Forrester study models 466% ROI from unified observability with Dynatrace

AI is adding new operational demands to already complex technology environments. Technology leaders must improve reliability and developer productivity, control costs, and demonstrate business value while understanding how AI models and agents affect the applications and infrastructure around them.

Dynatrace commissioned Forrester Consulting to evaluate the potential business value of unified observability. Its Total Economic Impact™ study modeled a 466% three-year ROI, $20.3 million in net present value, and payback in less than six months for a composite enterprise.

The sections below explain the sources of that value and what technology leaders should consider when evaluating their own observability investments.

“We get so much value out of Dynatrace it would be difficult for me to consider performing a tender to change technology every two to three years. Dynatrace is one of the few exceptions the CEO and the CFO have approved. They don’t ask me every three years to go to the market [to see] what’s less expensive. They told me, ‘Keep Dynatrace because we see the value of it.’” — CIO, financial services.

“We get so much value out of Dynatrace it would be difficult for me to consider performing a tender to change technology every two to three years. Dynatrace is one of the few exceptions the CEO and the CFO have approved. They don’t ask me every three years to go to the market [to see] what’s less expensive. They told me, ‘Keep Dynatrace because we see the value of it.’” — CIO, financial services.

Operate with greater speed and efficiency

The study attributes $14.4 million to AI-powered incident detection and resolution efficiency. This is the single largest benefit category. What’s interesting is where the value sits.

The composite organization starts with 450,000 monitoring alerts annually across all severity levels. For its lowest-severity incidents, classified as P4, the model assumes 98% are auto-remediated. This reduces the time teams spend handling routine issues.

For critical incidents, the composite cuts war room time by 50%, identifying problems in about two hours instead of four. With 20 people involved in a critical-incident war room, shorter investigations free substantial operational capacity. Interviewees also reported reductions in meantime to resolution of up to 90%.

For CIOs and CTOs, these efficiency gains become even more valuable as AI systems become part of the application stack. Extending observability to LLM calls, agent orchestration, and inference pipelines gives teams the context to identify problems faster and resolve incidents more efficiently, helping them manage growing complexity while innovating.

Turn technology performance into business performance

The economic case is not limited to IT efficiency. Forrester modeled $3.1 million in increased profit from improved business performance, including the impact of reducing severe incidents by more than 50%. Interviewees also described improvements in digital experiences and release velocity that connected technology performance directly to customer and revenue outcomes.

That is the larger platform story: observability can create more value when teams can connect technical signals to the business services and experiences they support. The goal is not simply to make systems easier to monitor. It is to make better technology decisions with a clearer understanding of their business impact.

Give developers more time to innovate

Forrester modeled $2.0 million in three-year, risk-adjusted present-value benefits from improved developer productivity. The analysis assumes efficiency gains of 30% in years one and two, and 35% in year three across the portion of developer work influenced by observability. In practical terms, developers spend less time troubleshooting production issues and more time building, testing, and delivering new capabilities.

Interviewees described significant improvements in the speed of diagnosing and resolving issues. A principal site reliability engineer in telecommunications reported that diagnosing a production issue dropped from 45 minutes to five minutes. Gathering the logs and metrics needed to investigate an issue went from 15 to 20 minutes to approximately one minute.

Those productivity gains also translated into faster software delivery. A second interviewee at the same organization reported that major releases, which previously took 10 to 15 weeks, now complete in two to four weeks. Minor fixes that once took a week are often delivered the same day.

The benefits extend beyond individual productivity. Before Dynatrace, developers often relied on centralized monitoring teams to diagnose performance issues. One interviewee estimated that setting up and validating observability required 10 to 20 hours of coordination per project. Giving developers direct access to observability reduces those dependencies, removes coordination bottlenecks, and frees more time for innovation.

Control the economics of modern technology

Forrester modeled $1.3 million in three-year, risk-adjusted present-value benefits from operating cost containment and consumption visibility. The benefit combines three opportunities to manage spending.

• Infrastructure costs. The composite’s mainframe costs had been increasing by 8% annually. With visibility into the processes driving resource consumption, the organization holds spending flat despite growing workloads.
• Log management. The composite reduces annual log storage costs by 50% through more efficient data handling and reduced duplication.
• AI token consumption. The composite reduces token spending by 20% against a baseline that assumes annual AI spending growth of 30%.

