By Saju Sankarankutty, SVP and Unit Technology Officer, Cloud, Infosys
India leads the world in hybrid multi-cloud adoption (44 percent), concluded the 2024 report of an annual survey of global enterprise cloud deployment. While modern IT environments offer undeniable benefits, they also come with a few problems, such as lack of visibility and a larger attack surface. Managing complex networks with ever-growing traffic and data streams (including from employees’ mobile devices), while protecting data integrity and privacy, is a real challenge. Enterprises need to ensure low latency, high performance and secure network connections between on-premise and cloud assets, while battling slow network speeds, interrupted connectivity and congestion. All of this requires a significant amount of money – while definitive numbers are hard to come by, an accepted figure for network management cost is 5 percent of the total IT budget. Expense apart, network complexities can impact business operations to reduce productivity, compromise customer experience and dent organisational performance.
Artificial intelligence for IT operations (AIOps) is the answer to all these challenges.
Insights that empower networks
Powered by AI and machine learning technologies, AIOps improves network operations and delivery in a number of ways. Analysing historical and real-time data from multiple sources, including network applications, devices and servers, to uncover anomalous patterns, the system offers valuable, timely insights into network traffic, performance, and root cause of issues, to facilitate a range of activities, from infrastructure management to troubleshooting. For instance, it can highlight atypical network traffic patterns signalling cyberthreats or nefarious activity, in real-time, giving enterprises a chance to take preventive or early remedial action. What’s more, it prioritises alerts based on criticality to reduce alert fatigue and also identifies the issues needing urgent attention. Further, AIOps can analyse data from IT systems, applications and other sources (logs, metrics, network tools) to correlate events, and improve IT teams’ understanding of the relationships between them. Another benefit of AIOps is that it promotes information sharing and collaboration between different teams.
The power of knowing
Predictive Analytics – a key capability of AIOps – forecasts future network performance and problems, enabling early intervention and proactive maintenance. Further, early prediction of bottlenecks or additional requirements helps to optimise the management of network resources. For example, when organisations have advance warning about traffic surges, they can allocate capacity to prevent congestion and outages, and enhance overall network performance.
A range of mundane tasks, from incident response to work order generation to network configuration to proactive IT health checks and maintenance scheduling, can be automated with AIOps to reduce the load on IT staff and free them up to concentrate on more strategic activities. Integrating AIOps with ITSM systems makes it possible to automatically initiate workflows, generate tickets, and route issues to the right personnel for faster mean time to resolution (MTTR) and better operational efficiency.
Networks that take care of themselves
Smart, self-managing networks go well beyond routine automation to efficiently manage and scale themselves with little or no human intervention, and even automatically adapt to demand fluctuations or new applications. They can detect and isolate threats, fix vulnerabilities, and respond to cybersecurity events in real-time. Together, these capabilities drive improvements in network cost efficiency, security, error rate and reliability. Besides enabling intelligent decision-making, AI technologies bring about seamless communication and better experiences for users.
Real-world case studies
When traditional monitoring tools were unable to identify bottlenecks in a healthcare provider’s network that was seeing a slowdown in its electronic health records (EHR) system during busy hours, a switch to AIOps resolved the problem. By enabling observability across domains, the system highlighted that performance dipped when users logged in during shift changes. It also predicted slowdowns half an hour in advance and automatically provisioned additional resources to handle the surge in activity. The result was a 70 percent reduction in the most important EHR slowdowns, improvement in system responsiveness, and freeing up of IT human resources.
A network operator suffering a high rate of incidents, and consequently service interruptions, found that manual triage was slowing down both response and remediation. The company recognised that it needed to reduce the number of interruptions, rather than add more service staff. It decided to use AIOps to automate triage. The system not only automated the detection of service incidents but also leveraged explainable AI to bring the number of false positives to nearly zero. What-if analysis guided staff to prioritise incidents impacting the most customers. Network incidents reduced substantially to improve service availability by 60 percent and cut staffing needs by half.
The future of the network is AIOps
Managing network operations and delivery is becoming more complex as IT environments move to cloud, organisations become increasingly distributed, and remote employees connect to the enterprise network using mobile devices. Traditional network management cannot deliver the scalability, performance, security and reliability required to support enterprise operations. AIOps provides the answer by enabling observability and insights, automating extensive network operations, and predicting future events to allow timely, effective actions.
