Generative AI (GenAI) is rapidly moving from pilot projects to production environments. Across industries, CIOs and Chief Data Officers are under pressure to translate early success into enterprise-scale impact. Yet many GenAI initiatives stall before they scale.

Gartner predicts that 30% of GenAI proofs-of-concept will be abandoned by 2026 due to issues like poor data quality, escalating costs, and unclear business value. Meanwhile, McKinsey reports that 70% of leading organisations face difficulty integrating data into AI models, with challenges including poor data quality, governance gaps, and siloed infrastructure being the primary blockers.

Ultimately, GenAI models are only as powerful as the data they can access, and most enterprises are sitting on oceans of unstructured, disconnected, and poorly classified data. Without unified access and consistent governance, GenAI models are prone to producing hallucinations, duplications, and compliance failures.

To unlock real-world value, leaders must shift their focus from the model to the data layer,  from prompts to pipelines. This begins by breaking down data silos and building a connected, governed information architecture capable of supporting semantic search, responsible AI, and large-scale enterprise content management.

The cost of data silos in the GenAI era

GenAI models excel when they can reason over vast amounts of structured and unstructured data, retrieve historical knowledge, and contextually relevant surface insights. But data silos break this chain.

  • Unified enterprise search becomes impossible when documents are scattered across outdated platforms or stored without consistent metadata.
  • Consistent classification fails when governance varies across systems, increasing the risk of hallucinations, bias, or untrustworthy outputs.
  • Knowledge reuse is blocked when previous decisions, documents, or conversations are locked in legacy archives and unreachable to modern AI tools.

The outcome is irrelevant responses, duplicated work, poor uptake, and considerable compliance and AI governance risks.

Why traditional integration strategies fall short

Many organisations still rely on traditional approaches, like full-scale migrations or custom connectors to integrate enterprise content. But in the GenAI era, these methods often fail to deliver the scale, speed, and governance needed.

Migrations are slow, risky and expensive

Full migrations are disruptive and resource-heavy, often taking years to complete and introducing risks of data loss, downtime, or user pushback. McKinsey reports that only 13% of organisations fully achieve their cloud transformation goals, citing legacy complexity and cost overruns as key barriers.

Custom connectors don’t scale

McKinsey notes that 74% of cloud transformation failures are linked to over-customised architectures. Custom integrations may seem like a quick fix, but they create long-term technical debt. Each connector needs maintenance, security, and updates as systems evolve, increasing overhead and exposing organisations to integration failure. 

Visibility gaps increase governance risk

Disconnected systems lead to blind spots in data classification and lifecycle controls. Without holistic oversight, GenAI models can access unverified or sensitive data, increasing compliance exposure. In fact, 97% of organisations report gaps in their cloud risk management plans.

Traditional strategies can’t keep up with modern demands for real-time, governed access to enterprise content. To enable scalable, responsible AI, CIOs and CDOs need a smarter integration model.

The encompaas approach: Federated management, not full migration

Traditional approaches like replatforming and custom builds often fail due to their complexity. EncompaaS provides a smarter alternative, allowing GenAI adoption without data migration or duplication.

  • Federated access, zero data movement: EncompaaS integrates into modern and legacy content systems, like SharePoint, file shares, OpenText, and cloud archives, helping enterprises eliminate silos without redesigning their entire system.
  • Semantic enrichment of unstructured content: Using metadata extraction and natural language processing, EncompaaS makes unstructured data searchable through data discovery, semantic search, and intelligent search for GenAI and RAG pipelines.
  • Centralised governance across systems: EncompaaS enables data classification and retention across repositories, ensuring consistent AI data management and AI governance, even across siloed or shadow systems.
  • Context-aware normalisation for GenAI: Different systems store and tag content differently. EncompaaS uses context-aware normalisation to align content types and metadata, preparing enterprise content for GenAI applications like summarisation, insight extraction, or chatbot integration.

This federated approach allows CIOs and CDOs to modernise from the inside out, avoiding the disruption of full migration, while delivering governed, connected data to AI systems at scale.

Strategic use cases for CIOs and CDOs

EncompaaS turns fragmented, unstructured data into a governed, AI-ready asset. For CIOs and CDOs, it’s a strategic tool for applications like enterprise search, metadata management, and secure RAG at scale.

  1. Accelerate legal and regulatory response

EncompaaS enables legal and compliance teams to quickly find and summarise documents across systems using GenAI, with intelligent search, embedded governance, and no data duplication.

  1. Improve policy accuracy and decision support

Using semantic search, EncompaaS gives GenAI tools real-time access to the latest documents while enforcing document classification and sensitivity labels.

  1. Enable secure RAG at scale

For reliable retrieval-augmented generation (RAG), EncompaaS connects content systems, enriches metadata, and applies access controls, ensuring trusted, governed data without duplication.

GenAI use cases don’t have to trade off speed for safety. With EncompaaS, CIOs and CDOs can enable innovation while maintaining governance and control.

The strategic advantage: governed, connected, AI-ready data

CIOs and CDOs have long faced a trade-off between innovation and control. GenAI raises the stakes, demanding rapid, scalable access to knowledge without compromising compliance, accuracy or trust.

EncompaaS removes that trade-off.

By connecting siloed systems, enriching unstructured content, and applying consistent data classification, EncompaaS gives GenAI the context it needs without exposing sensitive or unverified information.

With federated access, centralised policy enforcement and embedded AI governance, EncompaaS provides the foundation for secure, scalable and responsible AI across legal, compliance, support and executive workflows.

Explore how the EncompaaS intelligent information management platform helps enterprises break down data silos to enable GenAI you can trust.