Live Webinar

From Cost to Competitive Edge: How MSPs Can Unlock Profit with LM Logs

Thursday, October 2, 2025
1:00 PM BST / 8:00 AM EST

Logs are no longer just backend data—they’re a strategic asset. Join us to see how LM Logs helps MSPs deliver smarter, faster support with AI-powered insights, flexible retention, and scalable pricing. Learn how to turn logging into a high-value, margin-protecting service.

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MSPs face growing pressure to meet rising client expectations, control costs, and deliver stronger SLAs. As observability becomes foundational to IT operations, logs are evolving from backend data into a strategic asset.

Join this webinar to discover how LM Logs helps MSPs move from reactive support to proactive, high-value service delivery.

What you’ll learn: 

• How LM Logs helps your team accelerate triage and root cause analysis by correlating logs, alerts, and metrics and detect anomalies in real time using AI-driven insights
• How to reduce escalations and improve resolution times with AI-summarised log data that’s easy to interpret, contextual anomaly detection amd automated tools that identify root causes and suppress noise

Plus create flexible, profitable log strategies. Whether your pricing model is tiered or flat-rate, LM Logs offers the flexibility to scale efficiently. With Log Partitions, you can:

• Customise retention by customer or use case
• Apply role-based access controls (RBAC)
• Set usage thresholds to manage cost predictably

Explore how to maximise the impact of LM Logs through a coordinated go-to-market strategy—featuring log-inclusive service tiers, tailored pricing models that protect margin, and differentiated hybrid observability that maps to client SLAs.

Live Webinar

Supporting Sustainability Reporting Requirements for Your Managed Service Clients

Thursday, September 18, 2025
1:00 PM BST | 8:00 AM EST

As global sustainability regulations evolve, managed service providers (MSPs) are uniquely positioned to guide clients through these changes and unlock new strategic value in the process.

The Corporate Sustainability Reporting Directive (CSRD) is now in effect, requiring enterprises to disclose risks and opportunities related to environmental and social issues in their annual reports. CSRD requirements apply for the first time to Financial Year 2024 reporting, published in 2025.

Join LogicMonitor and Electricity Maps to explore how MSPs can respond to this shift with LogicMonitor’s new Sustainability Monitoring solution.

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What to expect

See how MSPs can meet new sustainability reporting demands and unlock strategic value in the process.

• Gain visibility with LogicMonitor Sustainability Monitoring
• Explore a customer use case
• See a live demonstration

Why attend

Sustainability has become a core client expectation—not just a “nice to have.” This session will help you:

• Support clients with CSRD and ESG reporting requirements
• Deliver proactive, high-impact advisory services
• Differentiate your MSP with sustainability-focused solutions

Speakers

Anton Vedel Sales Lead Electricity Maps

Jordan Amos Regional Sales Manager, UKI LogicMonitor

Thomas Coventry-Brooker Head of Customer Technical Architects, EMEA LogicMonitor

Insights and Automation: 3 ways to modernize your MSP with LogicMonitor

How MSPs can leverage automation to enhance their service delivery and improve the productivity of their teams

Companies are embracing IT automation. As MSPs look to scale operations in 2022 and beyond, there are a number of technologies and methods available to improve efficiency and deliver a more seamless experience for end customers. Automation is one way to increase delivery velocity and onboard more customers with fewer engineering resources.

Download this whitepaper for actionable tips on how to use automated processes to save time, do more, continue to delight your end customers.

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.

See how agentic AIOps automates IT incident response.

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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.

An operations team at one of the Asia-Pacific’s largest managed service providers (MSPs) was drowning in their own success. Years of investment in monitoring tools and automation had created comprehensive visibility—and comprehensive chaos. Engineers opened dashboards each morning to find thousands of alerts waiting, with critical incidents buried somewhere inside.

The scale of the problem was overwhelming their capacity to respond effectively. As the business grew, meeting SLAs became increasingly difficult, and service quality suffered under the weight of alert fatigue.

The MSP needed a fundamental change in approach. That change came in the form of Edwin AI, an AI agent for ITOps. Implementing this AI-powered incident management product achieved measurable results within weeks. Alert noise dropped by 78%, incident volumes decreased dramatically, and the team shifted from reactive firefighting to strategic problem-solving.

Here’s how they transformed their IT operations.

