Beyond human oversight: A Legal leader’s guide to AI governance

Many Legal teams can't point to the specific day they decided to adopt Artificial Intelligence (AI) into their workflows.

The shift happened faster than most people realize. Generative AI (GenAI) became publicly available with the launch of ChatGPT in November 2022, and Legal teams quickly began to explore what this new technology could do for their workloads.  

Contract managers started using AI to summarize supplier agreements. Lawyers began running NDAs through tools to flag risky or non-standard clauses. Work came back drafted with AI assistance, often without anyone formally signing off on the practice.

Now in 2026, legal teams have decidedly moved past the evaluation and experimentation stage and operationalizing and scaling it across their organizations.

This maturation has the numbers to back it up. Thomson Reuters’ 2026 AI in Professional Services Report found that 47% of corporate legal departments surveyed said they were using Generative AI (GenAI), up from 23% in 2025. Adoption among law firms rose from 28% to 41% over the same period. More than 50% of current users also said they engage with GenAI weekly .

And this ubiquity is exactly why AI governance is increasingly important. Without it, risks may develop quietly and spread across the organization. Read on to learn how legal leaders can address these risks head on with proper AI governance.

From generative AI to agentic Legal agents

The scope of AI in Legal is expanding. AI is now embedded inside the tools legal teams already use every day. At Agiloft, we call this approach AI on the inside™: AI built directly into the platform rather than bolted on as a separate tool, so legal teams get AI contract review, contract analysis, and an “Ask AI” chatbot right where they're already working.

Agentic systems take this development a step further. Unlike a tool that responds to a prompt, an agent can independently plan, reason, and execute complex work with minimal human oversight. Unsurprisingly, the emergence of agentic AI in Legal has already drawn significant attention and questions related to ethics, accuracy, and where exactly to “keep humans in the loop.”

15% of respondents in a recent Thomson Reuters survey said their organizations were using agentic AI, while another 53% said they were planning or considering it. Meanwhile, Deloitte’s 2026 State of AI in the Enterprise report found that only 21% of surveyed business and technology leaders described their organization's governance model for agentic AI as mature, even though 74% expect to use AI agents by 2027.

At Agiloft, we're already bringing practical agentic capabilities into contract management for our customers. In November 2026, we announced three new execution-focused AI agents within our Ask AI ecosystem:

• Contract Request Agent: Helps business users find the right request form by describing what they need in their own words.
• eSignature Agent: Guides users through preparing an Adobe Acrobat Sign workflow, from selecting documents and signers to reviewing the details before sending.‍
• Bulk Update Agent: Updates contract data across multiple records from Ask AI search results, with a preview and explicit human confirmation before changes are made.

But don't worry, legal leaders: autonomous agents aren't running your departments or replacing the critical human judgment required for complex legal matters.

However, the trajectory is clear: Gartner recently predicted that by 2028, most enterprises will move away from AI that simply assists, like copilots and smart advisors, in favor of AI platforms with delegated authority to execute workflows independently.

What AI governance actually means in practice

The more a system is able to do, the more it matters that someone has defined what it's allowed to do. That is what AI governance is: the policies, controls, and human checkpoints that determine what an AI tool or agent can access, decide, and act on, and who is accountable when something goes wrong. Just as data governance ensures contract data is clean, accurate, and trustworthy, AI governance ensures the systems acting on that data are operating within clearly defined boundaries.

Those boundaries matter more with each step up the ladder of AI capability. A tool that summarizes a contract raises one set of questions. A system that checks an agreement against a playbook raises another. An agent that can move a contract through intake, flag missing information, trigger approvals, or update records on its own raises a different set entirely. The risk doesn't scale in a straight line, it compounds.

Why AI systems failure is ‘silent’ and what to do about it

Hallucinations are usually where those compounding errors becomes most noticeable. When an AI tool confidently states something false, be it a fabricated clause, a case citation that doesn't exist, or an obligation that was never in the contract, the instinct is to blame the model. The real problem is often the data underneath it: outdated or inconsistent inputs that leave the model to fill in gaps on its own. Clean data doesn't guarantee a perfect answer, but dirty data all but guarantees a confidently wrong one.

Noe Ramos, vice president of AI operations at Agiloft, points out that AI failures can be difficult to detect, explaining that to CNBC that “autonomous systems don't always fail loudly. It's often silent failure at scale.”

An AI system might update records with small inaccuracies that appear harmless at first. But over time, those errors can snowball.

“At scale over weeks or months, they compound into that operational drag, that compliance exposure, or the trust erosion,” Ramos says. “And because nothing crashes, it can take time before anyone realizes it’s happening.”

Where does contract management fit into AI governance?

As AI adoption increases across the legal profession, contract management has become a prime example of a business function that is built around governance. The idea is fairly straightforward: when AI is built directly into the same systems legal teams already use to manage their contracts, governance doesn't exist in a vacuum. It becomes an integral part of the workflow.

That’s the thinking behind Agiloft’s new contracts AI platform, Agiloft Astra. The platform operates under the Astra Clean Data Promise, ensuring that all customer data is used solely to provide the service and meet applicable legal obligations, rather than to train underlying models. Astra also builds in audit trails, human review points, and traceability back to source data, so teams can see what the system did, what information it relied on, and where a person still needs to weigh in. That traceability is exactly what keeps a hallucination from slipping through unnoticed. If a system can show its work, a wrong answer can be caught fast.

