Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Sean Sebring
SolarWinds
Learn more about SolarWinds

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.

As today's organizations continue to lean on the latest technology to streamline workflows, they should simultaneously leverage the right AI tooling and internal coordination to develop a mature observability practice.

Why Has Monitoring and Observability Become More Complex?

Although the IT industry has long projected a collective move to the cloud, the reality is a little bit more complicated. According to the report, only 6% of organizations are completely on the cloud, 21% are on-premises, and 73% have a combination of the two. The data also shows that organizations' M&O strategies are not aligned with their IT architecture. For example, while 17% of organizations operate a hybrid IT environment, only 10% use hybrid IT M&O strategies. Similarly, while 32% of organizations are primarily cloud-based, only 9% of organizations leverage cloud-native or cloud-inclusive strategies. When organizations monitor their environments with M&O tooling foreign to those environments, it creates blind spots.

These blind spots can translate to cascading consequences for today's businesses. First, it suggests that cloud migration and the overall configuration of modern IT environments is outpacing M&O strategies. As a result, blind spots are created throughout an IT environment. This can lead to minor inconveniences — such as slower responses from important software — to large catastrophes such as major outages that cost hundreds of millions of dollars.

How Those Complexities Affect Your IT Team

While a disconnected M&O strategy can impact your IT systems, it can also cause workflow — and workload — problems for your IT team. More than half (55%) of the IT professionals surveyed said they have too many monitoring and observability tools. Disparate M&O tooling can increase alert fatigue and firefighting, causing IT personnel to burn out. In addition, a lack of team coordination can create observability obstacles. About 3 in 4 respondents reported a lack of coordination and cooperation between teams — such as network and infrastructure or apps and database teams — contributed to an observability challenge.

In addition to affecting systems and teams, multiple, disconnected M&O tools can increase the cost of a tech stack while reducing return on investment. Alternatively, a unified observability approach, one that's enhanced by proper AI use and internal upskilling, can increase ROI, decrease MTTR, and improve IT team morale.

AI's Role in Managing M&O Complexities

AI can bridge the gap where current observability tools struggle to keep pace with modern IT software. With proper AI use, teams can enhance diagnostics, automatically categorize alerts, and automate system responses to help engineers. These benefits are further amplified if AI is embedded in a unified M&O platform that can display diagnostics — in both on-prem, cloud, and hybrid environments — through a single pane of glass. This enhances visibility, decreases workload on IT personnel, and removes the need for multiple M&O tools.

In addition to diagnostic and alert prioritization, AI can also help in root cause analysis and predict system capacity or performance issues. This can dramatically decrease MTTx metrics such as mean time to acknowledge, detect, and resolve.

Now, it's important to note that while AI presents definitive advantages for M&O operations, AI adoption is not always a streamlined process. IT teams must get buy-in from top decision makers while also ensuring a safe and secure installation and use of AI technology. Respondents in the report cited security concerns, skills gaps, budget constraints, and regulatory or compliance limitations as barriers to AI adoption.

This is why it's important for IT teams to take three important steps before using AI in their M&O workflows:

1. Establish the change management role with AI: Identify where manual processes and outdated systems are holding M&O back. Communicate these issues to leadership and define exactly how AI and automation can address these challenges.

2. Begin with AI access control measures: Implement strict access controls for AI technology before bringing AI online. This will be especially important for industries that have a high level of compliance and regulatory requirements.

3. Prioritize upskilling: Oftentimes, security issues or negative effects from AI use come from internal mistakes. In addition, C-suite executives may be pushing back against AI because they simply don't know enough about the technology. Bring in experts who can educate the entire company on the benefits of AI in monitoring and observability. Also, conduct regular training sessions to establish a culture of responsible AI use.

A Gap Too Expensive to Widen

The gap between modern technology and current M&O strategies is a liability that is too costly not to address. Today's organizations are only set to move faster in the adoption of innovative technology, meaning the scale at which monitoring and observability must occur will only increase. If today's companies don't move fast, that gap will widen. If today's IT teams unify their observability practice, responsibly leverage AI, and properly educate their workforce, they can not only catch up to modern IT solutions — they can stay ahead of the curve. 

