What We Learned from the Forrester TEI Webinar on Edwin AI’s 313% ROI

Artificial intelligence has quickly become a boardroom priority, but the conversation is changing. Organizations are no longer asking whether they should invest in AI; they’re asking how to prove it’s delivering measurable business value.

For IT leaders, that means looking beyond AI adoption, productivity claims, or token usage and measuring the operational outcomes that matter, such as fewer outages, less alert noise, faster investigations, improved service reliability, and more time for engineers to focus on strategic work instead of repetitive investigation.

That question—how do you actually measure AI ROI?—was at the center of a recent webinar featuring LogicMonitor’s Karthik Sj, Forrester Consultant Zahra Azzaoui, Forrester Principal Analyst Carlos Casanova, and Kris Manning, Head of Global IT Networks at Syngenta. 

The discussion unpacked the findings of the Forrester Total Economic Impact™ (TEI) study of Edwin AI, including how Forrester measures business value, the operational improvements enterprise customers reported, and why trust and explainability are just as important as AI capabilities when moving toward autonomous IT.

Below, we’ll recap the biggest takeaways from the discussion, and what they mean for IT leaders building the business case for AI.

AI Has Entered Its “Show Me the ROI” Era

For the past few years, the AI conversation has centered on possibility. Organizations launched pilots, experimented with copilots, and invested heavily in new AI capabilities. But as AI spending has grown, so have expectations from the executive team.

Today’s CIOs aren’t being asked how many AI tools they’ve deployed. They’re being asked what those investments have accomplished. Did they reduce outages? Improve operational efficiency? Free engineers to focus on higher-value work? Deliver measurable business outcomes?

As Karthik Sj noted during the webinar, AI usage alone isn’t a meaningful metric, saying “Just because you’re using more tokens doesn’t mean it’s useful for AI.”

That shift—from measuring AI activity to measuring business outcomes—is exactly what prompted the Forrester Total Economic Impact™ (TEI) study of Edwin AI. Rather than evaluating AI adoption or product capabilities, the study examined a more fundamental question: What measurable business value does AI deliver for enterprise IT operations?

Based on interviews with four enterprise customers, Forrester found that a composite organization using Edwin AI achieved:

• 313% return on investment over three years
• Less than six months to break even
• $3.6 million in total benefits
• $2.7 million in net present value

At first glance, a 313% ROI may seem ambitious. But as Forrester Principal Analyst Carlos Casanova pointed out during the discussion, IT organizations have spent decades trying to reduce the manual work of monitoring, correlating events, and investigating incidents. The opportunity has always existed. AI is simply making it possible to address those problems at a scale and speed that wasn’t previously achievable.

The financial results reflected that operational shift. Rather than measuring productivity in the abstract, the study quantified improvements customers experienced in day-to-day operations: less time spent managing alert noise, faster root cause analysis, improved service continuity, and lower operational overhead. As repetitive investigation was reduced, engineers could spend more time improving systems instead of manually piecing together signals across disconnected tools.

The obvious next question, then, is how did Forrester arrive at those numbers?

How Forrester Measured AI’s Business Impact

A 313% return on investment is an attention-grabbing statistic, but it’s also one that deserves scrutiny. That’s why the webinar spent time unpacking how Forrester arrived at its findings.

Rather than estimating hypothetical savings or comparing product features, the Forrester Total Economic Impact™ (TEI) methodology is designed to help organizations evaluate the potential financial impact of a technology investment using real customer experiences. It combines customer interviews, financial modeling, implementation costs, risk adjustment, and future flexibility to create a business case that organizations can adapt to their own environments.

“It’s not a prediction of what you’re going to get in terms of ROI,” stated Zahra Azzaoui, Consultant at Forrester. “Think about it as a framework that organizations can apply to their specific situations.”

For the Edwin AI study, Forrester began by interviewing four enterprise customers across industries including healthcare, retail, agriculture, IT services, and consulting. Those conversations formed the basis of a representative “composite organization”—a large enterprise with $2.5 billion in annual revenue, 5,000 employees, and a complex hybrid IT environment using Edwin AI’s Event Intelligence, AI Automation, and AI Agent capabilities.

Just as importantly, those interviews revealed remarkably consistent operational challenges. Customers described environments overwhelmed by alert noise, fragmented monitoring tools, manual investigation, and increasingly complex hybrid infrastructure. Engineers were spending too much time correlating signals across multiple systems simply to understand what was happening before they could begin resolving issues.

Those shared pain points became the foundation of the financial analysis. Instead of measuring AI adoption in isolation, Forrester evaluated how improvements in day-to-day operations, like reducing manual effort, accelerating investigations, improving service continuity, and simplifying monitoring workflows—translated into measurable business value over time.

The result isn’t a promise of a 313% ROI for every organization; it’s a structured framework that shows where AI created measurable value for the customers interviewed and provides a model IT leaders can use to evaluate the opportunity within their own environments.

The next question, then, is where did that value actually come from? The study identified five operational improvements that collectively drove the composite organization’s financial return.

