Navigating the Network Data Tsunami with AIOps

The upsides of investing in Artificial Intelligence (AI) are as promising as they are plentiful. Just take AI for IT Operations (AIOps), for example, which offers a way for organizations to redefine how they identify threats, uncover blind spots and resolve incidents before their users are affected.  

However, despite the promise, successful AI implementations are not always guaranteed. AI generates immense tides of data, under which IT systems struggle to stay afloat – so if your network can’t handle it, the entire investment could fail. Seeing long-term results from AIOps relies on how well your digital infrastructure deals with the inevitable information overload.  

Is Your Network Ready for AI? 

In the face of this AI data tsunami, many enterprises are discovering a major weakness: their networks are struggling to keep up. AI can only perform as well as the data it consumes. So if the infrastructure that supports it is challenged, it makes sense to consider a widespread modernization of the enterprise and cloud networks. 

The sooner the better, too, as according to some recent research, only 37% of business and IT decision makers consider their organization to be fully prepared to implement its AI strategy. It’s clear that AI is maturing at a rate much faster than enterprise networks can currently cope with. 

The main challenge is that the enormous volumes of information consumed and generated by AI need a robust yet agile network to serve as a backbone. Without that in place, data can quickly cause congestion, latency, and potentially downtime. For end users, this translates to sluggish apps, inaccurate outputs, and ultimately, poor digital experiences.  

These limitations even undermine the effectiveness of AIOps platforms themselves. If your business lacks computing power and data fluency, its AI models will be unable to perform in a timely or constructive manner – neutralizing any financial and efficiency gains, and impacting business growth. 

To extract full value from AI, you must ensure your network is primed for speed, scale, and intelligence. 

Make Your Network Smarter, Bigger, Faster, Stronger  

​​​While most organisations today would agree that AI is making networking more critical, it’s also increasing the demand for network resilience. For this reason, it’s wise to seek solutions that ensure vital data can move quickly and reliably across your network – regardless of which application, device or user it originates from. 

One way to achieve this is with application acceleration technology, which can efficiently move vast quantities of data around the most complex of networks. The technology helps to prevent bottlenecks – even under the heaviest of data loads – which means AI-powered applications count on reliable services, while enterprises gain invaluable agility. 

That said, performance relies on relevance and accuracy just as much as it does on speed – a balance that smart observability solutions can help you find. By autonomously filtering and prioritizing data at the edge, intelligent telemetry allows AI systems to listen and learn from the information sources that really matter. Irrelevant or low-quality inputs are discarded early, increasing model accuracy and, in turn, operational productivity. 

No matter what context AI models are deployed in, they’re only ever effective if the right kind of data has the power to flow freely at scale. And with the AI landscape evolving fast, it’s never been more important to ensure your infrastructure can facilitate speed, scale, and security – all at once. 

Reaping the Business Benefits of Successful AI Deployments 

By embracing acceleration and observability solutions, it’s possible for you to take a major step towards reducing network strain while lowering the cost of data management. In becoming more AI-ready, you can begin to realize new competitive advantages, such as: 

• Faster error response times: With streamlined data pipelines, AIOps for observability platforms can use intelligent analytics to proactively detect and address issues before user experiences are impacted. Recently, Meta shared that improving their system architecture resulted in a “50% decrease in MTTR (Mean Time to Repair) for critical alerts”, despite needing to process “more than 500,000 analyses per week”. 

• Measurable improvements in wider business metrics: Accelerated AI-ready networks reduce long-term IT operating costs, increase service availability, and unlock more responsive customer experiences. Just ask financial lender Klarna, whose AI assistant is “performing the ​​work of 700 employees”. As a result, their “average revenue per employee had increased by 73%”. 

• Greater innovation and agility: Free from the constraints of ill-equipped infrastructure, your teams no longer need to be stifled by reactive firefighting. Now, their attention can turn towards supporting business growth – like driving faster product rollouts, conducting digital research, or evolving to meet shifting compliance demands. 

Achieving Agility in the Age of AI 

AI doesn’t have to feel like yet another layer to add onto your digital estate – instead, you should view it as a performance multiplier. But to unlock its full accumulative potential, your network must be fit for purpose. Delaying could have costly consequences, ranging from damaged user experiences to incomplete AI insights and underwhelming innovation.  

That’s why you should consider leveraging technology to your advantage. There are solutions in the market that not only facilitate secure and agile movement between massive datasets, but also ensure that AI models will be trained on the most accurate and relevant information – setting the stage for successful digital transformation.  

After all, the organisations that continue to thrive in the era of AI won’t necessarily be the ones that achieve instant seamless deployment. In reality, it’ll be the ones that spend time adapting their infrastructure to evolve as it grows.