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AIOps for Networking - the Dawning of a New Era

"Humans and manual processes can no longer keep pace with network innovation, evolution, complexity, and change," said Jim Frey, VP of Srategic Alliances at Kentik. "That's why we're hearing more about self-driving networks, self-healing networks, intent-based networking, and other concepts. These approaches collectively belong to a growing focus area called AIOps, which aims to apply automation, AI and ML to support modern network operations."

Key findings in the new report from Kentik, The State of Automation, Artificial Intelligence, and Machine Learning in Network Management, include:

The move to cloud is still underway for a few, but multi-cloud is a reality for many

Moving to the cloud, and especially multi-cloud, is one of the driving factors behind the need for network automation. While 76% of our respondents indicated they were using cloud services, nearly a quarter (24%) report that their organization has not yet moved to the cloud.

Of those with cloud services, nearly half (47%) are working in a multi-cloud environment, so the complexity ramp is a swift one.

Network automation is taking shape

85% of respondents said their organization has one or more types of automation, and yet only 27% of respondents said their organization is "extremely prepared" or "very prepared" for full automation.

Progress is being made, however, as respondents feeling "extremely" or "very" prepared nearly doubled from 15% in Kentik's 2018 survey.

The energy sector leads the network automation trend

Outside of the technology industry, the energy sector is the most prepared for full automation. Thirty percent (30%) of energy sector respondents reported their organization is "extremely prepared" or "very prepared" for full automation.

Healthcare and government are behind the curve. In the healthcare industry, only 3% of respondents reported that their organization is "very prepared" for full automation. Government respondents led among industries "not at all prepared," with 21% of the sector noting this response.

Networking processes are least likely to be automated

Networking processes like compliance and incident response are least likely to be automated. The majority (53%) of respondents are using automation for network configuration — the only area to receive a majority response.

Policy management was the second-most automated process, cited by 40% of respondents.

Processes such as compliance, incident response, and cloud bursting received lower response rates. This may be due to the level of human interpretation and investigation that still needs to exist, as these processes are often regulated and/or are more directly associated with impacting a business and its revenue.

Machine learning is growing in importance for network management

Machine learning is growing in importance for network management, regardless of who you ask. Up 20% since our 2018 survey, 65% of respondents said that ML is now "extremely important" or "very important" for network management. This reflects both the steady maturation of and comfort with ML as a technology, as well as the relentless march of complexity, causing network pros to seek help in reducing time and effort required to monitor and troubleshoot network and application performance in large, complex environments.

AIOps adoption among network professionals is very early stage

AIOps adoption among network professionals is very early stage, but the industry appears ready for it to help with network management. Only 22% of respondents reported that their organizations are actively using or planning to use AIOps tools today. However, clear majorities are prioritizing automation and ML, which are two of the three major foundational elements of AIOps (the third is data integration and enrichment).

"While our industry still appears to be in the early phases of embracing AIOps as a collective strategy, our findings show that the rationale and commitment are there," added Frey. "It appears that AIOps for networking professionals could indeed be the dawning of a new era of efficiency, productivity, and responsiveness, despite rampant technology change and growth, ultimately empowering organizational success."

Methodology: The report compiled an analysis based on the survey responses of 388 executive and technical-level attendees at Cisco Live U.S. 2019.

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AIOps for Networking - the Dawning of a New Era

"Humans and manual processes can no longer keep pace with network innovation, evolution, complexity, and change," said Jim Frey, VP of Srategic Alliances at Kentik. "That's why we're hearing more about self-driving networks, self-healing networks, intent-based networking, and other concepts. These approaches collectively belong to a growing focus area called AIOps, which aims to apply automation, AI and ML to support modern network operations."

Key findings in the new report from Kentik, The State of Automation, Artificial Intelligence, and Machine Learning in Network Management, include:

The move to cloud is still underway for a few, but multi-cloud is a reality for many

Moving to the cloud, and especially multi-cloud, is one of the driving factors behind the need for network automation. While 76% of our respondents indicated they were using cloud services, nearly a quarter (24%) report that their organization has not yet moved to the cloud.

Of those with cloud services, nearly half (47%) are working in a multi-cloud environment, so the complexity ramp is a swift one.

Network automation is taking shape

85% of respondents said their organization has one or more types of automation, and yet only 27% of respondents said their organization is "extremely prepared" or "very prepared" for full automation.

Progress is being made, however, as respondents feeling "extremely" or "very" prepared nearly doubled from 15% in Kentik's 2018 survey.

The energy sector leads the network automation trend

Outside of the technology industry, the energy sector is the most prepared for full automation. Thirty percent (30%) of energy sector respondents reported their organization is "extremely prepared" or "very prepared" for full automation.

Healthcare and government are behind the curve. In the healthcare industry, only 3% of respondents reported that their organization is "very prepared" for full automation. Government respondents led among industries "not at all prepared," with 21% of the sector noting this response.

Networking processes are least likely to be automated

Networking processes like compliance and incident response are least likely to be automated. The majority (53%) of respondents are using automation for network configuration — the only area to receive a majority response.

Policy management was the second-most automated process, cited by 40% of respondents.

Processes such as compliance, incident response, and cloud bursting received lower response rates. This may be due to the level of human interpretation and investigation that still needs to exist, as these processes are often regulated and/or are more directly associated with impacting a business and its revenue.

Machine learning is growing in importance for network management

Machine learning is growing in importance for network management, regardless of who you ask. Up 20% since our 2018 survey, 65% of respondents said that ML is now "extremely important" or "very important" for network management. This reflects both the steady maturation of and comfort with ML as a technology, as well as the relentless march of complexity, causing network pros to seek help in reducing time and effort required to monitor and troubleshoot network and application performance in large, complex environments.

AIOps adoption among network professionals is very early stage

AIOps adoption among network professionals is very early stage, but the industry appears ready for it to help with network management. Only 22% of respondents reported that their organizations are actively using or planning to use AIOps tools today. However, clear majorities are prioritizing automation and ML, which are two of the three major foundational elements of AIOps (the third is data integration and enrichment).

"While our industry still appears to be in the early phases of embracing AIOps as a collective strategy, our findings show that the rationale and commitment are there," added Frey. "It appears that AIOps for networking professionals could indeed be the dawning of a new era of efficiency, productivity, and responsiveness, despite rampant technology change and growth, ultimately empowering organizational success."

Methodology: The report compiled an analysis based on the survey responses of 388 executive and technical-level attendees at Cisco Live U.S. 2019.

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...