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Choose the right AIOps service provider: Learn to evaluate ITSM performance after implementation

AIOps for ITSM (IT Service Management) finds the most significant use case in creating genuine, actionable incidents for timely defect detection. AIOps for IT Service Management (ITSM) combines AI, ML Algorithms, and Data analytics to help perform anomaly detection, event correlation, and causality determination. It also helps interpret vast amounts of data generated by IT systems, applications, and infrastructure. 

When it comes to CTOs and product owners, AIOps solutions are a must for improving their key performance indicators (KPIs) – uptime, incident response, remediation time, and predictive maintenance to prevent potential outages. Business KPIs connected to AIOps include client satisfaction, employee productivity, and website or App metrics such as page loading time, conversion rate, system availability, data availability, user experience, etc. Another important KPI to measure is the success of strategies deployed in downsizing IT operations costs and resource costs.

AI in IT Operations and IT resource empowerment

Dark clouds waver over your IT Team’s future – As if ERPs, CRMs, cloud management tools, IoT devices, employee devices, virtual machines, and VPN network management was already taking a toll. Today, IT teams also need to enable remote system configuration, remote IT ticket resolution, remote employee productivity, remote workforce monitoring, and hundreds of more emerging business needs. Therefore, it is becoming untenable to rely on human skills alone to manage IT service and support.

IT Ops powered by AI plays a key role in reducing the stress that is bound to impact business continuity and daily operations. It will also enhance key IT Ops metrics related to software development lifecycle (SDLC), including development, testing, deployment, and maintenance. It will vastly improve anomaly detection by fully automating root-cause analysis, failure predictions, failure alerts, and IT resource management.

Unlock the following benefits by integrating AIOps in the Software Development Lifecycle using our proven Automation Testing tool – IntelliSWAUT

  • Accelerate the testing of your SaaS products before taking them to the market
  • Anticipate future IT infrastructure and performance issues and plan accordingly
  • Use defect classification to find the right type of developer for timely resolution
  • Get defect prediction to help identify the best ways to resolve any system issue
  • Leverage defect prevention using AIOps for faster software development lifecycle
  • improve testing strategies and the quality of software for best end-user experience

Is your ITSM partner on the right track for your AIOps implementation?

The below checklist will help evaluate your journey:

  • Assess Your Current Tools: Begin by assessing your current toolset and identifying the third-party integrations that are critical for your IT operations. This may include monitoring tools, log analyzers, incident management systems, ticketing systems, and more.
  • Identify Integration Points: Determine the integration points where AIOps can provide value. For example, you can integrate AIOps with your monitoring tools to enhance anomaly detection and root cause analysis or integrate it with your incident management system to prioritize and automate incident response.
  • Evaluate IT AIOps Solutions: Research and evaluate IT AIOps solutions that align with your integration requirements. Look for solutions that offer flexible integration capabilities, such as APIs, connectors, or plugins, to seamlessly connect with your existing tools.
  • Define Integration Approach: Based on your assessment and requirements, define the approach for integrating AIOps with your current tools. This may involve developing custom integrations, utilizing pre-built connectors, or leveraging middleware platforms to facilitate the integration.
  • Establish Data Flow: Determine how data will flow between your existing tools and the AIOps platform. Identify the data sources, such as log files, metrics, events, or alerts, and define the mechanisms for collecting, aggregating, and forwarding the data to the AIOps platform for analysis.
  • Implement Integration: Execute the integration plan by implementing the necessary configurations, APIs, or connectors to establish the connection between your current tools and the AIOps platform. Ensure proper authentication, access control, and data privacy measures are in place.
  • Test and Validate: Conduct thorough testing to ensure the integration is working as expected. Validate that data is being properly collected, analyzed, and correlated within the AIOps platform, and that insights and recommendations are accurately shared with your existing tools.

What must your AIOps implementation partner help you achieve?

Get a clear and visible impact on business operations and KPIs.