The Solution: Let Edwin AI Do the Sorting

The MSP implemented Edwin AI, LogicMonitor’s AI agent for ITOps, to process alert streams from their existing observability infrastructure. Edwin AI operates as an intelligence layer between their current tools, ingesting raw alerts from across the technology stack, identifying patterns, eliminating duplicate issues, and surfacing incidents that require human attention.

Instead of engineers manually connecting related events across different systems, Edwin AI performs correlation work automatically and routes consolidated incidents directly into ServiceNow.

The implementation created immediate operational changes:

• Alerts from multiple monitoring tools flow into a unified stream
• Redundant notifications are grouped together before ticket creation
• Related events from different systems are connected to provide complete incident context
• Each actionable event arrives in ServiceNow with full background information

Engineers now receive incidents with the context needed to begin troubleshooting immediately. Edwin AI eliminated the need to hunt through multiple systems to understand system failures. By converting fragmented alert streams into structured incident workflows, it allows technical teams to apply their expertise to resolution rather than information gathering.

See how Edwin AI streamlines incident response.

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The Results: From Reactive to Strategic

Edwin AI delivered measurable improvements within weeks of implementation, including:

• 78% reduction in alert noise. Engineers focus on genuine issues rather than filtering false positives
• 70% deduplication rate. Repetitive tickets eliminated at the source, reducing confusion in ServiceNow
• 67% alert correlation across systems. Related incidents linked automatically for complete context
• 85% drop in ITSM incident volume. Fewer, more meaningful tickets reduce cognitive load on engineering teams

These improvements freed up significant engineering time. The team can now concentrate on high-impact incidents and resolve them more efficiently. With fewer context switches between low-priority alerts, engineers gained capacity for proactive system improvements.

The operational transformation benefited both customers and staff. Service quality improved while engineer burnout decreased. The MSP gained a clearer path toward operational excellence through intelligent incident management.

How to Create a Smarter Workflow, Not Just a Faster One

Edwin AI restructured the MSP’s entire incident management process by converting raw alerts into comprehensive, contextual incidents. Engineers receive complete information packages rather than fragmented data requiring manual assembly.

Each incident now includes:

• A clear timeline showing when alerts triggered
• Correlated signals demonstrating how issues connect across systems
• Guided actions with suggested resolution steps

Engineers work with complete narratives that explain what happened, the business impact, and recommended responses.

ServiceNow evolved from a ticket repository into a comprehensive source of truth. Edwin AI feeds deduplicated and correlated events into the ITSM system, ensuring each ticket contains full context rather than isolated alert fragments.

According to the operations lead: “Edwin AI gives us clarity on what’s actually meaningful. We see the complete picture instead of puzzle pieces.”

This workflow transformation changed how the team approaches incident management, shifting from information gathering to solution implementation.

What’s Next: Building Toward Autonomous Operations

The MSP’s success with Edwin AI has opened the door to even more ambitious operational improvements. With alert noise under control and workflows streamlined, they’re now exploring how AI can move beyond correlation to autonomous decision-making.

Their roadmap includes agentic AIOps capabilities that will surface instant, context-aware answers pulled from telemetry data, runbooks, and historical incidents. Root cause analysis summaries will be delivered directly in collaboration tools like Slack and Teams, accelerating team decision-making. And Edwin’s GenAI Agent will also provide runbook-based recommendations that combine Edwin’s pattern recognition with the MSP’s own operational expertise.

The long-term vision extends beyond faster incident response to fundamentally different operations. Instead of engineers reacting to system events, AI will handle routine remediation while humans focus on complex problem-solving and strategic improvements. This evolution from reactive to proactive to autonomous operations represents the next phase in IT operations maturity.

Their operations lead frames it simply: “We’ve proven AI can sort the signals from the noise. Now we’re working toward AI that can act on those signals automatically.”

Accelerate your incident response.

Read the guide →

Why It Matters for Every MSP

AIOps environments have reached a complexity threshold that challenges traditional management approaches. Hybrid architectures, escalating customer demands, and continuous service expectations create operational loads that strain human capacity.

This MSP’s transformation demonstrates a replicable approach: intelligent alert filtering eliminates noise before it reaches human operators, automated correlation and deduplication prevent redundant work, and engineers gain capacity for strategic initiatives that drive business value.

The operational model shift from reactive alert processing to proactive system management addresses the fundamental scalability challenge facing managed service providers today.

