The launchpad for responsible AI

The hype is loud. The data reality is quieter – and more critical.

AI promises speed, scale, and smarter decisions. But behind the scenes, many initiatives stall. Not because the tech isn’t impressive, but because the data isn’t ready. It’s scattered, misunderstood, or quietly biased.

AI doesn’t just need data – it needs data with clarity, context, and care. That means:

• Shared definitions: For example, does “customer” mean the same thing across our systems?
• Lineage and trust: Where did this data come from? Can we stand behind it?
• Ethical framing: Are we reinforcing bias, or challenging it?

Without governance, AI is a black box that could easily be running “garbage in, garbage out”. But with governance, AI becomes a strategic partner underpinned by trusted, curated data.

But according to a recent Salesforce survey, entitled the State of Data & Analytics, only 43% of data and analytics leaders have established formal data governance frameworks and policies, yet 88% believe advances in AI demand new approaches to governance.

Old-school governance was often about locking things down. Today, data governance needs to unlock potential while protecting what matters. That means:

• Enabling access without compromising privacy
• Automating policy enforcement at scale
• Supporting agile, iterative data use for model training

Consequently, we often use the term “data enablement” rather than data governance, as it talks to how the controls can help or enable data curation.

Modern governance frameworks – especially those that use AI themselves – can do both: safeguard and accelerate. For example:

• Data product thinking: Treat data as a product with ownership, SLAs and AI-powered anomaly detection.
• ‍Embedded governance: Automate policy enforcement directly in data workflows – privacy, quality and compliance built in.
• ‍Federated stewardship: Empower domain teams with lineage, impact analysis and governance nudges.
• ‍Trust signals and observability: Generate trust scores to assess data quality, lineage and usage transparency.
• ‍Ethical and responsible AI by design: Monitor models for bias, drift, and explainability – embedding ethics into every stage.

The business case is clear: risk down, trust up, ROI accelerated

Good governance isn’t just a compliance checkbox. It’s a business enabler, helping to:

• Reduce risk and meet regulations (think GDPR, reputational resilience)
• Build trust – with customers, regulators, and internal teams.
• Speed up AI development and improve model performance.

Organisations with mature governance avoid problems – and move faster, deliver better experiences, and get more value from their data. We once worked with an organisation where governance wasn’t a gate – it was part of how they moved. It delivered real value as it was built into their data flows, so teams could access trusted, well-documented data without delays or second-guessing. That clarity helped them launch faster, scale AI responsibly, and make decisions with confidence.

In short, poor data governance is a tax on your day-to-day operations.

Data governance and AI governance: two sides of the same coin

These are deeply connected disciplines. Data governance ensures the inputs are reliable; AI governance ensures the outputs are ethical, explainable, and aligned with business goals. Together, these form the foundation of responsible AI.

Responsible AI is the practice of designing, developing, and deploying artificial intelligence systems in ways that are ethical, transparent, and aligned with human values – ensuring trust, fairness, and accountability throughout the AI lifecycle.

Final thought

AI without data governance is like a rocket without a launchpad. Ambitious, yes – but it’s not going anywhere and could do significant, unpredictable damage.

If we’re serious about AI, we must be serious about data governance. Not as a gatekeeper, but as a guide. Not as a checkbox, but as a catalyst.

At The Data Practice, we've developed a proven framework for aligning strategic AI and outcomes with business value. A significant number of AI and data transformation projects fail far too often; our methodology helps organisations navigate these challenges; implementing appropriate and seamless data governance frameworks and processes, to deliver meaningful, measurable results.

Please feel free to reach out to me or the team to explore how data and AI governance can support your transformation – building trust, enabling clarity, and turning strategy into impact.

‍

About the author

Karen Gibson is Data Governance and Data Capability Lead at The Data Practice. She has deep expertise in leading high-performing teams and embedding ethical, secure and scalable data practices across many complex organisations.

‍

Photo credit: Military_Material

‍