The Total Economic Impact™ Of LogicMonitor’s Edwin AI

Reduced Alert Noise And Triage Effort

Evidence and data. Interviewees reported that prior to implementing LogicMonitor Edwin AI, their L1 operations teams were inundated with high volumes of infrastructure, network, and application alerts — many of which were duplicative, low priority, or nonactionable. L1 engineers spent significant time reviewing, validating, and dismissing alerts that did not require intervention, while important signals were frequently obscured by noise. The alerts’ limited context also required teams to manually check multiple tools to understand severity and downstream impact, which slowed triage, routing, and escalation.

After implementing Edwin AI, interviewees reported substantial reductions in alert noise due to AI‑driven event correlation, deduplication, and suppression of nonactionable alerts. Edwin AI grouped related events into single incidents and determined which alerts required human attention, preventing a large portion of irrelevant alerts from reaching queues. As a result, their engineers spent less time processing noise and more time focusing on alerts that required action.

Interviewees also noted that fewer alerts entering L1 queues reduced overall backlog and improved throughput. With less work entering triage and many repetitive patterns handled autonomously, their engineers were able to process remaining alerts more quickly and with fewer interruptions. In parallel, Edwin AI enriched alerts with contextual information, such as related events and recommended handling guidance, which reduced manual cross‑checking across tools and further shortened triage and routing time.

• The chief customer service officer in IT services shared: “The number of alerts was simply too high. On a daily basis, we were averaging more than 10,000 alerts, which made it impossible to proactively manage alerts and incidents. We also had too many systems and platforms in place, which meant the tooling was fragmented. That fragmentation contributed directly to the alert noise.”
  The platform owner at the same organization continued: “When a device went down, we might get five or six different alerts for the same device. There was no intelligence saying, ‘This is the main issue and the others are related.’ The volumes were high enough that teams were getting overwhelmed. That lack of correlation was probably the biggest distraction for us, and we needed something like Edwin AI to deal with that.”
  The network manager at the same organization concluded: “We also had multiple alarms sent to different people, so we didn’t know who was managing which part. In many cases, two people could end up reviewing the same alert. That created a lot of operational inefficiency.”

• The IT infrastructure manager in retail pharmacy said: “We were getting so many alerts and so many false positives. It took time for the team to get to the real incidents. After implementing Edwin AI and getting through the initial tuning period, within a month we saw nearly a 90% reduction in alerts. For example, if there was a power failure at a retail site, previously we would get separate alerts for network, server, and other components. Edwin AI grouped those together and tied them back to a single power outage.”

• The global head of IT networks in agriculture explained: “We wanted to reduce alert volume because our network engineers had effectively become ticket administrators. Most of their time was spent updating, moving, and closing tickets. We could have continued tuning and automating over months or years, but Edwin AI removed the need for staff to spend time doing that manually.”
  The same interviewee continued: “We enabled Edwin AI in a passive mode initially to make sure it was making the right decisions. Almost immediately, we saw a 90% reduction in alerts. At first we thought we’d broken something, but the engineers reviewed the results and confirmed Edwin AI was making sound decisions.”

• The chief technology and strategy officer in IT consulting explained: “AI is simply better at correlation than humans. With the amount of data involved, people can correlate simple things like the same server showing multiple errors at the same time. But when the volume increases, it becomes much harder to see what’s related and what isn’t. Edwin AI can recognize when many systems are emitting the same signals at the same time and group those together as a single issue. That’s where we see the real benefit. It prevents multiple related alerts from being treated as separate problems. It’s not about replacing our employees; it’s about enhancing what they already do by reducing noise and using machine learning to surface clearer signals.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite operates a hybrid monitoring environment that spans infrastructure, network, and applications. It generated 350,000 alerts annually in the prior environment.

• In the prior environment, alert volumes exceeded L1 capacity, resulting in only about 50% of alerts being reviewed or triaged. The remainder were deprioritized or went unaddressed due to resource constraints.

