AI governance: AI starts with trusted data

It’s clear now that the introduction of ChatGPT in late 2022 was a breakthrough moment in the history of technology and, perhaps, the evolution of society itself. Around the world, enterprises are now fiercely competing to develop AI applications.

To stay competitive, you need to ramp up your AI initiatives — and launch applications that leverage the groundbreaking capabilities of LLM technology. But successfully integrating AI initiatives into your roadmap will require a rigorous approach. To ensure AI is used responsibly, forward-thinking enterprises are leveraging AI governance.

AI governance is the application of rules, processes and responsibilities to drive maximum value from your automated data products by ensuring applicable, streamlined and ethical AI practices that mitigate risk and protect privacy.

Why AI governance is important

The truth is data is the backbone of AI, and if the data is bad, the AI models trained on it will produce results that look good but are fundamentally flawed. The implications for companies building AI applications are profound.And as AI’s impact grows, so does the scrutiny. The European Union’s AI Act is an example of regulations emerging to ensure ethical AI use.Non-compliance can lead to:

Legal repercussions
Loss of customer trust
Brand damage

Company policies and government regulations

Both organizational policies and governmental regulations work hand in hand. While government regulations set the standard, company policies can further refine and detail AI usage specific to organizational needs.

Key features to look for

A well-defined AI governance solution is built on a framework that should encompass:

Clear AI objectives and principles
Data quality and integrity checks
Model transparency and interpretability measures
Regular audits and updates
Ethical considerations, including fairness and bias checks
Legal and data compliance

As data becomes increasingly critical to organizational decision-making, the emphasis on data governance becomes more and more business-critical. Whether you’re looking at traditional data governance or the emerging practice of AI governance, the presence of a clear, comprehensive framework is essential in today’s data-driven world.

Use cases by use case:

Model lifecycle management: Accelerating AI development through automated quality controls and documentation flows
Enterprise risk management: Ensuring model transparency and regulatory compliance across AI initiatives
Innovation pipeline: Enabling rapid experimentation while maintaining rigorous governance standards

Use cases by industry:

Financial services: Powering trusted AI adoption across risk modeling, fraud detection, and personalization
Healthcare: Enabling compliant AI innovation in diagnostics, care optimization, and clinical workflows
Manufacturing: Accelerating smart factory initiatives while maintaining complete model visibility