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What Is the Deal with AIOps? - Part 2

Akhilesh Tripathi
Digitate

Start with What Is the Deal with AIOps? - Part 1

What to Keep in Mind While Considering AIOps

Unlike some AI initiatives, AIOps doesn't always necessitate the use of a data scientist, so don't let hiring expenses put your AIOps initiatives on hold. It is always nice to have IT team members with AI skills, but this becomes less critical as more intelligent solutions come into prominence that offer AIOps features out of the box, a readily deployable option for IT.

Look for products which have a lot of out of the box features, great adapters to integrate and collaborate with other IT systems, and are easily extensible. If these products have some experience and credibility in the market, that is an added advantage.

Build a Business Case Upfront

AIOps is an emerging technology, and like any other new technologies, many stakeholders may be apprehensive of the benefits, sometimes dragging their feet on engaging. Build a real world business case upfront, and show some value very quickly, which should help take the program forward.

This factor is important while choosing any commercial AIOps product — one should choose a product that has the capability to give hard benefits measurable in hard currencies (along with some cool features obviously) as that would help in getting a business case easily.

Data Availability

At the heart of AIOps is intelligence and that is derived from data, so data availability would help augment the benefits. Though a near-perfect data set is generally a dream, arranging for clean data through system monitoring enhances accuracy and the success of AIOps projects.

Like any other promising new technology, AIOps can fall prey to its own hype

New technology's virtues or vices are often oversold in today's culture, so CIOs need clarity about their goals and what is realistically possible with AI and where in IT AIOps it should be applied.

Change Management

AIOps being new and also transformational in nature, it will naturally have resistance from some sets of users. Socializing the benefits clearly would go a long way in driving adoption.

What Results Should Be Expected from AIOps?

CXOs can bring AIOps into IT for several different benefits.

Business Assurance

Business assurance is where organizations will see the most bang for their buck, as AIOps helps to keep revenue-generating systems up and running and quickly remediating the issues that do come up. Also, by enabling this, IT departments become very relevant to business, thus elevating their image.

IT Agility and Customer Experiences

AIOps also pays off when it is applied to specific problems, like increasing IT agility or creating better customer experiences by creating a unified view of your IT estate, connecting business functions to applications and infrastructure, and improving customer experience by fulfilling their requests quickly and solving their application problems faster.

Better Alignment with Business Goals

AIOps enables the CIO to better align with business needs, as the IT team will be able to proactively take action through automated capabilities and self-heal algorithms to rectify any issues before they can impact a company's operations or revenue-driving business functions. This leaves CIOs with more time to proactively strategize and plan IT initiatives that will support larger business goals.

As a result of AIOps deployment, CIOs will be increasing the team's bandwidth and would be able to reassign IT team members to tasks that help to grow and transform the organization.

Increased Efficiency

Through autonomous operations, a lot of problems get solved and a lot of routine tasks get handled in automated fashion, thus increasing the efficiency of the IT team.

Reenergizing the Task Force

AIOps will also continuously adapt and increase its ability to address more complex problems the longer it has been deployed. This will enable IT to adapt its role and become more of a critical business enabler. Team members will be able to focus on higher-value, interesting work that helps organizations in talent retention and gives them a competitive advantage at the same time.

Traditional IT Operations rely on significant manual efforts that are untenable to scale in today's digitally enabled enterprise. In IT Operations, high efficiency gains, better insights, faster detection (MTTD) and resolution (MTTR) of problems can be expected through well-tuned and well adopted AIOps. Most companies that adopt AIOps report an increase in effectiveness within the business functions.

Those who are considered "high performers" with AI are much likelier to report significantly higher gains. Ultimately, AIOps solutions enable companies to easily deploy "high performing" AI/ML-based solutions to reduce manual efforts and adopt IT changes rapidly with minimal cost.

The organizations that are quickest to adopt AIOps will find themselves better positioned to navigate the ever-evolving move towards digital transformation than their competitors — enabling competitive advantage and the ability to better deliver business outcomes.

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.

