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Top 10 AIOps use cases and challenges
AIOps transforms reactive IT operations into proactive, automated systems with predictive analytics, but it requires strategic planning to overcome implementation challenges.
Organizations continue to look to AI and machine learning to find new ways of boosting efficiency, increasing reliability and improving business outcomes through automation, real-time intelligence and advanced analytics. Improving IT service management lets companies retain agility and satisfy service requirements cost-effectively, making it an essential part of any IT strategy.
One way of understanding what drives an organization toward an AIOps platform is to consider its strategic approach to IT service management (ITSM). Many companies remain trapped in a reactive state when it comes to availability, security incidents, root cause analysis and optimization. These organizations don't create the opportunity to transition to a proactive approach. An effectively planned and implemented AIOps strategy creates exactly this opportunity, enabling a complete shift toward forward momentum and agility.
Those planning AIOps implementations must manage expectations. The upfront costs are significant, often centering on licensing, integration and training. However, the long-term benefits include a reduction in IT service overhead costs along with improved staff and service efficiency.
10 AIOps use cases
Organizations have numerous options for integrating AIOps into their infrastructure, and each choice can potentially enhance service availability, security posture and resource utilization. Many of these benefits reflect similar AI deployments in other areas, such as code generation or data analysis.
Your organization could benefit from an AIOps deployment in the following ways:
- Predictive incident mitigation. A combination of AI and machine learning (ML) provides improved analytics to forecast outages, capacity issues and performance degradation before they affect users.
- Real-time incident detection and alerting. Real-time monitoring enables faster incident identification, escalation and correction.
- Automated incident response and remediation. Automated responses to identified incidents can include reconfiguration, resource optimization and compliance measures designed to ensure availability.
- Automated root cause analysis. AI and ML systems can pinpoint incident sources, including mitigation recommendations or automated responses.
- Capacity planning. Data-driven capacity planning for performance and availability enhances existing predictive models.
- Resource optimization. Enable resource optimization and scaling by recommending procedures or automating optimization processes.
- Service desk automation and intelligent ticketing. Automated routine service desk ticket remediation or escalation delivers greater efficiency, quicker responses and rapidly completed tickets.
- Data-driven performance optimization. Supplement trend analysis with optimization and tuning recommendations or automation.
- Security incident detection and response integration. Augment existing security procedures with security incident identification and automated responses, leading to an improved security posture.
- Performance monitoring and optimization across hybrid and multi-cloud environments. This approach addresses performance concerns across complex hybrid and multi-cloud deployments by integrating disparate tools and data for a holistic view.
The overall benefits of an AIOps deployment typically include improvements in compliance and satisfaction with service level agreements. AIOps offers the opportunity for continual improvement based on long-term data collection and ML.
Less tangible but equally essential returns include improved customer satisfaction and reduced IT employee burnout. IT staff are more likely to have time for strategic initiatives instead of reactive incident responses.
Consider agentic AIOps vs. traditional AIOps
One aspect of AIOps that warrants further examination is the distinction between agentic and traditional AIOps. These two approaches must be considered when discussing use cases and implementation challenges:
- Agentic AIOps. Moves beyond an advisory or information-gathering role to an automated decision-making and action-taking part of the IT ops team.
- Traditional AIOps. Provides data analysis, anomaly identification and incident prediction capabilities for the human IT ops team to act on, with little to no automation or independent decision-making.
Any AIOps plan must determine which approach suits the organization best or whether a hybrid strategy is more appropriate. Agentic AIOps is often best for automating routine responses to incidents and root cause analysis results, especially in complex environments.
10 AIOps implementation challenges
Moving your organization to AIOps presents its own set of challenges. Various technical, budgetary and cultural difficulties slow or prevent deployments. Many of these concerns are the same as those raised when introducing any new technology into an organization, while others are unique to AI. Consider how your organization might address the following challenges:
- Business strategy. The lack of a clear business strategy and objectives makes it difficult to implement AIOps and demonstrate its value to the organization.
- Data silos. Data silos and disparate systems make data quality and integration a challenge and hinder comprehensive analytics.
- Qualified AI staff. An industrywide skills gap makes effective deployment and management challenging, while a lack of organizational readiness impedes effective use.
- Tool utilization. Challenges with tool selection, sprawl and redundancy hinder efficient AI data analysis and effective human oversight.
- Vendor management. Immature evaluation criteria can lead to vendor lock-in or the selection of a less-than-optimal provider.
- Legacy system integration. Linking legacy infrastructure and modern AI-driven tools can be technically challenging due to compatibility issues.
- Resistance to change and AI. Cultural pushback from IT staff can stem from fears of job loss, loss of autonomy, loss of professional identity and skills recognition, and a reluctance to trust automation.
- Cost. Budget constraints, underestimated costs and unrealistic expectations weigh against the high initial investment in technology and skilled talent, potentially impeding deployment.
- Expansion of service. Select scalable AIOps platforms that can adapt to future business needs, including complexity and volume.
- IT ops efficiency and alert fatigue. The increased volume of messages and alerts can overwhelm existing support services, especially with a high number of false alerts.
Addressing challenges
Overcoming these challenges means addressing several key aspects of your IT operations department, including staff, technology, data and budget. Use the following approaches to mitigate AIOps deployment challenges:
- Develop IT employee buy-in by aligning the AIOps goals with the company's strategic initiatives, providing regular communication and encouraging participation. Be sure to alleviate concerns with AI replacing human IT staff.
- Offer upskilling opportunities and encourage IT staff participation in planning.
- Ensure effective vendor vetting and selection, with an eye toward integrating legacy systems, future growth and scaling.
- Retire legacy systems and services where possible.
- Recognize the significant upfront costs of AIOps as a balance against long-term savings and efficiency.
- Emphasize data centralization and normalization to ensure consistency and compatibility.
Conclusion
An effective AIOps deployment can dramatically transform an organization's IT department from a reactive, siloed environment into a proactive, integrated and agile driving force within the business. However, IT leaders must determine whether the company contains the standard use cases necessary to realize an acceptable ROI.
Consider how this list of benefits applies to your organization's strategy and how many of the stated challenges you're likely to encounter. Then, plan a strategy that addresses cultural concerns and fosters employee buy-in.
Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to Informa TechTarget, The New Stack and CompTIA Blogs.