According to their operations lead: “Modern ITOps generates a storm of signals no human team can sift alone. AI lets our people do more with less and still raise the bar on service. It turns complexity into a competitive advantage.”

MSPs operating without AI-powered incident management face mounting pressure as alert volumes continue growing while human capacity remains fixed. Organizations implementing intelligent automation now establish operational advantages that become increasingly valuable over time.

For MSPs evaluating their incident management approach, this transformation offers a clear example of how AI can turn operational complexity from a burden into a competitive advantage.

As one of Australia’s leading managed service providers, Tecala is trusted by a wide range of organizations to deliver IT services that drive performance, security, and innovation. But their ability to scale was hitting a wall.

Manual workloads. Infrastructure bottlenecks. Slow onboarding that delayed client value. These legacy challenges were getting in the way of Tecala’s growth and service excellence.

A fast, future-ready transformation Tecala knew they needed to modernize—fast. Their teams were stretched, and onboarding new customers was taking far too long. That’s when they turned to LogicMonitor.

With LM Envision, Tecala transformed their monitoring strategy in just 90 days.

Here’s how LogicMonitor helped:

•  70% faster onboarding. Tecala reduced new customer onboarding time by more than two-thirds—accelerating time-to-value and improving the customer experience.
• 20% more engineering capacity. With automation in place, Tecala reclaimed valuable engineering time and redirected it to innovation and high-impact initiatives.
•  Single-pane visibility across customers. LogicMonitor’s multi-tenant architecture allowed Tecala to monitor and manage all client environments through one unified platform—without compromising on data separation or security.
• Ready for the future with AI and automation. The LogicMonitor platform laid the foundation for agentic AIOps with Edwin AI and seamless ServiceNow integration, empowering Tecala to reduce alert fatigue, enable proactive incident response, and support usage-based billing.

Why it matters for MSPs

Managed service providers face constant pressure to scale efficiently, deliver faster results, and protect service quality across a growing portfolio. Tecala’s story proves that the right observability platform can unlock all three—while setting the stage for AI-powered operations.

With LogicMonitor, MSPs can:

• Automate onboarding and service delivery
• Free teams from low-value, manual monitoring tasks
• Build smarter services with predictive analytics and AIOps
• Scale customer environments without adding risk or overhead

The outcome?

Faster time-to-value, greater efficiency, and more strategic focus—for Tecala and the customers they serve.

Assessment

The Modern MSP Blueprint: Evaluating Your Business Evolution Readiness

Break through the MSP ceiling with a self-assessment that actually moves the needle.

Your clients’ tech environments grow more complex every quarter. Meanwhile, your competitors are scrambling to reinvent themselves. Where do you fit in? Our MSP Evolution Assessment cuts through the noise to show you exactly where you stand—and what moves will actually drive growth.

You’ll be able to:

• Benchmark your current capabilities against industry standards
• Identify specific growth opportunities in your service delivery
• Prioritize investments that will yield the greatest returns
• Create a roadmap for transforming into a Next-Gen MSP
• Gain competitive advantage in an increasingly complex market

What we evaluate:

Our assessment examines your business across five critical dimensions:

1. Hybrid Infrastructure Capabilities
   How well you manage environments across cloud and on-premises systems
2. AI, Data Integrity & Innovation
   Your readiness to leverage AI and data-driven insights
3. Customer Retention Excellence
   How effectively you retain and grow your existing client relationships
4. Tool Rationalization
   The efficiency and effectiveness of your technology stack
5. Co-Managed Services
   Your ability to partner with clients’ internal IT teams

Once you’re done, turn your insights into real transformation. Contact our MSP experts to help you:

• Analyze your results in depth
• Identify your biggest growth opportunities
• Build a practical roadmap for evolution
• Implement strategies that deliver measurable results

Do the assessment

Whitepaper

6 ways MSPs can automate with LogicMonitor

Automation is invaluable to managed service providers seeking to streamline customer onboarding, reduce alert noise, and improve uptime.

By automating the end-to-end monitoring process, MSPs can unlock their potential for growth, shifting the focus from manual labor to strategic initiatives.

On-demand webinar: Unlocking the Path to Automation with LogicMonitor

Watch webinar

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The Ultimate Guide to Monitoring and Automation for MSPs

Download the eBook

This eBook covers:

• The importance of automation for MSPs
• Different types of automation
• Automation pros and cons
• Choosing the right platform for automation

Download eBook