devopsy.kinsta.cloud

Category: IT Administration

  • Survey Surfaces Raft of Enterprise IT Challenges

    Survey Surfaces Raft of Enterprise IT Challenges

    A survey of  enterprise IT leaders found less than a third (32%) are extremely confident that they have the right technology, processes and people in place to execute an effective approach to IT management.

    The survey, conducted by Rocket Software, polled 275 U.S. IT leaders in organizations with more than 1,000 employees. A full 93% of IT leaders are also committed to maximize the value of investments in existing platforms.

    As a result, the top priorities for IT leaders are hybrid cloud computing (65%), data and content management (68%) followed by DevOps and infrastructure and application modernization, tied at 58%, and automation (57%).

    A total of 62% noted they have a much greater focus on efficiency due to the economic landscape, with faster DevOps processes, automated processes and increasing overall output as the top three improvements that would be most impactful for increasing efficiency.

    The survey also suggested many organizations still have much work to do to achieve those goals, with 63% of respondents spending anywhere from six to 15 hours on manual data entry and analysis each week.

    Phil Buckellew, president of infrastructure modernization for Rocket Software, said most organizations are still trying to determine the right level of DevOps to adopt. Few organizations have implemented DevOps workflows on an end-to-end basis, with many employing a mix of DevOps and waterfall processes to build and deploy applications across a hybrid cloud computing environment, he noted.

    Overall, IT leaders are most worried about improving overall IT performance (60%), data security (50%), process risk and compliance (46%) and the need to improve agility (41%), the survey found. Respondents also noted that they measure success within their IT organization by increased efficiency (71%), optimized resources (67%) and reduced risk (63%).

    The survey makes it clear that, despite challenges, enterprise IT organizations are committed to hybrid cloud computing as they become more comfortable with using various platforms that are fit for purpose, said Buckellew.

    Mainframes, for example, continue to play a major role in running online transaction processing (OLTP) applications alongside analytics applications that are now being infused with artificial intelligence (AI) capabililities, noted Buckellew.

    A quarter of survey respondents also identified providing cloud applications with access to mainframe data as an area where they need additional tools, followed by automation (22%).

    The biggest automation obstacles are the complexity of implementing automation (18%), resistance to process change among workforce (18%) and lack of skillsets to implement automation (18%), the survey finds.

    As always, the biggest challenge in enterprise IT environments is often simple inertia. Organizations that have been building and deploying applications on mainframes, for example, have been relying on waterfall processes for decades. Many of them have now adopted DevOps processes to varying degrees, but few have completely replaced legacy approaches to building and deploying software, noted Buckellew.

    It’s not likely the management of complex hybrid cloud computing environments will be simplified any time soon, but progress continues to be made. Soon, AI may automate many existing manual tasks. In the meantime, however, the number of enterprise IT organizations standardizing on a single IT platform remains few and far between.

  • Survey Sees Value But Limited Understanding of Platform Engineering

    Survey Sees Value But Limited Understanding of Platform Engineering

    Platform engineering is emerging as method for managing DevOps at scale, but a survey suggested that many organizations are still not clear on the concept.

    A survey of 100 DevOps engineers, platform engineers, developers, site reliability engineers and IT managers conducted by Civo, a cloud service provider, found 85% of respondents work for organizations that have partially or fully embraced a DevOps culture, with nearly two-thirds (65%) identifying platform engineering as the next evolution of DevOps. Nearly, a third (30%) of respondents work for organizations that are already currently evaluating whether they should adopt platform engineering.

    Overall, the survey found 50% of the respondents at organizations that have adopted platform engineering reported improved standardization, with 43% also seeing faster product delivery.

    However, only 41% of the respondents in DevOps roles that were familiar with platform engineering said they had a moderate to high level of familiarity with the concept.

    Saiyam Pathak, director of technical evangelism at Civo, said the survey makes it clear that, in terms of understanding how platform engineering best practices should be implemented, there is need for more clarity. Many organizations initially adopted DevOps to spur innovation, so any approach to centralization shouldn’t come at the expense of developer productivity, he noted.

    Historically, DevOps adoption of DevOps workflows in most organizations has been driven from the bottom up. As a result, it’s fairly common for multiple application development teams within an organization to have adopted multiple DevOps platforms to manage workflows. Advocates for platform engineering are making a case for eliminating many of those redundant platforms in favor of, for example, a single continuous integration/continuous delivery (CI/CD) platform that serves as a corporate standard.

