In a new series of articles, the Thomson Reuters Legal team looks at the impact of AI on legal judgment and how legal organizations can best preserve collaboration through designed workflows.

AI has crossed a threshold in legal practice and has become a participant in how legal teams think. Once AI-generated outputs enter shared workflows, they stop being neutral inputs and start shaping how teams frame problems, focus their attention, and reach conclusions.

The resulting risk is a quiet erosion of collective reasoning, beginning with the individual and compounding sharply at the team level.

Framing the problem

The first structured output in a legal workflow often becomes the working definition of the issue. A summary appears in a chat thread, and a list of issues is circulated before a meeting. From that point forward, the team engages with the structure already in place.

The timing and authority of AI-generated drafts now are arriving earlier in the workflow, looking more complete and requiring less effort to produce than any human-created first draft. As a result, they tend to shape interpretation before independent reasoning has a chance to develop.

Yet, no one intends for this to happen. Even when teams are told an output is exploratory, the effect persists because the structure becomes the implicit agenda.

Why the “tool” model falls short

Legal organizations have long approached AI the same way they approached earlier legal technology — as a tool supporting human effort. That framing is now incomplete. Traditional tools primarily helped professionals retrieve, organize, and process information. Generative AI (GenAI) can go further by proposing issue structures, arguments, interpretations, and conclusions, thereby contributing more directly to the substance of legal reasoning. And at the level of human interaction, AI increasingly functions as a collaborative actor.

Within a legal workflow, however, every AI-generated contribution must remain an input to be interpreted and validated by human judgment. Managing AI as a tool through access controls or accuracy benchmarks addresses what the system produces, but it does not adequately address what happens when those outputs start shaping how teams think together. This is a collaboration challenge rather than a simple technology-management one.

Indeed, much of the industry’s AI risk conversation still centers on accuracy, which mostly concerns incorrect summaries and unreliable outputs. While this is a notable risk, it is not the most consequential one. The more damaging consequence is an AI output looking plausible with no one responsible for interrogating what it may have missed or hallucinated. Too often, a well-structured AI-assisted document that looks complete becomes the basis for discussion without anyone asking what was left out.

The breakdown, then, emerges in the quality of the collective reasoning that forms around it.

Delegation versus collaboration

The clearest way to understand AI-mediated legal work is to separate two modes that look similar but are different in practice: delegation and collaboration.

Delegation transfers work, while collaboration requires interpretation. Delegation treats AI output as the conclusion, while collaboration treats it as a contribution in which effort is invested by the team interrogating the output, testing what is missing, and deciding what matters. From the outside, both can look identical because documents are produced, teams engage, and work progresses.

The difference, however, is whether the team has exercised independent judgment. One useful test is to set aside the AI output and ask team members to reconstruct, challenge, and defend the reasoning in their own words. If they can do so, AI has supported the team’s reasoning rather than substituted for it.

Interestingly, most AI-risk discussions focus on the individual, namely the lawyer who over-relies on an AI output. While this risk is real, it's manageable, because one person's judgment lapse is very often visible and correctable.

Within a legal workflow every AI-generated contribution must remain an input to be interpreted and validated by human judgment.

Teams are different. When multiple people interact with the same AI output, each may assume someone else has already validated the reasoning. Responsibility diffuses, and the habit of questioning assumptions, surfacing alternatives, and challenging the framing quietly erodes. A team can collectively move forward without anyone ever reconstructing the logic end to end.

The result is synthetic confidence, or the appearance of rigorous collective review without corresponding ownership or any testing of the underlying reasoning.

Unfortunately, a coherent, structured AI draft invites continuation rather than challenge. Team members add comments, adjust wording, reorganize a section, and the document evolves and gets approved. From the outside, this looks like collaboration. From the inside, however, the edits cluster around phrasing and tone rather than questioning underlying assumptions. Further, primary sources are not revisited, and alternative interpretations never surface.

The result is delegation in the same failure mode described above, but harder to catch because it is distributed across several people instead of being concentrated in just one.

Moreover, the erosion of collective reasoning may remain hidden in routine work because a polished product can mask an untested reasoning process. It becomes unmistakable under pressure when a novel issue arises or someone must defend the analysis on grounds and reasoning that the AI output did not supply.

Worse yet, accuracy alone does not prevent this failure mode. Even a technically correct output can anchor a team to an incomplete framing, and the polish of the output may make that framing less likely to be questioned in the first place.

However, two key questions can help teams test for this failure mode at critical points during an AI-assisted matter, and again before the work is finalized:

1. First, did the team reason through the problem together, or did it simply manage around AI's output?
2. And, if AI disappeared tomorrow, could the team still construct and defend the analysis?

Designing for cognitive resilience

Asking these two questions after the fact helps a team notice when it has drifted into disguised delegation. However, the more durable fix is to prevent the drift in the first place by building cognitive resilience into the workflow itself. Organizations that preserve strong collective reasoning do not rely on individual vigilance alone. They build structured practices that require interpretation to occur.

We recommend the following. First, designate a team member as a challenger that would be responsible for identifying omissions, questioning assumptions, and surfacing alternative interpretations. Then, require independent articulation by asking team members to explain the conclusion and supporting reasoning in their own words.

Finally, return to primary sources at defined decision points rather than allowing AI-generated summaries to become the substitute for the underlying record or authority.

These practices may not be easy at first. They require deliberate effort, but they also help keep reasoning active, visible, and shared.

The erosion of collective reasoning may remain hidden in routine work because a polished product can mask an untested reasoning process.

Further, reframing AI as a collaborative actor for the legal team’s work has direct implications for the legal organization’s leadership. AI strategy, properly understood, is itself a collaboration strategy. Leaders must establish clear responsibility for challenging the initial framing, verifying source material, and explaining the basis of the final conclusion. Without that accountability, human review can become a procedural checkpoint rather than a true exercise of judgment.

AI governance must extend beyond the conditions under which AI is accessed and the quality of what it produces. It must also address how teams interpret, test, and rely on AI-generated contributions. Access controls, acceptable-use policies, and accuracy standards remain necessary, but they do not by themselves demonstrate that human judgment has been exercised before a conclusion is accepted.

Indeed, effective human oversight requires more than placing a reviewer at the end of the workflow. Governance should require the people who draft, review, and approve AI-assisted work to demonstrate that they have interpreted, challenged, and validated the AI’s contribution.

AI is reorganizing how legal professionals think together inside teams. Those organizations that recognize this and design their collaboration practices accordingly will capture AI’s genuine value without quietly sacrificing the judgment that defines legal .

Of course, the ultimate test is not whether a single human reviewed the output, but whether the team can still explain, challenge, reconstruct, and defend the reasoning as its own.

In our next installment in this series, we will look at how AI is reshaping collaboration between junior and senior lawyers‍