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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...