    At the core of those efforts is the assumption that centralization will reduce costs by eliminating redundancy. At a time when more organizations than ever are sensitive to the cost of IT, there is always going to be a natural tendency to rationalize platforms. Challenges arise, however, when centralization results in a loss of flexibility. Many of the teams adopt DevOps because they want to be able to add and replace tools as they see fit. If there is a corporate standard that narrows their tool options, those teams may start to look for ways to do an end-run around the corporate standard.

    The philosophical IT battle between advocates for centralization and proponents of decentralization has been around since the first PCs showed up in enterprise IT environments. As always, organizations need to strike a balance between both extremes, said Pathak.

    It’s not clear how much platform engineering might accelerate the pace at which applications are built and deployed, but the total cost of DevOps should decline as various tools and platforms are consolidated. The challenge, of course, is that centralizing DevOps workflows is likely to disrupt the application development workflows on platforms that may about to be rationalized.

    Regardless of what platforms become the corporate standard, the one thing that is certain is the cultural challenges associated with successfully making that transition will loom large within most organizations.

  • Leaders in Resilience See ROI From Observability

    Leaders in Resilience See ROI From Observability

    Observability is maturing throughout the software industry. Observability is often defined as merging metrics, logs and traces to get a fuller picture of an application’s state. This can help pinpoint disruptions or security incidents and offer a clearer path to remediation. Observability is touted to advance the application monitoring practices of yesteryear, but are investments paying off?

    According to recent findings, many organizations are using observability tools and seeing a positive outcome. A full 73% of organizations reported using observability tools for over a year. And impressively, 86% of observability leaders said the ROI on their observability tools has exceeded their expectations. The practice appears to be paying dividends for organizations that have gone “all-in” with mature observability processes.

    The State of Observability 2023 report released by Splunk demonstrated how observability leaders are reaping a positive return from observability tools. According to the report, observability is helping to meet performance expectations, avoid downtime and enhance resilience in general. However, not all organizations are on the observability maturity bandwagon. Below, we’ll examine some of the key trends from the report and consider the benefits of refining your observability efforts.

    Now that observability is more commonplace, dedicated roles are emerging to support these initiatives. In fact, 87% of respondents now employ specialists who work exclusively on observability projects. Site reliability engineers likely make up a portion of these experts, as observability data can be used to advance efforts around meeting service-level objectives (SLOs).

    But although observability might be more common than a few years ago, not every observability practice is mature or standardized. Just 40% have a formal approach to resilience that has been institutionalized organization-wide. Because of this, only 43% are confident in their team’s ability to meet its application reliability and performance objectives. It should be noted that this number increases significantly for those with more mature observability adoption, which we’ll expand on below.

    Furthermore, not all applications are cloud-native. Organizations reported operating and maintaining, on average, 165 internally developed business applications, half of which are hosted in the public cloud and half of which are on-premises. Due to the persistence of legacy modalities, observability tools must continue to support a hybrid architecture if they are to be comprehensive and useful.

    And although we’ve previously covered how many organizations are drowning in a sea of observability data, observability leaders now say their ROI on observability tools far exceeded their expectations. Perhaps this indicates that, with more open standards, analysis centralization and fewer false positives, teams are finding ways to avoid observabiliy tooling sprawl.

    Lastly, the report unveiled desires to loop AI and ML into the observability pipeline. In fact, 66% of observability teams already use AI/ML, while 26% are in the process of deploying the technology. And 91% said AIOps is an important enabler of their observability goals. Certainly, AI is becoming a pervasive instrument to accelerate many areas, including observability.

    Benefits of Observability

    An impressive 64% reported that their investment into observability has exceeded their expectations. So, what are some of the top benefits of observability? Well, the report found the topmost benefits to be areas like decreasing problem detection and resolution times, identifying “true positive” incidents, and enhancing application security.

    Observability is being deployed as a tool to evolve traditional security efforts, as it uncovers a deeper understanding of application behaviors. This can help security teams correlate errors to specific diagnoses and spot potential threats in a quick manner. As such, 59% said observability helps them uncover security issues thanks to the intelligence capabilities of observability solutions. And 55% say they can uncover more security vulnerabilities thanks to the visibility afforded by observability.

    Observability can also be used to improve visibility into hybrid architectures. A full 86% said it’s important to have flexible observability solutions that cover hybrid architectures, though half identify this as an area for improvement. Although we like to think that cloud-native development is the status quo, many enterprises still oversee a mixture of on-premises, private cloud and multi-cloud environments. A unified, agnostic observability layer is thus essential to peek into all these disparate environments to grapple with disparate data outputs.

