Artificial intelligence for IT operations, or AIOps, is the application of artificial intelligence (AI) capabilities—such as natural language processing and machine learning models—to automate, streamline and optimize IT service management and operational workflows.
AIOps leverages big data, analytics and ML capabilities to:
By integrating separate manual IT operations tools into a single intelligent, automated IT operations (ITOps) platform, AIOps enables IT operations teams to respond quickly—and often proactively—to slowdowns and outages, with end-to-end visibility and context.
It helps businesses bridge the gap between diverse, dynamic and difficult-to-monitor IT landscapes and siloed IT teams on one hand and user expectations of app performance and availability on the other. With the proliferation digital transformation initiatives across business sectors, many experts see AIOps as the future of IT operations management.
AIOps can incorporate a range of AI strategies and features, including data output and aggregation, algorithms, orchestration and visualization.
Algorithms codify IT expertise, business logic and goals, enabling AIOps platforms to prioritize security events and make performance decisions. Algorithms form the basis for machine learning (ML) and enable platforms to establish baselines and adapt as environmental data changes.
Machine learning uses algorithms and techniques—such as supervised, unsupervised, reinforcement and deep learning—to help systems learn from large datasets and adapt to new information. In AIOps, ML helps with anomaly detection, root cause analysis (RCA), event correlation and predictive analysis.
AIOps programs gather data from various network components and data sources. Analytics interpret the raw data to create new data and metadata that helps both systems and teams identify trends, isolate problems, predict capacity demands and manage events.
Automation features within AIOps tools enable AIOps systems to act based on real-time insights. For example, predictive analytics may anticipate an increase in data traffic and trigger an automation workflow to allocate additional storage as needed (in keeping with algorithmic rules).
Data visualization tools in AIOps present data through dashboards, reports and graphics, so that IT teams can monitor changes and make decisions beyond the capabilities of AIOps software.
AIOps uses a big data platform to aggregate siloed ITOps data, teams and tools in one place. This data can include:
AIOps platforms then apply focused analytics and ML tools to:
The journey to AIOps is different in every organization. Once business leaders distill an AIOps strategy, they can start to incorporate tools that help IT teams observe, predict and respond quickly to IT issues.
When choosing tools to improve AIOps, many teams consider the following features:
AIOps platforms can provide organizations varying levels of automation, depending on their IT needs and AIOps strategy.
With a domain-agnostic approach, AIOPs software collects data from a wide range of sources to solve problems across various operational domains (networking, storage and security, for example). These tools offer a comprehensive, holistic view of overall performance, helping organizations address issues that span multiple areas.
However, they might not provide the detailed insights IT teams need to tackle specific pain points or cater to unique industry needs. The broad nature of domain-agnostic tools means they excel in offering a general overview, but they might fall short in delivering targeted incident management solutions for nuanced challenges.
Domain-centric AIOps tools focus on a specific domain, whether it's an IT environment or a particular industry. Though these tools don’t cover the entire IT landscape, they are highly specialized, with AI models trained on datasets specific to their domain. This specialization enables them to provide precise insights and solutions.
For instance, in a network context, a domain-centric tool can accurately identify the cause of a bottleneck by understanding standard network protocols and patterns. And thanks to its specialized training and focus, it can determine whether the slowdown is the result of a distributed denial-of-service (DDoS) attack or a simple system misconfiguration.
Regardless of the type of tool an organization chooses, it’s important that teams:
Both AIOps and DevOps are methodologies designed to enhance IT operations, but they focus on different aspects of the software lifecycle.
DevOps aims to integrate development and operations teams to foster collaboration and efficiency throughout the software development process. It streamlines and automates coding, testing and deployment processes and accelerates continuous integration and continuous delivery (CI/CD) pipelines, enabling faster, more reliable software releases.
DevOps also uses tools such as infrastructure as code and collaboration platforms to break down silos between teams and make sure that software updates can be delivered quickly, without compromising quality.
Whereas DevOps focuses on accelerating and refining software development and deployment, AIOps uses AI to optimize the performance of enterprise IT environments, ensuring systems run smoothly and efficiently. AIOps platforms use ML and big data analytics to analyze vast amounts of operational data to help IT teams to detect and address issues proactively.
When used in tandem, AIOps and DevOps services can help business create a complementary, comprehensive approach to managing the entire software lifecycle.
AIOps services can help businesses tackle several use cases, including:
Root cause analyses (RCAs) determine the root cause of problems to remediate them with appropriate solutions. RCA helps teams avoid the counterproductive work of treating symptoms of an issue, instead of the core problem.
For example, an AIOps platform can trace the source of a network outage to resolve it immediately and set up safeguards to prevent the same problem from occurring in the future.
AIOps tools can comb through large amounts of historical data and discover atypical data points within a dataset. These outliers help teams identify and predict problematic events (data breaches, for instance) and avoid the potentially costly consequences of those events (negative PR, regulatory fines and declines in consumer confidence, among other issues).
Modern applications are often separated by multiple layers of abstraction, making it difficult to understand which underlying on-premises servers, storage resources and networking resources are supporting which applications. AIOps helps to bridge this gap.
It acts as a monitoring tool for cloud infrastructure, virtualization and storage systems, reporting on metrics including usage, availability and response times. Furthermore, AIOps uses event correlation capabilities to consolidate and aggregate information so that users can consume and understand information more easily.
For most organizations, cloud adoption is gradual, not wholesale. This often results in hybrid multicloud environments (including many interconnected parts that rely on technologies such as APIs and microservices) with multiple dependencies that can change too quickly and frequently to document. By providing clear visibility into these interdependencies, AIOps can dramatically reduce the operational risks associated with cloud migration and hybrid cloud approaches.
DevOps speeds development by giving development teams more power to provision and reconfigure IT infrastructure, but teams still must manage the architecture. AIOps provides the visibility and automation IT teams needs to support DevOps without excessive human oversight.
The primary benefit of AIOps is that it enables ITOps teams to identify, address and resolve slowdowns and outages faster than they could by manually sifting through alerts from multiple tools and components. This enables businesses to achieve:
By cutting through IT operations noise and correlating operations data from multiple IT environments, AIOps can identify root causes and propose solutions faster and more accurately than humanly possible. Accelerated problem identification and incident resolution processes enable organizations to set and achieve previously unthinkable MTTR goals.
Automatic identification of operational issues and reprogrammed response scripts reduce operational costs and drive more precise resource allocation. It also reduces IT staff workloads and frees up staffing resources for more innovative and complex work, improving the employee experience.
Integrations within AIOps monitoring tools facilitate more effective collaboration across DevOps, ITOps, governance and security teams. And better visibility, communication and transparency enable these teams to improve decision-making and respond to issues faster.
With built-in predictive analytics capabilities, AIOps platforms continuously learn to identify and prioritize the most urgent alert. This helps IT teams address potential problems before they lead to unplanned downtime, disruptions and service outages.
Discover how IBM® Turbonomic helps manage cloud spend and application performance, with a potential 247% ROI over 3 years.
Learn best practices and considerations for selecting a cloud optimization solution from PeerSpot members who use Turbonomic.
Learn how users of IBM Turbonomic achieved sustainable IT and reduced their environmental footprint while assuring application performance.
Rethink your business with AI and IBM automation, which helps make IT systems more proactive, processes more efficient and people more productive.
Step up IT automation and operations with generative AI, aligning every aspect of your IT infrastructure with business priorities.
IT automation software from IBM Z plays a crucial role in providing high-end solutions that monitor, control and automate an extensive range of system elements across your enterprise's hardware and software resources.