How AIOps Is Poised To Reshape IT Operations

By Michael Szabados, chief operating officer at NETSCOUT

The contemporary business landscape is transforming akin to a second Industrial Revolution. This time, it’s not about steam and steel machines – it’s artificial intelligence (AI). Just as Marc Andreessen prophesied in 2011 that "software is eating the world," we now stand on the precipice of an era where AI is the new devourer, remaking whole industries in its image.

For the record, I’m not talking about the GenAI tsunami represented by chatbot breakthroughs like ChatGPT. It is a transformation far more intrinsic to how companies run. It’s AI applied to automating the control mechanisms for how our infrastructures operate, our applications perform, and our networks stay highly available.

It’s about doing all these things because the data volumes and software updates moving through today’s complex hybrid IT environments far exceed the ability of humans to handle anymore. In this overwhelming world, we can’t distinguish every problematic signal from a wall of noise or diagnose the root cause of each poor user experience from its symptoms.

Organizations across the globe are racing to harness AI’s potential in this way, but the race is fraught with obstacles. The complexity of AI systems, growing cyber risks, and a pervasive shortage of skilled personnel are just a few hurdles companies must overcome. Moreover, traditional IT methodologies are proving inadequate for the challenges at hand.

Related:Who Does or Doesn’t Need AIOps Tools?

Enter AIOps

AI Operations (AIOps) – leveraging artificial intelligence and machine learning (ML) to enhance and automate various aspects of IT operations – represents a seismic shift in managing technology ecosystems. It’s not simply about using AI – it’s about integrating AI at the core of IT operations. Analytics platforms such as Splunk and ServiceNow that incorporate AI and ML, mine vast and varied data sources, enabling automation of workflows that once demanded manual oversight. This automation isn’t just about efficiency; it’s also a gateway to new IT, security, and business optimization applications.

However, just as the trustworthiness of a GenAI chatbot is a function of the quality of the data that feeds it, fulfilling the very mission of the AIOps transformation is contingent upon the accuracy, relevance, and timeliness of the data feeding its platforms.

Traditionally, data types available for AIOps have been confined to different flavors of telemetry data – logs, traces, metrics – that report on network elements’ health. This is a necessary but incomplete view of the system. These data provide snapshots – important yet static – of system components without considering their interactions, which are the lifeblood of network resilience, security, and scalability. The observability that AIOps platforms create is impressive, in other words, but they could be generating even greater payback with a more precise, more meaningful dataset.

Related:Generative AI in ITOps: Its Potential and Limitations

Better Data, Better AIOps

A meaningfully different, as yet underutilized, high-value data set can be derived from the rich, complex interactions of information sources and users on the network, promising to triangulate and correlate with the other data sets available, elevating their combined value to the use case at hand. 

The challenge in leveraging this source is that the raw traffic data is impossibly massive and too complex for direct ingestion. Further, even compressed into metadata, without transformation, it becomes a disparate stream of rigid, high-cardinality data sets due to its inherent diversity and complexity.

A new breed of AIOps solutions is poised to overcome this data deficiency and transform this still raw data stream into refined collections of organized data streams that are augmented and edited through intelligent feature extraction.

Related:Will Generative AI Shake Up Security Operations Centers?

These solutions use an adaptive AI model and a multi-step transformation sequence to work as an active member of a larger AIOps ecosystem by harmonizing data feeds with the workflows running on the target platform, making it more relevant and less noisy.

By allowing feature extraction and applied AI/ML to run close to the raw sources of information, these modeling solutions for AIOps significantly reduce noise and false positives. This allows AIOps platforms to evolve from alert and incident de-duplication to more effective coordination of multiple intelligence feeds. Automation becomes reactive and proactive, characterized by quicker response times, reduced costs, and heightened confidence. It stands to reason that if your AIOps platform is processing smaller amounts of more relevant and valuable data, with all the unimportant noise filtered out, you can pursue specific use cases faster and far more efficiently.

Using this adaptive modeling approach to AIOps, companies can:

Toward a New World of Higher-Order Automation

In training and deploying AI-driven processes and platforms, the compactness and relevance of data are crucial. With these new AIOps adaptive modeling solutions, decisions can be based on proof versus conjecture, evidence versus suspicion, and meaningful connections instead of approximate correlations. They enable AIOps platforms to reach their targeted outcomes faster and with a level of trust that was previously unattainable.

As organizations adapt to new AI-centric modes of operation, we are witnessing the emergence of a new digital economy. It’s an economy where AI is an enabler and the central axis of innovation and value creation. However, the risks are outsized, too. To successfully mitigate the chance of unwanted outcomes and loss of control, the biggest lever companies call pull today is the truthfulness, relevance, and timeliness of the data they feed their AI platforms.

About the Author

Michael Szabados has served as NETSCOUT’s Chief Operating Officer since April 2007, focusing on the implementation and execution of the company’s vision and strategy. He has also served on NETSCOUT’s Board of Directors as Vice Chairman since February 2019. As COO, he has played a pivotal role in leading, integrating, and adapting NETSCOUT’S functional areas as the Company grew dramatically through organic expansion and multiple acquisitions.

His career at NETSCOUT began in 1997 when he joined the Company as Vice President of Marketing, charged with increasing overall visibility and market awareness. His responsibilities expanded in 2001 to encompass product development, manufacturing, and customer support when he was promoted to Senior Vice President, Product Operations.

 A veteran of the enterprise networking industry, Mr. Szabados held senior leadership roles with companies including UB Networks, SynOptics/Bay Networks, and MIPS Corporation following engineering and product management roles at Intel Corporation and later at Apple. Mr. Szabados holds a BSEE from UC Irvine and a Master of Business Administration from Santa Clara University.