Total Economic Impact™ study finds LogicMonitor Edwin AI delivered a 313% ROI and payback in 6 months or less
LogicMonitor Edwin AI's Total Economic Impact™ Study by Forrester finds organizations achieved 313% ROI and payback in less than 6 months with Edwin AI.
Forrester Consulting’s Total Economic Impact™ study found that a composite organization based on interviewed customers achieved 313% ROI and payback in less than 6 months with LogicMonitor Edwin AI.
Based on interviews with seven decision-makers at five organizations, Forrester aggregated the findings into a composite organization to evaluate costs, benefits, and risks over three years
The modeled return came from reducing alert noise, accelerating root cause analysis, cutting customer-impacting downtime, lowering SLA exposure, and reducing the cost of legacy event-management workflows
The study gives IT and business leaders a clearer framework for assessing the financial impact of AI-driven event correlation, investigation, and incident response in complex environments
AI for IT operations has a credibility problem. The market is crowded with claims about speed, automation, and intelligence, while buyers are left doing the harder work of separating measurable impact from vendor language.
A new commissioned study from Forrester Consulting gives that conversation firmer ground. In The Total Economic Impact™ Of LogicMonitor Edwin AI, Forrester Consulting found that a composite organization based on interviewed customers achieved 313% ROI over three years and payback in less than 6 months with LogicMonitor Edwin AI.
Those figures matter because they are grounded in detailed interviews, a composite organization constructed from real customer experience, and a three-year financial analysis of costs, benefits, flexibility, and risk. The full study goes further, showing where the return came from, what assumptions shaped the analysis, and which operational constraints were dragging performance before Edwin AI was introduced.
What Forrester studied
Forrester Consulting conducted interviews with seven decision-makers at five organizations with experience using Edwin AI, then aggregated those findings into a composite organization. In the study, that organization is a multinational enterprise with $2.5 billion in annual revenue, 5,000 employees, and business-critical applications running across hybrid cloud and on-premises environments.
Before adopting Edwin AI, the organizations interviewed described a familiar set of issues, including fragmented monitoring tools, high alert volumes, manual triage, slow root cause analysis, inefficient escalation, and rising SLA risk. That combination is expensive, pulling experienced engineers into repetitive work, slows response when incidents matter most, and adds friction across the incident lifecycle.
Forrester’s study models what changes when that work is handled with more context, stronger correlation, and more automation built into the workflow.
Where the financial impact came from
The study found that the composite organization’s three-year, risk-adjusted present value benefits came from five main areas.
1. Lower alert noise and triage effort
Forrester found that the composite organization began reducing alert noise early, reaching a 75% reduction in Year 1 and a 90% reduction at the optimized Year 3 state, producing $1.7 million in three-year risk-adjusted value. Those gains contributed to $1.2 million in total Year 1 benefits across the model and helped the composite organization achieve payback in less than 6 months.
That is one of the clearest findings in the study because the gains begin early — in some customer environments, Edwin AI has started surfacing value within an hour and reduced incidents within the first 30 days — and build over time. Noise slows triage, creates duplication, obscures priority, and keeps engineers focused on validating issues that never needed intervention. In the Forrester model, Edwin AI reduced that burden by correlating related events, suppressing non-actionable alerts, and routing meaningful incidents for human review.
2. Faster root cause analysis
Forrester found that the composite organization reduced time spent on root cause analysis for complex incidents by 60% in Year 1, with gains building toward a 70% reduction by Year 3 and yielding $798,981 in three-year risk-adjusted value.
Root cause analysis is where fragmented telemetry becomes expensive. When engineers have to move manually across metrics, events, logs, topology, tickets, and infrastructure context just to identify the likely source of an issue, response slows and senior resources get pulled into work that should be shorter and more targeted. In the study, Edwin AI helped shorten that path by surfacing correlated incident context, probable root-cause insights, and AI-assisted investigation.
3. Reduced downtime for customer-facing services
Forrester found that the composite organization improved resolution of P1 and P2 incidents causing customer-facing outages early, with a 20% reduction in incidents and a 40% reduction in MTTR in Year 1. By Year 3, MTTR improved by 50%, generating $1.1 million in three-year risk-adjusted value from improved business continuity.
The study also references $8.4 million in revenue recaptured from reduced P1/P2 downtime over three years. That figure is useful because it connects incident response to business consequences directly. It also reinforces the speed of the business case: Forrester found that the composite organization paid back its Edwin AI investment in less than 6 months. Less downtime protects revenue-generating services, preserves continuity, and reduces the cost of delayed diagnosis.
4. Fewer SLA-breaching incidents
Forrester found that the composite organization lowered SLA-breaching P1 and P2 incidents early, with a 30% reduction in Year 1 and improvements building to 40% by Year 3. Those avoided penalties and related costs yielded $446,250 in three-year risk-adjusted value.
This is one of the more practical findings in the study because it is tied to exposure that leadership teams already understand. When incident detection and resolution improve, service penalties decline. That has immediate relevance for organizations managing contractual obligations, availability targets, or regulated service environments.
5. Lower legacy event-management overhead
Forrester found that the composite organization reduced time spent managing the alerting and event-management layers of the prior monitoring environment by 50% in Year 1, while also reducing duplicative tooling spend by 70% in the same year. By Year 3, management time fell by 70%, producing $377,400 in three-year risk-adjusted value.
That category deserves attention because complexity rarely lives in one platform. It accumulates in scripts, custom logic, point tools, integrations, and manual tuning layered across the environment. The study attributes part of Edwin AI’s value to reducing that maintenance burden over time.
Move faster toward Autonomous IT with LogicMonitor Edwin AI
Forrester found that Edwin AI helped teams move faster from alert to action while reducing the effort and cost required to manage incident workflows.
Across the interviews, teams were spending too much time sorting alerts, validating symptoms, tracing dependencies, and moving between systems before they could get to the actual issue. Edwin AI improved that workflow by reducing noise, surfacing context sooner, and helping teams investigate and respond faster.
As one global head of IT networks in agriculture told Forrester: “Our engineers got half their day back with Edwin AI. It’s fun coming to work again because they have time to deal with higher-level issues. They’re involved in more projects and exciting rollouts that they could have never done before because everyone was managing tickets all the time.”
That is what makes the Autonomous IT business case stronger: Edwin AI improves operational capability without adding proportional cost or administrative burden. In the Forrester model, that showed up as faster investigation, lower service risk, reduced legacy event-management maintenance, and lower costs tied to overlapping tools and prior workflows.
Interviewees described the progress in practical terms:
Cleaner signal: Fewer duplicative and non-actionable alerts.
Faster investigation: More context upfront, less manual correlation across disconnected systems.
Lower service risk: Faster triage and root cause analysis helped reduce downtime and SLA exposure.
Less workflow friction: Teams spent less time maintaining custom alert logic, routing layers, and legacy event-management workflows.
Those changes point to a more credible path toward Autonomous IT: faster movement from visibility to context to action, with less manual drag, lower overhead, and better economics.
Want the numbers behind the impact? Read The Total Economic Impact™ Of LogicMonitor Edwin AI for the full methodology, assumptions, cost model, and benefit breakdown.
Margo Poda leads content strategy for Edwin AI at LogicMonitor. With a background in both enterprise tech and AI startups, she focuses on making complex topics clear, relevant, and worth reading—especially in a space where too much content sounds the same. She’s not here to hype AI; she’s here to help people understand what it can actually do.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