    The Leaders of the Observability Pack Win

    Service interruptions can have negative consequences for organizations, including lower customer satisfaction, loss of revenue and loss of customers. In fact, 76% of respondents reported downtime can cost up to $500,000 per hour.

    Thankfully, observability has emerged to help improve overall resilience and avoid these negative repercussions. But, to truly reap the benefits, you must have a mature practice in place. As defined by the report, “observability leaders” have a more formal approach to resilience and have standardized this across their organization. And 89% of those leaders are completely confident in their ability to meet their application availability and performance requirements. The catch-22 with observability is that you must invest more energy into formalization and knowing when the effort is worthwhile is a moving target.

    A solid observability practice is supported by standardized metrics, logs and traces across the organization. Furthermore, to make observability behave correctly, you will need flexible tooling that can leverage data from multiple sources, such as the network, infrastructure, servers and databases.

    The State of Observability 2023 report surveyed 1,750 observability practitioners from around the globe, making it the most extensive study on observability to date. For more insights, you can pick up a copy of the report behind an email gate here.

  • BMC Adds Generative AI Capabilities to Helix Management Platform

    BMC Adds Generative AI Capabilities to Helix Management Platform

    BMC today revealed it is embedding generative artificial intelligence (AI) capabilities across its BMC Helix service and operations management platforms.

    BMC CTO Ram Chakravarti said the company is incorporating the open source GPT-J large language models (LLMS) to build generative AI models. Those models are optimized for specific domains using data collected by BMC and imported from other data sources, like the Jira project management software from Atlassian. That persistent layer of storage is designed to aggregate multiple types of data within what amounts to a Helix data lake.

    The generative AI capabilities added to BMC’s portfolio include natural language summaries of issues and events captured by BMC Helix Operations Management, along with an ability to create a historical analysis of a user’s specific environment to identify recurring issues more easily.

    BMC Service Management, meanwhile, can now correlate incidents and provide resolution summaries that can be stored. It’s also now possible to run clustering analysis on chat archives to find new or improved utterances that help virtual agents answer queries better and execute workflows.

    BMC initially opted to build its own LLMs because general-purpose LLMs—such as the one created by OpenAI—are subject to “hallucinations.” Hallucinations are created when incorrect data is included in a model. BMC is training its LLMs on a narrow range of trusted data to ensure the recommendations and subsequent automations it creates are valid.

    Over time, BMC will add additional LLMs; the company hasn’t yet decided if there are use cases where it makes sense to rely on an existing general-purpose LLM to add additional capabilities, noted Chakravarti.

    Organizations can also opt to use a containerized version of the BMC LLM if they prefer, but BMC will encourage most organizations to invoke generative AI via its Helix SaaS platform, he added.

    BMC Helix is an IT services management (ITSM) platform that integrates with a range of DevOps platforms. Helix is part of a larger existing BMC Connected Digital Ops initiative that seeks to unify ServiceOps and DevOps with AIOps, DataOps and AutonomousOps. Generative AI will automate a range of tasks from within Helix that can then be extended to DevOps platforms such as Jenkins.

    It’s still early days as far as applying generative AI to processes, so organizations should focus on use cases where it will add some immediate value, said Chakravarti. Once they develop a level of trust in the automation created and executed, they can then pragmatically expand the number of generative AI use cases where they make sense, he added.

    One way or another, generative AI is going to be applied to the management of IT. Many routine tasks that conspire to limit the productivity of IT teams today will be eliminated, but there will always be a need for a human to remain in the loop to ensure the right outcomes are achieved. However, it will also soon become possible for existing teams to manage IT at levels of scale that were previously unattainable.

    Image source: Photo_Credite_Indira-Tjokorda via Unsplash

  • PagerDuty Signals Commitment to Adding Generative AI Capabilities

    PagerDuty Signals Commitment to Adding Generative AI Capabilities

    PagerDuty has signaled its intention to add generative artificial intelligence (AI) capabilities to its cloud platform for managing IT operations via integrations with large language model (LLM) providers that it will disclose at a future date.

    These capabilities will make it possible to invoke natural language via PagerDuty Operations Cloud to automatically generate status updates, create drafts of incident postmortem reports and even co-author workflows in any programming language to automate remediation of an issue.

    Damon Edwards, senior director of product for PagerDuty, said as AI continues to evolve, the goal will be to minimize the need for human involvement as much as possible. That doesn’t mean there won’t be a need for humans to be involved, but it does mean that many rote tasks associated with managing an IT incident can be automated, he added.

