Nov 2025 | Data Quality | Data Readiness, Generative AI

Discover 5 warning signs your data isn’t ready for Generative AI and learn practical steps to improve data quality for successful AI adoption.

Generative AI (GenAI) is designed to produce original, realistic outputs, making it a versatile tool for businesses in areas like chatbots, data analysis, automation, and customer behaviour prediction. The future of analytics is intelligent, automated and fast-moving, but none of it works without a solid foundation of trusted, governed, high-quality data. GenAI is only as good as the data it learns from. In fact, one of the strongest messages from this year’s Gartner Data & Analytics Summit was clear: trusted, well-governed data is the foundation for successful AI adoption.

While AI adoption surged from 55% in 2023 to 72% in 20241, only 11% of companies2 have successfully scaled production-grade3 GenAI—highlighting a critical gap between early experimentation and enterprise-wide transformation. This underscores the urgent need for robust data strategies to unlock GenAI’s full potential.

Top risks of poor data quality in AI projects

  1. Misleading insights
    GenAI models rely on patterns in your data to generate outputs. If your data is inaccurate, outdated or biased, the insights and recommendations produced will reflect those flaws, potentially leading to poor decisions.
  2. Wasted resources
    Training GenAI models on low-quality data can be costly and inefficient. You might invest significant time and money only to end up with models that underperform or require extensive re-work. Additionally, data silos across departments make it difficult to standardise inputs, especially without the right technology, resulting in poor GenAI outputs.
  3. Compliance and privacy issues
    Poor data governance can lead to non-compliance with regulations like GDPR or the Australian Privacy Act. GenAI systems that process personal data without proper controls can expose your business to legal and reputational risks.
  4. Reputational damage
    When GenAI systems produce inaccurate, biased or inappropriate outputs due to poor data quality, the consequences can be public and far-reaching. Whether it’s a chatbot giving misleading information or a data breach from poor governance, these missteps can quickly impact customer trust and damage your brand’s reputation.

Steps to prepare your data for AI adoption

Before implementing a successful GenAI strategy, your business must ensure strong data quality and governance. That means more than just having a lot of it—it needs to be clean, complete, and well-organised. Here are a few practical steps to help you get there:

  • Data profiling: Understand the current state of your data by identifying gaps, inconsistencies, and anomalies.
  • Cleansing workflows: Automate the process of removing duplicates, correcting errors, and standardising formats.
  • Validation rules: Ensure critical data elements like names, addresses, and contact details are accurate and consistently meeting quality threshold.
  • Audit trails: Maintain transparency and accountability by tracking changes and data ownership.

These foundational practices not only improve data quality but can also build trust in the insights your GenAI tools will generate.

Start your journey to AI-ready data

If you’re looking for support in getting started, Experian Data Quality offers tools and expertise to help you assess, clean, and connect your data, laying the groundwork for successful AI adoption.

Talk to our experts about how Experian’s data quality solutions can help you build a trusted foundation for GenAI success.

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  1. McKinsey & Company. The State of AI. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
  2. McKinsey & Company. Moving past GenAI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/moving-past-gen-ais-honeymoon-phase-seven-hard-truths-for-cios-to-get-from-pilot-to-scale#/
  3. A robust, secure, validated, controlled, and scalable deployment of large language models and other GenAI technologies in enterprise environments that can meet real-world demands while ensuring high performance, reliability, and compliance with industry standards.