AIOps injects intelligence into the heart of IT operations

As an IT manager in a bustling ecommerce company, you dread the start of the busy season. Downtime means lost sales and angry customers, and it all comes down to you and your team make sure the website and network are up and running, all the time.

You’re monitoring the system around the clock, but the sheer volume of data is overwhelming. Monitoring tools are spitting out a stream of alerts, and your team is scrambling to keep up — often chasing down false positives or reacting to issues only after they have affected users.

Now, picture a different scenario: instead of being bombarded with a flood of alerts, your team is proactively alerted to potential issues before they escalate into real problems. The system both detects and predicts anomalies, using machine learning to analyze patterns and identify the root causes. This is the power of using AI in IT operations, or AIOps for short.

How does AIOps differ from traditional IT operations monitoring?

Traditionally, organizations have deployed data collectors that feed into tools monitoring their campus, edge and data center infrastructure. These tools send alerts to IT teams for incident management and resolution. Each flagged incident requires a detailed analysis of the collected data so the network operations center can create a ticket and assign it for remediation.

This manual approach often involves multiple vendors, which leads to slower resolution times and higher costs, especially once an IT environment becomes more complex. This puts strain on IT budgets that could be used more strategically.

Trends in digital transformation — including network modernization, rapid detection and response in cybersecurity, and the rise of hybrid cloud environments — are now pushing organizations to seek more efficient alternatives.

In this context, AI is already delivering significant operational value by automatically resolving network incidents before they reach an organization’s IT engineers, reducing ticket-handling times across devices, users and routes. It also improves task automation and delivers greater efficiency, accuracy, improved security and compliance, sustainability gains and cost savings, especially in large-scale deployments.

• ALSO READ → AIOps complements platform-oriented security for unrivaled peace of mind

What are the core requirements of an AIOps strategy?

When you want to implement AIOps in your organization, start by evaluating your operations strategy for your enterprise IT architecture. An audit of your assets, skills and partnerships will help you to identify parts of your IT stack to automate immediately or in the future, and to decide whether to keep components in-house or outsource them to a service provider — which could lead to cost savings.

Also keep the following in mind:

• Data-driven IT operations involve collecting data continuously from multiple sources into a powerful data lake, augmented by analytics algorithms and machine learning, to identify patterns, anomalies and correlations — no matter the complexity of your IT stack.
• You need enough input from all your applications, data sources and application programming interfaces; a solid command management database; and an integrated software-asset-management platform populated as far as possible through auto-discovery.
• The output of your AI-driven components has to feed into integrated service-workflow channels to ensure the success of your newly automated operations.
• You can opt for a direct AIOps investment or a comanagement strategy with the help of a partner to evolve your IT operations.

Consider a hybrid approach

Many organizations would like to move to dark network or security operations centers that operate with minimal human intervention and rely heavily on automated systems and AI to monitor, detect and respond to issues.

However, considering the range of management tools available from multiple suppliers, organizations will, for now, focus on hybrid operations centers that have both automated and manual functions. In other words, their engineering teams’ skills, certifications and experience will remain in the mix, complemented by data-driven insights from the automation engine.

This approach reduces human error and gives organizations more detailed feedback, including information about resource planning and vulnerability tracking.

How can I transition smoothly to AIOps?

There are some important points to consider when you’re making this shift:

• When you move to AIOps, you have to eradicate data silos in your organization to benefit fully from this transformative technology.
• Although AIOps can be applied to existing technologies, we recommend some level of digital transformation to enable real-time data analysis and automation. A partner like NTT DATA can help you source the right skills and rethink your budget in this regard.
• Adapting your existing workflows to incorporate AIOps capabilities might involve redesigning processes to be more proactive and predictive.
• Change management, including reskilling and retooling your workforce, is critical to helping your teams use the new technology effectively.
• Linked to the previous point, your workplace culture needs to embrace change and innovation. Encourage collaboration and communication between your IT, development and business teams. AIOps works best when you have a holistic view of your IT landscape.
• Keep monitoring the performance of your AIOps solutions. Use key performance indicators to measure their impact on your IT operations so you can make adjustments as needed.

How does AIOps improve service availability?

Broadly speaking, AIOps provides a unified view of your entire service-delivery ecosystem, so you can ensure consistent service quality across channels. Your IT teams gain deeper knowledge of this ecosystem than they would have from traditional monitoring and event-chasing.

You can also start challenging your IT vendors to improve their management tools, based on detailed insights into their actual performance.

Partnering with an expert service provider gives you access to a wider range of technology and, therefore, even better results. Using NTT DATA’s AI and machine-learning tools — like our SPEKTRA network-management platform — we resolve up to 95% of incidents before they can affect service experience. We also see five times fewer tickets per device, a 30% reduction in ticket-handling time, and improved security — and our clients find it much easier to predict usage and manage the capacity required to support it.

We’re also integrating a centralized data platform, infusing GenAI and digital experience-management tools into carpeted and non-carpeted environments to support the rising number of IoT and client-facing devices that need to be monitored properly.

How does AIOps save me money?

AIOps reduces the need for manual intervention by automating routine tasks and fixes, incident management and data analysis. This leads to substantial cost savings, as it allows your IT team to operate more efficiently and resolve more incidents in less time.

At NTT DATA, we see our clients making faster and more cost-efficient progress with their digital transformation projects after applying AIOps to their IT environments. The correlated insights they gain into capacity, vulnerabilities and licensing fees help their IT teams to simplify their investment decisions.

Once they combine these gains with an as-a-service model such as network as a service, they can migrate from traditional, restoration-focused supplier SLAs to integrated, outcome-based SLAs with a full-stack partner like NTT DATA.

Why do I need the help of a partner?

As I’ve set out above, a partner with a variety of in-depth skills, available on demand, and the ability to work across multiple technologies will help you stabilize, transform and strengthen your organization’s network much faster.

When you find the right partner to achieve these outcomes, you’ll see the benefits of proactive, data-driven management and the ongoing modernization of your IT environment. This is why, at NTT DATA, we’re integrating AI and machine-learning tools like SPEKTRA across our delivery centers and cloud, network, security and collaboration solutions.

Our global expertise enables us to provide observability all the way from the device layer to the application layer, spanning a range of operational and customer-facing technologies.

In this way, we add valuable human know-how into the AIOps mix to give you the best of both worlds.