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Why Enterprises Must Consider AIOps

Gareth Smith
Eggplant

The demand for AIOps has accelerated as organizations struggle with the increased complexities of IT systems, a disparate workforce, and the explosive growth of operational data. Gartner even recently stated that "there is no future of IT operations that does not include AIOps." For the modern enterprise, relying solely on traditional analysis or humans results in missed opportunities and potentially increases risk.

So, what is AIOps?

It's about leveraging intelligent technologies, including AI and ML, to automate an organization's operations to provide a real-time understanding of issues in order to improve the reliability and quality of services. In addition, once adopted, it minimizes the time spent firefighting.

Another key benefit of AIOps is that it removes the barriers and wasted cost of siloed IT operations and provides enterprises with a platform to increase agility incrementally.

By using insights from uncorrelated data across systems, organizations can predict and fix operational problems before they occur. As a result, enterprises can reduce the time spent resolving these problems along with minimizing the impact. In addition, the intelligent technologies provide data-driven insights that help inform better decision making and improve the quality of services. By utilizing the power of AI and ML, this ultimately becomes fully automated and should prevent major outages from occurring.

Starting Your DevOps Journey

When it comes to starting your AIOps journey, it's best to focus on visibility. First, organizations should use the insights to hone in on critical operational indicators such as reliability, quality, mean time to resolution, and identify the major stumbling blocks.
Then IT teams should plan changes around those vital areas with additional, more proactive, AIOps capabilities while continually using the data generated to demonstrate progress and validate the shift to AIOps.

One of the advantages of AIOps adoption is that you can do it incrementally. With this approach, the insights initially uncovered can highlight otherwise unknown problems without disruption. Then augment the visibility aspects with more automation and more powerful analytics across a more comprehensive set of data sources. This allows the enterprise to become familiar with AIOps while proving its value before rolling it out further.

At the same time, there are some common pitfalls that enterprises need to be cognizant of. For example, the common problem of inertia with IT teams also applies to AIOps. Existing IT teams are overstretched and lack the time to investigate new technologies and initiatives, especially ones with more advanced AI and ML capabilities. However, given the automation and efficiency benefits of AIOps, this incremental migration should be seen as a good investment for a much-improved result.

As Abraham Lincoln put it, "give me six hours to chop down a tree and I will spend the first four sharpening the axe."

Gareth Smith is General Manager at Eggplant

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Why Enterprises Must Consider AIOps

Gareth Smith
Eggplant

The demand for AIOps has accelerated as organizations struggle with the increased complexities of IT systems, a disparate workforce, and the explosive growth of operational data. Gartner even recently stated that "there is no future of IT operations that does not include AIOps." For the modern enterprise, relying solely on traditional analysis or humans results in missed opportunities and potentially increases risk.

So, what is AIOps?

It's about leveraging intelligent technologies, including AI and ML, to automate an organization's operations to provide a real-time understanding of issues in order to improve the reliability and quality of services. In addition, once adopted, it minimizes the time spent firefighting.

Another key benefit of AIOps is that it removes the barriers and wasted cost of siloed IT operations and provides enterprises with a platform to increase agility incrementally.

By using insights from uncorrelated data across systems, organizations can predict and fix operational problems before they occur. As a result, enterprises can reduce the time spent resolving these problems along with minimizing the impact. In addition, the intelligent technologies provide data-driven insights that help inform better decision making and improve the quality of services. By utilizing the power of AI and ML, this ultimately becomes fully automated and should prevent major outages from occurring.

Starting Your DevOps Journey

When it comes to starting your AIOps journey, it's best to focus on visibility. First, organizations should use the insights to hone in on critical operational indicators such as reliability, quality, mean time to resolution, and identify the major stumbling blocks.
Then IT teams should plan changes around those vital areas with additional, more proactive, AIOps capabilities while continually using the data generated to demonstrate progress and validate the shift to AIOps.

One of the advantages of AIOps adoption is that you can do it incrementally. With this approach, the insights initially uncovered can highlight otherwise unknown problems without disruption. Then augment the visibility aspects with more automation and more powerful analytics across a more comprehensive set of data sources. This allows the enterprise to become familiar with AIOps while proving its value before rolling it out further.

At the same time, there are some common pitfalls that enterprises need to be cognizant of. For example, the common problem of inertia with IT teams also applies to AIOps. Existing IT teams are overstretched and lack the time to investigate new technologies and initiatives, especially ones with more advanced AI and ML capabilities. However, given the automation and efficiency benefits of AIOps, this incremental migration should be seen as a good investment for a much-improved result.

As Abraham Lincoln put it, "give me six hours to chop down a tree and I will spend the first four sharpening the axe."

Gareth Smith is General Manager at Eggplant

Hot Topics

The Latest

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...