Davos 2026: LogicMonitor CEO on AI's 'Hard Realities' at Scale

DAVOS, SWITZERLAND – January 16, 2026 – As global leaders converge on Davos for the 2026 World Economic Forum Annual Meeting, the dominant topic is undeniably artificial intelligence. But amidst the sweeping conversations about AI's potential to reshape society, a more pragmatic and urgent discussion is emerging—one focused not on what AI could do, but on what it takes to make it work reliably, securely, and at scale.

At the center of this crucial conversation is Christina Kosmowski, CEO of the AI-first observability platform LogicMonitor. Kosmowski is scheduled to speak on a series of high-profile panels, bringing a vital dose of realism to the alpine summit. Her message, echoed in a recent company announcement, is that scaling AI isn't about magic; it's about confronting the "hard realities" of trust, resilience, and operational discipline when AI moves from controlled experiments into the complex, high-stakes world of live business operations.

The Reality Check for AI at Scale

While boardrooms and headlines celebrate AI's transformative power, a different story is unfolding within enterprise IT departments. A staggering number of AI initiatives—some estimates suggest over 80%—never make it out of the experimental phase, a phenomenon known as "pilot purgatory." The reasons are complex, rooted in the immense operational challenges of deploying AI in real-world environments.

Enterprises grapple with fragmented data silos, poor data quality, and legacy infrastructure that buckles under the weight of AI's computational demands. Models that perform flawlessly in a lab often degrade when exposed to the dynamic, unpredictable data of a live business, a problem known as performance drift. This gap between AI ambition and operational reality is where businesses falter, investments sour, and credibility is lost.

Kosmowski’s agenda at Davos directly confronts this issue. Her discussions are set to pivot away from theoretical potential and toward the foundational requirements for success. The core argument is that AI's value can only be unlocked when it is observable—that is, when organizations have complete visibility into their entire technology stack, from on-premises servers to multi-cloud services and edge devices. Without this visibility, scaling AI is like trying to navigate a storm without a compass. As LogicMonitor's pre-Davos messaging states, "competing in an AI-first economy takes more than ambition. It takes visibility, discipline, and the guts to invest before things go sideways."

A Practitioner's Voice on a Global Stage

Kosmowski’s presence at Davos is significant, positioning her as a leading practitioner's voice on responsible AI adoption. She joins panels alongside executives from major global firms like Automation Anywhere, Bloom Energy, and Publicis Sapient, as well as venture capital leaders from NEA. Her participation in forums such as the CNBC Panel on the digital economy and the official World Economic Forum panel, "How High Can Unicorns Fly?", underscores the growing recognition that technological infrastructure is a critical component of economic strategy.

Her perspective is shaped by leading an organization that operates at the very infrastructure layer where AI's promises meet reality. LogicMonitor's platform is designed to provide the control and insight necessary to manage the complex digital systems that power modern enterprises. This experience provides a grounding counter-narrative to the often-unfettered optimism surrounding AI. According to company materials, Kosmowski aims to dig into "how organizations actually scale AI without breaking their workforce, their infrastructure, or their credibility."

This focus on operational resilience and tangible outcomes aligns with a broader theme at Davos 2026: deploying innovation responsibly. As leaders question whether they are truly redesigning work for an AI-native future or simply bolting on new tools, Kosmowski’s emphasis on building a resilient foundation offers a clear path forward. Her message is that trust in AI isn’t promised; it's built through rigorous monitoring, transparent governance, and demonstrable reliability.

The Engine of Trust: Observability and Edwin AI

To understand LogicMonitor's perspective is to understand the concept of AI-first observability. Unlike traditional monitoring tools that react to failures, observability platforms are designed to provide deep, proactive insights across a company's entire digital footprint. They unify data from infrastructure, networks, and applications into a single, intelligent view, enabling IT teams to predict and prevent issues before they impact users.

At the heart of LogicMonitor's platform is Edwin AI, which the company describes not as a simple AI add-on but as a native generative AI system purpose-built for IT operations. Edwin AI functions as an "AI agent for ITOps," designed to autonomously observe, analyze, and act on the torrent of data generated by modern hybrid environments. Its agentic architecture uses a system of specialized agents to provide predictive insights and guided troubleshooting.

This technology directly addresses the core challenges of scaling AI. For instance, one of the biggest hurdles for IT teams is "alert fatigue"—an overwhelming volume of notifications that makes it impossible to distinguish critical signals from noise. Edwin AI is engineered to reduce this noise by up to 95%, correlating thousands of raw events into a handful of actionable incidents with human-readable summaries. By predicting potential outages and pinpointing the root cause of problems with high accuracy, it enables organizations to build the resilience necessary to support business-critical AI applications. This focus on a purpose-built, explainable AI for observability differentiates LogicMonitor from competitors who may offer broader monitoring suites or wrap existing services around third-party AI models.

The New Economics of an AI-First World

The discussions Kosmowski will lead are not just technical; they are fundamentally economic. In an AI-first economy, the ability to deploy and manage AI effectively is becoming a primary determinant of competitive advantage. Companies that succeed will be those that treat AI not as a series of isolated projects but as a core operational discipline.

This requires a strategic shift in investment. Instead of focusing solely on developing novel algorithms, leading enterprises are investing in the underlying platforms that ensure those algorithms run reliably and efficiently. The ability to maintain flawless digital experiences, protect profit margins by preventing costly downtime, and accelerate innovation securely is now directly tied to the quality of an organization's observability infrastructure.

The World Economic Forum panel, "How High Can Unicorns Fly?", provides a perfect backdrop for this argument. The question is no longer just about securing funding or achieving a high valuation; it's about building a sustainable, resilient business that can deliver on its promises. As Kosmowski’s platform contends, "AI doesn’t win by magic. It wins by being run well." This new reality demands that leaders move beyond the hype and embrace the disciplined, visibility-driven work required to make AI a true engine of growth and stability.