AIOps Platforms Statistics


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AIOps Platforms Statistics 2023: Facts about AIOps Platforms outlines the context of what’s happening in the tech world.

LLCBuddy editorial team did hours of research, collected all important statistics on AIOps Platforms, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

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Top AIOps Platforms Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 53 Aiops Platforms Statistics on this page 🙂

Aiops Platforms “Latest” Statistics

  • In line with Gartner by 2023, 40% of DevOps teams will add artificial intelligence to application and infrastructure monitoring tools for its operations platform capabilities.[1]
  • Following the deployment of 22% on hybrid cloud platforms, another 20% are anticipated to be private cloud-based.[1]
  • By 2023, 40% of all business workloads, up from 20% in 2020, will be implemented in cloud infrastructure and platform services.[1]
  • Compared to less than 10% in 2025, 50% of businesses will have developed artificial intelligence orchestration platforms.[1]
  • By the end of 2023, hyperscale cloud providers will offer and manage 20% of deployed edge computing platforms, up from less than 1% in the previous year.[1]
  • In order to evaluate 100% of data streams and metrics, data is streamed into a single, centralized analytics platform, regardless of the business’s original data architecture and silos.[1]
  • LogicMonitor also predicted that by 2020, 41% of corporate workloads would use public cloud platforms.[2]
  • To cut operational activities by up to 99% of occurrences, it provides the crucial layer of intelligence and integrates performance monitoring and IT service management platforms.[3]

Aiops Platforms “Other” Statistics

  • The use of Robotic Data Automation requires less need for human involvement in the process and the amount of work required for data management may be reduced by up to 70% or 80%.[4]
  • According to a recent Forrester report, 68% of the firms planned a poll to actively engage in AIOPS-enabled performance monitoring solutions over the course of the next year.[5]
  • A little over 44% of people think AI would somewhat diminish the number of ITSM jobs, while 32% don’t think it will have any impact on job security.[6]
  • Only 13% of ITSM staff members claim to be actively engaged in their organization’s DevOps initiatives, despite the fact that DevOps encompasses operations.[6]
  • The digital transformation process may be aided by its operations services, and leaders of the digital transformation and its operations share 72% of the same objectives.[6]
  • Other company sectors that have been negatively impacted by IT service outages include R&D (38%), marketing (37%), finance (31%), production (27.9%), and customer service (26.3%).[6]
  • Significant obstacles when it comes to IT landscapes were a 61% dependence on manual procedures, a 69% surplus of tools, a 60% skills gap, and a 62% prevalence of outdated systems.[6]
  • According to market studies over the next years, there will be an increase in demand for solutions that can assist enterprises in retrieving, analyzing, and extracting value from IT operations data at a compound annual rate of 36.5%.[6]
  • The percentage of more than 300 it professionals working in development and operations who believe that the surge in digitization has made capacity planning more difficult is 39.5% in the state of digital operations.[6]
  • According to 76% of survey respondents, demand for new digital goods and services grew in 2004.[1]
  • According to a recent new relic report, 89% of the 750 senior it decision makers questioned globally feel that machine learning and AI are crucial to the way that businesses manage their it.[1]
  • About 80% of IT executives want to automate time-consuming processes for crises according to Bhanu Singh, senior vice president of OpsRamp.[1]
  • Poor data quality, cited by 34% of respondents in a 2021 Rackspace Technology poll, was the biggest factor in machine learning R&D failure.[1]
  • By 2024, 25% of conventional large enterprise CIOs will be held responsible for the operational success of digital businesses, thereby taking on the role of “COO by proxy.”.[1]
  • In order to combat the exponential expansion of data that is projected to surpass current storage technologies by 2024, 30% of digital organizations will demand DNA storage experiments.[1]
  • To support mission-critical data and analytics exchange and governance, 75% of enterprises will have implemented numerous data hubs by 2024.[1]
  • Digital workplace service professionals will be able to devote 30% less time to endpoint maintenance and repair by 2024 thanks to endpoint analytics and automation.[1]
  • With the help of new operational procedures and hyper-automation technology, businesses will reduce operational expenses by 30% by 2024.[1]
  • Organizations with IT teams that comprehend consumer demands will perform 20% better on customer experience measures by 2024.[1]
  • By expanding into paid virtual encounters by 2025, 40% of physical experience-based enterprises will boost their financial performance and outperform rivals.[1]
  • Over 20% of all goods will be produced, packaged, transported, and delivered by 2025 without being touched 4.[1]
  • In order to enable revolutionary business models, by 2025, more than 50% of enterprises will adopt a dispersed cloud option at their preferred location.[1]
  • 43% of businesses in the sector feel confident with the functioning of their apps.[1]
  • Additional respondents 83% AIOPS solution implementation typically takes three to six months, with 25% of respondents to the OpsRamp poll stating that it takes more than six months.[1]
  • Due to the increasing usage of cloud-native apps and infrastructure, Gartner anticipates that by 2024, more than 75% of big organizations in developed countries would employ container management.[1]
  • According to the AIOPS exchange research, over a million event alerts are received daily by over 40% of IT firms, with 11% getting over 10 million alerts daily.[1]
  • Most clients want to drastically reduce the price of level one assistance and reduce the price of alerts by 30% or 40%.[1]
  • Hyperautomation is also being fueled by technological advancement organizations should be able to do 25% more work autonomously by 2023, according to Gartner.[1]
  • More people are adopting Forrester’s terms “intelligent application and service monitoring” than its data suggest, with 51% currently doing so and another 21% intending to do so within the next year.[1]
  • 30% of IT operations activities will be shifted from support to continuous engineering by 2024 thanks to advancements in analytics and autonomous remediation capabilities.[1]
  • By 2023, 40% of businesses would even use AIOPS for application and infrastructure monitoring, according to Gartner.[7]
  • A study by BBC research projects that the worldwide market for AIOPS will more than treble from 30 billion in 2021 to 94 billion in 2026, growing at a CAGR of 26.1%.[7]
  • AIOPS users report a favorable return on investment in 81% of cases, according to a study by enterprise management associates.[8]
  • 59% of businesses, according to the loom systems survey, are still in the exploratory phase, making it difficult for clients to understand precisely what they are selling.[8]
  • According to Gartner, by 40% of businesses will be utilizing AIOPS for application and infrastructure monitoring.[8]
  • In addition, 42% of IT businesses employ more than 10 distinct monitoring solutions for their IT infrastructures, according to a recent BigPanda study.[8]
  • After adding AIOPS, the mean time to repair has become at least 30% quicker and our reaction time to identify and take action has grown, Kamath claims.[8]
  • Over 50% of firms, according to a 2021 PWC analysis, have either completely enabled or started adopting AI into their workflow.[9]
  • Over 90% of IT DevOps and site reliability engineering workers, according to Transposit research, cited an increase in service problems.[9]
  • Top experts predict that enterprises will use hyper-automation and AIOPS to save operational expenses by as much as 30% by 2024 as these technologies continue to evolve.[9]
  • According to Gartner, major businesses will exclusively employ AIOPS and digital experience monitoring solutions to track their infrastructure and apps from 5% in 2018 to 30% in 2023.[10]
  • By 2020, 83% of business workloads are anticipated to be on the cloud, according to LogicMonitor.[2]
  • The IT/technology department was the one with the highest reported usage of AI (47%), followed by R&D (36%), and customer service (24%).[2]
  • Almost 84% of respondents said AI and machine learning would eventually make their position easier to handle.[2]
  • The AIOPS market was estimated to be worth USD $13.51 billion in 2020 and is anticipated to reach USD $40.91 billion by 2026, growing at a CAGR of around 21.05% during the five-year forecast period of 2021 – 2026.[2]