One interviewee described using Dynatrace to understand the cost of individual prompts and identify the ten most expensive queries. That visibility helped the team identify opportunities for caching and adjust which models handled different tasks. These examples show how understanding consumption can guide specific decisions about infrastructure, data handling, and AI usage.

For CIOs and CTOs, this is a governance gap before it’s a cost problem. Most organizations scaling LLM usage can’t tell which models, which calls, or which agents are driving spend. You can only govern what you can see, and by the time it surfaces as a finance conversation, the inefficiency is already architectural.

Consolidate tools and reduce operational complexity

Forrester modeled $3.0 million in three-year, risk-adjusted present-value benefits from tool consolidation. The composite organization retires all duplicated application performance monitoring and log management tools by year three.

Separately, one interviewee reported replacing two legacy APM products and saving roughly $1.7 million in annual licensing costs. Decommissioning a standalone log analytics platform eliminated another $800,000 in annual costs.

Consolidation also simplifies investigation. Before consolidating, interviewees kept logging data separate from performance data, requiring teams to switch between systems to identify root causes. Major incidents could bring together 20 or more people, sometimes including external vendors, each investigating a different part of the environment. Bringing that information together helps teams establish a shared understanding of the problem and coordinate their response.

This is one of the hardest ROI categories to surface in a business case, because the costs sit in different contracts, owned by different teams, renewing on different cycles. Nothing aggregates them. The Forrester model does, and it’s often the first time a CIO or CTO sees the full number.

Underneath the licensing savings is an operational cost that’s harder to price. Before consolidating, interviewees described keeping logging data separate from performance data, which meant root cause analysis required jumping between systems. It’s what one called a swivel-chair problem. Incident response meant assembling war rooms of 20 or more people, sometimes including external vendors, each investigating their own piece and comparing notes before remediation could start. Consolidation reduces the operational surface area the organization manages when something goes wrong, which shows up in both incident cost and engineering capacity.

Reduce regulatory reporting effort

Forrester modeled $765,000 in three-year, risk-adjusted present-value benefits from more efficient regulatory reporting. The composite organization reduces report preparation time by 50% as Dynatrace helps teams gather incident details, identify affected systems, and document business impact.

One financial services interviewee reported using Dynatrace to populate four of the seven sections in its DORA incident reports. This reduces manual information gathering and gives technology and business teams more time to review and complete reports within required deadlines.

From observability to autonomous operations

One interviewee, a staff technical program manager in telecommunications, is running AI-driven remediation today on a single contained infrastructure use case, with control policies in place to maintain human oversight before any wider rollout.

The workflow they described: a Kubernetes cluster is detected with pods saturating at 90% CPU. A problem card is created and passed to ServiceNow, which opens an incident and calls an agent. The agent recognizes the cluster has hit its autoscaling ceiling, spins up additional capacity, then kills the pod when traffic drops, closes the ticket, and generates a root cause analysis. Their stated direction is for the network operations center and SRE team to become the developers and monitors of automated remediation rather than its executors.

Across the interviews, the barrier to scaling automation wasn’t ambition or model quality; it was foundational visibility, governance, and operational confidence. Interviewees were explicit that without unified, high-quality observability data, AI use cases couldn’t be reliably operationalized. As one technology leader put it, “The platform serves as our source of truth and source of confidence.”

For CIOs and CTOs, this reframes the platform investment question. Observability is not only a cost to optimize; it is part of the foundation for AI and automation that can operate reliably at scale. The practical question for the next 24 months is whether your observability foundation is mature enough to support increasingly autonomous operations with the visibility, governance, and confidence they require.

The bigger picture

Forrester’s model found 466% ROI, $20.3 million NPV, and payback in under six months. But the more important finding is where that value comes from. The benefits span incident response, developer productivity, cost governance, tool consolidation, business outcomes, and preparation for autonomous operations. Together, they reflect the value of a unified view across applications, infrastructure, user experience, business services, and AI systems.

That distinction matters when evaluating observability investments. A tool is often assessed on features and price. A platform is measured by the outcomes it enables across the organization over time. As AI becomes a larger part of the technology stack, the quality of the data, context, and visibility supporting those systems will increasingly shape how effectively organizations can scale them.

For technology leaders, the study provides a practical starting point for an observability business case. Use its six benefit categories to assess where your organization spends time and money today, which improvements matter most, and what assumptions should inform your own investment decision.

Read the full Forrester TEI study for methodology and complete findings.