    For example, the reports that are run to determine the root cause every time there is an incident can be automated, noted Edwards. In addition, summarizations of status reports or postmortems after incidents were resolved can be made readily available to any member of an IT team on demand, he added.

    Those capabilities will also make it easier to onboard new members to an incident management team as required, said Edwards. Today, it’s difficult to add new members to a team after an incident has unfolded because it takes time to bring them up to speed, he noted.

    In addition, generative AI also makes it easier to capture a lot of the tribal knowledge within an IT organization that would otherwise disappear when staff either leave the company or shift roles, said Edwards.

    The generative AI additions to the PagerDuty platform extend an artificial intelligence for IT operations (AIOps) capability that PagerDuty made available this spring. The PagerDuty AIOps platform leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates and begin surfacing valid recommendations to optimize workflows.

    It’s not clear at what pace AI will be adopted within enterprise IT organizations, but it is rapidly becoming pervasive. Each DevOps team will need to decide the degree to which they can rely on AI to manage IT processes, but as IT processes become more complex, there is a clear need for AI to navigate all the dependencies that exist in an IT environment. As more routine tasks are automated, however, there will also inevitably be a realignment of roles and responsibilities within an IT organization. The overall goal is not so much to eliminate IT staff as much as it is to enable them to manage IT environments at a much higher level of scale.

    In the meantime, DevOps teams need to plan for not only what is possible using generative AI today but also tomorrow. After all, there’s not a lot of point in training someone to take on a new task if that task is automated a few months from now.

  • Logz.io Taps AI to Surface Incident Response Recommendations

    Logz.io Taps AI to Surface Incident Response Recommendations

    Logz.io this week added a supervised machine learning capability to its observability platform that reduces mean-time-to-remediation by surfacing recommendations for resolving incidents.

    Asaf Yigal, vice president of product for Logz.io, said the Alert Recommendation capability added to the Logz.io Open360 platform uses artificial intelligence (AI) to model the steps a DevOps team needs to complete to resolve an incident.

    The goal is to reduce the amount of time required to resolve incidents at a time when IT environments are becoming increasingly complex, he added.

    In fact, a recent Logz.io survey found 75% of respondents said it currently takes them hours to resolve production issues, with only 14% satisfied with their current mean-time-to-resolution (MTTR). A total of 41% specifically cited monitoring and observability of Kubernetes environments as a primary challenge.

    Alert Recommendation is the latest in a series of investments in AI that Logz.io has made. Previously, Logz.io integrated the ChatGPT generative artificial intelligence (AI) platform to surface links to related information and best practices for resolving IT issues.

    In general, AI tools should make it possible to manage IT at a level of scale that eliminates many of the low-level data engineering and analytics tasks that previously required manual effort from a DevOps engineering team. If, for example, the observability platform is generating recommendations to address issues, there may be less of a need to create runbooks that DevOps teams typically create to address a wide range of known issues.

    Along with making it simpler to discern the root cause of an issue, AI also makes it possible for less experienced members of a DevOps team to resolve an issue using guidance generated by the observability platform, noted Yidal.

    In effect, AI technologies are reducing the cognitive load required to be an effective member of a DevOps team, he added.

    One way or another, it’s not so much a question of whether AI will be applied to DevOps as much as it is to the degree. Many of the manual tasks that often conspire to create DevOps bottlenecks should be significantly reduced in the months ahead as more advances are made. The challenge now is determining how best to reallocate DevOps expertise in anticipation of those advances.

    Of course, there’s always going to be some sense of trepidation when it comes to AI. However, many of the tasks that are about to become automated tend to be tedious. Many DevOps professionals would just as soon see those tasks become automated in the expectation that more time will become available to take on more complex challenges.

    Regardless of the motivation, the way IT is managed is about to change. There may be plenty of instances where AI does not live up to its initial hype, but as AI models are exposed to more data, they will become more accurate. That doesn’t mean, however, there won’t always be a need for a DevOps engineer to ensure those algorithms are behaving as expected.

  • Red Hat Enhances Insights to Simplify RHEL Management

    Red Hat Enhances Insights to Simplify RHEL Management

    This week at its Red Hat Summit event, Red Hat introduced an expanded set of capabilities to Red Hat Insights that simplify management of Red Hat Enterprise Linux (RHEL).

    Managing Linux across the hybrid cloud is complex, and it’s made even more so because of current macroeconomic conditions and the challenge of finding and retaining highly skilled DevOps and IT professionals, said Gunnar Hellekson, vice president and general manager, Red Hat Enterprise Linux, Red Hat.