• After implementing Edwin AI, the composite reduces alert noise by 75% in Year 1, 85% in Year 2, and 90% in Year 3.

• In the prior environment, on average, it took 7 minutes to manually triage and route each alert.

• With Edwin AI, the composite reduces the time to manually triage and route alerts by 10% in Year 1, 15% in Year 2, and 20% in Year 3.

• The fully burdened hourly rate for L1 engineers is $65.

• For this benefit, the composite has a productivity recapture rate of 50%. Resources spend half of the time they save on activities that generate business value, but not all reclaimed time is dedicated to value-added work.

Risks. Forrester recognizes that these results may not be representative of all experiences. The following factors may impact this benefit:

• Operational infrastructure, network, and application monitoring alerts in the prior environment.

• Average manual triage and routing time per alert in prior environment.

• Average fully burdened hourly rate of resources reviewing and triaging alerts.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.4 million.

Accelerated Root Cause Analysis For Complex Incidents

Evidence and data. Interviewees shared that LogicMonitor Edwin AI reduced the time L2 and L3 engineers spent performing root cause analysis for complex incidents by introducing AI-driven diagnostics, AI-assisted investigation capabilities, and automated remediation into incident workflows. In prior environments, engineers at the interviewees’ organizations manually performed root cause analysis after escalation by correlating telemetry across logs, metrics, and dependent systems to determine the underlying cause of incidents, which required significant effort to gather, interpret, and validate data from multiple sources.

Interviewees noted that Edwin AI applied an AI agent that analyzed incident context and telemetry to identify the most likely root cause and prioritize issues based on impact to provide engineers with precorrelated insights rather than raw data. This allowed their engineers to engage with Edwin AI to ask questions, explore anomalies, and receive guided recommendations rather than manually piecing together data across tools. In addition, interviewees said automation executed known remediation steps for recurring issues, which reduced the need for manual troubleshooting and eliminating repetitive diagnostic effort for known failure patterns.

As a result, engineers at the interviewees’ organizations spent less time manually correlating data and diagnosing incidents and were able to more quickly validate AI-identified root causes and execute remediation actions. The time they saved was then redirected toward higher-value activities, including improving monitoring coverage, refining detection logic, and addressing backlog of operational improvements and preventative engineering work.

• The chief customer service officer in IT services explained: “For me, the main objective was scale: How do we scale effectively and efficiently and move from being reactive to more proactive? There is already an expectation that we should be identifying incidents before they arrive, and that’s where our terminology and thinking started to shift. We were interested in how Edwin AI could enable us to focus more on the predictive rather than just the reactive. That’s a key aspect of delivering on our commitments and being able to scale while adding value to both our people and our customers. We wanted our expert resources to focus on critical tickets instead of investigating 10,000 alerts a day, which doesn’t help with identifying the root cause before customers are impacted.”
  The same interviewee continued: “More efficiently identifying the root cause has enabled us to be more effective and smarter in how we deal with incidents. That leads to being more proactive in spotting issues and fixing them more quickly instead of manually going through thousands of alerts to determine where the real problem is.”
  This interviewee concluded: “The intent was to redeploy our resources toward more value‑added services and enable us to add more customers without continuously increasing headcount. We’re in a growth and scaling phase as a business, and we are using Edwin AI to help us scale without linear headcount growth. From a strategic planning perspective, automation and AI with Edwin AI have been fundamental to enabling our people to do more, support more customers, and build the business for scale.”

• The chief technology and strategy officer in IT consulting said: “For us, it’s really about spending more time in a proactive mindset instead of a reactive one. Before Edwin AI, a lot of time was spent repeatedly investigating the same issues and manually trying to understand what was actually causing incidents. Edwin AI helps accelerate that process by clarifying the underlying issue sooner, which frees up our people from repetitive troubleshooting. As a result, they can spend more time having meaningful conversations with customers and application teams and focusing on work that actually adds value.”

