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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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Gartner highlighted the six trends that will have a significant impact on infrastructure and operations (I&O) for 2025 ...

Since IT costs can consume a significant share of revenue ... enterprises should (but often don't) pay close attention to the efficiency of IT operations at scale. Improving operational cost structures even fractionally can yield major savings for larger organizations, often in the tens of millions of dollars ...

Being able to access the full potential of artificial intelligence (AI) and advanced analytics has become a critical differentiator for businesses. These technologies allow for more informed decision-making, boost operational efficiency, enhance security, and reveal valuable insights hidden within massive data sets. Yet, for organizations to truly harness AI's capabilities, they must first tap into an often-overlooked asset: their mainframe data ...

The global IT skills shortage will persist, and perhaps worsen, over the next few years, carrying a collective price tag of more than $5 trillion. Organizations must search for ways to streamline their IT service management (ITSM) workflows in addition to, or even apart from, hiring more staff. Those who don't find alternative methods of ITSM efficiency will be left behind by their competitors ...

Embedding greater levels of deep learning into enterprise systems demands these deep-learning solutions to be "explainable," conveying to business users why it predicted what it predicted. This "explainability" needs to be communicated in an easy-to-understand and transparent manner to gain the comfort and confidence of users, building trust in the teams using these solutions and driving the adoption of a more responsible approach to development ...

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