What Is the Deal with AIOps? - Part 2

Akhilesh Tripathi
Digitate

Start with What Is the Deal with AIOps? - Part 1

What to Keep in Mind While Considering AIOps

Unlike some AI initiatives, AIOps doesn't always necessitate the use of a data scientist, so don't let hiring expenses put your AIOps initiatives on hold. It is always nice to have IT team members with AI skills, but this becomes less critical as more intelligent solutions come into prominence that offer AIOps features out of the box, a readily deployable option for IT.

Look for products which have a lot of out of the box features, great adapters to integrate and collaborate with other IT systems, and are easily extensible. If these products have some experience and credibility in the market, that is an added advantage.

Build a Business Case Upfront

AIOps is an emerging technology, and like any other new technologies, many stakeholders may be apprehensive of the benefits, sometimes dragging their feet on engaging. Build a real world business case upfront, and show some value very quickly, which should help take the program forward.

This factor is important while choosing any commercial AIOps product — one should choose a product that has the capability to give hard benefits measurable in hard currencies (along with some cool features obviously) as that would help in getting a business case easily.

Data Availability

At the heart of AIOps is intelligence and that is derived from data, so data availability would help augment the benefits. Though a near-perfect data set is generally a dream, arranging for clean data through system monitoring enhances accuracy and the success of AIOps projects.

Like any other promising new technology, AIOps can fall prey to its own hype

New technology's virtues or vices are often oversold in today's culture, so CIOs need clarity about their goals and what is realistically possible with AI and where in IT AIOps it should be applied.

Change Management

AIOps being new and also transformational in nature, it will naturally have resistance from some sets of users. Socializing the benefits clearly would go a long way in driving adoption.

What Results Should Be Expected from AIOps?

CXOs can bring AIOps into IT for several different benefits.

Business Assurance

Business assurance is where organizations will see the most bang for their buck, as AIOps helps to keep revenue-generating systems up and running and quickly remediating the issues that do come up. Also, by enabling this, IT departments become very relevant to business, thus elevating their image.

IT Agility and Customer Experiences

AIOps also pays off when it is applied to specific problems, like increasing IT agility or creating better customer experiences by creating a unified view of your IT estate, connecting business functions to applications and infrastructure, and improving customer experience by fulfilling their requests quickly and solving their application problems faster.

Better Alignment with Business Goals

AIOps enables the CIO to better align with business needs, as the IT team will be able to proactively take action through automated capabilities and self-heal algorithms to rectify any issues before they can impact a company's operations or revenue-driving business functions. This leaves CIOs with more time to proactively strategize and plan IT initiatives that will support larger business goals.

As a result of AIOps deployment, CIOs will be increasing the team's bandwidth and would be able to reassign IT team members to tasks that help to grow and transform the organization.

Increased Efficiency

Through autonomous operations, a lot of problems get solved and a lot of routine tasks get handled in automated fashion, thus increasing the efficiency of the IT team.

Reenergizing the Task Force

AIOps will also continuously adapt and increase its ability to address more complex problems the longer it has been deployed. This will enable IT to adapt its role and become more of a critical business enabler. Team members will be able to focus on higher-value, interesting work that helps organizations in talent retention and gives them a competitive advantage at the same time.

Traditional IT Operations rely on significant manual efforts that are untenable to scale in today's digitally enabled enterprise. In IT Operations, high efficiency gains, better insights, faster detection (MTTD) and resolution (MTTR) of problems can be expected through well-tuned and well adopted AIOps. Most companies that adopt AIOps report an increase in effectiveness within the business functions.

Those who are considered "high performers" with AI are much likelier to report significantly higher gains. Ultimately, AIOps solutions enable companies to easily deploy "high performing" AI/ML-based solutions to reduce manual efforts and adopt IT changes rapidly with minimal cost.

The organizations that are quickest to adopt AIOps will find themselves better positioned to navigate the ever-evolving move towards digital transformation than their competitors — enabling competitive advantage and the ability to better deliver business outcomes.

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.