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How do you plan to govern AI at your organisation?

· 3 min read
Dan Peacock
Dan Peacock
Chief Hustler

A practical place to begin this conversation is by asking: what does AI actually require to be successful Despite the noise around models, innovation, and tools, the reality is simple:

AI is only as good as the data that feeds it.

For AI to deliver trustworthy, scalable value, organisations need three foundational capabilities:

  1. Good-quality data.
  2. Data with business context.
  3. Data stored in a consistent structure.

Let’s tackle these one by one.

Good Quality Data​

High-quality data isn't an optional extra — it's the core ingredient of any reliable AI system. Poor data leads directly to poor decisions, model drift, and compliance risks. To govern AI effectively, organisations must ensure:

  • Clear data ownership and stewardship
  • Automated quality checks (accuracy, completeness, timeliness)
  • Controls to prevent downstream contamination

AI governance starts with the governance of the data itself.

Data and context​

AI models don’t understand your organisation unless your data is described in a way that reflects how your business actually works. This means:

  • Enriching data with definitions, rules, and relationships
  • Capturing business meaning directly in the data layer
  • Ensuring metadata and lineage are built-in, not bolted-on

Context is what transforms raw data into information and AI-ready intelligence.

Data stored in a consistent structure​

Consistency is essential for repeatability, scalability, and trust. If every team shapes data differently, AI cannot operate reliably.

  • Consistent structure enables:
  • Faster model development and retraining
  • Reduced operational cost
  • Easier cross-team collaboration
  • Transparent and auditable AI behaviour

A unified data model is the foundation of robust AI governance.

So where should organisations start?​

At the data preparation layer. Always. This is where the strategic control points live — quality, lineage, security, context, consistency. If these elements are not embedded before AI, no governance framework on the surface will fix the problems underneath.

Key takeaways from recent data events​

Recent industry discussions (from CDO forums, cloud summits, and AI governance roundtables) consistently highlight three lessons:

  1. The biggest AI failures trace back to ungoverned or poorly prepared data — not the models.
  2. Organisations that model, cleanse, and contextualise their data up-front accelerate AI adoption dramatically.
  3. AI governance must be built on top of data governance, not separate from it.

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