Also Read

How Useful is Aiops Platforms

One of the key benefits of AIOPs platforms is their ability to automate many tedious and time-consuming tasks that were previously handled manually by IT professionals. Tasks such as monitoring system performance, detecting and resolving incidents, and analyzing trends in data can now be done in real-time and with a level of accuracy that is unmatched by human operators. This not only saves time and resources but also ensures that potential issues are detected and addressed before they have a chance to escalate into larger problems.

Another advantage of AIOPs platforms is their ability to aggregate and analyze vast amounts of data from multiple sources. This allows businesses to gain deeper insights into their IT environment and make better-informed decisions about how to optimize performance and minimize downtime. By correlating data from various sources, AIOPs platforms can also help to identify patterns and trends that may not be immediately apparent to human operators, enabling businesses to proactively address potential issues before they impact operations.

One area where AIOPs platforms have shown great promise is in the realm of predictive analytics. By using historical data and machine learning algorithms, these platforms can identify patterns and anomalies that could indicate future issues and help businesses to prevent them before they occur. This proactive approach can significantly reduce downtime and improve overall system reliability, ultimately leading to a better user experience and higher levels of customer satisfaction.

Despite their many benefits, AIOPs platforms are not without their challenges. One of the primary concerns surrounding these platforms is the potential for bias in the algorithms that power them. If not properly trained and monitored, AIOPs platforms could inadvertently reinforce and perpetuate existing biases, leading to skewed and inaccurate results. It is crucial for businesses to ensure that their AIOPs platforms are developed and maintained in an ethically responsible manner to avoid this pitfall.

Another potential drawback of AIOPs platforms is the initial investment required to implement and integrate them into existing IT environments. While the long-term benefits of increased efficiency and reduced downtime can outweigh these costs, some businesses may find it challenging to justify the upfront expense. It is important for businesses to carefully weigh the potential benefits of AIOPs platforms against their costs and ensure that they align with their overall IT strategy and objectives.

In conclusion, AIOPs platforms have the potential to revolutionize IT operations and bring about significant improvements in efficiency, reliability, and performance. By automating tasks, aggregating data, and leveraging predictive analytics, these platforms can help businesses to optimize their IT infrastructure and stay ahead of potential issues. While there are challenges to overcome, the benefits of AIOPs platforms make them a valuable investment for businesses looking to streamline their operations and enhance their overall IT performance.

Reference

  1. webinarcare – https://webinarcare.com/best-aiops-platforms/aiops-platforms-statistics/
  2. mordorintelligence – https://www.mordorintelligence.com/industry-reports/aiops-market
  3. softwarereviews – https://www.softwarereviews.com/categories/aiops
  4. cloudfabrix – https://cloudfabrix.com/blog/aiops/aiops-in-2022-and-beyond-5-trends-you-should-be-aware-of/
  5. sciencelogic – https://sciencelogic.com/solutions/aiops
  6. techbeacon – https://techbeacon.com/enterprise-it/20-it-ops-stats-matter
  7. aidataanalytics – https://www.aidataanalytics.network/data-science-ai/articles/what-is-aiops
  8. cio – https://www.cio.com/article/196239/what-is-aiops-injecting-intelligence-into-it-operations.html
  9. eweek – https://www.eweek.com/big-data-and-analytics/aiops-trends/
  10. gartner – https://www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

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