AI has become a core part of many business strategies. Organizations are building models, processing data at scale, and often seeing early results that look promising. Yet beyond experimentation, the story is frequently different. Only a small portion of AI initiatives ever make it into daily operations. Research from Union Bank of Switzerland (UBS) shows that by the end of 2025, only around 17 percent of organizations had AI running fully in production. This highlights a critical reality: the biggest challenge with AI is not adoption, but operating it consistently. 

Once AI is embedded into business operations and expected to run reliably across complex hybrid environments, new challenges emerge. Performance becomes harder to maintain, small changes introduce risk, and visibility across the system diminishes. This is where enterprise AI becomes essential—as an approach to running AI with clear production standards, measurable outcomes, and operational discipline. 

What Is Enterprise AI?

Enterprise AI refers to the implementation and operation of AI that is designed from the outset to function as a production system. The focus is not on how advanced a model is, but on whether the surrounding infrastructure, operational processes, security, scalability, and governance are in place to sustain AI over time. 

This is what distinguishes enterprise AI from experimental AI. Rather than operating in isolation, enterprise AI is integrated directly into applications and business processes, enabling it to run consistently across on-premises environments, data centers, and cloud platforms. 

Benefits of Enterprise AI for Hybrid and Multi-Cloud Businesses

For organizations operating in hybrid and multi-cloud environments, enterprise AI helps ensure AI runs consistently and remains under control across different platforms. With the right foundation, enterprise AI delivers benefits such as: 

  • Maintaining operational consistency across environments 
  • Strengthening AI governance and control 
  • Enabling predictable and scalable AI growth 
  • Improving operational efficiency 
  • Accelerating business responsiveness to change 

Challenges of Running Enterprise AI at Production Scale

Most organizations do not struggle to build AI models. The real challenge begins once AI is expected to operate consistently as part of day-to-day business. When AI moves from controlled environments into live systems, technical and operational complexity increases—and the limitations of existing foundations become clear. 

Common challenges organizations face when enterprise AI reaches production include: 

Increasingly Complex AI Infrastructure

AI workloads demand intensive compute resources, GPU acceleration, and well-balanced storage and networking architectures. Without infrastructure designed specifically for AI, performance becomes harder to sustain and increasingly unpredictable as scale grows.  

Fragmented Operations

Disconnected AI platforms, data pipelines, and deployment environments often create silos between data science and IT teams. This fragmentation slows the transition from experimentation to production and increases operational risk.  

Limited End-to-End Visibility

Without proper observability, organizations struggle to understand what is happening across the AI stack. Service performance, resource consumption, and downstream impact on applications often go unseen until issues surface in production.  

Why Enterprise AI Requires an Integrated Foundation

As AI becomes embedded into core business systems, fragmented approaches no longer work. Separate platforms, infrastructure, and operational tools increase complexity, introduce risk, and make it difficult to run AI consistently across environments. 

An integrated approach enables enterprise AI to operate in a more controlled manner by: 

  • Unifying AI platforms, infrastructure, and observability into a single operational foundation 
  • Reducing complexity and risk caused by disconnected tools 
  • Maintaining consistent security and governance across environments 
  • Accelerating the transition from experimentation to production 

Keeping Enterprise AI Consistent Across Hybrid Environments

Hybrid environments are now the norm for most organizations. AI must run consistently whether it is processed in an on-premises data center or in the cloud. Consistency is critical so models can be managed, updated, and operated in the same way—without adding operational overhead. 

For enterprise AI to be production-ready, organizations need a foundation that supports build, run, and observe processes end to end. When these elements are not aligned, AI becomes difficult to scale and can disrupt business operations. 

Build, Run, and Observe: A Unified Foundation for Enterprise AI

Running enterprise AI in production requires a different mindset. AI should not be treated as a one-off model development effort, but as a system that must be built, operated, and monitored continuously.  

The Build, Run, and Observe approach simplifies these challenges by clearly separating—but tightly connecting—how AI is developed, how it runs in production, and how it is monitored during operation. 

This approach can be delivered through three complementary solutions: 

Build AI with Red Hat OpenShift AI

Red Hat OpenShift AI provides an enterprise Kubernetes-based AI platform to manage the full AI and machine learning lifecycle, from development to production deployment. It enables teams to build, train, and manage AI models consistently across environments without being locked into a single vendor or infrastructure. 

With standardized MLOps practices, OpenShift AI simplifies collaboration between data scientists and IT teams while accelerating the transition from experimentation to production-ready AI services. 

Read More: A Deep Dive into Red Hat OpenShift AI: A Smart Solution for Cloud-Native Optimization 

Run AI on Enterprise-Grade Infrastructure from DELL

To operate reliably in production, AI requires infrastructure designed for sustained AI workloads. DELL Technologies delivers AI-ready infrastructure built on PowerEdge servers, supporting CPU and GPU configurations for training, fine-tuning, and inference. 

A balanced architecture across compute, storage, and networking ensures predictable performance as AI scales. With validated reference architectures, DELL helps organizations reduce deployment complexity and operational risk when running AI in enterprise environments. 

Observe AI End-to-End with Dynatrace

Dynatrace provides end-to-end observability for enterprise AI environments, including those running on Red Hat OpenShift AI. The platform automatically collects and correlates metrics, logs, traces, and business events to deliver full visibility across the AI stack. 

Using AI-driven analytics, Dynatrace helps teams monitor AI service performance, detect anomalies early, identify root causes, and manage response times and resource usage in real time—before issues impact business operations. 

Deliver Production-Ready Enterprise AI with Virtus

Delivering enterprise AI at production scale requires an integrated foundation—covering how AI is built, operated, and observed. Virtus Technology Indonesia (VTI), part of CTI Group, acts as a partner that helps organizations implement the Build, Run, and Observe approach end to end. 

Contact the Virtus team today to discuss how to build a production-ready enterprise AI foundation tailored to your business needs.