Agentic AI has entered day-to-day operations. Systems with the ability to act, learn, and adjust are already cutting noise, speeding remediation, and giving engineers time back for work that moves the business.
In a recent webinar, Karthik SJ, General Manager, AI at LogicMonitor, and Mike Cervasio, Global Practice Manager, AIOps at World Wide Technology, explored what makes this new phase of AIOps actionable. They discussed how organizations can gauge readiness, test adoption through pilots, and scale with confidence.
See the full conversation between LogicMonitor and WWT.
WWT and many of the enterprises it advises are moving beyond experimentation, reflecting a broader evolution in enterprise IT. Organizations are embedding Agentic AIOps into daily operations, signaling a broader shift toward systems that not only detect problems but also decide and act on them.
Organizations are moving from monitoring, which provides reactive visibility, to observability, which connects signals for proactive insight. AIOps brings automation into that workflow. The next step—Agentic AIOps—adds autonomy.
This shift is enabled by advances in generative AI, large language models, and reasoning systems. With these tools, IT operations can go beyond static automation to adaptive, context-aware responses. Agentic systems do more than flag issues; they correlate telemetry, surface the root cause, and initiate remediation.
For IT teams, the impact is measured in time and focus. Noise is reduced, triage is accelerated, and engineers are freed from constant firefighting.
“With Edwin AI, we’ve moved from monitoring to observability to AIOps. Agentic AIOps is the next level—systems acting on your behalf, not just telling you what went wrong.”
Mike Cervasio
Global Practice Manager, AIOps at World Wide Technology
What It Takes to Be Ready for Agentic AIOps
Understanding what Agentic AIOps delivers is one step; preparing an organization to adopt it is another. The technology is here, but the capacity to use it effectively depends on how teams, processes, and cultures adapt.
The obstacles to adoption are rarely technical. Most stem from organizational inertia: siloed tools, knowledge concentrated in a handful of subject-matter experts, and a culture still anchored in manual firefighting. These patterns fuel alert fatigue and slow down response.
Readiness depends on three areas:
Unified observability to provide a reliable foundation across domains
Executive sponsorship to drive prioritization and manage change
Pilot projects that prove value quickly, typically by instrumenting a critical user journey and benchmarking before and after
In the webinar, Karthik emphasized that adoption should be measured by how it improves the quality of work for IT teams, not how it replaces them: “It’s about uplifting the quality of their work. Instead of waking up at 3 a.m. to troubleshoot, they can focus on projects that move the business forward.”
Don’t wait for perfect data or exhaustive preparation. Modern AI can generate value from the environment you already have. Starting small and building confidence is often the most effective path forward.
How Enterprises Build Momentum with Agentic AIOps
Adopting Agentic AIOps is less about a single deployment and more about building momentum through deliberate steps. For many enterprises, the journey begins only after a wake-up call—a major outage, a prolonged application failure, or another disruption that exposes how fragile manual processes and siloed tools have become. These moments shift observability and automation from side projects to board-level priorities.
From there, organizations need a structured path. The first step is assessing where they are on the maturity curve—monitoring, observability, automation, or autonomy—and identifying the right entry point. With that baseline in place, adoption moves through a staged sequence:
Measure performance: Instrument a critical application or user journey to establish a baseline
Run a showcase pilot: Deploy Edwin AI against that baseline to prove value quickly
Apply event-driven automation: Use AI insights to shrink MTTx and prevent business disruptions
Expand gradually: Start with suppression and triage, then extend autonomy to more complex workflows
WWT often guides customers through this process. Their readiness workshops capture pain points and translate them into a strategic blueprint, which is then tested in safe environments like its AI Proving Ground. This gives enterprises confidence that the approach will work before it touches production.
As Mike pointed out, this journey is about organizational design. Many enterprises are creating new roles such as Head of Observability and investing in SRE teams to embed resilience at the architectural level.