• The IT infrastructure manager in retail pharmacy explained: “When customers report poor network performance and we can’t find anything obvious initially, the team will go into Edwin AI and use the tooling to ask, ‘Do you see anything unusual?’ In several cases, it’s surfaced a particular metric that looks off. It doesn’t solve the problem 100% of the time, but it helps identify the most likely cause and gives the team better direction on where to focus.”

• The global head of IT networks in agriculture said: “The biggest benefit for us is that engineers now spend more time fixing problems than administering tickets. That was the first major improvement. Edwin AI also surfaced issues we would never have seen otherwise. Within the first few weeks, it identified a global BGP [border gateway protocol] issue that was close to bringing the company down. Without Edwin AI, we wouldn’t have caught it in time.”
  This interviewee continued: “Edwin AI now automatically closes tickets when problems resolve. It recognizes when an issue is no longer present and closes the ticket unless it’s something we’ve defined as requiring further investigation. We don’t ignore issues, but this automation reduces unnecessary work.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization has a team of 15 L2 and L3 engineers responsible for root cause analysis for complex incidents.

• In the prior environment, the engineers spent an average of 30% of their time on root cause analysis.

• With Edwin AI in place, the engineers experience a 60% productivity lift on root cause analysis in Year 1, 65% in Year 2, and 70% in Year 3.

• The fully burdened hourly rate for L2 and L3 engineers is $103.

• For this benefit, the composite has a productivity recapture rate of 50%. Resources spend half of the time they save on activities that generate business value, but not all reclaimed time is dedicated to value-added work.

Risks. Forrester recognizes that these results may not be representative of all experiences. The following factors may impact this benefit:

• Resources dedicated performing root cause analysis of complex incidents.

• Percentage of resource time spent on root cause analysis.

• Average fully burdened hourly rate for resources performing root cause analysis.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $659,000.

Improved Business Continuity Due To Reduced Downtime

Evidence and data. Interviewees explained that before implementing LogicMonitor Edwin AI, customer-facing P1 and P2 incidents required manual investigation across multiple monitoring systems, including logs, metrics, and infrastructure telemetry. This often led to extended mean time to resolution as their engineers manually correlated signals, identified likely causes, and coordinated remediation across teams. In many cases, the lack of early signal prioritization and automation contributed to prolonged service degradation and increased application downtime. These inefficiencies reduced overall business continuity as critical applications remained unavailable longer than necessary during incidents, which directly impacted revenue-generating systems and, by extension, customer experience.

With Edwin AI, interviewees’ organizations experienced a reduction in both the frequency and duration of customer-facing outages. Edwin’s AI agent analyzed incident patterns and telemetry to identify probable root causes more quickly and prioritize high-impact issues, which sped up diagnosis during active incidents. In addition, automation executed known remediation steps for routine and recurring issues to reduce manual intervention and further shorten resolution times. Faster identification of incident drivers, coupled with accelerated remediation, reduced incident duration and mitigated customer‑facing outages, thus improving application availability and ensuring continuity of critical business operations for the interviewees’ organizations.

• The global head of IT networks in agriculture explained: “Networks are living, breathing things and they break, but incidents are now consolidated into a much smaller number of tickets. We get much more targeted tickets, which means we spend far less time diagnosing. We know about problems quicker, we get to the root cause faster, and we’re not being bombarded with noise like a site issue triggering 50 device alerts. Edwin AI does that correlation work for us and tells us roughly where the issue is, and most of the time that’s exactly where it is. That way, we can get to work on resolving it much sooner.”

• The IT infrastructure manager in retail pharmacy shared: “We had issues at one of our distribution centers where handheld devices were failing. We couldn’t see anything obvious in the network monitoring or management platforms. The only sign was some packet loss, but it wasn’t clear where it was coming from. Using Edwin AI, the team correlated that metric with system logs and discovered a misconfiguration that flooded a server with requests and caused the system to go down. We resolved it in about an hour and a half. Without Edwin AI, it would have taken 4 to 5 hours to find.”

