Enterprise AI Is Stuck. Cognizant Just Built a Platform to Get It Moving Again.

Every Fortune 500 CIO has a version of the same story. A pilot that worked. Results that impressed. And a deployment that never happened. That customer service chatbot that reduced call volume by 30% in a regional test. The AI-powered finance tool that cut reporting time in half for one department. The results are real. And yet, most of those pilots are still sitting exactly where they started, in a controlled environment, serving a fraction of the users they were built for, waiting for a scale-up that never comes. The industry has a name for this. It is called ' pilot purgatory '. And right now, it is one of the most expensive problems in Corporate America. The Scaling Problem The conversation around enterprise AI has been dominated by questions of capability. Can the models perform the task, can they match human accuracy, or can they be trusted to make decisions. Those questions are largely settled. The models work. But the real question, the one that's hardly ever asked, is why so few of them ever leave the lab . Research tells a consistent story. According to a 2025 McKinsey global survey , 44% of CFOs said their organizations were already using generative AI for more than five use cases, yet the same report noted that most organizations have yet to scale AI beyond pilots across enterprise processes. Gartner has projected that by the end of 2026, 40% of enterprise applications will include task-specific AI agents, which implies that today, the majority still do not. The gap between experimentation and operationalization is not a gap in ambition. It is a gap in execution. The reasons are familiar to anyone who has sat in an enterprise technology meeting. Infrastructure costs are prohibitive. Scaling AI from a pilot to thousands of users requires GPU capacity, data pipelines, and governance tooling that most companies have not built. Data is messy. A pilot can run on clean, curated data, but production deployment has to survive contact with the full complexity of enterprise data environments. Legal and compliance teams pump the brakes. Regulated industries in particular face mounting scrutiny over AI governance, auditability, and model accountability. And employees do not trust the systems enough to rely on them for consequential decisions. Each of these is a real barrier. But they are not equally solvable. And that distinction matters enormously for anyone trying to move AI from pilot to production. What Cognizant Is Betting On On March 16, 2026, Cognizant announced the launch of the Cognizant AI Factory, a multi-tenant, enterprise-grade cloud platform built on Dell Technologies and NVIDIA infrastructure, designed specifically to address the infrastructure side of the pilot-to-production problem. The platform is built around a straightforward premise. Most enterprises are not failing to scale AI because they lack good ideas or capable models. They are failing because they do not have the underlying infrastructure to support AI at enterprise scale without building it all from scratch themselves. "Enterprises everywhere are racing to operationalize AI, but too often run into barriers around scale, cost and governance," said Sriram Kumaresan , Global Head of Cloud and Infrastructure Services at Cognizant. "Cognizant AI Factory changes the equation. By pairing best-in-class infrastructure with intelligent orchestration and enterprise-grade guardrails, we're supporting clients to turn AI into a durable engine for business value." The platform runs on Dell AI Factory with NVIDIA, combining Dell's PowerEdge servers, PowerSwitch networking, and PowerScale storage with NVIDIA's AI Enterprise software, NIM microservices, NeMo for LLM lifecycle management, RAPIDS for accelerated data pipelines, and CUDA-X libraries. The full-stack managed service covers the complete AI lifecycle, from ideation and experimentation through to deployment, orchestration, and ongoing operations. The Fractional GPU Innovation The most technically distinctive element of the Cognizant AI Factory is its proprietary Fractional GPU technology, built on NVIDIA's Multi-Instance GPU framework. The technology is designed to create secure, isolated GPU slices that allow multiple business units to run AI workloads concurrently within a single unified environment, without compromising data integrity or security. This matters more than it might initially appear. GPU access has been one of the most significant cost barriers preventing US enterprises from scaling AI. Dedicated GPU infrastructure at the scale required for enterprise-wide deployment is expensive, prohibitively so for many mid-market companies, and inefficient even for large enterprises where workloads are uneven across departments. A shared infrastructure model that allocates GPU capacity dynamically, the way cloud computing allocated server capacity a decade ago, could fundamentally change the economics of enterprise AI. Accord

Enterprise AI Is Stuck. Cognizant Just Built a Platform to Get It Moving Again.

The Scaling Problem

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