    The enhanced Insights capabilities add a layer of abstraction to help reduce enterprise Linux complexity across the hybrid cloud and make RHEL management easier by lowering skill level requirements for managing Linux server estates, the company said.

    Using a single UI, entry-level systems administrators and IT support teams can access Red Hat Insights’ predictive analytics to identify potential bugs, misconfigurations or security vulnerabilities and remediate them. Systems administrators of all skill levels can detect, assess and push fixes for potential problems without a deep understanding of Linux management systems like Red Hat Satellite Server and without having to interact with the command line, the company said.

    These enhancements help organizations maintain services and systems even in uncertain macroeconomic times when hiring is constrained. Red Hat research also claimed that these enhanced capabilities enabled IT organizations to find IT issues up to 90% faster and remediate them nearly 66% faster across the hybrid cloud.

    A Red Hat Insights image builder service also helps lower the skills level needed to manage Linux estates by enabling IT teams to build standardized, optimized operating system images, including the creation of their own ‘golden images’ that adhere to organization-specific needs for security and compliance. These images can be deployed across public clouds, hybrid clouds, data centers and the edge using the same console.

    As the economic downturn continues, organizations are looking to do more with less and identify ways to make the most of the resources, talent and skillsets they already have. Red Hat is making the case for adding a layer of abstraction to allow less-highly-skilled IT and DevOps team members to contribute to managing Linux estates across hybrid, multi-cloud and edge deployments.

    “The demands of managing Linux across the hybrid cloud are stretching the ability of resource-constrained IT departments to keep pace,” said Hellekson in a statement. “CIOs need to be able to extend existing skillsets and lower the skill barriers for overseeing Linux estates, which is exactly what the management capabilities of Red Hat Insights are designed to do.”

  • Nobl9 Adds Tools to Make SLO Queries More Efficient

    Nobl9 Adds Tools to Make SLO Queries More Efficient

    At its SLOconf event today, Nobl9 added a Query Tester tool that validates whether queries made against the data its service level objectives (SLO) management platform collects will deliver meaningful results. Nobl9’s SLO management platform collects data in real-time from data sources such as New Relic, Datadog and Dynatrace to help customers meet SLOs.

    In addition, a Query Checker tool will now automatically put any new SLO into a testing state to automatically validate that the query against that SLO will run correctly.

    The company has also improved the precision of its calculations to provide DevOps teams with more accurate, reliable and actionable insights into the performance of services and added a Metrics Health Notifier toolset that provides insights into anomalous data being collected from various sources as well as what data might be missing. Nobl9 has also added a Custom Query Delay tool that gives DevOps teams more control over when data is pulled along with access to data source logs.

    Kit Merker, chief growth officer for Nobl9, said the overall goal is to improve the overall resiliency of its platform for managing SLOs.

    Finally, Nobl9 also announced that its platform is now available for the Google Cloud Platform (GCP) in addition to existing support for Amazon Web Services (AWS). As part of that GCP support, Nobl9 made available an educational site that provides example of how DevOps team can take advantage of the large language models (LLMs) that Google is making to improve SLOs or generate an SLO that is automatically converted into a set of YAML files.

    A survey of 314 IT professionals conducted by Dimensional Insight on behalf of Nobl9 found 69% have implemented SLOs, with more than three-quarters of those respondents crediting SLOs for preventing business disruptions. A total of 80% also noted their organization has an increased focus on system reliability, with just over a quarter (27%) saying SLOs saved their organizations a half million dollars or more. A full 95% said SLOs are also driving better business decisions.

    However, 97% also reported it is difficult to manage SLOs because of a lack of reporting, lack of application and environment support and interoperability issues.

    Nobl9 is trying to spur greater adoption of SLOs by making available an open source SLO specification that defines a common interface for constructing SLOs across a Git-based workflow. Nobl9’s platform based on that specification has now been integrated with 24 data sources, said Merker.

    SLOs, of course, are not a new idea. They have been employed as a metric to track the performance of IT services for decades. However, as more microservices-based applications are built and deployed, it’s becoming more challenging to maintain SLOs across applications that have many more dependencies than legacy monolithic applications.

    Ultimately, each IT team needs to provide some sort of objective benchmark that assesses their overall effectiveness at delivering application services. SLOs-as-code are intended to make it simpler to gather the metrics that confirm whether service levels are being achieved. Those SLOs are then tracked and integrated across any set of application services a DevOps team cares to track.

