When to Trust Legal AI: How Task Complexity Impacts Accuracy

This article was first published in The AI Journal.

In legal work, speed matters—but trust matters more. As generative AI rapidly expands into contract review and risk detection, the question legal teams face isn’t whether to adopt AI, but where and when to trust it. And the answer lies in task complexity.

Where Legal AI Excels: Simple Tasks and NLU/NLG

The most common uses of GenAI are possible because of language models like Natural Language Understanding (NLU) and Natural Language Generation (NLG), two distinct but complementary branches of Natural Language Processing (NLP).

NLU is the process by which machines analyze and interpret human language in a way that allows them to understand meaning, intent, and context, so the system can make decisions or take actions based on it. This is where legal AI thrives and is a best fit—tasks with structured inputs and outputs, clear benchmarks, and well-scoped objectives. 

Contract review is a prime use case for AI because its workflows follow a sequence of rule-based steps. Reviewing an NDA is straightforward. AI can quickly identify missing or risky clauses, suggest redlines that reflect preferred language or fallback position, or summarize key terms or issues. 

In NLG, machines generate human-like language based on data, rules, or structured information to create coherent, contextually appropriate, and readable text (e.g., case summaries or drafting clauses and responses). In more complex legal tasks, AI uses NLU to surface issues in a first-pass review attempt and NLG to suggest edits to risky or missing clauses. 

However, we must be wary of the bias GenAI models can reflect based on the datasets and information they are trained on. Maintaining accuracy while using legal AI is critically important because of the high-stakes, nuanced, and risk-sensitive nature of legal work. Mistakes by AI systems can cause hallucinations, inefficiencies and delays, missed critical risks, negotiation missteps, difficulties in verification, and malpractice risk. 

AI is powerful and increasingly pervasive in legal workflows. Yet despite AI’s promise and presence, its reliability varies dramatically based on the task at hand. The more structured and predictable the task, the more accurate AI can be. The more complex, strategic, or judgment-driven, the more AI requires human oversight. 

Trust in AI depends on understanding where it thrives—and where it falls short.

Where Legal AI Struggles: Complex Judgment Needs Human Expertise

Accuracy is not just a “nice to have” in legal AI—it’s mission-critical. For AI to be trusted and useful in this space, it must be more than fast. It also needs to be reliable, explainable, and verifiable. AI systems need expert oversight and judgment to help alleviate some of the risk of biases, especially in high-risk domains like law.

For example, in contract review, AI tools can flag standard indemnification language as high risk, but still miss a newly inserted carve-out that shifted liability. This error can only be caught by legal review. Unfortunately, by the time it’s updated and approved, the review has likely delayed negotiations. So while AI can flag risks and make suggestions, it lacks the expert judgment required to negotiate the contract. 

In other words, AI can assist in more complex matters, but it can’t replace legal reasoning, commercial judgment, or strategic communication.

The search for legal AI tools can be daunting, particularly in a market ripe with solutions that promise the moon. When researching vendors, it’s important to understand that numbers related to accuracy can be misleading. Easier tasks inflate performance metrics—scoring 98% accuracy on simple redlines and replacements should be expected, not celebrated. Pay special attention to accuracy claims in instances where lawyers are required to offer judgment, and dig into how the technology calculates this level of accuracy.

The trustworthiness of AI mirrors the complexity of the task. In straightforward workflows, AI operates in a high-trust zone. But as complexity grows, the zone of trust narrows, demanding human expertise to bridge the gap.

The solutions with the best levels of accuracy don’t rely on AI alone. Automation isn’t expertise. Rather, they combine AI technology with human knowledge in legal content written, tested, and guided by legal expertise. Trustworthy outputs depend on human-grounded inputs. When the AI is layered by rulebooks, issue lists, and playbooks created by lawyers, not LLMs, users can be confident that the quality and credibility come from domain expertise and not just technical capability. 

Here’s what humans can do that AI can’t:

• Business and legal judgment: Knowing when to stand firm, when to compromise, and how to balance risk.
• Strategic guidance: Shaping negotiation strategies based on deal context and desired outcomes.
• Relationship management: Building trust with counterparties and stakeholders beyond pure contract terms.

For example, deciding what to negotiate in a contract isn’t a purely legal or mechanical choice. While AI can identify contract risks, only an experienced professional knows external factors like deal timing or recent conversations that might influence their willingness to accept certain risks. These strategic calls are uniquely human and best made by legal and contract professionals.

Conclusion: How Legal Teams Can Decide When to Trust AI

It’s no surprise that legal teams are increasingly adopting AI into their workflows—particularly with structured tasks like contract review or document summarization. Legal AI tools amplify legal expertise, allowing attorneys to work more efficiently and focus on high-value, strategic aspects of their roles. 

But there is a level of trust that must be verified before incorporating tools into daily workflows. When determining if AI can be trusted, ask the following questions: 

• Is the task highly structured? (e.g., standardized NDAs)
• Does the risk of error have legal or business consequences?
• Is there a trusted legal playbook layered into the AI?
• Is there a process for verifying or overriding AI suggestions?
• Can human review be triggered automatically for edge cases?

As technology continues to mature and improve, AI tools will eventually take on more complex tasks. And challenges will still exist. AI will always carry some bias based on the data sets on which they are trained. 

Legal teams don’t need to choose between AI and humans—they need tools that combine both. Human expertise will always be needed to set guidelines and make key decisions, especially in more complex legal matters. The best legal AI is guided by legal expertise and purpose-built with guardrails that ensure trust and accuracy at every stage.