• The senior director of infrastructure operations in healthcare said: “There has been a decline in the [duration] of customer-impacting downtime events we’ve had and a lot of that comes down to our people being able to respond faster. As we continue to reduce incidents, we expect those numbers to keep improving. We still generate over 100,000 alerts a month, but a lot of work has gone into balancing configurations so we’re not alerting too soon and not missing something critical. Reducing alerts also helped reduce the number of actionable incidents overall.”

• The network manager in IT services shared: “We definitely had instances where we were able to spot issues with Edwin AI before they happened; before a firewall or a device went into a state where it was essentially not passing traffic, or before a CPU or memory issue caused a problem. Before [Edwin AI], we had customers going down and calling out saying, ‘If you’re monitoring, how are you not capturing those instances?’ A lot of these issues creep up over time like CPU gradually increasing. We should have spotted that before it happened so we could work on it and fix it. There have definitely been fewer of those missed instances.”
  This interviewee continued: “The biggest benefit I see overall — and something customers really appreciate — is that we now have much more time within the engineering team to focus on critical vulnerabilities and patch management, which we were lacking before. We are now following industry standards and best practices. We’re not missing any critical vulnerabilities, and the environments we manage are securely patched and properly monitored.”

• The IT infrastructure manager in retail pharmacy said: “We had a couple of incidents that impacted the organization, and our ability to triage those incidents was slowed or diminished because we didn’t have tooling that was smart enough to point engineers in the right direction or give them the context they needed. These were incidents where the time to resolution would have been dramatically better if Edwin AI had been in place.”
  The same interviewee continued: “Edwin AI allows us to correlate what was happening at the server OS level and the database level, and how that then related to the application server. It was able to bring all of that together and point to something as it was occurring, so we could get onto it straight away and avoid an issue. In other cases, it allowed us to quickly find what the problem was and fix it; whether that was a job being stuck or a memory leak on a server impacting the database.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• In the prior environment, the composite experienced 30 P1 and P2 incidents annually that result in customer‑facing application outages

• With Edwin AI in place, the number of P1 and P2 incidents the composite experiences is reduced by 20% in Year 1, 25% in Year 2, and 30% in Year 3.

• In the prior environment, each P1 and P2 incident that resulted in customer-facing application outages lasts an average of 4 hours.

• With Edwin AI, the composite experiences reduces MTTR for P1/P2 incidents by 40% in Year 1, 45% in Year 2, and 50% in Year 3.

• For every hour of uptime for Edwin AI-monitored customer-facing applications, the composite organization generates $300,000.

• The composite organization has an operating margin of 10%.

Risks. Forrester recognizes that these results may not be representative of all experiences. The following factors may impact this benefit:

• Number of P1/P2 incidents that cause customer-facing application outages in the prior environment.

• Average duration of a P1/P2 incident in the prior environment.

• Revenue generated per hour of uptime for Edwin AI-monitored customer-facing applications.

• Operating margin.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $872,000.

Avoided Service Penalties From Reduced Downtime

Evidence and data. Interviewees said that SLA breaches represented a material operational and financial risk prior to implementing Edwin AI, both for MSPs with contractual service‑credit obligations and for enterprises operating under internal or regulatory service‑level requirements. When critical services were not restored within defined timeframes, the interviewees’ organizations faced financial penalties, compliance exposure, or formal remediation actions.

Interviewees noted that SLA breaches were primarily driven by delayed detection of P1 and P2 incidents and prolonged resolution during customer‑facing outages. In prior environments, their engineers relied on manual correlation of alerts and telemetry across multiple systems to identify root causes and coordinate remediation, which extended incident duration and increased the likelihood of exceeding SLA thresholds.

With Edwin AI, interviewees explained that the frequency of SLA‑breaching incidents declined as AI‑driven correlation accelerated identification of probable causes and prioritized high‑impact incidents. As a result, the interviewees’ organizations experienced fewer SLA violations and reduced exposure to SLA‑related financial penalties, service credits, and compliance‑related obligations; in several cases, SLA penalties were significantly reduced or even completely eliminated following implementation.

