How to Build Your First AI Accountability Policy

In the balance of risk versus reward, the scales are tipping in favor of legal AI tools. In 2025, 59% of legal professionals thought that the benefits of using AI outweighed the risks. That number jumped to 92% in 2026. But capable tools still require human judgment — knowing when to trust an output, when to verify it, and who’s responsible when something goes wrong isn’t something the technology resolves on its own.

96% of the State of AI in Legal respondents said they would use AI more extensively in legal work if accountability for errors were more clearly defined. Yet only 49% report having a clear policy in place.

Someone has to be the one doing that checking, and someone has to own it when they don’t catch what AI got wrong. The stakes are real: U.S. courts imposed over $145,000 in AI hallucination sanctions in the first quarter of 2026 alone. 

This guide walks through where that trust breaks down and how to build a working accountability policy in a day.

Nobody agrees who’s responsible 

Nearly every survey respondent shared that they’d be willing to use AI more if accountability for errors were more clearly defined, and yet just shy of half of organizations have those rules in place. The explanation isn’t about skepticism, reluctance, or the tools themselves.

Doubts about the tools themselves aren’t what’s holding people back. The percentage of legal professionals who think the benefits of AI outweigh the risks leapt from 59% in 2025 to 92% in 2026.

People aren’t holding the tools at arm’s length, either. 49% act directly on AI outputs without review for routine tasks, and 74% act directly for routine tasks and select complex ones too. People are already extending real reliance to these tools, with the degree depending on what’s at stake.

The driving force behind the gap is that nobody agrees who’s responsible when something goes wrong. Asked who’s primarily accountable for an AI error:

• 37% say the legal team as a whole
• 23% say the individual who used the tool
• 20% say it’s shared, with no single owner
• 15% say IT

The responses scatter across six categories without landing on any one answer, which underlines how unresolved the question still is. Deciding whether AI was worth using for legal tasks turned out to be the easy part, and untangling who’s on the hook when something goes wrong is harder.

There’s an established standard to build from

Most legal teams building an AI review standard have two options: invent one from scratch or borrow from one that already exists. The ABA’s first formal ethics opinion on generative AI, issued in 2024, sets guidance for two of the harder questions in AI governance.

How much review is enough? The opinion’s logic is straightforward: review effort should match what’s at stake.

• Using AI to brainstorm a few approaches to a contract clause? Less verification is needed, especially once you’ve used the tool enough times to have a reasonable basis for trusting its output.
• Using AI to review and summarize a batch of lengthy contracts? Commit to more scrutiny up front. The opinion’s own example is to test the tool by manually reviewing a smaller sample first and confirming the summaries are accurate.
• Already ran that test and confirmed the tool’s reliability? You don’t have to manually re-review the entire set every time after that.

Who owns the rule versus who enforces it? The opinion splits this into two separate jobs. One person, or team, decides what the policy actually is. The policy includes which tools are approved, what gets checked, and who signs off. A separate responsibility, which can sit with the same person on a small team, is making sure people are actually following that policy day to day. A written policy that nobody’s checking against real work isn’t doing its job.

Build your AI accountability policy in one day

45% of respondents have discussed AI accountability policies but never formally defined them. 

If you’re in the camp that wants AI accountability guidelines but doesn’t have them yet, you can get a baseline down in a day. Here’s what to consider and how to share it in a way that builds your team’s credibility as the organization’s AI authority.

Step 1: Decide who needs to be in the room

Survey respondents couldn’t agree on which team or person holds responsibility for AI errors, but that might be for the best. Legal teams undeniably play a role, but they can’t singlehandedly create a policy if they want it to stick. 

The tools your team uses touch contract data, vendor relationships, employee information, and regulatory exposure—all of which belong to functions outside legal. Irene Liu, Executive Director of Stanford Law’s AI initiative and Founder & CEO of HypergrowthGC, was blunt about the spread during Ironclad’s recent State of AI in Legal panel: 

In practice, that means inviting:

• Legal to own the policy and be accountable for what it says
• IT or security if the tool touches client, contract, or employee data
• Compliance if your industry carries specific regulatory exposure like financial services, healthcare, or government contracting.
• Whoever owns adjacent policies like data retention, vendor management, and outside counsel guidelines, so you’re not contradicting something that already exists

You don’t need to go to these people with a huge ask on day one. A single one-hour working session with a specific output like a tiering decision or even a named owner per task, is easier to get on the calendar than a standing governance meeting. The broader structure can grow from what you put on paper today.

