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Your AI Pilot Worked. Your Infrastructure Wasn’t Ready.

A retail group builds an AI assistant to help store managers forecast stock. In the pilot, five stores love it. Then the company tries to open it to two hundred stores and everything slows down. Data sits in three different places, the servers that ran the pilot cannot absorb the load, and the team spends more time moving workloads around than improving the model.

The model was never the problem. The ground it was standing on was.

Why Infrastructure Is the Quiet Bottleneck

The conversation around AI usually starts with models and use cases, which is the exciting part. The less glamorous part is where the data lives, how quickly it can be reached, and whether the same workload can run in the data center today and in the cloud next quarter without being rebuilt. Findings from Nutanix's 2026 Enterprise Cloud Index lean in this direction: organizations are discovering that their readiness to run AI depends heavily on how flexible and manageable their infrastructure is.

An environment built piece by piece over ten years rarely behaves like one system. Every extra silo adds cost, delays, and another place where something can break during a busy week.

Indonesian organizations tend to feel this more sharply than most. A bank with branches from Medan to Makassar, or a retailer with stores spread across several islands, often runs systems in more than one place with uneven connectivity between them. Rolling AI out across that footprint exposes every seam.

Three Questions Worth Asking This Quarter

Can we run a workload where it makes the most sense, on-premises, in a public cloud, or at the edge, without a separate toolset for each? Can we grow capacity in steps instead of buying for a peak that may never arrive? And can the people who manage this today keep up, or are they already stretched?

Honest answers to these usually show where to start. Often it is not a giant overhaul, just consolidating one or two stubborn pieces so the rest can follow.

Making the Jump From Pilot to Production

Perkom and Nutanix usually start with the unglamorous questions: where the data sits today, how many consoles your team juggles, and what happens when a workload needs to grow. Nutanix Cloud Platform lets virtual machines, containers and data services run across the data center, the cloud and the edge under one management layer, so the pilot and the rollout stop being two separate projects.

The move does not have to be dramatic. Perkom plans the migration in stages around your daily operations, so the foundation changes while the business keeps running. If an AI project is waiting on infrastructure that is not ready, an infrastructure review is a low-pressure way to see how far apart the two really are.

Author: Ghea Devita, Marketing Communication PT Perkom Indah Murni