Where the ROI Comes From

The Forrester study found that the composite organization’s return wasn’t driven by a single breakthrough. It came from a series of operational improvements that reinforced one another. By reducing manual work, improving visibility, and accelerating investigations, Edwin AI changed how engineering teams spent their time—and, ultimately, how they operated.

Cut Through Alert Noise

The largest source of value came from solving one of IT’s oldest problems: too many alerts and not enough signal.

Customers described environments where engineers spent hours reviewing notifications, manually correlating events, and determining what actually required attention. By automatically correlating related events and filtering out unnecessary noise, Edwin AI helped the composite organization reduce alert noise by up to 90%, generating $1.4 million in productivity savings.

As Forrester Consultant Zahra Azzaoui observed, reducing alert noise doesn’t simply make engineers more efficient—it “changes how they operate day to day.” Once teams spend less time sorting through alerts, they can focus on solving the issues that actually matter.

Accelerate Root Cause Analysis

Reducing noise is only the first step. Engineers still need to understand why an incident occurred.

Before implementing Edwin AI, customers described manually piecing together logs, metrics, and infrastructure data across multiple monitoring tools before they could even begin remediation. By automatically correlating signals and surfacing likely root causes, Edwin AI helped the composite organization reduce time spent on root cause analysis by up to 70%, creating $659,000 in value..

For Kris Manning, Head of Global IT Networks at Syngenta, that operational shift was the real win. Rather than spending hours “inside the innards of a tool,” his team could focus on “improving the services that we provide to our customers.”

Improve Service Reliability

Faster investigations save engineering time, of course, but they also improve business outcomes.

According to the study, earlier detection and quicker diagnosis helped the composite organization reduce mean time to resolution (MTTR) for priority incidents by up to 50%, resulting in $872,000 in value.

As Manning explained during the webinar, “the technology bit of it isn’t as important as the business outcomes.” The goal is improving service reliability, reducing outages, accelerating recovery, and ultimately delivering a better experience for users.

Reduce SLA Risk

Customers also reported improvements in how they identified and prioritized critical issues.

With better correlation and richer operational context, teams were able to respond to high-impact incidents sooner, reducing SLA breaches by up to 40% and generating another $367,000 in savings for the composite organization.

Rather than reacting to every alert, teams could focus their attention where it would have the greatest business impact.

Simplify Operations

Finally, customers described another source of value that often goes unnoticed: simplifying the operational overhead of legacy monitoring.

Instead of replacing existing monitoring investments overnight, organizations layered Edwin AI into their environments and gradually reduced the manual effort required to manage fragmented event management workflows. The composite organization reduced that effort by up to 70%, contributing another $310,000 in operational savings.

As Forrester Principal Analyst Carlos Casanova noted, organizations have been trying to eliminate this kind of operational toil for decades. AI finally made it practical to automate work that had long consumed engineers’ time.

Taken together, these findings reinforce one of the webinar’s central themes: AI creates value not because it helps engineers work a little faster, but because it fundamentally changes how they spend their time. As repetitive investigation, manual correlation, and alert triage are automated, engineering teams can focus more of their expertise on improving reliability, preventing outages, and delivering strategic value to the business.

The Biggest Shift is How Engineers Spend Their Time

While the financial outcomes of the Forrester study are compelling, one of the webinar’s most interesting discussions centered on something less tangible: how AI changes the day-to-day work of engineering teams.

Kris Manning described an environment familiar to many enterprise IT organizations. His team managed a global network spanning more than 90 countries and 440 sites, generating enormous volumes of operational data. Before Edwin AI, engineers spent much of their time mentally correlating information across multiple tools, separating real issues from noise, and constantly tuning monitoring systems.

The challenge was never a lack of engineering expertise. On the contrary, it was where that expertise was being spent. As Manning explained, his team wanted to move away from “spending all of this time inside the innards of a tool” and instead focus on “improving the services that we provide to our customers.”

That shift echoed what Forrester heard across its customer interviews. As Zahra Azzaoui noted, engineers weren’t simply working faster. They were “shifting away from that reactive ticket management… towards more proactive reliability and improvement work,” allowing organizations to create value well beyond shorter investigations.

That’s an important distinction. The goal isn’t simply to reduce mean time to respond or mean time to resolution, although those metrics remain important. It’s to reduce the amount of engineering effort spent on repetitive investigation in the first place.

AI increases team productivity, reduces repetitive investigation, and gives skilled engineers more time to improve network services rather than constantly reacting to operational noise. And ultimately, that’s where AI creates its greatest value. It amplifies engineers, instead of replacing them. By automating correlation, investigation, and prioritization, AI gives engineering teams more time, more context, and more capacity to improve reliability, strengthen critical services, and focus on the work that moves the business forward.

Why AI ROI Goes Beyond Cost Savings

The Forrester study quantified substantial financial returns, but some of the most valuable outcomes never made it into the ROI model.

As Zahra Azzaoui explained during the webinar, customers highlighted “a number of unquantified benefits that were not included in the financial analysis, but were still really important to the overall value story.” Those outcomes may be harder to measure consistently across organizations, but they often shape the long-term success of an AI initiative.