Step 2: Map your real AI use cases to a risk tier

Individuals might already have a sense of which tasks they do and don’t trust to AI, but writing it down makes it visible for everyone. Start by listing out six or seven things your team actually does with AI right now. Then, sort each one into a tier using the trust-level table above and the ABA’s verification principle as your guide.

Another useful place to start is any stories your team has about an AI output that was wrong, or nearly wrong, in a way that mattered. It’s faster to build a rule around something that already happened than to argue about a hypothetical.

Tier	Tasks	Tool	Review required
1 – Act on directly	Summarizing a contract, tagging contract metadata, routine risk scoring	General AI tool	Minimal. Spot-check a handful of outputs per quarter, not a per-document review.
2 – One-person review	Drafting a first-pass memo, flagging risky clauses, conducting legal research	Specialized legal AI tool	One named reviewer signs off before it leaves the building. Can be a peer, doesn’t need to be senior.
3 – Senior sign-off required	Negotiating contract terms, making a final recommendation, preparing for litigation	Human-led, AI as support	A named senior reviewer signs off before anything moves externally.

Step 3: Write the rule and contingency for each tier

Getting the tiers right is half the work. The other half is being specific enough that two different people on your team would handle the same task the same way. Here’s what that documentation looks like for a single Tier 2 task. The fields stay the same across every use case, only the answers change.

Task: drafting a first-pass legal memo

• Tier: 2 (one-person review)
• Primary reviewer: Senior associate or legal ops lead
• Backup reviewer: Deputy GC, or next available senior team member
• Turnaround expectation: Same day for standard tasks, 24 hours for anything complex
• What the handoff looks like: AI output shared alongside the source documents it drew from
• What review means here: Reviewer confirms cited authority is real and current; flags any client-confidential information that shouldn’t have been included in the prompt
• How sign-off is recorded: Accepted or rejected in your CLM’s activity log, or documented in a shared working doc if the task happened outside your contract platform

Using a list like this to detail specifics is what transforms your policy into a real process. It also gives you something to point to when you need to explain exactly what review happened and when.

Step 4: Build the process for when something goes wrong

In addition to planning your review, you should figure out how you’ll respond if an error slips through the cracks. In practice, that means agreeing on four things before anyone needs them:

• Who gets notified when an error is caught, whether it’s the reviewer, a shared inbox, or a Slack channel with the relevant team leads
• What gets logged, like a short description of what happened, what tier the task was in, and what changed afterward
• How the postmortem runs. Gather whoever was close to it, find the failure point, fix what’s fixable, and add the guardrail that was missing.
• When the tier itself gets revisited. If the same failure type happens twice, the tier may be wrong.

Here’s an example: a reviewer catches an error in a Tier 2 contract summary before it goes out. They flag it in the team’s shared Slack channel and log a one-sentence note in the working doc, ‘AI summarized the termination clause incorrectly; summary now requires cross-check against original before sending.’ At the next monthly check-in, the team moves that task to Tier 3.

Share what you’ve built

Once the day is over, get 30 minutes on the calendar with the broader team you pulled together in step one. It’s not a formal rollout so much as a quick walkthrough of the document you just created, including what the tiers are, who owns review at each level, and where the policy lives so people can actually find it.

Come prepared to answer two questions most teams will have: what happens if I’m the reviewer and I’m not sure, and what do I do if I catch an error after something already went out. Having answers to both ready makes the session more useful than a simple read-through.

The visibility matters as much as the policy itself. Among legal professionals at organizations with a clearly defined AI error policy, 87% rate their legal team’s AI adoption as ahead of other business functions. At organizations without one, that number is 51%.

The work that doesn’t end

Even after the day above, some questions about AI accountability won’t have a clean answer. Mary O’Carroll, legal ops legend and CEO of LegalEng Consulting Group, put it plainly on Ironclad’s recent State of AI in Legal panel:

Responsibility for complex failures has always been scattered across the people who built the system, used it, and were supposed to catch its errors. 

AI hasn’t changed what legal has always relied on: judgment, documentation, and knowing who was in the room. Teams that start formalizing accountability processes now are treating today’s answer as the floor, not the ceiling.

The data behind this piece comes from Ironclad’s 2026 State of AI in Legal report, which goes deeper on where trust breaks down by task and role, where in-house and law firm teams are landing differently on AI accountability, and what legal teams with clear governance in place are doing that others aren’t. Download the full report here.

Ironclad is not a law firm, and this post does not constitute or contain legal advice. To evaluate the accuracy, sufficiency, or reliability of the ideas and guidance reflected here, or the applicability of these materials to your business, you should consult with a licensed attorney.