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Automation Will Propel the Future of IT

Phil Tee

The IT industry has been changing shape and scope for decades. Over the course of my career, I've witnessed first hand how innovative technologies have assisted IT professionals to overcome their most daunting challenges.

Currently, we're seeing artificial intelligence for IT operations or "AIOps" take center stage in the IT industry. If AIOps hasn't been on your horizon yet, look closely and expect it soon. The leading analyst firm Gartner has predictedthat "by 2020, approximately 50 percent of enterprises will actively use AIOps technologies together with APM to provide insight into both business execution and IT operations, up from fewer than 10 percent today."

So what can we expect from automation and AIOps as it becomes more commonplace? Let's dive in.

Automation Boosts IT Productivity

If technology is handling tasks previously owned and managed by a human, will it eliminate their role?

Automation is no enemy to IT teams

No, with man and machine together, businesses can thrive and employees can feel like their jobs are safe. I understand that change can be difficult, but automation is no enemy to IT teams. PwC found that 73 percent of workers believe that technology can never replace the human mind. It's clear that human beings — not automation — will continue to be the driving force behind IT.

The common IT experience is reactive, rather than proactive work. This forces teams to slog through endless monitoring of various dashboards or countless service tickets. When we apply automation technologies, a new type of proactive work is possible: one where professionals have dedicated time to improve products, platforms, and services.

Alongside this productivity boost, employees may also see more opportunity to finally wrap up work on time. IT professionals can take the much-needed disconnection from devices, and instead focus their time out of work on their family, hobbies or passions. Late nights in the office can become a thing of the past, as AI manages monitoring and other menial tasks.

With Automation, IT Receives Some Much-Needed Recognition

In Moogsoft's Heard from the Herd podcast, Jill Lehman, Vice President of Corporate Services & Chief People Officer at Ontario Systems, and Andy Brown, CEO and Founder of Sand Hill East (and a Moogsoft board member), shared their perspective on automation working alongside IT teams.

From Jill's perspective, "Learning agility is what happens when you accept automation or the different technologies that help you do work, which means that once a task is automated, people have the opportunity to pivot to a new type of thinking or work that expands upon and innovates from their foundational knowledge."

Andy shared this bit of wisdom: "There are definitely common traits that I find in the most successful people. For example, you may have heard the phrase ‘listening is at the heart of being innovative,' but to that, I would also add: if you know everything, you can't learn anything. I truly believe that listening is important and a skill to develop. Listen to the client, investors, and advisors. Take what you hear and learn to apply it."

This insight shows a significant difference between humans and machines: humans have the ability to listen, think critically, and apply their learnings at a much faster and more knowledgeable level, while machines do exactly as they're told. The most advanced machine-learning algorithms cannot surpass the human mind, especially when it comes to quick, reactive decision making.

IT Has All The Power and Potential to Grow

The future is automated, and my hope is that we can be excited about this shift. Change is important, but Gartner paints a bright outlook: emergent technology like AI will create 2.3 million jobs by 2020.

We're at the dawn of something big, akin to the excitement buzzing around the Industrial Revolution. We're taking great strides toward a new type of life: one where we can all live and work comfortably and enthusiastically alongside machines.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Automation Will Propel the Future of IT

Phil Tee

The IT industry has been changing shape and scope for decades. Over the course of my career, I've witnessed first hand how innovative technologies have assisted IT professionals to overcome their most daunting challenges.

Currently, we're seeing artificial intelligence for IT operations or "AIOps" take center stage in the IT industry. If AIOps hasn't been on your horizon yet, look closely and expect it soon. The leading analyst firm Gartner has predictedthat "by 2020, approximately 50 percent of enterprises will actively use AIOps technologies together with APM to provide insight into both business execution and IT operations, up from fewer than 10 percent today."

So what can we expect from automation and AIOps as it becomes more commonplace? Let's dive in.

Automation Boosts IT Productivity

If technology is handling tasks previously owned and managed by a human, will it eliminate their role?

Automation is no enemy to IT teams

No, with man and machine together, businesses can thrive and employees can feel like their jobs are safe. I understand that change can be difficult, but automation is no enemy to IT teams. PwC found that 73 percent of workers believe that technology can never replace the human mind. It's clear that human beings — not automation — will continue to be the driving force behind IT.

The common IT experience is reactive, rather than proactive work. This forces teams to slog through endless monitoring of various dashboards or countless service tickets. When we apply automation technologies, a new type of proactive work is possible: one where professionals have dedicated time to improve products, platforms, and services.

Alongside this productivity boost, employees may also see more opportunity to finally wrap up work on time. IT professionals can take the much-needed disconnection from devices, and instead focus their time out of work on their family, hobbies or passions. Late nights in the office can become a thing of the past, as AI manages monitoring and other menial tasks.

With Automation, IT Receives Some Much-Needed Recognition

In Moogsoft's Heard from the Herd podcast, Jill Lehman, Vice President of Corporate Services & Chief People Officer at Ontario Systems, and Andy Brown, CEO and Founder of Sand Hill East (and a Moogsoft board member), shared their perspective on automation working alongside IT teams.

From Jill's perspective, "Learning agility is what happens when you accept automation or the different technologies that help you do work, which means that once a task is automated, people have the opportunity to pivot to a new type of thinking or work that expands upon and innovates from their foundational knowledge."

Andy shared this bit of wisdom: "There are definitely common traits that I find in the most successful people. For example, you may have heard the phrase ‘listening is at the heart of being innovative,' but to that, I would also add: if you know everything, you can't learn anything. I truly believe that listening is important and a skill to develop. Listen to the client, investors, and advisors. Take what you hear and learn to apply it."

This insight shows a significant difference between humans and machines: humans have the ability to listen, think critically, and apply their learnings at a much faster and more knowledgeable level, while machines do exactly as they're told. The most advanced machine-learning algorithms cannot surpass the human mind, especially when it comes to quick, reactive decision making.

IT Has All The Power and Potential to Grow

The future is automated, and my hope is that we can be excited about this shift. Change is important, but Gartner paints a bright outlook: emergent technology like AI will create 2.3 million jobs by 2020.

We're at the dawn of something big, akin to the excitement buzzing around the Industrial Revolution. We're taking great strides toward a new type of life: one where we can all live and work comfortably and enthusiastically alongside machines.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...