Mean time to detect (MTTD):

Use Mean Time To Detect (MTTD) as a measure to evaluate how AIOps reduces the time to detect IT incidents and anomalies. By leveraging AI and ML algorithms, AIOps can analyze vast amounts of data in real-time, identifys patterns, and detects anomalies quickly. Decreased MTTD allows IT teams to respond promptly to issues and minimize the impact on business.

Mean time to acknowledge (MTTA):

By measuring Mean Time To Acknowledge (MTTA), once any issue is detected, IT teams must acknowledge it and decide who will handle it. AIOps use machine learning to automate that decision-making process and ensure that the correct teams work on the problem as soon as possible.

Mean time to restore/resolve (MTTR):

Mean Time To Resolve (MTTR) is a measure to evaluate how AIOps helps accelerate incident resolution by providing intelligent insights, automated incident prioritization, and recommendations for remediation. By leveraging AIOps capabilities, IT teams can resolve incidents more efficiently, reducing MTTR and ensuring faster restoration of services.

Parameters to evaluate the benefits of integrating AIOps tools

Service availability:

AIOps must enable proactive monitoring, anomaly detection, and predictive analytics, which helps prevent service outages and reduce downtime. By leveraging AIOps to identify potential issues and take preventive measures, businesses can improve their service availability, ensuring uninterrupted access to their services for customers.

Incident Reduction:

AIOps must help in identifying the root causes of incidents and help address them proactively. By leveraging intelligent insights and recommendations, AIOps must identify recurring issues, highlight areas of improvement, and facilitate preventive actions. This leads to a reduction in the number of incidents and minimizes their impact on business operations.

User-reported vs. monitoring detection:

IT operations must be able to detect and resolve issues before the end user is aware. For example, if application or website performance is slowing by milliseconds, the IT team needs to receive an alert and fix the problem before the slowness develops and affects users. AIOps must support dynamic thresholds to guarantee that alerts are created automatically and forwarded to the appropriate teams for inquiry or auto-remediation when regulations need it.

Time savings and associated cost savings:

AIOps must help automate the entire process of incident identification along with the setup, design, configuration, deployment, and maintenance of IT infrastructure. Automation of IT operations must save resource costs while providing flawless support to all the different tasks and workflows required by the organization.   

Evaluate AIOps tools based on the following outcomes

Integrates with existing tools and processes: It must be able to seamlessly integrate and draw insights from multiple monitoring tools used for different purposes that are valuable for different functions/teams.

Justifies spends on IT management toolsets: It must be able to activate shared visibility across all tools, domains, and teams so that your IT teams can monitor their utilization and measure their effectiveness from one place.

Helps monitor and refine usage: It must continuously monitor and gather feedback from users to identify areas of improvement. These improvements can manifest in terms of optimized data flow, enhanced functionality, improved speed of data retrieval, etc.

Enhances training and adoption: It must include easy training support for non-technical teams to handle AIOps capabilities. The more user-friendly the tool is, its adoption and usage among different teams will also grow as a part of day-to-day operations.

Specific impact areas that your AIOps implementation partner must fulfill

Enhanced user experience:

The partner must be able to deliver prompt resolutions to tickets raised by employees delivered through a modern interface that improves adoption among users.

Maximized ROI:

The partner must offer open-source solutions to handle challenges with increased observability and contextual analysis to help the IT team optimize its return on investment (ROI).

Improved IT team efficiency:

Your partner must be able to increase insights into volatility and vulnerability of IT systems to minimize downtime incidences and boost productive outcomes.


As the trend shifts from human-centric Operations to AI-centric Operations, the Development of AIOps techniques will also transition from building tools to creating human-free, end-to-end solutions. AIOps brings outcomes such as proactive issue detection, faster incident response, improved root cause analysis, predictive analytics, enhanced resource utilization, increased efficiency, cost savings, and intelligent decision-making. These outcomes enable organizations to improve their IT operations’ reliability, performance, and agility, ultimately delivering better customer service.

Our Sun Technologies team is an expert in assisting clients in implementing enterprise-wide AIOps—from cloud to the data center to mainframe and everywhere in between—as a corporate IT solutions pioneer.

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