Generative AI is only as accurate, responsible and trustworthy as the data that fuels it. While large language models (LLMs) offer significant potential, their performance is deeply influenced by the quality and structure of the enterprise information they rely on.

According to our Pathway to GenAI Competitive Advantage report, 54% of business leaders identified hallucinations, algorithmic bias and data errors as key challenges undermining GenAI performance. These issues are often the result of models being trained or prompted on unstructured enterprise data that is fragmented, outdated or poorly governed.

This is particularly concerning given that 70% to 90% of enterprise data is unstructured and may be entirely missing from AI pipelines. From shared drives and email archives to collaboration platforms and legacy systems, structured, semi-structured and unstructured content is scattered across environments that often lack integration, visibility and governance.

Without a way to discover, understand and manage this content, GenAI initiatives are likely to produce incomplete or misleading outputs, erode trust, and increase exposure to regulatory and reputational risk.

The unstructured data challenge

In most enterprises, unstructured data management is still an unsolved problem. Information is scattered across systems that were never designed to work together: file shares, archives, cloud storage, on-prem document management tools, the list goes on. Often, no one knows exactly what’s stored where or whether it’s accurate, relevant or compliant.

This lack of oversight can be a strategic obstacle. In fact, 69% of business leaders cited data accuracy and reliability as the top barrier to unlocking GenAI value, according to our research.

The sheer scale of the problem often leads to inaction. Many teams assume they need to clean and govern everything before GenAI can be deployed responsibly. That’s not the case.

You can start small and specific, identifying the pockets of content relevant to a given GenAI use case, and preparing that data first.

Whether you’re building an AI agent for policy search or applying generative AI to customer queries, EncompaaS helps you find and prepare the content that matters without needing to overhaul everything at once.

How EncompaaS transforms unstructured data into GenAI-ready assets

The EncompaaS platform uses next-generation AI to discover, understand and organise enterprise data at scale, in place, and across both cloud and on-prem environments. It turns unstructured, semi-structured and structured content into governed, contextualised information that GenAI can use with confidence.

Here’s how it works:

  • Enterprise-wide discovery and classification – EncompaaS automatically scans and classifies data across repositories, identifying sensitive information, business-critical content and redundant, obsolete or trivial (ROT) data. This supports fast, precise targeting of information for specific generative AI applications.

  • Contextual enrichment and metadata layering – Through AI-powered enrichment, the platform applies business context, relationships, ownership and sensitivity labels to content, improving data integrity and interpretability for GenAI and generative machine learning models.

  • Normalisation of diverse formats – EncompaaS converts diverse file types and unstructured formats into consistent, AI-ready structures. This makes analysing unstructured data simpler and more scalable.

This ensures that the data used to train, prompt or inform AI agents is accurate, explainable and aligned with business intent.

Reducing risk in practice

The risks of overlooking unstructured enterprise content are well-documented yet frequently underestimated. Poorly managed data can undermine AI performance, introduce regulatory exposure, and erode trust in GenAI systems.

The EncompaaS platform offers enterprise-grade controls that ensure data is accessible, trusted and governed.

Here’s how EncompaaS mitigates risk in practice:

  • Preventing poor model outputs – Incomplete, outdated or irrelevant content leads to hallucinated or misleading AI responses. EncompaaS ensures GenAI systems are grounded in relevant, governed and trustworthy information.
  • Ensuring responsible data sourcing – With EncompaaS, you maintain full visibility and control over where your data is sourced from and whether it’s suitable for use in AI modelling or real-time GenAI responses.
  • Meeting compliance and security requirements – Whether you need to comply with GDPR, HIPAA, APRA CPS 234 or internal data retention policies, EncompaaS applies governance policies at the source, so you can scale GenAI without compromising compliance.

A strategic partner in unstructured AI readiness

The potential of generative AI is significant but it cannot be realised without access to clean, compliant and contextualised information. In regulated, data-rich environments, unstructured content is often the most valuable (and the most overlooked) asset in preparing for AI.

EncompaaS turns fragmented and inconsistent enterprise content into a governed, high-quality data foundation, ready to support accurate, explainable and secure GenAI applications at scale.

Whether you’re deploying GenAI in knowledge search, contract analysis, policy interpretation or customer support, the integrity of your unstructured data will determine the success of your AI initiatives.

EncompaaS provides the visibility, control and governance needed to move forward with confidence.

Contact us to learn how we help regulated organisations make unstructured data AI-ready responsibly and at scale.