Nexus Cove: Pages

Why Most AI Initiatives Fail

According to industry research, 85% of AI projects fail to deliver on their promises. The primary reason? Poor data quality and inadequate data infrastructure. Organizations face:

Unknown data quality issues that undermine AI model performance
Incomplete understanding of available data assets across the organisation
Governance and compliance risks that could lead to regulatory issues
Uncertainty about data readiness for specific AI use cases
Difficulty prioritising which data issues to address first
Lack of standardized data quality metrics to measure improvement

Without a thorough assessment of your data foundation, AI investments are built on unstable ground.

AI Data Audit

The Nexus Cove Data Methodology

Data Discovery & Quality

We start by building a comprehensive inventory of your data assets, mapping how data flows across your systems and pinpointing the domains most critical to your business objectives.

Governance & Compliance

We review your data governance framework to ensure roles and responsibilities are clearly defined. This includes assessing data ownership, access controls, privacy protections, and alignment with industry regulations such as GDPR or HIPAA.

AI & Infrastructure Readiness

We evaluate how prepared your data is to support AI initiatives, identifying gaps that could hinder implementation and recommending improvements to accelerate AI adoption.

Model Performance Validation

For organisations already leveraging AI or data classification models, we conduct a deep-dive analysis of model effectiveness.

Deliverables

Our AI Data Audit delivers a complete package of insights and recommendations:

Data Quality Dashboard
Gap Analysis Report
Data Governance Assessment
AI Readiness Roadmap
Technical Recommendations

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