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Sometimes, you can ask AI a detailed legal question and come away with a smart first draft for your next case document.
And sometimes it cites a Supreme Court case that literally doesn’t exist.
When we asked legal professionals what concerns they had about incorporating AI into their legal workflow, “information accuracy concerns,” was the top pick, alongside security issues.
Experts call these errors hallucinations because AI doesn’t actually think when we ask it a question. It connects words and ideas that often go together based on its data inputs and training. (It’s also why sometimes it spits out long paragraphs of jargon that sound good but really don’t say anything real.)
The difference between an AI environment that produces smart, easy-to-use outputs and junky hallucinations comes down to the system that you use, not the specific AI tool. The data quality, workflow design, and system context all matter for how it performs for your legal team. In this post, we’ll talk through what that looks like when managing important legal workflows, like your contracts, and how you can implement AI for your legal organization in a smarter way.
The rapid adoption of AI is breaking your systems
92% of legal professionals currently use AI in their day-to-day work for drafting and reviewing documents, conducting case research, or negotiation support. Doing so saves time, improves communication, automates mundane tasks, and allows you to be more strategic.
Source: 2026 State of AI in Legal Report, Ironclad
Speeding up your work sounds great. Who doesn’t want to get boring tasks like redlining done more quickly? The problem is that teams rush so fast to adopt AI into their workflows that they fail to think about whether or not their current systems can actually support this new technology. That means they’re layering new tools onto existing fragmented processes and wondering why everything keeps breaking.
Why the same AI produces different outcomes in different environments
Right now, many legal teams struggle with fragmented, manual processes that slow down their overall workflow. There may be duplicate sources of truth or a lot of copying-and-pasting happening to get contracts out the door. Layering AI over this doesn’t fix it if it’s not part of a system-wide platform that solves your workflow problem.
“The difference between successful AI implementation and creating chaos in your organization is making your contracts observable,” says Suha Saya, Director of PMM, Jurist, here at Ironclad. “To me, observable contracts means that you can see the entire lifecycle, from first intake request through red line, approval, signature, obligation, and renewal. And that you can see this in a searchable form across multiple versions of the document. Who approved what and why?”
For many organizations, contracts remain trapped in individual files that aren’t searchable, or worse, paper files you have to track down in person. And once legal hands it off to another team, it’s unlikely they’ll know exactly where it is.
Take, for example, a common sales-to-legal pipeline with contract management. If you’re finalizing a quote for a customer in your CPQ tool, each product or pricing schedule is going to determine what contracts and clauses need to be included, especially if you’re selling a specialist product with a complicated package. If those systems aren’t connected, you’re stuck manually copying and pasting each line item and hoping you’ve got the governance correct.
AI can’t fix a broken workflow like this.
For artificial intelligence to be successful in your organization, you need three elements:
- A strong workflow design: A cross-functional legal flow that maps to how your team actually gets work done
- High data quality: A well-trained model that has access to real legal data, not just what’s on the Internet
- System context: Additional inputs from your specific organization so AI understands what your protocols, stances, and contract/negotiation history looks like so it can give you outputs that match what matters for your team, not just every legal team
“Without observability of what’s happening throughout the contract lifecycle, it’s impossible to add AI into your workflow and do it well,” adds Saya. “There is so much noise right now about which model is smarter or faster, but that’s the wrong conversation. A model can have access to thousands of tokens, but if it doesn’t know your specific organization’s negotiation history, your playbooks, or your approval logic, it’s just reasoning in the dark or the internet. The system and context matters so much more than the specific model.”
How to fix the observable contract problem
Your legal team needs the same kind of observable system that the rest of your organization has. “Every other enterprise function has an observable system. Finance has the ERP, where every transaction is logged. Sales has a CRM, where every interaction is tracked. Support has ticketing. It gives these teams full lifecycle visibility. And then there’s legal,” says Saya. “Contracts govern all of these relationships, but they’re stored everywhere and nowhere.”
The reason contracts have historically been unobservable is partially due to a technical limitation. Contracts tend to be PDF files (historically known as unstructured, or unsearchable, data.) Much of the negotiation details end up in email threads. Your team collaborates in Slack or in person without recording who approves what. Or single point solutions like contract repositories may store contracts in one place but still don’t allow you to search or surface insights.
AI allows you to break through these issues to make the previously unsearchable, searchable. “Contracts for so long were seen as unstructured data, and AI has completely changed that,” says Saya. “Now, if you’ve got your contracts in one place, AI can pull key terms out and read that unstructured data in a way previous technology just couldn’t. Now, the challenge is that it can read this data and surface information about approvals, obligations, renewals, and so on, but can your team actually use this information with the workflow you have in place?”
What “observable contracts” actually means in practice
To take your contracts from black box to observable, you need the right system. For many legal teams, that’s a dedicated Contract Lifecycle Management (CLM). These tools take a holistic approach to the entire contract, so you can automate management processes, and more importantly, extract business intelligence from your contracts instead of letting them languish over time.
Renewals are a prime example: Has your team ever been caught by surprise by an unexpected software renewal? CLM tools add features such as automated notifications that give you time to start the renegotiation process.
With a CLM, your contracts become structured data across the contract lifecycle: Searchable, traceable, and actionable clauses, obligations, changes, and approvals that your team can use to be more strategic in your legal operations.
This, in turn, better connects your team to the rest of the enterprise organization, so that you’re moving contracts through the entire lifecycle more quickly, and with more information and buy-in from your stakeholders.
Where AI performs well in contracting environments today
Now, if you add AI on top of a CLM, things get interesting.
Source: 2026 State of AI in Legal Report, Ironclad
“When AI is embedded into the system of record, every contract that goes in and out creates structured data,” explains Saya. “This allows the system to get smarter over time. That means your playbooks improve because they learn from your negotiation history. It means your redline suggestions get more precise, because they’re grounded in what your organization actually accepted in the past. That gives your team a super powerful compounding effect.”
It’s not just about speed. Using AI on the system level integrates your workflows together, with structured templates, standardized clause libraries, and cleaner metadata that surfaces insights about your overall contracts and performance you can use every time you’re negotiating a new contract. This, in turn, creates a feedback loop across the contract lifecycle that gives your team cross-functional visibility and more strategic insights.
“The only way that AI can improve over time is to have your organization’s context, the rules and standards by which you operate. A regular AI tool is just using the internet and whatever prompt you put in, but when it’s embedded into a system that’s already designed for legal specialists, you can get a much more customized answer for a legal question and more tailored recommendations for your deal playbooks, for example. That kind of thing would take months to do without the system in place,” says Saya.
With the right system, AI becomes a powerful part of your legal organization
Adding a layer of AI to a system that integrates into your entire legal workflow is what makes it so powerful, rather than handling one-off requests. By turning your contracts into observable, structured data that can be analyzed and understood by the entire business, your team has access to a system that makes their job so much easier.
“In an ideal world, your CLM has all of your contract data, and the AI runs on top of that. Your redlines are smarter. Your playbooks are more efficient. And ultimately, your team saves time by establishing clear standards and requirements for all of your processes,” says Saya. “When you have the right data, AI can do anything.”
Learn more about how legal professionals use AI every day in the 2026 edition of our State of AI in Legal Report.
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.