  • Atlassian Embeds Generative AI Across Cloud App Portfolio

    Atlassian Embeds Generative AI Across Cloud App Portfolio

    At its Team 23 conference today, Atlassian announced it is adding an artificial intelligence (AI) capability to the enterprise editions of its cloud portfolio to automate a wide range of routine tasks.

    Sherif Mansour, distinguished product manager for Atlassian, said Atlassian Intelligence uses natural language to drive a virtual agent that resolves routine help desk issues from within Slack or Microsoft Teams. Atlassian Intelligence can also identify incidents that have been previously resolved along with issues that are related to one another.

    In addition, IT teams can also leverage Atlassian Intelligence to create summaries for projects being tracked using the company’s Jira project management software, noted Mansour.

    The overall goal is to streamline incident management processes while also making it simpler for application stakeholders to collaboratively participate in software development, he added.

    Generative AI capabilities are likely to be infused into almost every imaginable application going forward, but Atlassian is clearly at the forefront of an effort to leverage AI to improve productivity. For example, instead of spending time handling routine issues that increase the amount of daily toil, IT teams should be able to spend more time resolving more complex issues, noted Mansour.

    The cumulative effect of AI should result in a significant decrease in the backlog of tasks that many IT teams would otherwise have to manually perform, he noted.

    It’s not clear just yet how much generative AI will impact the way software is developed and how IT is managed, but it’s already apparent that fundamental change is on the way. The Atlassian Intelligence approach is intended to augment the capabilities of IT teams so they can do more with the same number of or fewer personnel.

    The challenge IT teams will face is identifying tasks they couldn’t previously accomplish simply because there wasn’t enough time available.

    Fundamentally, generative AI changes the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. That makes it possible for anyone to make use of large language models used to create generative AI platforms to, for example, automate a routine task or, one day soon, create code.

    At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of IT organizations, the ultimate impact should involve less drudgery as many of the tasks that make managing IT tedious are eliminated.

    Hopefully, as IT continues to evolve, there will still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has been for the past four decades or more.

  • PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty this week made generally available an artificial intelligence for IT operations (AIOps) platform that leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates.

    Jonathan Rende, senior vice president and general manager for PagerDuty, said that approach enables the PagerDuty AIOps platform to begin surfacing valid recommendations sooner than other AIOps platforms. Other platforms first need to be integrated into IT environments by an expensive team of consultants and integrators, he added. The PagerDuty approach makes the benefits of AIOps immediately available to both IT operations teams and application development teams alike, he said.

    The arrival of PagerDuty AIOps comes on the heels of the launch of PagerDuty Process Automation, a platform for orchestrating automation across multiple platforms that is integrated within PagerDuty Operations Cloud. That capability makes it possible for organizations to centralize the management of islands of automation that have emerged across the enterprise without having to replace any framework that has already been deployed, said Rende. There are also more than 700 integrations between PagerDuty AIOps and the PagerDuty Operations Cloud platform.

    Like other platforms, PagerDuty AIOps relies on machine learning algorithms and an AI model created by PagerDuty to correlate events so that the platform can put them in context. Incident response recommendations are then made, which an IT team can accept and automatically implement or reject. The platform then uses those inputs to surface more accurate recommendations over time, said Rende.

    The platform will also automatically group apply defined rules coupled with its machine learning capabilities to aggregate alerts. That capability reduces the overall alert noise that might otherwise be generated, noted Rende.

    Organizations are looking to invest in AI and automation more heavily at a time when many of them are reducing the overall size of their IT staff, said Rende. The challenge with AI, however, is that it can take six months or more for a rival AIOps platform to learn enough about an IT environment to begin making recommendations. The PagerDuty approach takes advantage of the data within the PagerDuty incident management platform to train an AI model more quickly, in a way that reduces the time to generate valid recommendations to a matter of days, noted Rende.

    PagerDuty has been building that AI model for the last four and a half years with that specific goal in mind, he added.

    It’s not clear how quickly organizations are embracing AIOps, but as IT environments continue to evolve, many once-manual tasks are being automated. There is a natural fear of the impact those advances might have on specific roles within an IT organization, but as IT environments get more complex, there will always be a need for IT personnel that can focus on higher-order tasks, said Rende.

    Overall, the rate of IT staff turnover should also decline as much of the toil that contributes to burnout is also eliminated.

    It may be a while yet before AI becomes pervasively applied to manage IT, but the proverbial genie is not going back into the bottle. The challenge is determining precisely where the line between the man-machine interface with IT environments is today versus where it might be tomorrow.