It’s time to transition from monitoring to observability. Where do you start? 

As IT environments grow more complex, traditional monitoring tools are struggling to keep up. The rise of cloud-native architectures, microservices and containerized applications has created highly interconnected systems that need a more comprehensive approach to visibility.

These trends have driven the evolution of observability as a discipline, which goes beyond tracking system metrics to provide full insight into system behavior. By correlating telemetry data across distributed environments, observability solutions help teams identify root causes faster, resolve issues proactively and improve system reliability. With the help of modern observability tools, one organization increased service level availability by 70%.

The transition to observability is also being driven by necessity. Legacy monitoring tools are being retired in favor of observability platforms that can handle today’s technology demands. For example, IBM’s own Tivoli® is being phased out for Instana®, a next-generation observability solution.

Here’s a look at why and how organizations are moving to observability right now, based on expert insights from IBM’s Drew Flowers, Americas Sales Leader for Instana. Whether you’re actively migrating or just evaluating options, the following discussion can help clarify the state of play today. 

At a high level, monitoring tells you what is happening, but observability explains why. Monitoring detects symptoms of a problem, while observability provides the context needed for deeper diagnostic analysis.

Traditional monitoring captures predefined metrics such as CPU usage and network latency, offering a snapshot of system performance but little insight into why an issue is occurring. For example, monitoring might flag high CPU usage during performance degradation, but it won’t explain the root cause.

Observability takes system intelligence further by correlating multiple telemetry data types—metrics, events, logs and traces (MELT data)—to provide a complete, real-time view of IT environments. This view enables organizations to not only detect issues but also pinpoint their causes, anticipate failures and analyze complex behaviors across distributed systems.

Because observability extends beyond traditional monitoring, it can offer real-time insights that improve system performance, enhance resilience and optimize costs.

Key benefits include:

• Faster issue resolution: Automated diagnostics eliminate the need for manual correlation across tools, which can reduce mean time to detect (MTTD) and mean time to repair (MTTR) in complex IT environments.
  
  
• Proactive problem-solving: AI-driven analytics can predict failures before they impact customers or infrastructure, shifting teams from reactive firefighting to proactive operations.
  
  
• Optimized efficiency: Detailed visibility into resource consumption helps organizations monitor usage, scale efficiently and manage cloud costs.
  
  
• Greater resilience: AI-powered anomaly detection reduces alert fatigue by prioritizing incidents based on impact, while automated remediation streamlines workflows.
  
  
• Stronger collaboration: By breaking down silos, observability gives teams a shared data source, leading to faster incident resolution and better decision-making.
  
  
• Business alignment: Connecting system health with key performance indicators (KPIs) gives leadership visibility into how technology affects operations, customer experience and revenue, enabling more informed decisions.

While observability solutions have been on the market for years, many organizations are choosing now to make the move from traditional monitoring to observability.

Organizations that delay the transition to observability risk technical debt and a competitive disadvantage, while organizations that make the move gain faster issue resolution and greater efficiency. McKinsey highlights how observability can transform IT resilience, with one organization cutting incidents by 90% and slashing response times from hours to seconds.

Aside from the withdrawal of many legacy monitoring tools from the market, two of the most important factors driving observability adoption include increasing IT complexity and AI innovation.

With the complexity of modern IT environments—including hybrid cloud infrastructures, microservices and containerized workloads—traditional monitoring tools are no longer cutting it. These solutions, designed for stable, monolithic applications, cannot effectively manage the sophisticated technological ecosystems of modern enterprises.

Common limitations of traditional monitoring include:

• Gaps in visibility across distributed systems, leading to undetected failures and unexpected downtime
  
  
• Slow incident resolution, delaying recovery efforts and increasing operational disruptions and costs
  
  
• Increased MTTD and MTTR, making it harder to meet service level agreements (SLAs) and maintain reliability
  
  
• Limited insight into cascading failures, resulting in misdiagnoses, recurring outages and prolonged performance issues

Observability solutions help address these limitations by providing comprehensive, real-time insights into technology infrastructure. These insights make it easier to spot and address issues faster, reducing downtime, protecting revenue and maintaining customer trust.

Artificial intelligence (AI) is transforming observability by helping teams analyze vast amounts of telemetry data, filter noise and surface critical issues in real time without manually sorting through logs and alerts.

Artificial intelligence for IT operations, or AIOps, takes it a step further by using machine learning to detect patterns, reduce false positives and correlate events across complex systems. As a result, IT teams can cut through alert fatigue and isolate real issues more quickly.

By integrating observability with AIOps, organizations can streamline incident response, reduce downtime and improve system reliability without extra manual effort. This shift moves teams from reactive troubleshooting to proactive system optimization, leading to faster insights and fewer disruptions.

Moving from traditional monitoring to observability doesn't need to be intimidating. With a thoughtful approach, organizations can make this transition smoothly while gaining immediate benefits.

While much of a migration depends on which partner or service an organization chooses (for more information, see "Choosing the right observability solution"), several key principles can help ensure success.

Before choosing an observability platform, clearly define your organization’s specific goals and what you need it to accomplish. Otherwise, you risk choosing a solution that lacks key capabilities or is overly complex for your use case.

