The question we hear most often from legal-ops and in-house counsel is some version of this: "We are looking at AI redlining tools. What actually changes?" It is a fair question, and the honest answer is more nuanced than most vendor materials suggest.
AI-assisted contract review does change the workflow in meaningful ways. It also does not change some things at all. Getting clear on which is which will save your team a lot of disappointment, and help you set realistic expectations with attorneys, contract managers, and the business stakeholders who are waiting for faster turnaround.
What the Mechanical Work Looks Like Without AI
Manual contract review, for a typical vendor agreement or commercial contract, involves several distinct tasks that get conflated but have different time profiles. There is the initial read-through to understand the structure and flag high-risk sections. There is clause comparison: checking whether the counterparty's indemnification language differs from your standard position, whether the limitation of liability is mutual, whether auto-renewal provisions match what you have accepted before. There is the actual redlining: editing the document, adding comments, proposing alternative language. And there is the negotiation round-trip: tracking which redlines the counterparty accepted, which they pushed back on, and what compromise positions are acceptable.
For a 25-page commercial agreement, a thorough first pass by a skilled paralegal or junior attorney takes somewhere between 90 minutes and three hours, depending on contract complexity and how many deviations from standard language appear. Senior attorney review of the high-risk sections adds another 30 to 60 minutes. That time estimate assumes the reviewer knows the company's standard positions and has access to prior versions. When those resources are hard to find, the time climbs.
Where AI Assistance Compresses Time
AI-assisted redlining is genuinely fast at the mechanical comparison work. Given a set of playbook positions (acceptable language, fallback language, never-accept positions), an AI layer can surface clause deviations in seconds. A contract that deviates from your standard indemnification position in three places and has a non-standard limitation of liability cap gets flagged immediately, with the specific sections highlighted and the nature of the deviation identified.
That first-pass work, the flagging and initial markup that a paralegal might spend an hour on, now takes under a minute. For high-volume review situations (NDAs, vendor onboarding agreements, vendor addenda to master agreements), this compression is significant. A legal-ops team that was processing 40 NDAs per week at 30 minutes each is now doing the flagging pass in bulk, and the human reviewer focuses on the deviations rather than the whole document.
The compression is also meaningful for clause standardization. When AI is working from a well-defined playbook, it applies those positions consistently. A human reviewer, especially when tired or under time pressure, will occasionally let a clause through that should have been flagged. AI does not have that fatigue problem.
What AI Cannot Do in a Review Workflow
Here is where a lot of the overselling happens. AI-assisted redlining, as it exists today, does not replace attorney judgment on the questions that actually determine deal terms. Those questions are contextual in ways that current models handle poorly.
Consider an indemnification clause that the counterparty has modified to include indirect damages on a capped basis. The AI can flag that this deviates from your standard mutual-and-uncapped-but-market position. But whether you accept it depends on things the AI does not know without explicit instruction: how important this vendor is, whether there are strategic reasons to close quickly, whether your company has had prior indemnification disputes that make this issue more sensitive than usual, and whether the legal team's general counsel has a view on this specific category of exposure.
The judgment call is still human. So is the negotiation strategy. An experienced attorney reading a counterparty's proposed changes often draws inferences about how much flexibility they actually have: a heavily lawyered draft with unusual positions may signal that their legal team is cautious rather than that those positions are firm. An AI layer does not have that intuition, and treating its outputs as a substitute for counsel review on material terms would be a mistake.
We are not saying AI should not be involved in the review process. We are saying it is a mechanical acceleration tool, not a judgment replacement. The productivity gain is real; the risk reduction you get from faster flagging is real. But the senior legal time required to make final calls on deviated terms does not compress proportionally.
The Playbook Quality Problem
AI redlining is only as good as the playbook it works from. This is underappreciated. A well-functioning AI review layer requires a clearly documented set of positions: what your standard language is, what alternative language is acceptable, what positions are non-negotiable, and what questions require escalation to senior counsel. Most legal teams have this knowledge spread across people's heads, prior redline history, and informal institutional knowledge.
Building the playbook is the hard part, and it is work that would be valuable even without AI in the picture. When we talk to teams that have implemented AI redlining and feel like they are not getting the benefit they expected, the root cause is usually a thin or inconsistent playbook. The AI is flagging things inconsistently because its reference points are inconsistent. The fix is not more AI sophistication, it is better playbook documentation.
This is why we build playbook management as a core function in Pactthread. The clause library and position documentation is not a nice-to-have addition to the redlining feature, it is the foundation the redlining accuracy depends on. When teams use it properly, review quality improves across both the AI-assisted passes and the human review that follows.
The Version Control Issue in Practice
One change that is easy to underestimate is what happens to version management when redlining rounds multiply. A negotiation with four rounds of redlines in a traditional workflow is a filing challenge: which version is current, who made which change in which round, and what was accepted and what was not? Legal teams often end up with multiple "final" Word documents and have to reconstruct the negotiation history manually when a question arises six months later.
AI-assisted review tools that maintain clean version lineage solve this problem as a side effect. Every markup is tracked, every round is timestamped, and the lineage from first draft to executed version is queryable. This matters less in the moment than it does during a dispute or an audit, when someone needs to reconstruct why a particular term ended up the way it did.
We have seen teams discover value in this retroactively. They adopted the redlining tool to speed up current reviews, and then found that the version history it maintained allowed them to answer questions about old contracts that previously required an hour of hunting through email attachments.
Setting Expectations With Your Team
If you are introducing AI-assisted redlining to your legal or procurement team, the framing matters. Positions that overstate what it will do ("this will cut our review time in half") create backlash when attorneys find themselves still spending significant time on high-risk clauses. Positions that undersell it ("it just flags things, you still have to do all the work") leave value on the table.
The accurate framing is this: for high-volume, lower-complexity contract types (NDAs, vendor service agreements, standard amendments), AI redlining will materially reduce first-pass time. For complex commercial agreements with significant deal terms, it will front-load the deviation-flagging work, so human review is more focused and efficient. It will not eliminate the need for experienced legal judgment on material issues, and it will improve your playbook coverage as a byproduct of being forced to document your positions explicitly.
That is a genuinely useful set of improvements. It is also a realistic one, which is the only kind worth building a workflow around.