Shifting from ‘humans in the loop’ to ‘humans on the loop’

When AI first existed primarily inside a handful of sanctioned pilots, governance was a fairly straightforward exercise: approve a tool, define a use case, and decide what data it can touch. But this model gets harder to sustain once AI capabilities show up across dozens of applications and an unpredictable number of individual workflows.

Governance needs to sit at the heart of business operations for AI to scale safely across the enterprise. The NIST AI Risk Management Framework calls for an approach to governance that runs throughout the entire AI lifecycle, with defined roles, clear accountability, and ongoing monitoring.

Deloitte’s report also notes that organizations seeing the most success with agentic AI are those ones that built governance frameworks early on, spelling out clear boundaries for agent autonomy and establishing real-time monitoring systems that track agent behavior and flag any anomalies. The organizations doing this well also tend to bring Legal, Compliance, IT, and executive leadership into the conversation together, rather than leaving AI governance siloed to a single function.

A workable governance model needs to answer a handful of concrete questions:

• Who approves an AI use case, and who owns it afterward?
• What information can the system access?
• Which decisions should AI make, and which require a human?
• What actions need to be logged?
• What triggers escalation?
• How does an AI tool get retired, or its permissions changed, when the underlying risk changes?

Ramos notes that organizations tend to underestimate how much access they're granting AI systems in the name of efficiency, and that the edge cases a person would catch by instinct rarely get encoded into the system itself. Her recommendation is to shift from “humans in the loop to humans on the loop.”

“Humans in the loop review outputs, while humans on the loop supervise performance patterns and detect anomalies and system behavior over time, mitigating those small errors that can increase at scale,” she says.

Ramos’s point is especially relevant to legal teams, which often use AI to process sensitive client and contract data. The governance questions above become particularly important in high-stakes workflows such as contract negotiation. AI can flag deviations from approved language, suggest fallback clauses, or compare terms against a provided playbook. But should it be allowed to approve a deviation on its own? Should it be able to send a redline to the counterparty without human review?

Legal teams need clear boundaries around where AI can assist and where human judgment must remain firmly in control, and World Commerce & Contracting supports this approach. Not all contracts carry the same level of risk, so organizations need to distinguish between tasks that can be safely automated and those that should remain human-led with AI support.

Don’t treat governance as just an IT issue

It's tempting to hand AI governance off to IT and consider the job done. But AI creates issues involving confidentiality, privacy, contractual obligations, regulatory exposure, and professional responsibility, and those are areas where Legal carries the accountability.

That distinction matters for who owns governance. IT can build the infrastructure and monitor uptime, but it can't define what “silent failure” looks like in a compliance context, or decide when a drifting AI-drafted clause crosses from acceptable to actionable. That judgment call belongs to Legal.

The American Bar Association’s Formal Opinion 512 makes this responsibility clear, stating that lawyers using GenAI must understand the capabilities and limitations of the tools they use and to review AI-generated work appropriately before relying on it. That responsibility also includes defining where AI has no business operating at all.

With that in mind, Legal leaders should be asking questions like:

• Are employees putting confidential contract terms or client information into tools that haven't been approved?
• If an AI-assisted decision gets challenged later, can we show who reviewed it and what controls were in place?
• Are outside counsel and vendors held to the same expectations on confidentiality, AI use, and data handling?
• Do our contracts with AI vendors clearly address data use, security, confidentiality, audit rights, and responsibility?

Why contract management and governance go hand in hand

Contract management is the perfect function to introduce to AI because it already encompasses much of an organization's risk in one centralized place. A single agreement can define data rights, confidentiality obligations, security requirements, indemnification, liability, audit rights, renewal terms, and restrictions on how information can be used.

Across an entire portfolio, those terms add up to a record of what the business has committed to and what Legal needs to keep watch over, which is exactly the kind of ongoing tracking that obligation management is built to handle.

Consider a legal team asked to identify which vendors have the right to use customer data for model training, or Procurement needing to flag agreements with unusual auto-renewal terms before a deadline, or Compliance trying to understand which contracts carry obligations affected by a new regulation. These questions become difficult to answer when contract data is scattered across email threads, shared drives, spreadsheets, and disconnected systems.

That same structure and organization becomes even more important once AI enters the picture. If an AI system is asked to identify obligations, compare clauses, flag risk, or summarize a portfolio, the reliability of its output depends on the quality of the contract data underneath it. AI doesn't remove the need for good information management. If anything, it makes the cost of skipping that work more visible, and faster to show up.

What Legal leaders should do now

Good governance doesn't have to slow AI adoption down. In many cases, it does the opposite: it gives Legal a clearer way to determine where AI belongs, where it doesn't, and what controls are needed to scale it responsibly.

The organizations that best navigate AI won't necessarily be the ones using the technology the most. They'll be the ones that can articulate, with confidence, where AI is used, what it can access, what decisions it influences, and what record exists if someone later asks how an outcome was reached.

“There’s pressure among AI operations leaders to move really quickly,” Ramos said. “Yet you’re also challenged with not crippling experimentation, because that’s how you learn.”

“The next wave isn't going to be less ambitious, but more disciplined.” The organizations that mature fastest, Ramos says, will be the ones that don't avoid failure, but learn to manage it.

Don’t let AI governance become an afterthought at your organization. Contact our team to learn how Agiloft’s no-code CLM platform helps you build guardrails around AI from Day One.

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