As CIOs Address App Sprawl, Observability Can't Be an Afterthought

Bill Lobig
IBM Software
Learn more about IBM

App sprawl has been a concern for technologists for some time, but it has never presented such a challenge as now. As organizations move to implement generative AI into their applications, it's only going to become more complex. In fact, a recent Canva report found that 72% of CIOs see application sprawl as a challenge — and with 71% of CIOs expecting to adopt 30-60 new apps this year, this complexity is poised to keep growing.

Potential solutions include consolidating applications, optimizing workflows, and automating IT processes to reduce strain on technologists so they can tackle the issue of app sprawl head-on. While these are all valid and necessary approaches, observability is a necessary component for understanding the vast amounts of complex data within AI-infused applications, and it must be the centerpiece of an app- and data-centric strategy to truly manage app sprawl.

Cracking the Code for AI App Sprawl Challenges

In a year of elevated global IT spend, ensuring investments aren't wasted is a necessity for overwhelmed technology leaders, who not only must make decisions around which technologies to implement, but also make sense of application performance amid growing tides of vast and complex data.

When AI enters the mix, it's even more important to have complete visibility, which many organizations still lack. Observability tools and practices can help technologists address AI app sprawl by providing visibility into the performance, behavior, and dependencies of AI applications. Unfortunately, many teams are still attempting to work with incomplete visibility, meaning they simply don't know what they don't know.

Simply put, traditional application performance monitoring (APM) tools can provide visibility to a certain degree, but they weren't built to necessarily account for the influx of generative AI applications that modern enterprises are dealing with.

End-users for generative AI-infused applications demand continuous availability and frictionless experience. However, with a lack of real-time visibility, they will feel when an outage or delay compromises their experience, particularly as applications span numerous platforms. Not to mention, the implementation and understanding of complex generative AI models' behaviors are still something many organizations are working to figure out. These potential blind spots could create significant performance, compliance, and security issues.

Observe the Full Stack So You Can Add to It

Organizations can better understand the internal state of their AI applications by analyzing their external outputs with observability. When this is connected to business outcomes and effectively addressed, technology teams can lay the groundwork for a well-functioning application monitoring and management process. So, when generative AI is introduced to the environment, the foundation is already in place to effectively see and optimize application processes.

As generative AI applications join the enterprise equation, observability tools are a must-have for facilitating the delivery of higher-quality software at a faster pace — these are best enabled through:

■ Finding and fixing the "unknown unknowns": You can't fix what you can't see. Unfortunately, many monitoring practices and tools can only address flaws that are previously known. Observability uncovers conditions that would be impossible to find manually or with traditional platforms. It then monitors the correlation to different performance flaws and gives context for discovering root causes, resulting in quick and easy remediation.

■ Detecting and remediating issues early on: With observability, monitoring is integrated into the initial stages of software development. It's then easy to pinpoint and rectify new code issues before they affect the service level agreements (SLAs) and customer experience.

■ Self-healing application infrastructure and automated resolution: Observability can be coupled with automation capabilities to anticipate issues from system outputs and resolve them autonomously without requiring manual intervention.

■ Scaling and load balancing: We need to be able to observe and control the current load on systems but also help forecast future demands. The data can be used to optimize applications in real-time without having the end-users feel any impact.

■ Cost management: From optimizing workloads to computational resources, SREs can and should find ways to save on VM, GPU, cloud and inferencing costs through observability.

Observability is crucial for organizations to address AI app sprawl and overcome myriad challenges that come along with it, especially as generative AI becomes a mainstay across enterprises. By following observability best practices and deploying the right automated tools, organizations can proactively identify and resolve issues and ensure all AI applications are always available and friction-free.

Bill Lobig is VP, Automation Product Management, IBM Software

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As CIOs Address App Sprawl, Observability Can't Be an Afterthought

Bill Lobig
IBM Software
Learn more about IBM

App sprawl has been a concern for technologists for some time, but it has never presented such a challenge as now. As organizations move to implement generative AI into their applications, it's only going to become more complex. In fact, a recent Canva report found that 72% of CIOs see application sprawl as a challenge — and with 71% of CIOs expecting to adopt 30-60 new apps this year, this complexity is poised to keep growing.

Potential solutions include consolidating applications, optimizing workflows, and automating IT processes to reduce strain on technologists so they can tackle the issue of app sprawl head-on. While these are all valid and necessary approaches, observability is a necessary component for understanding the vast amounts of complex data within AI-infused applications, and it must be the centerpiece of an app- and data-centric strategy to truly manage app sprawl.

Cracking the Code for AI App Sprawl Challenges

In a year of elevated global IT spend, ensuring investments aren't wasted is a necessity for overwhelmed technology leaders, who not only must make decisions around which technologies to implement, but also make sense of application performance amid growing tides of vast and complex data.

When AI enters the mix, it's even more important to have complete visibility, which many organizations still lack. Observability tools and practices can help technologists address AI app sprawl by providing visibility into the performance, behavior, and dependencies of AI applications. Unfortunately, many teams are still attempting to work with incomplete visibility, meaning they simply don't know what they don't know.

Simply put, traditional application performance monitoring (APM) tools can provide visibility to a certain degree, but they weren't built to necessarily account for the influx of generative AI applications that modern enterprises are dealing with.

End-users for generative AI-infused applications demand continuous availability and frictionless experience. However, with a lack of real-time visibility, they will feel when an outage or delay compromises their experience, particularly as applications span numerous platforms. Not to mention, the implementation and understanding of complex generative AI models' behaviors are still something many organizations are working to figure out. These potential blind spots could create significant performance, compliance, and security issues.

Observe the Full Stack So You Can Add to It

Organizations can better understand the internal state of their AI applications by analyzing their external outputs with observability. When this is connected to business outcomes and effectively addressed, technology teams can lay the groundwork for a well-functioning application monitoring and management process. So, when generative AI is introduced to the environment, the foundation is already in place to effectively see and optimize application processes.

As generative AI applications join the enterprise equation, observability tools are a must-have for facilitating the delivery of higher-quality software at a faster pace — these are best enabled through:

■ Finding and fixing the "unknown unknowns": You can't fix what you can't see. Unfortunately, many monitoring practices and tools can only address flaws that are previously known. Observability uncovers conditions that would be impossible to find manually or with traditional platforms. It then monitors the correlation to different performance flaws and gives context for discovering root causes, resulting in quick and easy remediation.

■ Detecting and remediating issues early on: With observability, monitoring is integrated into the initial stages of software development. It's then easy to pinpoint and rectify new code issues before they affect the service level agreements (SLAs) and customer experience.

■ Self-healing application infrastructure and automated resolution: Observability can be coupled with automation capabilities to anticipate issues from system outputs and resolve them autonomously without requiring manual intervention.

■ Scaling and load balancing: We need to be able to observe and control the current load on systems but also help forecast future demands. The data can be used to optimize applications in real-time without having the end-users feel any impact.

■ Cost management: From optimizing workloads to computational resources, SREs can and should find ways to save on VM, GPU, cloud and inferencing costs through observability.

Observability is crucial for organizations to address AI app sprawl and overcome myriad challenges that come along with it, especially as generative AI becomes a mainstay across enterprises. By following observability best practices and deploying the right automated tools, organizations can proactively identify and resolve issues and ensure all AI applications are always available and friction-free.

Bill Lobig is VP, Automation Product Management, IBM Software

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 ...

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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 ...

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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 ...

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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 ...