Sean Sebring is Solutions Engineering Manager at SolarWinds

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Sean Sebring
SolarWinds
Learn more about SolarWinds

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.

As today's organizations continue to lean on the latest technology to streamline workflows, they should simultaneously leverage the right AI tooling and internal coordination to develop a mature observability practice.

Why Has Monitoring and Observability Become More Complex?

Although the IT industry has long projected a collective move to the cloud, the reality is a little bit more complicated. According to the report, only 6% of organizations are completely on the cloud, 21% are on-premises, and 73% have a combination of the two. The data also shows that organizations' M&O strategies are not aligned with their IT architecture. For example, while 17% of organizations operate a hybrid IT environment, only 10% use hybrid IT M&O strategies. Similarly, while 32% of organizations are primarily cloud-based, only 9% of organizations leverage cloud-native or cloud-inclusive strategies. When organizations monitor their environments with M&O tooling foreign to those environments, it creates blind spots.

These blind spots can translate to cascading consequences for today's businesses. First, it suggests that cloud migration and the overall configuration of modern IT environments is outpacing M&O strategies. As a result, blind spots are created throughout an IT environment. This can lead to minor inconveniences — such as slower responses from important software — to large catastrophes such as major outages that cost hundreds of millions of dollars.

How Those Complexities Affect Your IT Team

While a disconnected M&O strategy can impact your IT systems, it can also cause workflow — and workload — problems for your IT team. More than half (55%) of the IT professionals surveyed said they have too many monitoring and observability tools. Disparate M&O tooling can increase alert fatigue and firefighting, causing IT personnel to burn out. In addition, a lack of team coordination can create observability obstacles. About 3 in 4 respondents reported a lack of coordination and cooperation between teams — such as network and infrastructure or apps and database teams — contributed to an observability challenge.

In addition to affecting systems and teams, multiple, disconnected M&O tools can increase the cost of a tech stack while reducing return on investment. Alternatively, a unified observability approach, one that's enhanced by proper AI use and internal upskilling, can increase ROI, decrease MTTR, and improve IT team morale.

AI's Role in Managing M&O Complexities

AI can bridge the gap where current observability tools struggle to keep pace with modern IT software. With proper AI use, teams can enhance diagnostics, automatically categorize alerts, and automate system responses to help engineers. These benefits are further amplified if AI is embedded in a unified M&O platform that can display diagnostics — in both on-prem, cloud, and hybrid environments — through a single pane of glass. This enhances visibility, decreases workload on IT personnel, and removes the need for multiple M&O tools.

In addition to diagnostic and alert prioritization, AI can also help in root cause analysis and predict system capacity or performance issues. This can dramatically decrease MTTx metrics such as mean time to acknowledge, detect, and resolve.

Now, it's important to note that while AI presents definitive advantages for M&O operations, AI adoption is not always a streamlined process. IT teams must get buy-in from top decision makers while also ensuring a safe and secure installation and use of AI technology. Respondents in the report cited security concerns, skills gaps, budget constraints, and regulatory or compliance limitations as barriers to AI adoption.

This is why it's important for IT teams to take three important steps before using AI in their M&O workflows:

1. Establish the change management role with AI: Identify where manual processes and outdated systems are holding M&O back. Communicate these issues to leadership and define exactly how AI and automation can address these challenges.

2. Begin with AI access control measures: Implement strict access controls for AI technology before bringing AI online. This will be especially important for industries that have a high level of compliance and regulatory requirements.

3. Prioritize upskilling: Oftentimes, security issues or negative effects from AI use come from internal mistakes. In addition, C-suite executives may be pushing back against AI because they simply don't know enough about the technology. Bring in experts who can educate the entire company on the benefits of AI in monitoring and observability. Also, conduct regular training sessions to establish a culture of responsible AI use.

A Gap Too Expensive to Widen

The gap between modern technology and current M&O strategies is a liability that is too costly not to address. Today's organizations are only set to move faster in the adoption of innovative technology, meaning the scale at which monitoring and observability must occur will only increase. If today's companies don't move fast, that gap will widen. If today's IT teams unify their observability practice, responsibly leverage AI, and properly educate their workforce, they can not only catch up to modern IT solutions — they can stay ahead of the curve. 

Sean Sebring is Solutions Engineering Manager at SolarWinds

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...