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GenAI Stack Consolidation: A CIO Blueprint to Escape Pilot Purgatory and Deliver Enterprise ROI

Unburden.cc • • 3 min read

Your Singapore team’s chatbot cut support costs by 18%. Tokyo’s supply-chain model shaved six days off lead time. Yet, board members still ask the critical question: “Where is the enterprise-wide ROI?”

Across APAC, 73% of GenAI pilots never scale. They are trapped by duplicative tools, siloed data, and zero governance. The culprit is an ad-hoc stack that devours 70–80% of project budgets on plumbing, not profit.

Key Takeaways for CIOs

  • Unify data into a governed fabric to slash integration effort and meet APAC sovereignty rules.
  • Standardize on one MLOps golden path to eliminate shadow AI and speed model reuse.
  • Deploy a single control plane for compliance, cost transparency, and board-ready ROI metrics.

This article operationalizes the Consolidate pillar of our Centralize. Consolidate. Control. framework. Follow the three mandates below to convert scattered experiments into a revenue-driving GenAI platform.

Mandate 1: Unify the Data Fabric into a Single Source of Intelligence

Generative AI is only as powerful as the data it can access. Today, every new pilot rebuilds its own pipelines—a redundancy that consumes 70–80% of resources and makes Retrieval-Augmented Generation (RAG) unreliable.

The fix is an intelligent metadata layer, not merely another warehouse. A unified data fabric gives every model a governed, real-time view across on-prem lakes, regional clouds, and sovereign data zones. This is critical for meeting data requirements, such as those outlined in Indonesia's AI strategy and similar APAC regimes.

Companies leveraging AI-driven forecasting and data intelligence solutions inside this fabric report 40% faster deployment and 25% lower compliance costs, effectively turning data sprawl into a competitive advantage.

Mandate 2: Standardize the MLOps and Tooling Stack

Shadow AI flourishes when one team uses Azure AI, another Vertex AI, and a third experiments with open-source models. The result is security gaps, duplicated effort, and zero model reuse.

Establish one board-approved MLOps golden path—complete with a container registry, feature store, model catalog, and drift monitoring baked in. This shared runway lets data scientists publish models that any business unit can consume, mirroring the AI-powered success and transformation seen in scaled enterprises.

Think of this standardization as consolidation without service interruption: the business keeps running efficiently while the foundational infrastructure gets stronger and more secure.

Mandate 3: Implement a Centralized Governance and Cost Management Layer

Fragmented systems obscure data lineage, model behavior, and—crucially—total cost of ownership. A single control plane is essential to answer the board’s two recurring questions: “Are we compliant?” and “What is the ROI?”

Integrate policy engines, audit trails, and real-time cost dashboards into the same pane of glass. Leading providers are already embedding this oversight inside AI-first platforms, enabling CIOs to shift the conversation from project cost to strategic value.

Robust governance is also the prerequisite for successfully building enterprises in the age of AI at regional scale without incurring regulatory surprises.

Execute these three mandates to collapse tool sprawl, free up budget for innovation, and turn GenAI into a predictable profit engine—escaping pilot purgatory for good.