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Today's IT Challenges Identified - and Solved by AIOps

Akhilesh Tripathi
Digitate

The pandemic has spurred organizations to rapidly shift away from traditional in-house IT infrastructures to modern and agile IT systems that support enterprise-wide digital transformation efforts, such as cloud migration and automation enablement. These digital transformation initiatives have been critical to business continuity in the midst of an unprecedented global market upset that came in the form of COVID-19.

Along with the shift to modern and agile IT systems comes an increase in the volume of data created by various digital systems and solutions. Traditional IT management solutions that involve manual efforts for tedious and repeatable processes cannot keep up with the pace of these rapid changes and leaves IT teams facing challenges surrounding infrastructure complexities, long delays in isolating and resolving IT faults, and inconsistent and variable quality of operations.

AI-driven software can help to overcome these challenges by acting as an intelligence tool to assess enterprise system behavior and detect anomalies, resolve IT incidents and even prescribe and proactively take action to prevent the disruption of IT operations.


Below are the findings from the recent Autonomous Enterprise Survey that uncovered trends around the ongoing adoption of artificial intelligence (AI) for IT operations (AIOps) and the technology's benefits to business users.

Today's IT Operational Challenges

To better understand the business and departmental need for AIOps, let's look at the top IT operational challenges organizations face today. The primary challenge organizational IT reported is dealing with too many routine and redundant tasks, with 82% reporting this as their top IT problem. The next biggest challenges were a lack of capabilities to proactively detect and correct system issues, and a need for flexibility to scale with business needs — with 64% of respondents claiming each of these challenges.

The benefits AIOps delivers to businesses perfectly address these current IT challenges. Well-built AIOps solutions leverage advanced AI-based reasoning to detect and correct system issues automatically — simultaneously reducing manual effort spent on managing IT operations by up to 60%. Many businesses recognize these benefits, with 82% reporting that AIOps is necessary for future growth and transformation of businesses.

Qualitative survey responses explained the thought process behind this prioritization of AIOps solutions. The complexity of hybrid and multi-cloud infrastructures, increasing transactional volumes and the criticality of online business systems make it impossible for traditional IT systems to manage every application and infrastructure without automation. The overwhelming amount of data in today's business operations is simply too much for manual analysis.

In addition, the insights provided by AIOps mean the performance of various tasks and decisions can be greatly improved while reducing the manual effort needed to conduct routine tasks — so IT teams can focus on more business-critical tasks that require their immediate attention.

Key Business Drivers for AIOps

There are multiple benefits to AIOps solutions that have significant impacts on an organization's ability to increase revenue and better plan for the future. 91% of organizations identified the removal of manual processes as the most critical benefit, with improved agility and reliability coming in second at 82%. However, AIOps can also support the need to build predictability and resolve problems faster, with 73% identifying this as another key business benefit.

Thanks to AIOps, organizations can adapt to changes fast, and evolve innovative processes that enable sustained growth

The ability to automate rote processes and increase reliability, as well as the ability to better plan for the future are critical to business leaders today, according to a recent PwC survey. Organizations want to give their leadership the confidence that they can remain efficient while withstanding stresses and disruptions. An agile IT operation, supported by AIOps, is an efficient vehicle to achieving resilience in today's constantly evolving, fast-paced business environment. Thanks to AIOps, organizations can adapt to changes fast, and evolve innovative processes that enable sustained growth.

The Obstacles in the Way of AIOps Deployment

With the challenges and benefits of AIOps well addressed and understood, why aren't all companies currently investing in AIOps initiatives?

There are several barriers to adoption, but the biggest obstacle highlighted by 73% of respondents is a lack of experience with intelligent IT solutions.

While this is to be expected for new technologies such as AIOps, as they are still evolving and nascent, organizations should not let it stop them from exploring the possibilities of AIOps further.

In addition, a lack of staffing/talent with appropriate technological skills was identified as a barrier to AIOps adoption by 45% of organizations. That said, digital transformation initiatives happen from the top down. Securing the sponsorship of company leaders is critical to any organizational change, and 55% of organizations claim they lack executive support for, or a strategic approach to, AIOps deployment.

The survey clearly identifies a need for companies to incorporate AIOps, or at the very least intelligent automation, into their organizational culture and strategy to meet the business goals of today's environments as they compound in complexity. AIOps offers a scalable solution to resolve current enterprise IT challenges by automatically detecting, resolving and preventing IT issues. Ultimately, AIOps helps to minimize revenue risk and improve business agility by ensuring zero downtime of critical applications.

However, to properly leverage the full advantages of AIOps, both IT and executive leadership teams must sync their knowledge and understanding of AIOps tools and technology, or risk falling behind their competition.

