As artificial intelligence (AI) and other emerging technologies continue to advance in scope and sophistication, pressure is increasing on IT teams to quickly deploy these tools. That pressure is exacerbated by relentless resource constraints and increasing struggles to retain talent.
These increasing pressures have substantial implications for how IT professionals spend their day. While an IT team’s initial priority is ensuring service availability and reliability, a substantial portion of their time is often spent on crisis management—time that could be better spent innovating.
This is why AIOps, the application of AI to IT operations, has gained widespread popularity when leveraged with generative AI. AIOps alleviates routine tasks and helps foster innovation by simplifying common problems, detecting anomalies, and accelerating automated responses.
BMC Product Director.
Overcoming complexity
Contemporary IT teams are tasked with monitoring hybrid and complex environments, often relying on a wide range of tools. Among them, certain platforms stand out for their intuitive user experience and their ability to integrate applications, heuristics and workflows into a cohesive framework aimed at improving operational efficiency.
The purpose of these systems is to be as accessible as possible, even to less qualified top-level operations teams. By providing AI and machine learning-based insights at all skill levels, raw data can be transformed into actionable insights and recommendations.
Sophisticated causal AI can get to the root cause of complex problems when data sources and tools have been effectively consolidated. But generative AI goes even further: it translates these causes into digestible summaries, providing proactive predictions and solutions. At the same time, generative AI can also leverage operational, service and DevOps management to save IT teams time.
Avoid problems by proactively identifying them
Using traditional and reactive monitoring tools leaves organizations vulnerable to a variety of weaknesses. Many only notify teams about problems after they have already occurred, resulting in emergency troubleshooting, slower systems, and possible shutdowns. As systems grow in complexity, anticipating and addressing problems before they arise becomes crucial. Proactivity ensures business continuity, which must include the impact of change risk management (both in scope and severity).
Ideally, organizations should be informed of issues before they impact operations rather than struggling to mitigate the impact after issues arise. This includes the use of predictive AI that can identify capacity and resource issues, as well as potential service interruptions or outages, and implement automated measures to resolve them.
Optimizing large amounts of data to improve business performance
Organizations are shifting toward advanced enterprise-grade tools equipped with machine learning capabilities, and the speed at which these systems evolve often outpaces human monitoring and management. These tools process and analyze large amounts of data from systems in complex IT environments, transforming this data into actionable insights and driving automated responses. IT professionals can then automate actions based on a comprehensive understanding of their systems operations and their impact on business objectives.
Organizations can better optimize valuable IT resources by leveraging their data analytics and automated actions. This allows them to prioritize tasks while improving value creation and innovation activities.
Considering the challenges
Due to the variety and complexity of modern IT infrastructures, networks and applications, as well as the heterogeneity of data that systems produce, machine learning models and AI are often considered necessary for IT operations.
KPIs such as failure prediction, mean time to repair, and root cause analysis have become a typical primary focus for IT teams. However, due to the complexity and volume of data employees are dealing with, they struggle to keep up to date quickly enough to make significant progress on these metrics. If an organization relies on manual, labor-intensive processes to meet these metrics, it is difficult to scale and standardize efforts cost-effectively.
Despite this, organizations will also face challenges when implementing AIOps technologies to automate these processes. These challenges may include:
- Data quality: It is crucial that the large amounts of data sources used to power these tools are constantly monitored for bias and errors. Low-quality data can cause problems ranging from faulty results to misuse.
- Scale and complexity: As IT operations grow and data and tools expand, there is the added challenge of resolving and modeling assets that span from cloud to mainframe and from application to network.
- Silos: Typically, IT teams are siled rather than under a single umbrella, leading to data inconsistencies and a lack of standardization.
- Confidence in automation: It can be difficult to get the right context to isolate the root cause and recommend actions based on historical events.
Laying the foundation for AIOps
For AIOps to be successfully integrated, organizations must integrate existing tools, provide advanced AI/ML out of the box, and accelerate automation. Business leaders should consider the use cases that are important to the organization and start small to demonstrate their value. By doing this, AIOps can improve the quality and speed of business decisions.
A solid AIOps strategy also requires cultural considerations. Organizations need to standardize processes to simplify automation, improve governance to support new roles, and effectively address organizational change management. In practice, this means that internal goals must be aligned, teams must be equipped to accept failure and grow from it, and interdisciplinary collaboration must be encouraged. A cultural shift towards open and consistent communication will ensure that employee resources are used effectively and that everyone is working towards a common goal.
Ultimately, if approached strategically, AIOps can substantially optimize IT operations. It lays the foundation for automation to be deeply embedded in all IT activities, transforming organizational efficiency and innovation.
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