Data governance has long been seen as a necessary but often rigid function, but as AI adoption accelerates, governance is transforming. Traditional models focused on control and compliance, often slowing down innovation. However, as AI-driven decision-making becomes central to business operations, governance must evolve to be more agile, proactive, and seamlessly integrated into data workflows. Organizations need governance frameworks that enable responsible AI usage, ensure data quality, and maintain compliance while avoiding becoming a bottleneck to progress.

At our recent webinar, data governance expert and CEO of First CDO Partners, Morgan Templar, spoke to Secoda CEO, Etai Mizrahi about how governance is evolving to unlock AI-driven value, manage unstructured data, and create real business advantages.

Here are the key takeaways from the discussion.

1. Governance is the foundation of AI success
2. Unstructured data is the next governance challenge
3. Governance is moving beyond compliance
4. AI governance will become a standalone function
5. Governance at scale requires automated workflows

Governance is the foundation of AI success

AI is only as good as the data it’s built on. 

A central theme of the discussion was the shift in how data governance is perceived: not as an obstacle to AI, but as its foundation. Without well-governed, high-quality data, AI models are prone to bias, inconsistency, and failure.

Morgan Templar, CEO of First CDO Partners, noted:

"With AI coming around the corner, governance is an absolutely critical enabler. Without properly governed data, AI isn’t just ineffective—it’s dangerous."

To make governance a springboard for AI, organizations must:

• Automate governance workflows to remove friction and ensure policies are consistently applied without slowing down data teams.
• Embed governance into AI pipelines, making sure models are trained on compliant, high-quality datasets from the start rather than addressing issues post-deployment.
• Improve data transparency, so teams can track lineage, understand data sources, and assess whether their AI models are working with reliable information.

Governance must support AI adoption, not hinder it. By making data governance automated and proactive, companies can accelerate AI initiatives while maintaining trust and compliance. 

Governance at scale requires automated workflows

One of the biggest shifts happening in data governance is the move away from manual approvals and static policies. Instead, organizations are embedding governance directly into workflows to ensure data remains compliant, secure, and high-quality without adding unnecessary friction.

Key shifts happening in governance in 2025:

• AI-powered classification to tag and govern data in real-time
• Automated access controls based on metadata and user roles
• Self-service governance that empowers teams to find, understand, and use data independently while ensuring governance rules are enforced automatically.

"If you’re still treating governance as a manual process, you’re already behind. The future is fully integrated, automated governance that happens in the background." – Etai Mizrahi

Companies that integrate governance into their workflows will move faster, reduce risk, and create a more data-driven culture without governance becoming a bottleneck.

Unstructured data is the next governance challenge

Most organizations govern only a fraction of their data: the structured, transactional data that’s easy to manage. However, unstructured data—including emails, chat logs, contracts, and internal documentation—often makes up the majority of a company’s data footprint and is largely unaccounted for in governance strategies.

"With AI, everything is discoverable. If you don’t govern unstructured data, you’re not governing at all." – Morgan Templar

What businesses need to do:

• Leverage AI-powered classification to automatically detect and tag sensitive or business-critical unstructured data.
• Enrich metadata to improve searchability, context, and access management.
• Extend governance policies to cover all data types, ensuring regulatory compliance across structured and unstructured sources.

Why it matters: Unstructured data often contains intellectual property, customer insights, and sensitive business information. 

Failing to govern it increases security risks, hinders AI initiatives, and limits overall data visibility. Companies that can unlock the value of unstructured data while maintaining security and compliance will gain a significant advantage.

Governance is moving beyond compliance

For many years, governance was seen primarily as a risk mitigation function—focused on compliance, regulatory adherence, and avoiding data breaches. But leading organizations are now treating governance as a competitive advantage, using it to improve operations, increase efficiency, and even create new revenue streams.

Some emerging governance-driven business opportunities include:

• Faster AI deployment: Streamlined governance means teams can confidently train and deploy AI models without legal or ethical concerns.
• Operational cost savings: Poor data quality leads to inefficiencies. AI-driven data quality monitoring can reduce unnecessary cloud storage costs by ensuring only valuable data is retained.
• Data monetization: High-quality, trusted data can be packaged and sold as a revenue-generating asset.

"Companies that get governance right aren’t just avoiding fines—they’re unlocking new revenue streams." – Etai Mizrahi

Rather than viewing governance as a limitation, companies that invest in modern governance frameworks are discovering new ways to scale, innovate, and gain a competitive edge.

AI governance will become a standalone function

As AI continues to reshape business operations, AI governance is beginning to separate from traditional data governance. While data governance ensures data integrity and compliance, AI governance focuses on ethical AI use, model fairness, and decision accountability.

"AI governance isn’t just about compliance anymore—it’s about making decisions that impact hiring, automation, and ethical AI use." – Morgan Templar

To prepare, businesses should:

• Define AI governance responsibilities within the organization—whether in risk management, compliance, or a dedicated AI governance team.
• Develop AI risk policies that address bias, model drift, and regulatory concerns.
• Implement AI monitoring tools that continuously track AI models to ensure fairness, accuracy, and compliance.

AI-driven decisions will soon impact hiring, automation strategies, and even corporate ethics. Companies that fail to put governance guardrails in place risk financial, legal, and reputational consequences as AI adoption scales.

Final Thoughts: What’s Next for Governance?

As we look ahead, the role of governance will surely continue to evolve along with AI, and other technological advances we can’t even predict. Organizations must shift from reactive governance models (focused on compliance) to proactive strategies that drive efficiency, security, and AI innovation.

The biggest takeaway? Governance isn’t just about control—it’s about making data work smarter. It’s time to reframe the way we think about data governance.

🔹 What’s the key takeaway? Governance must be seen as a scalable, automated function that enables AI, business growth, and competitive differentiation—not just a compliance requirement.

📢 Want to continue the conversation? Connect with our panelists on LinkedIn, watch the replay above, and stay tuned for more discussions about the future of data governance.