One recurring theme was faster onboarding. With better alert correlation and immediate visibility into new environments, organizations were able to stabilize new deployments more quickly and begin delivering value sooner.

Customers also described better leadership visibility. Centralized dashboards and AI-driven insights gave IT leaders a clearer understanding of system health, making it easier to identify emerging risks, communicate operational performance, and make more informed decisions across the business.

For many organizations, Edwin AI also supported broader AI-first transformation initiatives. Rather than treating AI as a standalone experiment, teams embedded intelligence into day-to-day IT operations while demonstrating measurable progress toward larger modernization goals.

Interviewees operating in regulated industries pointed to additional benefits around compliance readiness and data sovereignty, while several customers described their relationship with LogicMonitor as a strategic advantage, citing opportunities to collaborate on product development, test new capabilities, and help shape the product roadmap.

None of these outcomes appear in a traditional ROI calculation, but they’re often what determine whether an AI initiative delivers lasting value. Cost savings help justify the investment. Better visibility, stronger governance, faster time to value, and closer alignment between IT and business objectives help organizations maximize it.

The Future Isn’t More Automation; It’s Better Automation

When people talk about autonomous IT, it’s easy to imagine a future where AI simply takes over more work. But the webinar panelists described something more deliberate. The goal isn’t automation for automation’s sake, but instead, it’s building enough trust to automate the right work at the right time.

Yesterday, AI helped teams understand what was happening across their environments.

Today, it investigates incidents, identifies likely root causes, and recommends the next best actions.

Tomorrow, it will automate routine, low-risk operational tasks so engineers can spend less time on repetitive work and more time improving critical services.

But moving beyond that isn’t simply a technology challenge. It’s a human one.

As Manning explained, “the real breakthroughs are yet to happen, and the real business value will be realized when people’s levels of trust increase.” Organizations will only allow AI to take on greater responsibility as they gain confidence that it is making the right decisions.

Casanova expanded on that idea. Trust doesn’t come from asking engineers to blindly accept AI recommendations. It comes from transparency and explainability. Teams need to understand how an AI system reached its conclusions before they’ll allow it to act autonomously in production.

That becomes even more important as AI moves from recommending actions to taking them. A human mistake might affect one system. An AI operating at machine speed can repeat that mistake thousands of times before anyone notices. As a result, organizations need governance, guardrails, and clear visibility into AI-driven decisions before they can confidently expand automation.

The future of autonomous IT, then, isn’t about removing engineers from the equation. It’s about creating an operating model where AI handles the repetitive analysis it’s uniquely suited for while engineers provide the judgment, oversight, and expertise that machines still can’t.

As organizations mature their AI strategies, success won’t be measured by how much they’ve automated. It will be measured by how intelligently—and how confidently—they’ve automated.

Key Takeaways for IT Leaders

As AI adoption matures, success will be measured less by how much AI an organization uses and more by the business outcomes it delivers. The webinar surfaced five lessons for IT leaders evaluating AI for operations:

• Measure business outcomes, not AI activity: Token consumption, model usage, and AI adoption are poor indicators of value. Focus instead on operational metrics such as alert reduction, faster investigations, improved service reliability, engineering productivity, and reduced downtime.
• The biggest returns come from improving operations: The Forrester study found that ROI wasn’t driven by a single breakthrough—it came from multiple operational improvements that compounded over time, from reducing alert noise to simplifying monitoring workflows.
• The real opportunity is giving engineers their time back: AI creates its greatest impact when it eliminates repetitive investigation, manual correlation, and alert triage, allowing engineers to focus on improving systems rather than constantly reacting to them.
• Trust determines how far organizations can automate: Explainability, transparency, and governance are what allow organizations to move from AI-assisted recommendations to autonomous action with confidence.
• Build your AI strategy around measurable business value: The most successful AI initiatives solve real operational problems, support broader business objectives, and demonstrate outcomes that leaders can clearly understand and defend.

From AI Experimentation to Business Value

The conversation around AI has entered a new phase. Organizations are no longer investing because AI is new—they’re investing because they expect measurable improvements in reliability, operational efficiency, and business outcomes.

The Forrester Total Economic Impact™ study provides a practical framework for evaluating those outcomes. More importantly, it reinforces a broader shift happening across enterprise IT. The most successful AI initiatives aren’t defined by the sophistication of their models, but by their ability to solve real operational problems and deliver measurable value.

The webinar also made something else clear. Autonomous IT isn’t simply the next technological milestone—it’s the next organizational one. AI is already capable of reducing alert noise, accelerating investigations, and recommending actions. The question now is whether organizations have the trust, governance, and operational maturity to let it take on greater responsibility.

For IT leaders, that means the conversation is no longer about whether to adopt AI. It’s about how to measure its impact, where to apply it first, and how to build the confidence to expand its role over time.

Ultimately, the future of IT operations won’t be defined by how much AI organizations deploy. It will be defined by how effectively they combine AI with human expertise to build more resilient systems, empower their engineers, and deliver better outcomes for the business.