Ask yourself—and other relevant stakeholders—what problems you’re trying to solve. Are you focused on reducing MTTD/MTTR, improving cloud cost efficiency or gaining deeper application insights?

Additionally, how much automation do you need? Some platforms provide out-of-the-box dashboards and AI-driven recommendations, while others require manual configuration and customization.

You should also consider whether the platform can integrate with existing tools. Ensuring compatibility with current DevOps pipelines, cloud infrastructure and security frameworks is crucial for a smooth transition.

Many organizations still rely on a patchwork of monitoring solutions—legacy application performance management (APM) tools, infrastructure monitoring and siloed logging platforms—that lack the depth of correlation required for observability. Be sure to assess your current toolset and identify redundancies.

Key auditing concerns include:

• Identifying redundant tools, which can lead to false alerts and complicate troubleshooting efforts
  
  
• Evaluating whether current logging or tracing solutions integrate with your observability platform or need to be replaced
  
  
• Assessing data coverage gaps, including what insights are missing from your current monitoring approach

Observability platforms—especially software as a service (SaaS) solutions—can change how data flows across networks, impacting data security policies and regulatory compliance. Security teams should be engaged early to prevent delays and last-minute compliance challenges.

Key security concerns include:

• Confirming security and compliance policies for external data transmission to prevent unauthorized access or compliance risks
  
  
• Reviewing authentication processes and role-based access controls (RBAC) to ensure that only the right people can access data
  
  
• Validating infrastructure readiness for on-premises deployments to handle observability data without performance bottlenecks

Organizations can underestimate the cultural shift necessary for observability adoption. Observability isn’t just an IT function. It impacts development, operations, security and business stakeholders. Without team alignment, adoption can stall, and data might not be used effectively.

Key considerations for cross-team alignment include:

• Understanding who’s responsible for setting up, managing and maintaining the observability platform
  
  
• Including developers early in the process to ensure proper instrumentation of applications for full-stack visibility
  
  
• Involving senior leadership to reinforce observability’s role as a major driver of business performance, customer experience and strategic decision-making

Success in observability is measurable—but only if organizations define clear KPIs from the start.

Key observability metrics for measuring success include:

• MTTD: How quickly are system anomalies identified?
  
  
• MTTR: How much time is saved in troubleshooting and resolution?
  
  
• Uptime and SLA adherence: Is system availability improving?
  
  
• Alert efficiency: Are redundant or low-priority alerts reduced?

When planning is complete, the next step is putting observability into action. Again, a significant part of the migration journey will be shaped by the partner or platform an organization chooses. However, these foundational practices can help ensure a smooth transition.

Observability adoption can vary widely based on team readiness, infrastructure and automation capabilities. Some organizations migrate in two weeks, while others take three to six months for full implementation.

Key factors that can affect migration speed include:

• Whether teams are ready and familiar with observability tools and workflows
  
  
• Whether you’re fully replacing existing monitoring solutions or gradually transitioning
  
  
• Whether your platform requires custom instrumentation 

Instead of migrating all at once, many organizations opt for a phased rollout. While this approach can take longer, it allows teams to introduce observability alongside existing tools, minimizing the potential for disruption.

Key steps in a phased rollout include:

• Deploying observability alongside existing monitoring tools to test system compatibility
  
  
• Incrementally instrumenting applications and infrastructure to ensure comprehensive data capture
  
  
• Gradually retiring legacy monitoring tools to refine alerting strategies and prevent disruptions

Even with a fully implemented observability platform, teams must be trained to interpret and act on insights effectively. Otherwise, they can misinterpret data, miss critical insights or implement observability ineffectively.

Key training focus areas include:

• Understanding MELT data for faster troubleshooting
  
  
• Optimizing alert configurations to prevent unnecessary noise and highlight critical incidents
  
  
• Encouraging proactive observation over reactive troubleshooting

The work doesn’t stop after deployment. To get the most out of your investment, consider tracking impact, gathering feedback and fine-tuning configurations to ensure that observability delivers real value.  

Look deeper than the data to confirm your teams can detect issues faster, collaborate more effectively and make better operational decisions.

Key follow-up actions include:

• Comparing pre- and postmigration performance metrics such as MTTD, MTTR, uptime and alert efficiency to identify early wins and track improvements
  
  
• Engaging teams to see whether observability has helped detect issues faster, uncover insights or inform strategic decision-making
  
  
• Assessing cross-team collaboration, including whether IT, DevOps and cybersecurity teams are working together more seamlessly 

Observability should evolve with your systems, teams and business needs. Actively refine and expand your observability capabilities to ensure you address gaps and get the most long-term value.

Ways to improve observability over time include:

• Optimizing telemetry configurations to improve data quality and reduce unnecessary collection
  
  
• Leveraging AI-driven capabilities such as predictive analytics to anticipate and prevent issues before they happen
  
  
• Expanding observability beyond troubleshooting, including using it for capacity planning, performance optimization and business strategy decisions

Choosing the right observability solution is critical for getting the most out of your transition. It should do more than just collect data. It should provide actionable insights, adapt to your infrastructure and scale as your organization grows.

Some factors to consider when evaluating platforms include:

• End-to-end visibility
• Deployment flexibility
• Advanced analytics and automation
• Scalability without performance tradeoffs
• Pricing model implications
• Open-source vs. commercial solutions