Across industries, the promise of GenAI is reshaping business ambition. But while the tools evolve rapidly, the data foundation they rely on remains the limiting factor. High-quality data is essential, but quality alone isn’t enough. To support scalable, responsible and effective AI, organisations must address a more complex need: data readiness.

Data readiness speaks to the context, completeness, governance and accessibility of data, ensuring AI systems are fed the right inputs in the right way. According to Gartner, over 60% of AI projects will fail to meet business SLAs by 2026 due to insufficient data readiness.

At EncompaaS, we’ve defined data readiness across five key dimensions: correct, complete, secure, achievable and non-biased. Each plays a critical role in shaping trustworthy, usable data for AI.

1. Correct: ensuring fitness for purpose

Accuracy in AI begins with context. Data must be curated with its intended use in mind, so models interpret it meaningfully, not just syntactically. This means labeling and structuring content based on business purpose, using metadata and conceptual filters to isolate what’s truly relevant.

EncompaaS enhances this process through transparent classification, semantic enrichment, and the application of sensitivity labels such as PII or PHI. While this builds confidence in the data, it also ensures that AI models and agents are grounded in appropriate, task-specific inputs.

Without this level of clarity and labeling, AI runs the risk of drawing on irrelevant or misleading content, producing hallucinated responses and undermining reliability.

2. Complete: eliminating gaps in context

In many organisations, enterprise data remains fragmented across disconnected systems, inconsistent formats and long-standing silos. According to Gartner, just 10% to 30% of enterprise data is structured; the remainder, up to 90%, is unstructured and often overlooked by AI pipelines.

These gaps create blind spots that affect the accuracy, governance and transparency of AI outputs.

EncompaaS helps close these gaps by discovering and connecting structured, semi-structured and unstructured content across the enterprise. By applying metadata and semantic enrichment at scale, the platform makes fragmented data searchable, compliant and ready for GenAI consumption without the need for migration.

This unified view of enterprise information ensures AI models have access to complete, context-rich data for more reliable insights.

3. Secure: embedding governance by design

The risks associated with AI begin much earlier than the point of model drift. They begin with the use of ungoverned or poorly managed data. As GenAI draws insights from increasingly large and diverse enterprise data sets, the need for robust data governance becomes a critical prerequisite, not a secondary consideration.

Effective data governance policies and standards must be built into the AI data supply chain from the outset.

EncompaaS supports this by enforcing access controls, tagging sensitive information, and applying policy-based retention and deletion rules. It maintains full data lineage, tracks usage, and provides auditability to ensure compliance with regulations such as GDPR, HIPAA and APRA CPS 234.

With governance embedded across the lifecycle, organisations can ensure their AI initiatives are secure, compliant and auditable from day one.

4. Achievable: making data accessible and actionable

More data does not mean more value. Many AI initiatives stall not because data is unavailable, but because it is too broad, inconsistent or difficult to interpret. For AI to produce meaningful results, the data must be relevant, targeted and immediately usable.

EncompaaS makes this possible through intelligent pre-filtering, semantic enrichment, and vector-based retrieval. By narrowing the scope to only the most valuable and contextually appropriate content, the platform improves model performance, reduces noise and accelerates time-to-insight.

By delivering focused, high-quality inputs to AI models, EncompaaS enhances performance, reduces processing overhead, and ensures AI outputs are aligned with operational needs and strategic objectives.

5. Non-biased: enabling fair and representative outcomes

Even technically accurate AI can fail if its outputs are biased, incomplete or unfair. Bias often stems from the data itself through exclusion, duplication, or an over-reliance on narrow sources.

Bias mitigation begins with balanced, representative data coverage. That includes filtering duplicate records, enriching underrepresented data types, and ensuring diverse content sources are captured and classified.

EncompaaS helps address this at the source. Its automated discovery, enrichment and classification workflows ensure that the data supporting GenAI is relevant and trustworthy, supporting enterprise AI governance frameworks that prioritise ethics, transparency and fairness.

Laying the groundwork for scalable, secure AI

The path to AI success begins with data. Specifically, data that is correct, complete, secure, achievable and non-biased.

These five dimensions of data readiness underpin EncompaaS’s approach to preparing enterprise information for GenAI. By discovering, understanding and governing structured, unstructured and semi-structured content in place, the platform enables organisations to scale AI with accuracy, compliance and confidence.

As GenAI becomes further embedded into enterprise operations, the most successful organisations will be those that view data readiness not as a one-off initiative, but as an enduring capability.

Discover how EncompaaS can help your organisation prepare its data for AI. Book a demonstration here.