AIOps has emerged as a transformative force for modern business IT estates. Why? Because it offers unprecedented benefits in terms of automation, visibility and security. But as adoption rates continue to increase across the board, enterprises are now learning that real results don’t come from hype alone.
A common issue for many has been caused by skipping a crucial step in the initial rush to deploy AI-powered tools: operational readiness. As a result, they’ve since found themselves burdened by a patchwork of monitoring tools and disparate data sources. Now, the focus must shift. To truly supercharge the power of AIOps, enterprises must try a more strategic approach – one built on simplicity, scalability and a clear understanding of their own digital landscape.
The challenges of scaling AIOps
Today’s CIOs are under pressure to deliver measurable and significant ROI from their AI investments. But this isn’t easy if they are navigating a complex digital infrastructure. It’s a concern echoed by the Riverbed 2024 Global AI & Digital Experience Survey, in which over 70% of business leaders agreed that while AI is still maturing, it’s been challenging to implement AI that works and scales.
One of the most significant obstacles is the sheer number of network monitoring and data management tools in play across modern IT environments. Renewing licenses for an oversubscribed suite is costly and, to make matters harder, it can often be unclear which solution offers the most value in different scenarios. Consolidating these assets can save money, time and network storage.
Clearly, complexity is a real barrier towards widespread adoption. Relying on too many moving parts also creates an inefficient and disjointed environment where AI models receive mixed messages and incomplete information. If telemetry data is siloed, outdated or irrelevant, AIOps will simply produce the wrong outputs – introducing risk rather than reducing it.
For AIOps to deliver value at scale, it has to draw from comprehensive and accurate datasets that span the entire enterprise. That means establishing unabridged visibility across user devices, cloud workflows, physical offices and remote workers. And to protect the productivity and resources of IT teams, it must also be easy to use.
Smart, open and simple AIOps
Overcoming the risks of complexity during AIOps deployment requires platforms that are not only powerful, but also unified and intuitive. Crucially, these need to consider all domains, users, use cases, locations, devices and systems, which is why it’s helpful to take the following steps:
- Equip the network with an observability framework that facilitates smart and open telemetry. Platforms with these capabilities not only capture data from every corner of the enterprise, but also intelligently filter meaningful signals from unwanted noise. In turn, that makes it easier for IT teams to respond quickly and accurately, without being overwhelmed by false alerts.
- Prioritise the unique contexts of end users with solutions designed to monitor frequently-used applications, such as video call software. This unified agent approach gathers together data from all of an enterprise’s essential communication channels, making IT management a much more convenient and accessible task.
- Choose the right type of AI support for the task at hand, because generative, predictive and agentic AIOps all offer different layers of insight and automation. Whether an enterprise needs real-time alert triage, foresight into potential threats or an intelligent assistant to manage time-intensive responsibilities, tailoring the nature of an AIOps selection makes all the difference to the extractable value.
What happens when AIOps is deployed well?
The primary conditions for AIOps readiness – clear visibility, clean data and context-aware telemetry – grant businesses of all sizes the benefit of truly understanding their entire IT environment. And when all these baseline principles for deployment are combined, the results start to speak for themselves.
For instance, one UK-based global bank wanted to boost the productivity of its operations while still keeping costs and end-user impact to a minimum. However, managing around 150,000 devices in its branches, contact centres and remote users was making it tough to minimise the regular bottlenecks that naturally occur across a vast network.
To combat this, the bank adopted an AI-led service desk that could automate the daily monitoring of performance issues like latency, as well as proactively send out reminders to users about rebooting their device or clearing their cache. In doing so, the organisation was able to:
- Clear over 300,000 remediations in 12 months by detecting and silently fixing issues before they can affect the end user.
- Save the service desk teams 7,000 hours of troubleshooting per year, streamlining man-hour costs and freeing IT teams up for more innovative tasks.
- Reduce password reset tickets by 30% after automatically distributing over 20,000 push alerts to the users that would usually rely on manual support.
Experience the full force of AIOps
A successful AIOps strategy is a lot like a parachute: it’s only truly effective if it’s fully open. For this reason, enterprises run the risk of falling short if they treat AIOps as a plug-and-play solution. Instead, it takes a concerted effort to consolidate digital assets in a way that prioritises transparency, productivity and the specific needs of the business.
That’s why supercharging AIOps deployment with the right infrastructure, tools and telemetry is the best way to maximise the performance – and therefore the value – that can be achieved from the initial investment. Only then, when IT teams are armed with technology that makes their operations more agile, scalable and intelligent, can enterprises confidently embrace the full force of AIOps.
Charbel Khneisser
Charbel is Senior Vice President – Global Solutions Engineering at Riverbed. He has almost 20 years experience in helping organisations solve today’s complex IT challenges, and in addition lectures in the Faculty of Engineering at Saint Joseph University to graduates undertaking a first year Master’s Degree.