• The network manager in IT services shared: “[Before Edwin AI], it was really hard to track down the issues and that caused a lot of SLA breaches and a lot of callouts from our clients. Customers were asking us to focus more on providing a proactive service by spotting issues before they happened and before they had a big impact on their environments.”
  The chief customer service officer at the same organization explained: “We did an exercise recently as part of a separate review with finance, and frankly speaking, the amount of rebates related to us missing SLAs has gone to zero since using Edwin AI, which is big for us. When we reviewed the last 12 months, anything SLA‑related where we weren’t able to respond or meet customer requirements had dropped completely. There were zero service credits related to us missing alerts or not responding.”

• The IT infrastructure manager in retail pharmacy explained: “We’ve definitely been able to reduce service penalties related to incidents that breach SLA. One example is our data delivery services; we provide sales data to marketing organizations, which depend heavily on our database and data platforms being available. Before, we had a significant number of issues affecting those systems. [With Edwin AI], we’re able to identify database-related problems much faster, and the number of incidents has dropped substantially. These are the types of services where penalties apply if we don’t deliver data on time, so reducing those disruptions has directly lowered our exposure.”
  The IT infrastructure manager continued: “LogicMonitor brought us into the high 80s in terms of availability, and with Edwin AI, we were able to quickly move into the mid-to-high 90s. At one stage, we were still around 87%, which put us below our target range but that improved rapidly. Now, we’re consistently operating between 98% and 99%. For example, we reached 98.8% in February, and we haven’t fallen below 97% since August of last year. If we exclude external factors like power or environmental incidents, we’re effectively meeting our 99% SLA targets.”
  This interviewee concluded: “The month before we introduced Edwin AI, we had 104 incidents that breached SLA. In the first month with Edwin AI, that dropped to 15. The highest we’ve seen since then was 65, which occurred during the early rollout when we were enabling additional metrics across teams. Even during that period of expansion, the reduction was still significant.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization operates a portfolio of customer-facing applications and services with defined SLA commitments, where missed SLAs result in financial penalties, service credits, or compliance‑related consequences. It experienced an average of 100 P1/P2 incidents annually that breach SLA in the prior environment.

• The composite assumes an average monthly contract value of $100,000 for services exposed to SLA breaches with penalties equivalent to 5% of contract value, which translates to an average cost of $5,000 per SLA-breaching incident.

• With Edwin AI, the composite reduction the number of SLA-breaching incidents by 30% in Year 1, 35% in Year 2, and 40% in Year 3.

Risks. Forrester recognizes that these results may not be representative of all experiences. The following factors may impact this benefit:

• Number of P1/P2 incidents that breach SLA in prior environment

• Average cost per SLA-breaching incident.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $367,000.

Legacy Environment Savings

Evidence and data. Interviewees explained that prior to adopting Edwin AI, their organizations operated fragmented monitoring environments composed of legacy, open-source, and custom-built observability tools that were supported by extensive integrations and supplementary event-processing logic. These distributed environments created significant software, integration, and maintenance overhead. Teams spent considerable time maintaining multiple monitoring systems, managing event and alert integrations between tools, and operating custom correlation and routing logic required to make monitoring telemetry usable for incident response. Interviewees noted that Edwin AI helped minimize the need for ongoing maintenance of fragmented alert processing workflows and correlation logic layered on top of monitoring tools, which reduced both operational overhead and the effort required to reconcile and interpret events across systems.

The global head of IT networks in agriculture said: “We had a variety of other tools, and they were all capable of producing alerts, but they were what I would call niche tools. They did very specific jobs, but what we didn’t have was a good, generic manager‑of‑managers type of tool. With traditional monitoring tools, it’s often difficult to see the wood for the trees. You end up spending an awful lot of time tuning the tool. That’s not to say you don’t do any tuning in an AI world, but there’s significantly more intelligence and a lot less tuning required.”

