How a Fortune 500 Company Eliminated 93% of IT Incidents in 72 Hours

Sometimes the biggest transformations begin with what sounds like the worst possible news. One day, this Fortune 500 technology company’s observability platform was running smoothly. The next, they learned their critical monitoring solution would be discontinued as part of a corporate buyout.

For a leading global IT vendor in data infrastructure serving customers across storage, cloud, and managed services, this was a potential catastrophe. Two of their most critical ITOps teams suddenly faced the prospect of operating blind, with no visibility into the systems that kept customer environments running.

The clock was ticking. They had weeks to find a replacement solution and deploy it before their existing platform went dark.

Within three weeks, they had deployed LogicMonitor Envision and Edwin AI. Within 72 hours of activation, they had eliminated 93% of their incident volume—turning what could have been an operational disaster into a dramatic improvement over their previous state.

“Too Many Alerts, Not Enough Signal”

Before the crisis forced their hand, this tech giant was already drowning in operational noise. Their engineering teams were trapped in a vicious cycle that will sound familiar to anyone managing complex IT environments at scale. They had thousands of incidents generated a day, with engineers spending their mornings sifting through a digital avalanche of duplicates, false positives, and context-free notifications. 

Fragmentation was killing their effectiveness. Internal systems generated one stream of alerts, customer-facing environments produced another, and the complete lack of correlation meant engineers were constantly switching contexts as they tried to piece together the bigger picture. A single underlying issue might trigger dozens of separate alerts across different systems, each treated as an independent incident requiring individual investigation.

“We were overwhelmed,” admitted one senior team member. “Too many alerts, not enough signal. We needed help focusing on what actually mattered.”

The distinction between monitoring and observability had never been more critical. Their existing setup could tell them when something crossed a threshold, but it couldn’t explain why systems were failing or how incidents connected across their hybrid infrastructure. When problems cascaded through their environment, engineers burned precious time assembling clues from disparate sources while customer-facing services remained compromised.

Even worse, this noise was actively undermining their ability to deliver on their commitments. SLAs were slipping as real problems got buried under false alarms. Service continuity suffered when critical issues went unnoticed in the flood of notifications. Customer experience degraded as engineers remained trapped in reactive mode. 

Ironically, in an age of unprecedented visibility into system performance, they were essentially operating blind.

Why LogicMonitor + Edwin AI

With their existing platform’s expiration date looming and operational chaos mounting, the technology leader faced an impossible timeline. They needed to evaluate, select, and deploy a replacement solution in weeks—all while maintaining service levels for critical customer environments.

The evaluation process revealed a harsh reality. Most enterprise monitoring solutions required lengthy implementations that could stretch for quarters. Complex integrations, extensive customization, and drawn-out deployment cycles were luxuries they simply couldn’t afford. They needed something that could deliver immediate value without the traditional overhead of enterprise software rollouts.

LogicMonitor Envision and Edwin AI emerged as the clear choice, but not just because of their technical capabilities. The combination offered something their crisis demanded: speed without compromise. LogicMonitor’s hybrid observability platform could provide the comprehensive visibility they needed across their complex infrastructure, while Edwin AI promised to solve their most pressing problem—the overwhelming noise that was paralyzing their operations.

What sealed the decision was Edwin AI’s precision approach to incident response. Unlike traditional rule-based systems that simply filtered alerts, Edwin AI could actually understand the relationships between incidents, automatically correlate related events, and accelerate root cause analysis.

The scalability factor proved equally crucial. Their organization needed to support two distinct but interconnected teams: internal engineering focused on infrastructure operations and managed services responsible for customer-facing environments. Most solutions would have required separate deployments with limited coordination between them. LogicMonitor and Edwin AI offered the flexibility to create synchronized deployments that could operate independently while sharing intelligence and insights.

Within days of their initial evaluation, it became clear that this combination could help them regain control of their operations while actually improving their capabilities beyond what they’d lost in the acquisition.

The 72-Hour Transformation

What happened after deployment defied even their most optimistic expectations. Within 72 hours of Edwin AI going live, the same teams that had been drowning were witnessing a complete transformation of their incident management reality.

The numbers were staggering. Edwin AI automatically reduced 93% of incidents flowing into ServiceNow based on external resolution signals and intelligent correlation. The digital avalanche that had consumed their days vanished, replaced by a manageable stream of genuinely critical issues that actually required human intervention.

But the real breakthrough was in speed and precision. Edwin AI cleared 1,300 false positives in under 10 minutes, eliminating hours of investigative work that would have pulled engineers away from real problems. Another 1,650 incidents were resolved within a single hour, turning what used to be day-long troubleshooting marathons into quick, focused responses.

The transformation extended far beyond the numbers. Engineers who had started each day bracing for an overwhelming barrage of notifications were suddenly able to focus on strategic work. SLA adherence improved dramatically as real issues were no longer buried under false alarms. Customer experience benefited as problems were identified and resolved before they could cascade into service disruptions.