Karthik stressed the importance of progress over perfection. Modern AI can deliver value even in messy environments, and waiting for flawless data only delays results. The real transformation comes when enterprises move beyond pilots and begin reshaping workflows—suppressing noise, accelerating triage, identifying root causes, and automating remediation.
For adoption, the pattern is consistent: start with small, well-defined projects, prove ROI quickly, and build trust across teams. Each success creates momentum for the next, making it easier to expand Agentic AIOps across domains and scale toward autonomy.
WWT’s Proof Point: Edwin AI in Production
WWT applies the same adoption model it brings to customers inside its own environment. The LogicMonitor hybrid observability platform, Envision and Edwin AI run in WWT’s Advanced Technology Center (ATC), across the supply chain, and within the network operations center (NOC). These deployments give WWT direct evidence of what Agentic AIOps delivers at scale.
In the ATC, Edwin AI ingests data from multiple domains, correlates incidents, and accelerates root cause analysis. For supply chain and NOC operations, the system reduces noise, shortens triage, and drives faster remediation. The impact is operational resilience: fewer delays, more accurate insights, and confidence that teams can resolve issues before they escalate.
Adoption followed the same sequence WWT recommends to its customers. Pilots began with suppression and triage, anchored by baselines that measured performance before and after deployment. Autonomy increased gradually, with human approvals in place until trust in the system was established.
As Mike explained: “WWT relies on LogicMonitor across our IT operations—from the ATC to supply chain and warehouse systems, and into the NOC at Softchoice. We use it ourselves because it works, and that gives us confidence when we recommend it to customers.”
For enterprises evaluating Agentic AIOps, WWT’s use of Edwin AI is ongoing proof that the approach can operate reliably in complex, high-stakes environments.
The Business Value of Edwin AI
The impact of Edwin AI shows up in both operational performance and team experience. Enterprises lower operating costs by automating routine L1 and L2 tasks. Downtime is reduced, SLA performance improves, and exposure to penalties is minimized. At the same time, engineers spend less time buried in alerts and postmortems and more time driving projects that matter to the business.
In production environments, customers have reported outcomes such as an 88 percent reduction in alert volume—results that translate directly into efficiency gains and a better quality of work for IT teams.
Key areas of value include:
Hard ROI: Reduced OpEx through automation of repetitive workloads.
Soft ROI: Lower downtime costs and improved SLA adherence.
Productivity: Faster resolution with AI-generated RCA summaries, runbooks, and postmortems.
Strategic reallocation: Engineers freed from firefighting can focus on transformation and innovation.
What Trips Teams Up, and How to Avoid It
The ROI is clear, but realizing it requires avoiding common missteps. WWT’s experience shows that adoption stalls less from technical limits than from organizational habits. The most frequent issues are:
Waiting for ideal conditions: Delaying until data is perfectly clean or tools are fully consolidated.
Project thinking: Treating AIOps as a one-off deployment instead of an ongoing shift in operations.
Cultural inertia: Teams accustomed to firefighting struggle to adapt to automation without strong alignment and sponsorship.
Successful programs take the opposite approach. They begin with focused pilots tied to critical user journeys, measure improvements against a baseline, and expand scope only after those results build trust.
This progression is visible in both WWT’s internal adoption and its customer engagements. As Mike explained, cultural readiness is as important as technology. And as Karthik emphasized, the purpose of Agentic AIOps is not substitution but improvement: shifting engineers away from repetitive triage so they can spend their time on work that advances the business.
Where Enterprises Go from Here
The WWT case study and customer outcomes make one point clear: Agentic AIOps is already working in production. Enterprises are reducing noise, cutting response times, and giving engineers room to focus on work that advances the business.
The next step is scale. That begins with assessing current maturity, piloting in controlled environments, and expanding as results build trust. WWT and LogicMonitor provide the structure for this progression—Edwin AI as the agentic platform, and WWT’s AI Proving Ground as the environment to test and validate before moving into production.
As Karthik noted during the webinar: “Start small, prove the value, and scale from there.”
See how Edwin AI can deliver measurable results in your own environment.
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.
