4 elements of AI copilots for incident management
Generative AI has immense potential to transform how IT operations, service management, and infrastructure teams function. However, integrating GenAI technologies, like copilots, often brings significant challenges, such as ensuring accuracy, addressing job displacement concerns, and demonstrating tangible value. Navigating the landscape of various vendors and implementation hurdles can be time-consuming and resource-intensive. We’ve created a tactical framework for evaluating AI copilots for ITOps and service management to smooth the path and help you achieve positive outcomes more quickly.
Explore use cases for AI copilots in incident management.
AI copilots can provide significant value, offering proven benefits in productivity, service reliability, and overall organizational value for IT. Nonetheless, many organizations need help to leverage the potential of generative AI. According to research from Lucidworks, only 25% of planned GenAI investments reach full implementation. Many projects don’t get beyond the pilot stage, delaying return on investment (ROI). Common factors hampering adoption include concerns about justifying high costs, uncertainty about data quality, and the accuracy of GenAI output.
When evaluating an AI copilot for your organization, look for the following five elements to avoid wasting resources and speed time to value.
Contextual understanding of your IT infrastructure
The more a copilot knows about your organization, the more purpose-built and relevant the outputs will be. Ensure the copilot can comprehend and analyze multiple data types from various aspects of your IT infrastructure. In addition to machine-generated and historical data, your human-generated institutional knowledge has immense value. An open, data-agnostic incident-management AI copilot that integrates these diverse types effectively will provide more accurate, relevant, and actionable insights. Gathering context for incident management requires that the copilot seamlessly integrates with elements across your infrastructure. For example, it needs data from:
- Existing monitoring tools and systems
- Documentation
- Relevant chat histories
- Knowledge articles
By creating actionable insights and enriching existing data with more context, a copilot enhances the output of your existing tools and increases their value. This interoperability is crucial to streamline processes and ensure that the copilot can access relevant information to facilitate better decision-making.
Purpose-built for incident management
All sorts of copilots exist on the market today. And more are on the way. Look for a strong focus on incident management and automating resolution, plus customizable features to address your team’s unique requirements. For example, you might want to customize suggested resolution steps or use specific templates for post-mortem documents. This level of programmability can enhance team productivity and support swift, efficient issue resolution. The ultimate goal is to deliver significant value to your operations.
Integration within existing workflows
Ensure that you can integrate AI copilot into the environments where your network operations center (NOC) and service desk teams already operate. Accessing the copilot from familiar tools and workflows reduces the learning curve, minimizes disruption, and enhances adoption. Teams can receive AI-powered insights directly in their existing chat and ticketing systems. Operators get quick responses without having to learn new tools or processes and jump between different screens.
Continuous learning and improvement
AI copilots produce substantial amounts of data, but the real benefit is their ability to use the data for ongoing improvement. Prioritize copilots that apply advanced analytics to identify trends, recommend preventive measures for recurring issues, and enhance overall infrastructure performance. A copilot that adapts and learns will become increasingly effective in supporting your IT operations, especially as teams scale.
Make a difference
A good AI copilot can significantly improve incident management by bringing situational awareness and consistency to response workflows. Evaluating AI copilots for ITOps and service management doesn’t have to be overwhelming. Focus on the essential criteria to more easily identify an AI solution that aligns with your organization’s needs and delivers value. A well-selected AI copilot will help address modern IT service-reliability challenges and empower teams to operate more effectively and efficiently. Innovation promises a future where operations teams benefit from instant AI-driven insights that complement and enhance human expertise rather than replace it.
Join us for a new webinar, Effectively Integrate GenAI into Incident Response, to explore how AI-powered incident response can enhance ITOps management to improve team efficiency and service reliability.