This interviewee continued: “I have six people on my NMS [network management system] team, five of whom work on our tooling stack, which includes LogicMonitor’s core platform and Edwin AI. That team doesn’t spend a lot of time inside LogicMonitor day to day, because much of their effort is focused on future solutions and automation. Before Edwin AI, those engineers would spend anywhere from 50% to 75% of their day on tuning. Now it’s closer to 20%. That reduction is because of the intelligence and automation in Edwin AI. We spend almost no time on manual tuning anymore. We still do some teaching when Edwin AI misses a new fault case, but that’s happening less and less as the model continues to learn.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• In the prior environment, the composite operated a fragmented monitoring ecosystem spanning legacy, open-source, and custom-built observability tools, with an estimated $50,000 in annual spend on redundant point solutions supporting alert ingestion, correlation logic, and integrations.

• With Edwin AI, the composite gradually decommissions overlapping monitoring and event-processing tools and reduces tooling spend by 70% in Year 1, 80% in Year 2, and 90% in Year 3.

• In the prior environment, the composite had three L3 engineers responsible for maintaining the monitoring and event management environment, including managing integrations, tuning correlation rules, maintaining alert routing logic, and reconciling cross-system event data. Each resource spent approximately 50% of their time on these activities in the prior environment.

• With Edwin AI, time spent managing the alerting, correlation, and event management layers of the prior monitoring environment decreases by 50% in Year 1, 60% in Year 2, and 70% in Year 3.

• The average fully burdened annual salary for an L3 engineer is $240,000.

• For this benefit, the composite has a productivity recapture rate of 50%. Resources spend half of the time they save on activities that generate business value, but not all reclaimed time is dedicated to value-added work.

Risks. Forrester recognizes that these results may not be representative of all experiences. The following factors may impact this benefit:

• Point solution spend in prior environment.

• Number of resources working on management of prior environment.

• Fully burdened annual salary for resources managing prior environment.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $310,000.

Unquantified Benefits

Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:

• Faster customer onboarding. Interviewees, particularly those whose organizations were operating managed service models, explained that Edwin AI accelerated the observability and stabilization portion of customer onboarding. By improving event correlation, reducing alert noise, and providing earlier visibility into new environments, their teams were able to bring customers into steady‑state operations quicker and with less manual effort. This reduced the time engineers spent cleaning up alerts, understanding undocumented environments, and stabilizing monitoring configurations during the early stages of new customer engagements.
  The chief technology and strategy officer in IT consulting explained: “For most MSPs, you’re going to lose money in the first months of any new contract. You need to onboard the customer, spend time, get documentation in place, and get observability up and running. There are always things you weren’t aware of at the start, and all of that takes time from your people. The more we can reduce that and the better we get at it, the less we overspend in those early months. It’s shadow spend; everyone knows it’s there, but it’s very difficult to calculate.”

• Strategic partnership with the LogicMonitor team. Interviewees described their relationship with LogicMonitor as a collaborative engagement centered on testing, refining, and providing structured feedback on Edwin AI and related platform capabilities. Rather than a transactional vendor model, the interviewees’ organizations worked directly with product and forward deployed engineering (FDEs) teams to evaluate new features in production-like environments and influence ongoing development priorities, particularly around AI-driven event intelligence. This engagement model ensured that enhancements reflected real operational requirements across complex environments and supported continuous platform evolution.
  The chief technology and strategy officer in IT consulting shared: “We’re working with LogicMonitor on other elements they’re taking to market, pushing, testing, and validating, because we’re a little bit different from some other providers. We’re very strongly opinionated about how things should function and work. That was part of why we chose LogicMonitor. There’s a very strong focus on product development and feedback cycling.”
  The platform owner in IT services explained: “We work very closely with LogicMonitor. We have access to everything that’s available, we ask for preview time, and we’re testing new features while providing input into what we’d like to see next, which they’re actively working to build.”
  The IT infrastructure manager in retail pharmacy said: “Our engineer, who was the SME [subject matter expert] on this, had direct access to their engineering teams and weekly meetings to give feedback and get advice if there was a better way of doing something. They were very engaging. This has been my most genuine partnership. I deal with many technology providers, including much larger commercial partnerships, but this one has been very genuine from the start.”

