by Jay Zammit, Chief Operating Officer, OnStak

Across industries, investment in artificial intelligence is surging. Yet despite this momentum, a sobering pattern continues to emerge: the majority of AI initiatives struggle to move beyond experimentation. Many stall before they reach production, and even those that are deployed often fail to deliver sustained value to the business.

The root issue isn’t whether the underlying technology works. It’s whether the organization knows how to make it work— in a way that is strategic, operational, and scalable. In practice, this means more than building models. It requires aligning those models with real business priorities, embedding them into workflows, and ensuring continuous visibility into their behavior and performance. Without these foundations—governance, infrastructure, observability, and data readiness—AI systems can easily drift into irrelevance, or worse, introduce new forms of risk and inefficiency.

Organizations that succeed do so by grounding their efforts in business outcomes from the start. They prioritize data readiness and design every solution for operational fit—not just technical novelty. Observability is treated not as an afterthought, but as a core business enabler, ensuring performance can be measured, outcomes traced, and improvements made in real time. This visibility protects investments, accelerates time-to-value, and enables what works to scale with confidence. Deployment is seen as the starting point—not the finish line—supported by the infrastructure, processes, and accountability needed to make AI a durable, high-impact capability.

Closing the Gap Between Potential and Performance

At OnStak, we help organizations close the gap between AI potential and AI performance. Our focus is on deploying solutions that don’t just prove a concept—but produce measurable, operational impact. Every implementation is designed to serve a business objective, built on resilient infrastructure, governed by best practices, and made observable from the start.

This approach has delivered measurable impact across a range of industries. At a hospital, we deployed a generative AI triage assistant and intelligent fall detection system to help reduce ER congestion and improve response times in high-risk scenarios. Predictive maintenance models and GenAI CoPilots were implemented for a manufacturer, accelerating root cause identification and minimizing unplanned downtime. In collaboration with a drive-thru service franchise, AI frameworks were delivered to improve service velocity and enhance forecasting, better aligning staff and inventory with real-time demand. A retailer leveraged our solutions to streamline store operations, while a financial services company adopted our deployments to strengthen fraud detection and automate compliance workflows—enabling faster, more confident decisions.

In each of these cases, AI didn’t succeed because the model was novel—it succeeded because it was designed to operate inside the business and deliver value where it matters most.

Why Observability Is Essential

To drive value from AI at scale, organizations need more than functioning models—they need the ability to manage, monitor, and improve those models in real time. Observability provides this foundation. It transforms AI from a set of isolated efforts into a continuously learning, business-aligned capability. When leaders have visibility into how AI systems are behaving, what’s influencing outcomes, and where performance is trending, they can make smarter decisions—faster.

This isn’t just a technical imperative; it’s a business one. Observability enables teams to detect early signs of degradation before they affect outcomes. It reveals whether models are operating on reliable data. It makes compliance auditable, and risk measurable. It turns AI from a black box into a source of insight—and accountability.

At OnStak, observability is not an add-on—it’s an essential layer of every AI deployment we deliver. Built on Splunk, our approach gives organizations visibility across data pipelines, infrastructure, and AI workflows. In high-performance environments, knowing how GPUs are being used—how much capacity is consumed, which workloads are throttling, and where power constraints or temperature may impact throughput—is also essential to maintaining reliability and controlling cost. Our GPU Performance Analytics capabilities make that insight accessible, helping teams eliminate blind spots and optimize the infrastructure powering their AI.

The organizations that embed observability into their AI strategies are the ones outperforming their peers. They resolve issues faster, adapt more intelligently, and scale AI with confidence—because they have the visibility to know what’s working and the agility to improve what’s not.

Let’s Make AI Work

So, if your goal is to build AI that performs—not just in a demo, but in the real world—start by building it the right way. Let’s make AI observable. Let’s make it operational. Let’s make it work.

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References
This blog draws on insights from industry-wide studies and analyses published between 2023–2025. Primary references include but not limited to the following:

The Root Causes of Failure for Artificial Intelligence Projects – RAND Corporation
The State of AI: How Organizations Are Rewiring to Capture Value – McKinsey & Company
State of Observability 2024 – Splunk