Two years ago, generative AI (genAI) burst onto the scene, transforming how tech providers and enterprises tackle real-world business challenges. Now countless companies report using genAI to speed up software development, create personalized content, and even troubleshoot network performance.
Though not without hype, genAI enables network teams to swiftly analyze diverse data sets, identifying the root cause, finding solutions, and resolving issues often before end users and customers even notice. Modern network environments are incredibly complex, supporting advanced applications that span the internet and cloud. While numerous systems work together to deliver swift application and network performance, they can generate a flood of alerts, often overwhelming network teams with noise.
GenAI, when applied properly, can extract the important signals amidst the noise and help reduce the time it takes network engineers to identify potential problems and prevent system errors from impacting end users and customers. AI can improve network resilience, reduce costs, and create a seamless customer experience for digital businesses.
Getting Started with GenAI
AI strategies must begin with tangible business challenges. For network teams, the best approach is to identify specific technology problems that directly impact business outcomes and need to be solved to enable business success—rather than exploring AI for the sake of using the technology.
“I would start by saying, ‘Let’s focus on the problem to be solved,’ and not focus on all the big buzzwords. There’s a big challenge: Everybody wants to say they’re doing AI, but let’s start with the problem we are solving,” said Frank Kelly, CTO, Hughes Business Solutions, during a Hughes panel discussion on AI and IT transformation.
The focus should be on defining the business problem, gathering the necessary data from the environment, and then choosing the right AI tools and techniques to address it. Building effective AI solutions often requires an iterative, experimental approach so it is important to start small and expect some failures.
“You may not have that immediate gratification when you send your team off to go build the next day, so I opt for small. It may take you five or six tries of something,” added Kelly. Starting with small, manageable AI projects and preparing for several iterations will help network teams discover the best applications of AI in their environments.
Learn more about how Hughes applies AI across its managed services offerings.
Data, Automation, and Governance with AI
Data is foundational to the success of genAI. GenAI depends greatly on building models and incorporating high-quality data into the models to ensure the expected and correct outcomes.
Having good, comprehensive data is critical to building effective AI models and solutions. For instance, the customer support team at Hughes was able to drive a 17% increase in customer satisfaction by pulling data from support call transcripts and contextual responses from support agents to improve service levels. For Hughes, being able to hear what support agents said and learn what they did to solve real-world problems better informed the AI on how to address these problems with automation. The team was able to build better troubleshooting tools for employees, enabling them to solve issues faster.
The team used AI-powered automation to improve employee efficiency and customer experience by reducing handle times by 30% for support calls. Automation makes it possible for network teams to keep pace with the volumes of data as genAI parses through the information and automatically enables actions to be completed with or without human intervention. Automation is necessary to enable teams to keep up with the volumes of data generated from today’s distributed systems.
Automation coupled with AI requires checks and balances, according to Rupinder Bir, Senior Director, Hughes AI Technical Expert, who also participated in the panel discussion. When using AI, it is critical to establish a strong data governance framework and auditing process to ensure the reliability and safety of AI-powered solutions before deployment.
“The new capabilities of AI are amazing, especially genAI, so we need to be careful on how to mitigate the risk so proper governance should be in place,” Bir said. He explained rigorous auditing processes are necessary to validate the outputs of the genAI models before exposing them to customers.
Organizations must approach deploying these technologies with a high degree of discipline and oversight to protect the customer experience and maintain trust.
How AI Makes Networks Smarter
AI assists in networking by analyzing data to identify patterns, predict potential issues, and optimize network performance. AI can help by adjusting non-optimal configurations, allocating bandwidth and prioritizing traffic based on real-time events, and improving efficiencies without manual intervention.
Here are a few other ways in which AI can benefit network teams:
Root-cause analysis: AI can quickly analyze network data and pinpoint the source of network issues, speeding problem resolution.
Security detection: AI can detect anomalous network behavior and spot potential security threats to automatically trigger responses to mitigate risks.
Automated configuration: AI can generate optimal network configurations and reduce manual configuration errors while improving deployment speed.
Traffic optimization: AI can analyze traffic patterns and adjust routing decisions, as well as bandwidth allocation, to optimize network performance.
Hughes has pioneered the application of machine learning to identify potential failures and applies AI to proactively mitigate them. With AI for IT operations, or AIOps, Hughes proactively identifies and heals network issues before they happen. AI streamlines network management, improves efficiencies, and enhances the user experience—making the network smarter.
Learn more about how Hughes uses AI and machine learning to deliver Managed Network Services.