• Alignment with broader AI‑first business objectives. Interviewees described Edwin AI as an important component of their organizations’ broader AI and digital transformation strategies. Rather than treating monitoring as a standalone operational function, their organizations positioned AI‑driven observability as foundational to demonstrating innovation to customers, delivering value at scale, and supporting long‑term growth. Edwin AI enabled the interviewees’ organizations to operationalize AI in day‑to‑day workflows while meeting required security and compliance standards.
  The platform owner in IT services said, “We wanted to dive into the AI space further, and this was an opportunity to showcase to our customers that we were using AI as part of a larger digital transformation.”
  The chief customer service officer at the same organization continued: “As a business, we’re leading with AI. We’re making investments across our end‑to‑end service portfolio, and monitoring is a key aspect of enabling the business to scale. That was part of our roadmap. When it comes to the customer success journey, it all starts with visibility; showing value and showing that we’re continuously investing in our systems and platforms to become more AI‑first.”

• Improved compliance readiness and data sovereignty support. Interviewees noted that Edwin AI strengthened their organizations’ ability to meet data sovereignty and regulatory requirements by operating within a secure, regionally hosted LogicMonitor environment designed to support local legislative and customer data residency expectations. This reduced the need for additional architectural adjustments or operational workarounds when serving regulated customers, including those in government, financial services, and nonprofit sectors.
  The chief customer service officer in IT services shared: “A big element of us investing in LogicMonitor products was data sovereignty. They have made a strategic investment to build the platform in Australia to meet local legislation. We were compliant before, but this enabled a solution that is fully security‑vetted and meets requirements for all the verticals our customers are in, which allows them to use Edwin AI to monitor their services.”

• Improved leadership visibility. Interviewees reported that dashboards, reporting, and centralized visibility within the core LogicMonitor platform, which were enhanced by Edwin AI, improved situational awareness across operational, technical, and executive teams. Rather than relying on fragmented monitoring tools, manual correlation of alerts, or ad hoc updates from teams, their users leveraged Edwin AI’s centralized intelligence layer and observability views to assess system health, performance trends, and emerging anomalies. This improved visibility, enabled faster interpretation of incidents and more confident decision-making, and supported earlier identification of potential issues before they escalated into customer-impacting events.
  The senior director of infrastructure operations in healthcare explained: “There are time savings for operational and technical leadership from having dashboards that give immediate awareness. We had an issue this morning, and because the dashboards were already built, there was immediate understanding of what was going on.”

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Edwin AI and later realize additional uses and business opportunities, including:

• Expanding AI-driven event intelligence toward autonomous IT and broader operational use cases. Interviewees indicated that the ability of Edwin AI Agents to correlate high volumes of alerts, reduce noise, and prioritize incidents created a strong foundation for expanding AI-driven intelligence beyond initial monitoring and triage use cases. As the interviewees’ organizations gained confidence in AI-driven correlation and event summarization, they saw increasing potential to apply these capabilities across a wider set of operational domains where large-scale event data must be interpreted quickly to support prioritization and decision-making.
  Interviewees explained that these capabilities represented early but meaningful steps toward more autonomous IT, where AI-driven systems augment operational teams by identifying emerging risk conditions, surfacing patterns across infrastructure and applications, and providing contextual insights that support proactive response. Interviewees said that over time, their organizations expect to extend usage beyond reactive incident management into adjacent workflows, such as problem identification, operational health analysis, post-incident learning, and service reliability oversight. Rather than viewing Edwin AI Agents as a point solution, interviewees described it as a foundational intelligence layer that can evolve alongside their IT maturity and enable progressively more autonomous operational workflows.
  The chief technology and strategy officer in IT consulting said: “We’re not just buying a piece of software in Edwin AI. It’s really a partnership around how they’re building the platform, what they’re doing with the AI modules, and how we can interact with those to do more and better on our side for the future.”

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).