Akhilesh Tripathi is CEO at Digitate

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Today's IT Challenges Identified - and Solved by AIOps

Akhilesh Tripathi
Digitate

The pandemic has spurred organizations to rapidly shift away from traditional in-house IT infrastructures to modern and agile IT systems that support enterprise-wide digital transformation efforts, such as cloud migration and automation enablement. These digital transformation initiatives have been critical to business continuity in the midst of an unprecedented global market upset that came in the form of COVID-19.

Along with the shift to modern and agile IT systems comes an increase in the volume of data created by various digital systems and solutions. Traditional IT management solutions that involve manual efforts for tedious and repeatable processes cannot keep up with the pace of these rapid changes and leaves IT teams facing challenges surrounding infrastructure complexities, long delays in isolating and resolving IT faults, and inconsistent and variable quality of operations.

AI-driven software can help to overcome these challenges by acting as an intelligence tool to assess enterprise system behavior and detect anomalies, resolve IT incidents and even prescribe and proactively take action to prevent the disruption of IT operations.


Below are the findings from the recent Autonomous Enterprise Survey that uncovered trends around the ongoing adoption of artificial intelligence (AI) for IT operations (AIOps) and the technology's benefits to business users.

Today's IT Operational Challenges

To better understand the business and departmental need for AIOps, let's look at the top IT operational challenges organizations face today. The primary challenge organizational IT reported is dealing with too many routine and redundant tasks, with 82% reporting this as their top IT problem. The next biggest challenges were a lack of capabilities to proactively detect and correct system issues, and a need for flexibility to scale with business needs — with 64% of respondents claiming each of these challenges.

The benefits AIOps delivers to businesses perfectly address these current IT challenges. Well-built AIOps solutions leverage advanced AI-based reasoning to detect and correct system issues automatically — simultaneously reducing manual effort spent on managing IT operations by up to 60%. Many businesses recognize these benefits, with 82% reporting that AIOps is necessary for future growth and transformation of businesses.

Qualitative survey responses explained the thought process behind this prioritization of AIOps solutions. The complexity of hybrid and multi-cloud infrastructures, increasing transactional volumes and the criticality of online business systems make it impossible for traditional IT systems to manage every application and infrastructure without automation. The overwhelming amount of data in today's business operations is simply too much for manual analysis.

In addition, the insights provided by AIOps mean the performance of various tasks and decisions can be greatly improved while reducing the manual effort needed to conduct routine tasks — so IT teams can focus on more business-critical tasks that require their immediate attention.

Key Business Drivers for AIOps

There are multiple benefits to AIOps solutions that have significant impacts on an organization's ability to increase revenue and better plan for the future. 91% of organizations identified the removal of manual processes as the most critical benefit, with improved agility and reliability coming in second at 82%. However, AIOps can also support the need to build predictability and resolve problems faster, with 73% identifying this as another key business benefit.

Thanks to AIOps, organizations can adapt to changes fast, and evolve innovative processes that enable sustained growth

The ability to automate rote processes and increase reliability, as well as the ability to better plan for the future are critical to business leaders today, according to a recent PwC survey. Organizations want to give their leadership the confidence that they can remain efficient while withstanding stresses and disruptions. An agile IT operation, supported by AIOps, is an efficient vehicle to achieving resilience in today's constantly evolving, fast-paced business environment. Thanks to AIOps, organizations can adapt to changes fast, and evolve innovative processes that enable sustained growth.

The Obstacles in the Way of AIOps Deployment

With the challenges and benefits of AIOps well addressed and understood, why aren't all companies currently investing in AIOps initiatives?

There are several barriers to adoption, but the biggest obstacle highlighted by 73% of respondents is a lack of experience with intelligent IT solutions.

While this is to be expected for new technologies such as AIOps, as they are still evolving and nascent, organizations should not let it stop them from exploring the possibilities of AIOps further.

In addition, a lack of staffing/talent with appropriate technological skills was identified as a barrier to AIOps adoption by 45% of organizations. That said, digital transformation initiatives happen from the top down. Securing the sponsorship of company leaders is critical to any organizational change, and 55% of organizations claim they lack executive support for, or a strategic approach to, AIOps deployment.

The survey clearly identifies a need for companies to incorporate AIOps, or at the very least intelligent automation, into their organizational culture and strategy to meet the business goals of today's environments as they compound in complexity. AIOps offers a scalable solution to resolve current enterprise IT challenges by automatically detecting, resolving and preventing IT issues. Ultimately, AIOps helps to minimize revenue risk and improve business agility by ensuring zero downtime of critical applications.

However, to properly leverage the full advantages of AIOps, both IT and executive leadership teams must sync their knowledge and understanding of AIOps tools and technology, or risk falling behind their competition.

Akhilesh Tripathi is CEO at Digitate

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.