Frequently asked questions

What is the Forrester Total Economic Impact™ (TEI) study of Dynatrace? The Forrester TEI study is an independent analysis commissioned by Dynatrace to evaluate the potential business value of a unified observability platform. Forrester interviewed eight decisionmakers using Dynatrace and created a composite enterprise to model the costs, benefits, and return on investment over three years. What ROI did the Forrester study find for Dynatrace? The study found that the composite organization achieved a 466% return on investment (ROI), generated $20.3 million in net present value (NPV), and reached payback in less than six months over a three-year period. How did Forrester calculate these results? Forrester interviewed decisionmakers from organizations using Dynatrace, quantified their reported benefits and costs, and aggregated the findings into a representative composite organization. The model included operational efficiency gains, developer productivity improvements, cost savings, tool consolidation benefits, and business outcomes. What was the largest source of business value in the study? The largest quantified benefit was AI-powered incident detection and resolution efficiency, which accounted for $14.4 million in risk-adjusted present value. This value came from reducing alert noise, automating root-cause analysis, shortening war-room investigations, and accelerating incident resolution. How does Dynatrace help reduce alert fatigue? According to the study, Dynatrace helps consolidate duplicate alerts, reduce false positives, and identify root causes automatically. In the modeled organization, 98% of low-severity alerts were avoided or resolved without requiring manual intervention. How does observability improve incident response? Interviewees reported significant reductions in mean time to resolution (MTTR), with some organizations achieving improvements of up to 90%. Faster problem identification and automated root-cause analysis helped teams spend less time investigating issues and more time resolving them. How does Dynatrace improve developer productivity? The study found that developers spend less time gathering logs, troubleshooting production issues, and coordinating with centralized monitoring teams. Interviewees reported that diagnosing some production issues fell from approximately 45 minutes to five minutes. Can observability accelerate software delivery? Yes. Organizations interviewed by Forrester reported shorter release cycles, faster issue resolution, and reduced operational bottlenecks. Some teams reduced major release timelines from 10–15 weeks to as little as two to four weeks. How does Dynatrace help control infrastructure and cloud costs? Dynatrace provides visibility into the processes, services, and workloads driving resource consumption. This allows teams to identify inefficiencies, optimize infrastructure use, and make informed decisions about capacity and spending. How does Dynatrace help manage AI costs? The study highlights visibility into AI token consumption as an emerging source of value. Organizations can identify expensive prompts, optimize model usage, improve caching strategies, and understand the cost impact of specific AI workloads. What is the connection between observability and AI governance? AI governance depends on understanding how AI systems operate, perform, and consume resources. Observability provides the visibility needed to monitor AI models, agents, prompts, token usage, and supporting infrastructure so organizations can manage cost, performance, and risk. How does platform consolidation create value? The study found that organizations reduced spending by retiring overlapping application performance monitoring (APM) and log management tools. Consolidation also simplified operations by allowing teams to investigate issues from a single platform rather than switching between multiple tools. Why is unified observability important for AI initiatives? As AI becomes part of the technology stack, organizations need visibility across applications, infrastructure, user experiences, business services, and AI systems. Unified observability helps teams understand how these components interact and support more reliable AI operations. What role does observability play in autonomous operations? Observability provides the trusted data, context, and operational confidence required for automated remediation workflows. Without high-quality observability data, AI-driven automation cannot reliably detect, diagnose, and act on issues. Can Dynatrace support AI-driven remediation? Interviewees described using Dynatrace data as part of automated remediation workflows that detect problems, trigger incident management processes, scale infrastructure resources, and generate root-cause analyses while maintaining appropriate governance controls. What business outcomes beyond IT efficiency did the study identify? The study modeled $3.1 million in additional profit from improved business performance. Interviewees also reported improvements in digital experiences, service reliability, and release velocity that contributed to customer and business outcomes. Who should read the full TEI study? The study is particularly relevant for CIOs, CTOs, platform engineering leaders, site reliability engineering (SRE) teams, observability leaders, and technology decisionmakers evaluating platform investments, AI readiness, operational efficiency, and cost governance. What is the key takeaway from the study? The primary finding is that observability is no longer just a monitoring function. According to the study, a unified observability platform can help organizations improve operational efficiency, accelerate software delivery, control costs, support AI governance, simplify technology stacks, and build a foundation for autonomous operations.

