Top 10 DevOps Challenges & How AIOps Can Help

DevOps was conceptualized to bridge the collaborative gap between developers and IT operations. Previously, developers worked independently of operations teams, shipping their work to the IT team and moving on. DevOps created a shared sense of ownership of a product, allowing development and ops teams to work in tandem for a more streamlined and efficient workflow.

DevOps allowed production teams to deploy new applications, services, and updates quicker as developers focused on building new and innovative applications and features without distracting escalations. So, DevOps was integral in enabling faster product release cycles through agile development methodology.

Benefits of DevOps include:

  • A streamlined product delivery
  • Reduced development complexity
  • Higher scalability and availability
  • A stable operations process
  • Better resource utilization
  • Collaboration
  • More sophisticated automation

However, even as DevOps was a game-changer for software production, challenges loomed over, making way for AIOps.

10 DevOps Challenges and How AIOps Solves Them

Many of the following DevOps challenges overlap, just as they do in the real world. Let’s dive in.

  1. Sprawling modern IT ecosystems 
    Legacy technology systems can substantially limit an organization’s prospects in an already competitive industry. However, shifting to modern IT ecosystems comes with its own challenges and complexities.

    With modern IT systems getting increasingly complexity, DevOps is met with the daunting challenge of gaining a reliable understanding of the infrastructure. AI automation can fill the gap by providing powerful insights into your entire IT ecosystem for seamless observability — giving you an eagle’s view and visibility.
  1. Humongous IT data volume
    DevOps is now business critical. However, application modernization leads to stacks that generate humongous data daily, quickly rendering humans incapable of monitoring, diagnosing, and troubleshooting issues.

    Detecting and resolving IT incidents within a data deluge then requires AIOps technologies. AIOps renders DevOps and development teams efficient even in making sense of observability and monitoring data that flows their way.
  1. Hiccups from service outages
    The data deluge SREs and developers have to sift through to resolve outages increases the operational complexity in DevOps. AIOps coupled with observability enhances the ability to detect issues quickly, automate resolution or escalate them to respective teams for a fast response.

    With DevOps as a standalone system, service outages aren’t rare. When they happen, outages can cause customer-facing events that directly impact business. AIOps can enable automated remediation, preventing losses from outages.
  1. Developer productivity and agility
    Guesswork and monotonous tasks in the development process hinder developer productivity within DevOps. When AI assists teams in finding relevant insights and information from voluminous and often unstructured data, it can maximize team morale, productivity, and agility in responding to IT incidents.

    Resultingly, an organization can innovate quickly and maintain its competitive edge without compromising on anything. AIOps event correlation can allow developers to gain more from specialized tools and processes they already have while being supported by automation to take care of the repetition.
  1. Automation and reliance on manual labor
    For developers to focus on innovation, IT operations need to be efficient and resourced. Sprawling modern IT environments can’t do with manual operations processes. Monotonous and low-value work without automation keeps talent stuck in the day-to-day and prevents them from prioritizing knowledge work.

    AI entering the DevOps stack allows humans to focus on critical tasks, IT incidents, and potential points of failure without getting into the nitty-gritty of observability metrics. AIOps allows automation of product delivery through CI/CD, release management, resiliency, and production availability.
  1. IT incident troubleshooting
    As incidents occur in real-time, it falls upon DevOps teams and SREs to understand their origination and impact and act accordingly. This is a challenge without AIOps. DevOps teams use their own set of tools, which are rarely integrated. So, when incidents occur, the challenge is to solicit immediate attention and response from multiple teams with different schedules and processes.

    Gaining real-time visibility of assets in complex IT systems is another challenge. Without automatic dependency discovery, DevOps cannot have an insight into how a change impacts the entire infrastructure. AIOps, with built-in asset discovery and automatic dependency mapping, can help plug in this gap.

    AIOps makes IT incident troubleshooting streamlined and efficient, limiting damage when critical incidents occur. It informs all concerned users in real time by mapping the affected areas of an IT ecosystem and even triggers preliminary action if an incident isn’t new. AIOps enriches data with context and accelerates response.
  1. Incident prediction
    With DevOps only, organizations end up putting out the same fires in the same way time and again. AIOps enable smart responsiveness. As incidents are detected and resolved throughout an organization, AIOps learn the resolution, and a streamlined post-mortem ensures similar incidents are resolved automatically in the future.

    AIOps can maintain a dynamic baseline of asset behavior and patterns, predict when a deviation occurs, perform event correlation and trigger an early response before service is impacted. Incident prediction by AIOps saves resources and compounds those savings in the long haul.
  1. Pre-defined rules for incident identification
    A critical value-add of AIOps is the self-learning capability. As networks and applications become more complex, rich, and varied, it’s nearly impossible to set pre-defined boundaries for good and bad behavior or performance.

    AIOps offers dynamic baselining, thresholding, and anomaly detection with both AI and humans in the loop. Over time, AIOps only bubble up the most critical incidents that need the expertise of IT operations teams, allowing them to focus on value-add areas instead of putting out fires repeatedly.
  1. Resource utilization
    For an organization, resources include human capital, money, and time. AIOps allows DevOps teams visibility where once there were blind spots. This means developers work more efficiently in tandem with operations teams and collaborate to speed up important outcomes.

    For operations teams, even the most basic of AIOps use cases, such as automated incident detection, alerting, event correlation, and automated response, can make a huge difference to how they spend their time and make an impact.
  1. Security & compliance
    DevOps struggles to make sense of MELT’s information with its limited automation and autonomy. AIOps can ensure better security and efficient compliance by proactively detecting threats and assessing compliance issues in a modern IT environment.

    AIOps move organizations forward on the path to becoming autonomous through full-stack observability, establishing causality, detecting risks, and proactive remediation.

DevOps faces a host of challenges, which translate into slower growth and innovation for organizations. A modern and future-proof AIOps solution such as CloudFabrix AIOps can make a world of difference. Learn more here.

Tejo Prayaga
Tejo Prayaga
Tejo Prayaga is a high-growth Product Management & Marketing leader. Tejo has extensive experience helping enterprises build, scale, and market innovative products and solutions that use modern technologies like Data Automation, Artificial Intelligence, Machine Learning, Microservices, Cloud Services, and more. Startup geek, Ex-Cisco, MBA, Speaker, and Toastmaster!!