Ali Siddiqui, CPO at BMC
Spotting trouble as quickly as it starts is often easier said than done, particularly in IT operations environments in which it is challenging to separate false alerts from anything that requires immediate attention. Enterprises can out the noise and instead focus on operational issues that matter by using proactive, intelligent capabilities that predict and prevent problems, all while offering insights. By doing this, they can go one step better than simply reacting quickly to problems.
Today, artificial intelligence (AI) is more prevalent than ever across service and operations management, both in distributed systems and the mainframe. In fact, according to research from Mordor Intelligence, the AIOps market that was valued at $13.51 billion in 2020 is projected to be worth $40.91 billion by 2026.
Every second counts when an application hiccups. Employee productivity, Net Promoter Score (NPS), business reputation and revenue can all take a hit when complex applications and services spanning on premises, cloud, and even mainframe do not work as intended. AIOps can address this by applying the appropriate sophisticated AI and machine learning (ML) algorithms and models. This replaces traditional rules-based processing methods for IT operations, introducing new speed and efficiency across the enterprise and, in turn, providing higher-order insights that lead to better decision-making.
Observability goes hand-in-hand with AIOps, but they are two distinct concepts that feed off and complement one another. When an organisation has both, it can use AIOps for more intelligent and dynamic monitoring with anomaly detection and advanced root cause analysis learning from a broad set of data, including events, logs, traces, metrics, and topology.
Observability is the notion of getting visibility into the full tech stack and gaining operational data insights, leveraging multiple sources and types of data, and being able to determine the state of a system based on that external data. The aspiration is full stack observability, allowing users to respond to situations that they were not aware of and apply intelligent automation to examine the IT landscape and take the appropriate action, whether it is compliance, a patch or the blocking or elimination of a threat. Essentially, the greater the observability and insights, the more powerful the actions and the more prepared users will be to respond to the unexpected.
Getting a full view of the business impact and moving from reactive to proactive can help organisations better prioritise business risks and problems to meet service level agreements (SLAs). In addition, they can enhance customer and employee satisfaction and retention while capitalising on differentiating opportunities for growth. It also opens the door for more innovation as it is better at adapting to rapidly evolving technologies and processes. This means employers can allocate top talent to innovation projects that they find more rewarding.
According to a 2022 PwC business survey, 62 percent of AI “leaders”, which refers to companies advancing with AI in the areas of business transformation, enhanced decision-making and modernised systems and processes, use the technology to support operations and maintenance. Deploying the right AIOps solution requires many considerations, including:
- Open cross-domain engagement, observability and actionability: with true enterprise wide, platform-driven management, IT can better predict and resolve issues quicker while providing always-on service for the business
- Predictive insights and failure prediction: AI/ML can identify patterns in the data, identify trends, and provide intelligent insights that would take significant human effort and investment
- Event noise reduction: analysis powered by AI/ML separates the real problems from noise to deliver a clearer view of the real issues causing event storms
- Intelligent alerting: by federating data from across the IT environment, including third-party solutions, AIOps can filter and correlate data and transform it into actionable events so that potential problems are proactively flagged before they affect customers or the business
- Cross-domain situational understanding and probable cause analysis: by applying advanced analysis to aggregated data across infrastructure and applications, IT can identify and focus on the true problem and respond, saving time and energy that can be better allocated elsewhere
- Intelligent automation: AI/ML algorithms, policies and insights continuously detect the state of the infrastructure and service-desk activity to take or recommend automated actions for faster, informed fixes
By adopting an AIOps posture, enterprises can advance and evolve their operations and gain better-quality insights that empower them to be nimble in the face of change and prepare for the future. Essentially, AIOps gives employees the chance to focus on driving operational excellence, which helps the business evolve into an Autonomous Digital Enterprise.
