When legal assistants mock AI document review, stop defending it and make the software prove itself

This guidance from Rohan Hall’s site addresses a management and technology-adoption problem, not the substance of any legal matter. Your first move should be to stop defending the purchase and investigate the team’s claim. Legal assistants work close to the documents, exceptions and correction burden. If the software repeatedly overlooks relevant language, misclassifies material or produces output that takes longer to verify than manual review, their criticism may identify a genuine implementation failure.

The mocking still needs to stop. Open ridicule undermines professional discussion and makes it harder to separate valid evidence from frustration. State that criticism is welcome when it is specific, documented and tied to the work. Personal attacks, sarcasm and attempts to embarrass colleagues are not a useful evaluation method. This distinction lets you protect professional standards without demanding artificial enthusiasm for a tool that has not earned trust.

If hostility extends beyond this one tool, use a structured approach to training employees who are openly hostile to AI. Training helps only after you determine whether the underlying problem is skill, fear, workflow design or poor software performance.

“It makes too many mistakes” is a serious allegation but not yet a useful diagnosis. Create a simple error log using representative work. For each disputed result, record what the software produced, what the reviewer expected, why the difference matters, how the error was found and how long correction took. Remove sensitive information from any discussion or evaluation environment that is not authorized to receive it.

Do not build the evaluation around a demonstration-perfect document. A useful test reflects the ambiguity, formatting variation and exceptions that appear in actual work. Equally, do not let the team select only spectacular failures. Representative samples prevent both management optimism and employee hostility from controlling the outcome.

The same principle applies when resistance sounds like “we have always done it this way.” The right response is not automatic deference or forced modernization; it is a measurable comparison. A broader framework for overcoming the “that’s not how we’ve always done it” mentality can help distinguish procedural wisdom from habit. If employees say the technology makes them feel incompetent, address that separately through support that preserves confidence during AI adoption.

A document-review system can appear productive because it processes material quickly. That metric is incomplete. If legal assistants must inspect every output line by line, trace unsupported conclusions or redo classifications, the software may have shifted labor rather than reduced it. Measure elapsed time, active review time, correction time and the severity of errors. A minor formatting issue and a missed consequential clause should not carry equal weight.

An error rate alone also lacks context. You need to know which errors occurred, how detectable they were and what happened downstream. Establish clear categories and use them consistently. The purpose is not to manipulate the numbers until the purchase looks defensible. It is to determine whether the system improves the work under controlled, responsible use.

Fear can distort the evaluation in either direction. People who believe automation threatens their jobs may emphasize every failure; leaders who sponsored the purchase may minimize those failures. If anxiety is driving the behavior, address employee fear around AI workflow automation openly rather than trying to resolve it through performance statistics alone.

Do not make “use the AI” the goal. The goal is an accurate, defensible and efficient review process. Define a narrow job for the software, a clear job for the reviewer and an escalation path for ambiguous or consequential material. Human review should not be a ceremonial approval added after the AI has effectively made the decision. It should be placed where professional knowledge changes the outcome.

This is better than enforcing adoption through status or sunk cost. Leaders facing similar defiance in creative work can apply the same evidence-first method described in how to handle designers resisting purchased AI software. When the larger obstacle is that employees refuse even to learn a new system, use a staged approach to introducing an AI system employees call too complicated rather than making immediate full compliance the first test.

You do not look foolish because a technology purchase encountered resistance. You will look ineffective only if you defend the tool regardless of evidence, tolerate disrespect indefinitely or impose a process that makes the work worse. Credible leadership means being willing to say both “the conduct is unacceptable” and “the software has to demonstrate value.” Those positions are fully compatible.

Meet with the assistants as a group to reset expectations, then speak individually with anyone whose conduct remains disruptive. Acknowledge that you sponsored the decision. Explain the evaluation process, invite documented criticism and name the professional behavior you expect. Do not demand that employees describe the software as good. Require them to participate honestly in testing, training and the agreed workflow.

Job anxiety deserves a direct conversation. Explain which responsibilities remain human, which repetitive activities are being evaluated for assistance and how employee expertise will shape the process. Avoid vague assurances that nothing will change. People trust concrete role definitions more than motivational speeches. The same approach is useful when a veteran team fears replacement, as outlined in talking to experienced employees who reject AI prospecting.

This problem sits inside a broader shift: artificial intelligence increasingly acts as an integrative force across other emerging technologies rather than developing in isolation. Rohan Hall examines that thesis in The Convergence of AI and the Top 10 Emerging Technologies. The practical lesson for leaders is that technical capability and organizational adoption must be evaluated together. Purchasing software does not eliminate the need for workflow design, human judgment and accountable implementation.

Rohan founded OceSha Ventures and its AI-first solutions, which builds and operates course creation, branded academies, AI assistants such as Lumi and business intelligence for businesses and organizations. Those offerings do not replace the evaluation of specialized legal document-review software. They illustrate the wider need to connect AI systems with reviewed business information, useful training and clearly defined operating processes.

For more of Rohan’s work across his ventures, writing and technology projects, visit Rohan Hall’s home page. The immediate decision, however, remains local and evidence-driven: test the document-review tool against real work, count the correction burden and keep human accountability explicit.