A Global Brand’s AI Hub Journey
Enterprise AI Governance from Pilot to Production

Client Problem

AI Ideas were Never the Problem: The retailer had volumes of AI use cases flowing in from every department of their organisation. Right from store operations to internal technology teams, customer-facing functions and hackathon initiatives, they were no less keen on fresh AI ideas. The challenge was no longer generating ideas but taking them successfully into production.

Every New Use Case Started from Zero: Every AI initiative followed a largely manual onboarding process. It took them months for a single use case to get started. Multiple teams had to coordinate infrastructure provisioning, configure AI resources, request approvals, establish development environments and validate access across different platforms. The major challenge was that every use case repeated the same lengthy process. This adversely affected the delivery process by slowing down the entire path to production, consuming valuable engineering efforts.

Platform Teams Became the Bottleneck for Innovation: The platform team became the centre of every request. Whether provisioning Microsoft Foundry projects, creating Databricks workspaces, configuring LLM endpoints or modifying model limits, almost every operational task depended on manual intervention. Even routine configuration updates required platform engineers to modify things manually.

Governance Was an Afterthought: Governance was another major challenge. Documentation, approvals and implementation evidence were scattered across multiple emails and portals. There was no way to understand who requested a use case, who approved it, how it evolved. The bare minimum to know whether the final implementation aligned with the original intent was also missing. AI adoption grew, this fragmented approach towards governance made traceability and audit readiness extremely difficult.

Two AI Platforms. Zero Unified Visibility: Like many other huge organisations, this one too relied on both Microsoft Foundry and Databricks. Each platform served an important purpose, but when it came to visibility, there was a lack of unified tracing. That meant every time teams had to move between these environments to understand each of them separately, making governance even more complex.

Root Cause Analysis

Considering the whole scenario, Cloudaeon did not consider these challenges as individual operational issues. We conducted a detailed assessment of how AI use cases were being onboarded across the organisation. The root cause analysis revealed that the organisation already had capable AI engineers, platform teams, governance functions, Microsoft Foundry and Databricks. Each team was performing its responsibilities effectively.

The real challenge was a lack of a standard operating model connecting business sponsors, governance teams, platform engineers and AI developers throughout the complete lifecycle of an AI use case.

As a result:

• Approvals became fragmented

• Infrastructure provisioning became repetitive

• Governance evidence became difficult to trace

• Pilot to production journey required significant manual coordination

Therefore, Cloudaeon concluded that the client did not need another AI platform. It needed a governed production control system.