AI governance is the next big priority in legal tech – here’s how CLM is leading the way 

Legal operations teams have never been shy about adopting technology that drives efficiency. CLM, analytics tools, and workflow automation have all earned their place by solving real problems. Artificial intelligence is following the same trajectory, moving rapidly from experimentation into core legal processes. 

But as AI becomes embedded in contract review, negotiation workflows, and enterprise decision-making, legal teams now must ask: Who is governing the AI that now helps shape legal risk? 

That’s where AI governance comes int AI governance is about oversight, accountability, and the ethical management of AI systems – making sure that powerful tools are used responsibly and transparently.  

The emphasis on AI governance comes at a critical time. Enterprise AI has exploded across businesses large and small; regulatory pressure is mounting across many sectors, and legal workflows have become among the first to feel both the promise and the peril of automation. 

And nowhere is this convergence clearer than in Contract Lifecycle Management (CLM), the software legal teams use to manage the very agreements that bind businesses together. Sitting at the intersection of risk, compliance, and data, CLM is now ground zero for responsible AI use. If the future of legal operations is going to be AI-powered, it’s going to need governance built in from the start. 

Why AI governance is gaining momentum 

If you walk into any legal department today and ask about AI, you’ll likely hear two sentiments: enthusiasm and concern. 

AI is transforming how contracts are analyzed, how obligations are tracked, and how risk is surfaced. According to Gartner research, enterprise legal leaders are prioritizing both AI and contract analytics – not just for efficiency, but because managing risk has become more complex than ever. Within that same research, 36% of General Counsel (GCs) said adopting AI or improving AI risk management is an urgent priority.  

This shift is directly tied to legal exposure, not just business value. Across the globe, policy frameworks like the European Union (EU) AI Act and the U.S. AI Bill of Rights are pushing companies to prove that AI systems are explainable, fair, and auditable. Regulators require transparency, and legal teams need decisions they can defend. 

There’s also the gnawing reality of data privacy and security. Contracts carry customer data, competitive terms, and sensitive obligations that, if mishandled, can expose businesses to regulatory penalties and brand damage. Without official oversight, AI tools can produce outputs no one can fully explain or trust. That’s how AI governance evolved from a nice-to-have to an enterprise must-have. 

Research conducted by Harvard Law School Forum of Corporate Governance found that while companies are rapidly deploying AI, corporate governance and board oversight – including discussions of transparency, risk, and legal exposure – are lagging far behind, meaning teams are using tools faster than they are governed.  

This gap has direct implications for legal leaders. 

What AI governance looks like in CLM software 

AI is already woven into the fabric of modern CLM platforms for better and, without governance, sometimes for worse. 

Here’s a sense of how AI is being used in Legal in 2026: Obligation extraction – pulling dates, duties, and thresholds from text that used to require hours of human review. 

• Risk scoring – assigning a numerical view of contract risk based on clause variations. 

• Contract summarization – helping stakeholders get the gist of complex agreements in seconds. 

• Workflow automation – routing contracts to the right reviewers based on rules and risk profiles. 

These are powerful capabilities, but they’re also critical functions where unchecked AI can make things worse, not better.  

Imagine a system that flags a contract as “low risk,” but can’t explain why – and then that contract goes straight to signature. Or consider an AI model that misses a critical obligation because it wasn’t trained on the right data. 

This is where governance matters.  

Good governance means: 

• White box AI, where AI-generated outputs are cited and traceable back to the source data and logic. 

• Access controls, so only approved users can trigger or approve AI outputs. 

• Audit trails, which log every AI interaction for later review. 

• Human-in-the-loop processes, so, people still review and validate high-impact decisions. 

Put simply: AI should assist people, not act as a black box oracle. 

What to look for in a CLM platform with strong AI governance 

If you’re evaluating CLM platforms today, here’s a practical checklist to guide you: 

• Transparent, explainable AI – not AI that “just works,” but AI you can see working. 

• Configurable AI agents and workflows – tools tailored to your organization’s unique risk appetite and legal standards. 

• Audit logs and usage tracking – evidence you can produce when someone asks, “Why was this contract approved?” 

• Data privacy certifications – proof that underlying data handling meets industry standards. 

• Human override options and AI confidence indicators – ways people can intervene when the stakes are highest. 

Effective governance allows innovation to scale without increasing risk. Agiloft’s data-first CLM platform, for example, embeds governance into its AI capabilities, giving teams transparency and control over how AI is used across the contract lifecycle. From configurable AI agents in Prompt Lab to audits and logs that track AI output usage, the focus is on AI that supports judgment rather than obscures it.  

The emerging role of legal professionals in AI governance 

There’s another development happening quietly alongside this: career evolution. 

As AI use becomes central to legal operations, organizations are creating roles that didn’t even exist five years ago – roles focused not just on law, but on governing the technology that processes law. 

Today’s legal teams are comprised of:  

• Legal AI analysts, who understand both contracts and models. 

• AI ethics counsel, who guide responsible use policies. 

• AI compliance officers, who ensure systems meet regulatory demands. 

• Legal ops professionals, who implement governance and oversee workflows. 

• Legal knowledge engineers, who are experienced lawyers with commercial practice experience and a passion for technology, supporting pre-trained artificial intelligence initiatives. 

CLM platforms become a kind of sandbox for these roles – a place where legal teams can experiment with AI at scale, define internal standards, and oversee tools that touch contracts across the enterprise. 

AI governance isn’t optional – it’s strategic 

We’re at a moment where the question is no longer “Should we use AI?” but “How do we use it responsibly?” 

AI governance is how legal teams protect value, reputation, and trust in an era where automation touches nearly every contract and compliance decision. CLM is the proving ground – the place where AI meets legal risk and data accountability. 

Robust CLM platforms like Agiloft that embed governance – not as an add-on, but as a foundation – are not just solving today’s problems. They’re preparing legal teams for a future where AI is pervasive, powerful, and explainable. 

For legal and procurement teams thinking about next year, next quarter, or the next decade, the message is clear: AI governance is essential to managing risk and enabling scale. 

Schedule a platform demo to see white box AI in action and experience governance-ready CLM that legal and procurement teams can trust.