“We’ve seen a dramatic reduction in alert noise and faster resolution times,” said one engineering lead. “Edwin AI helped our teams stay focused, maintain SLAs, and deliver a better experience to both internal and external users.”

Perhaps most remarkably, this wasn’t the result of months of fine-tuning and optimization. This was day-one performance from an AI system that understood their environment well enough to immediately distinguish between noise and signal—solving in hours what had taken their previous approach years to create.

Enterprise Deployment at Startup Speed

For the managed services team responsible for maintaining customer-facing environments, the deployment timeline was ambitious. Any disruption to their monitoring capabilities could cascade into service outages affecting dozens of enterprise customers. They needed a migration that was both lightning-fast and completely seamless.

Traditional enterprise software deployments are measured in quarters. Complex integrations, extensive testing phases, and gradual rollouts are standard practice for a reason: they minimize risk. But this team didn’t have the luxury of a cautious approach. Their existing platform’s shutdown date was fixed, and customers couldn’t experience even momentary gaps in service monitoring.

What followed was a masterclass in streamlined implementation. LogicMonitor and Edwin AI were deployed, configured, and activated in just three weeks—a timeline that would be aggressive for a greenfield deployment, let alone a critical system migration under pressure. The deployment required minimal configuration overhead, with Edwin AI’s intelligent capabilities adapting to their environment rather than demanding extensive customization.

Most importantly, the transition happened invisibly to their customers. Service monitoring continued uninterrupted throughout the migration, with no gaps in coverage or temporary blind spots that could have put customer environments at risk. The managed services team maintained their SLA commitments while simultaneously overhauling their entire observability infrastructure.

“We didn’t have time for a long rollout,” explained one team lead. “LogicMonitor and Edwin AI were up and running fast with zero disruption to our customers.”

The speed of deployment proved that modern AI-driven solutions could deliver enterprise-grade reliability without enterprise-typical implementation overhead. In an era where business moves faster than traditional IT deployment cycles, this kind of rapid, low-risk implementation capability represents a fundamental shift in how organizations can adopt new technology.

Beyond the Quick Wins: Building a Predictive Operations Engine

The dramatic success of their initial deployment represented a fundamental shift in how this company approaches IT operations. Having tasted the power of AI-driven incident management, they’re now positioning themselves at the forefront of a broader transformation in enterprise ITOps.

The immediate wins were impressive, but the real opportunity lies in what comes next. With Edwin AI proving its value in their most critical environments, the organization is now expanding their AI strategy across multiple dimensions. The reactive approach that had defined their operations for years is giving way to something far more sophisticated: truly predictive operations.

Their roadmap reveals the full potential of AI-powered ITOps. Generative and agentic capabilities are being activated to provide auto-summarization of complex incidents, accelerated root cause analysis that can trace problems across interconnected systems, and predictive insights that identify potential issues before they manifest as outages. These are improvements that represent a complete reimagining of how IT operations can function.

The expansion is equally ambitious. Having proven the concept with two critical teams, they’re now scaling agentic AI across additional internal and customer-facing environments. Each new deployment builds on the intelligence gathered from previous implementations, creating a network effect where the AI becomes more effective as it monitors more systems and learns from more incidents.

But perhaps the most transformative element is their shift from reactive to preventive operations. Using pattern recognition and historical data analysis, Edwin AI is helping them anticipate disruptions before they impact operations. Instead of waiting for alerts to fire, they’re identifying the conditions that typically precede problems and taking action while issues are still manageable.

“Our goal is to anticipate challenges before they happen,” explained one senior leader. “Edwin AI is a key partner in making that vision a reality.”

This evolution from crisis management to strategic advantage illustrates a broader truth about AI in enterprise operations: the organizations that will thrive aren’t just those that deploy AI to solve today’s problems, but those that leverage it to prevent tomorrow’s challenges entirely.

The New Reality of Enterprise IT Operations

We’ve reached an inflection point where AI has moved from experimental technology to operational necessity. Their story is a preview of how modern ITOps will function in an increasingly complex digital landscape.

And the broader implications are impossible to ignore. Enterprise IT leaders everywhere are grappling with the same fundamental challenges that nearly overwhelmed this organization: hybrid environments that span multiple clouds and on-premises systems, shrinking IT budgets that demand more efficiency from smaller teams, and user expectations that continue to expand while tolerance for downtime approaches zero.

Traditional approaches to IT operations simply can’t scale to meet these demands. The manual processes, reactive workflows, and human-intensive troubleshooting that defined IT operations for decades are breaking down under the weight of modern complexity. This company’s pre-AI state represents the inevitable outcome of trying to manage 21st-century infrastructure with 20th-century methods.

Edwin AI and similar intelligent automation platforms represent a fundamental evolution in how IT operations function. Engineers freed from the constant noise of false alarms can focus on strategic initiatives, complex problem-solving, and the kind of high-value work that actually moves organizations forward. The result is operations teams that can do more with less while maintaining higher service levels than ever before.