support teams. Tony has more than 28 years of technology, business and management experience in various Telecom and Information Technology roles.
