As artificial intelligence (AI) and other emerging technologies continue to advance in scope and sophistication, pressure is mounting on IT teams to rapidly deploy these tools. That pressure is exacerbated by relentless resource constraints and the increasing struggles to retain talent.
These growing pressures have substantial implications for how IT professionals spend their day. Although an IT team’s initial priority resides in ensuring service availability and reliance, frequently, a substantial portion of their time is invested into crisis management – time that could be better used 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 frequent problems, detecting anomalies, and accelerating automated responses.
Chief Product Officer at BMC.
Cutting through complexity
Contemporary IT teams are tasked with monitoring complex, hybrid environments, often depending on a wide range of tools. Among these, certain platforms stand out for their intuitive user experience and ability to integrate applications, heuristics, and workflows into a cohesive framework aimed at enhancing operational efficiency.
The purpose of these systems is to be as accessible as possible, even to less-skilled, first-level operations teams. By providing AI and machine-learning-driven insights to 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 takes this even further – it translates these causes into digestible summaries, providing proactive predictions and solutions. Simultaneously, generative AI can also leverage operational, service management, and DevOps to save time for IT teams.
Avoiding problems by proactively identifying them
The use of reactive, traditional monitoring tools leaves organizations vulnerable to an array of pain points. Many only notify teams of issues once they have already occurred, causing emergency troubleshooting, slower systems, and potential shutdowns. As systems grow in complexity, anticipating and addressing problems before they arise becomes crucial. Proactivity ensures business continuity, which should include change risk management impact (both scope and severity).
Ideally, organizations should be informed of issues before they affect operations rather than scrambling to mitigate impact after issues arise. This includes utilizing predictive AI that can identify capacity and resource issues, as well as potential service disruptions or declines, and implementing automated measures to resolve them.
Optimizing vast amounts of data to enhance business performance
Organizations are shifting towards advanced, enterprise-level tools equipped with machine learning capabilities – with the speed in which these systems evolve often exceeding human monitoring and management. These tools process and analyze a wealth of system data from 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 range and complexity of modern IT infrastructure, networks and applications, as well as the heterogeneity of the data the systems produce, machine learning models and AI are often seen as necessities for IT operations.
KPIs like 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 the data that employees are dealing with, they struggle to keep up fast 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 cost-effectively scale and standardize efforts.
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 vast quantities of data sources used to fuel these tools are constantly monitored for biases and errors. Low quality data can cause issues ranging from flawed outputs to misuse.
- Scale and complexity: as IT operations grow and data and tools expand, there’s the added challenge of resolving and service modelling assets that span from cloud to mainframe and application to network.
- Silos: typically, IT teams are often isolated rather than being under a single umbrella, leading to data inconsistencies and a lack of standardization.
- Automation confidence: it can be difficult to get the right context for root cause isolation and recommended actions based on historical occurrences.
Laying the groundworks for AIOps
For AIOps to be successfully integrated, organizations must integrate existing tools, provide out-of-the-box advanced AI/ML, and accelerate automation. Business leaders must consider the use cases that are important to the organization and start small to prove the value. By doing this, AIOps can enhance the quality and speed of business decisions.
A strong AIOps strategy also requires cultural considerations. Organizations need to standardize processes to simplify automation, enhance governance to support new roles, and effectively address organizational change management. In practice, this means internal goals need to be aligned, teams must be equipped to embrace failure and grow from it, and cross-functional collaboration needs to be encouraged. A cultural shift towards open and consistent communication will assure that employee resources are effectively used and that everyone is working towards a common objective.
Ultimately, if approached strategically, AIOps can substantially streamline IT operations. It lays the groundwork for automation to be deeply ingrained into all IT activities, transforming organizational efficiency and innovation.
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