Legal Contract AI: Balancing Efficiency and Trust for In-House Teams

Picture a legal team at a large global bank on a typical morning. Contract work is spread across multiple systems, with drafting, approvals, versioning, and comparisons happening wherever someone remembers to run them.

As one legal leader described it, “We basically use Word and a redline tool to run comparisons so we can work in two versions, then create a clean copy and the redlines, then send a comparison against the prior draft.”

These kinds of workflows explain why AI continues to show up in the efficiency conversation. But legal teams are proceeding cautiously and asking the tough question: Can AI help without introducing risk in work that has to be defensible, auditable, and accurate every time?

Top Legal Team Priorities

Source: November 2025 Docusign survey of 51 U.S.-based, full-time in-house legal professionals

At the same time, respondents also noted that momentum for AI is building. Ramping up the technology is a priority for 57%, and 69% plan to purchase contract analytics or AI tools in the near term. Herein lies the dynamic: Legal teams want progress, but they also want to separate acceleration from new sources of risk.

How contract complexity leads to uneven tech adoption across legal

Most in-house legal teams operate in two contracting realities. One includes high-volume, repeatable agreements like NDAs and procurement contracts where standardization is feasible. The other is high-stakes—strategic partnerships, major customer agreements, and multi-party deals—where context is a major influence and terms are heavily negotiated.

Automation tends to work best when inputs and outcomes are consistent. That’s why adoption tends to be more cautious in variable, high-risk deals—not because teams don’t want efficiency, but because the cost of a mistake is higher. This tension is reflected in the data: 35% of respondents said prioritizing which tasks should be digitized is a challenge.

The process gaps holding back legal AI adoption 

AI can’t fix a broken workflow. For many legal teams, the big constraint is that the underlying process still depends on informal knowledge, incomplete intake, and inconsistent handoffs.

Playbooks are a common example. They often live in spreadsheets, Google Docs, or just in the heads of senior attorneys. And while work doesn’t stop completely when those senior lawyers aren’t around, it can slow down as teams wait for clarification or oversight.

As one general counsel put it, “We have processes and procedures, some are well documented. Some have a lot of institutional knowledge with the people involved in the process. If they are out, all of a sudden you have a gap and you may not even know about it.”

Intake is another friction point. When requests don’t provide the full context about the deal, legal teams spend time chasing down information before they can focus on risk.

One in-house legal leader involved in contract review said, “One thing I would improve is when I’m asked to review a contract, I need a summary of the relationship. All the information, the value, the importance of the client, the priority, so I can focus on the right things.”

For many legal teams, getting the process right is the first step toward meaningful automation—with or without AI.

Tool sprawl creates friction, and integration is the real blocker

There’s a common perception that more tools make work easier. In practice, though, disconnected tools often create hidden work as users toggle between platforms.

And even where contract lifecycle tools exist, adoption can stall at basic features if day-to-day workflows still happen in Word, email, and shared drives.

As one legal leader at a global bank described it, “We have each tool for a specific function, and they don’t work well together.” 

Another described the ideal state as “one platform, to be able to log in to one system instead of copying and pasting info from one app to another.”

Beyond the technology itself, implementing new software often requires a significant culture change. Longtime attorneys are often asked to adopt new tools that don’t immediately feel easier than the workflows they’ve relied on for years. As one in-house legal leader noted, “Change management is harder for less tech-savvy employees.”

Survey results also found that the investment required for upgrading tech is a concern, but it isn’t what holds teams back most.

Why accuracy and trust define legal AI adoption

When AI enters the conversation, accuracy is usually the first test.

Many teams are comfortable using general-purpose tools for low-risk tasks, like Copilot for quick checks or basic summaries, but contracts raise the stakes. As one legal leader explained, “We would still have to have a human check it, so is it really saving time? Until we can be sure it’s accurate, we’ll probably only use it for minor tasks.”

Another was more direct: “The accuracy has to be very high. If it’s wrong, it creates more work, not less.”

Privacy, security, and integration challenges compound that hesitation. In the survey, each was cited by 53% of respondents as a significant hurdle to adding AI to legal workflows.

Measuring impact adds another layer of complexity. Many legal teams don’t track consistent baselines for contract cycle times, workload distribution, or error rates, so ROI gets judged informally. Does the tool get used, and does it make day-to-day work meaningfully better? For legal teams, the bar is clear: if AI outputs can’t be explained, validated, and audited, adoption won’t scale.

What in-house legal teams want from contract automation and AI

Focus isn’t on AI experimentation because “everyone else in the enterprise is doing it” but rather for progress that reduces friction, preserves control, and fits how legal work actually gets done. Across teams, priorities around contracts are remarkably consistent and practical:

Smarter intake that provides full context upfront

Intake is a common pressure point in the contracting process, especially when requests arrive without the basics of the deal, leaving attorneys to spend time contacting stakeholders and filling in gaps before they can assess risk.

Structured intake processes that require requestors to give key context up front can reduce rework and help legal triage faster.

Embedded playbooks that reduce unnecessary escalation

Teams want playbooks embedded directly into systems, not buried in spreadsheets or locked away inside a few experienced brains. Clear fallback language and escalation rules can help junior attorneys and legal support staff handle routine decisions without repeatedly escalating the same questions.

Fewer, better-integrated platforms that are easy to use

Consolidation keeps coming up: fewer logins, fewer handoffs, fewer places for versions to diverge. Ease of use matters as much as capabilities, particularly for long-tenured attorneys who won’t adopt tools that slow them down.

AI opportunities where rules and structure already exist

Legal teams gravitate toward AI use cases with defined inputs, clear governance, and outcomes that can be validated. In the survey, here’s where respondents felt most comfortable:

Where work requires discovery, nuanced judgment, or incomplete inputs, teams are more cautious, signaling that for now, the safest AI adoption path starts with structured workflows and expands as trust grows.

Early signals of where legal AI adoption is heading

In-house legal teams are not standing still. Adoption is deliberate and there are clear signs of forward movement, especially with tools that help teams analyze contracts at scale and reduce manual work in established workflows.

In the survey, respondents reported near-term investment plans:

Put simply, teams are starting where AI can be validated, governed, and integrated, then expanding as tools prove accuracy and traceability.

As one legal leader described their approach: “We use Copilot. I’ve asked it questions like ‘Tell me what the remedies are for this kind of termination,’ and it’s helpful to check your own analysis, or ChatGPT to ask if there is a better way to frame this. I typically put in what I think is already good just to see if it can do better.”

Legal’s path forward for contract AI

This cautious approach to AI reflects how legal teams work. Contracts have to move quickly, manage risk, and hold up under scrutiny, and progress depends on keeping those priorities in balance.

As one legal leader put it, “It’s important for legal teams to act as a business partner first, understand the company’s business and what level of risk is acceptable.”

AI tools will gain traction fastest when they support that role: reducing rework, improving visibility, and helping teams apply playbooks consistently, without asking legal to trade away control. 

For more insight into how in-house legal teams are thinking about contract AI today, explore our related analysis on where AI is gaining traction.