What is the Forrester Total Economic Impact™ (TEI) study of Dynatrace?

The Forrester TEI study is an independent analysis commissioned by Dynatrace to evaluate the potential business value of a unified observability platform. Forrester interviewed eight decisionmakers using Dynatrace and created a composite enterprise to model the costs, benefits, and return on investment over three years.

What ROI did the Forrester study find for Dynatrace?

The study found that the composite organization achieved a 466% return on investment (ROI), generated $20.3 million in net present value (NPV), and reached payback in less than six months over a three-year period.

How did Forrester calculate these results?

Forrester interviewed decisionmakers from organizations using Dynatrace, quantified their reported benefits and costs, and aggregated the findings into a representative composite organization. The model included operational efficiency gains, developer productivity improvements, cost savings, tool consolidation benefits, and business outcomes.

What was the largest source of business value in the study?

The largest quantified benefit was AI-powered incident detection and resolution efficiency, which accounted for $14.4 million in risk-adjusted present value. This value came from reducing alert noise, automating root-cause analysis, shortening war-room investigations, and accelerating incident resolution.

How does Dynatrace help reduce alert fatigue?

According to the study, Dynatrace helps consolidate duplicate alerts, reduce false positives, and identify root causes automatically. In the modeled organization, 98% of low-severity alerts were avoided or resolved without requiring manual intervention.

How does observability improve incident response?

Interviewees reported significant reductions in mean time to resolution (MTTR), with some organizations achieving improvements of up to 90%. Faster problem identification and automated root-cause analysis helped teams spend less time investigating issues and more time resolving them.

How does Dynatrace improve developer productivity?

The study found that developers spend less time gathering logs, troubleshooting production issues, and coordinating with centralized monitoring teams. Interviewees reported that diagnosing some production issues fell from approximately 45 minutes to five minutes.

Can observability accelerate software delivery?

Yes. Organizations interviewed by Forrester reported shorter release cycles, faster issue resolution, and reduced operational bottlenecks. Some teams reduced major release timelines from 10–15 weeks to as little as two to four weeks.

How does Dynatrace help control infrastructure and cloud costs?

Dynatrace provides visibility into the processes, services, and workloads driving resource consumption. This allows teams to identify inefficiencies, optimize infrastructure use, and make informed decisions about capacity and spending.

How does Dynatrace help manage AI costs?

The study highlights visibility into AI token consumption as an emerging source of value. Organizations can identify expensive prompts, optimize model usage, improve caching strategies, and understand the cost impact of specific AI workloads.

What is the connection between observability and AI governance?

AI governance depends on understanding how AI systems operate, perform, and consume resources. Observability provides the visibility needed to monitor AI models, agents, prompts, token usage, and supporting infrastructure so organizations can manage cost, performance, and risk.

How does platform consolidation create value?

The study found that organizations reduced spending by retiring overlapping application performance monitoring (APM) and log management tools. Consolidation also simplified operations by allowing teams to investigate issues from a single platform rather than switching between multiple tools.

Why is unified observability important for AI initiatives?

As AI becomes part of the technology stack, organizations need visibility across applications, infrastructure, user experiences, business services, and AI systems. Unified observability helps teams understand how these components interact and support more reliable AI operations.

What role does observability play in autonomous operations?

Observability provides the trusted data, context, and operational confidence required for automated remediation workflows. Without high-quality observability data, AI-driven automation cannot reliably detect, diagnose, and act on issues.

Can Dynatrace support AI-driven remediation?

Interviewees described using Dynatrace data as part of automated remediation workflows that detect problems, trigger incident management processes, scale infrastructure resources, and generate root-cause analyses while maintaining appropriate governance controls.

What business outcomes beyond IT efficiency did the study identify?

The study modeled $3.1 million in additional profit from improved business performance. Interviewees also reported improvements in digital experiences, service reliability, and release velocity that contributed to customer and business outcomes.

Who should read the full TEI study?

The study is particularly relevant for CIOs, CTOs, platform engineering leaders, site reliability engineering (SRE) teams, observability leaders, and technology decisionmakers evaluating platform investments, AI readiness, operational efficiency, and cost governance.

What is the key takeaway from the study?

The primary finding is that observability is no longer just a monitoring function. According to the study, a unified observability platform can help organizations improve operational efficiency, accelerate software delivery, control costs, support AI governance, simplify technology stacks, and build a foundation for autonomous operations.