General counsels and litigation leaders across the US legal market are navigating a familiar set of pressures. Timelines are tighter. Matter volumes are growing. And clients want more visibility on cost without any relaxation on quality. AI-powered document review has arrived as a genuine response to those pressures – and the speed and scale it delivers are real. But speed only holds its value if the output can be trusted. And that is where things get complicated.
What happens in practice looks different from what the technology promises
The challenge most legal teams run into isn’t access to AI tools. It’s what happens once those tools are deployed inside a real workflow. AI review platforms perform well under the right conditions – but those conditions need to be created and maintained. Without structured escalation paths, consistent quality checkpoints, and clear protocols for edge cases, outputs become harder to rely on. Decisions start lacking the context the model couldn’t fully capture. And teams, under time pressure, can become overly dependent on automation in ways that introduce rather than reduce risk.
The first-order effect is operational friction. The second-order effect is more consequential – compliance exposure, weakened privilege determinations, and review decisions that are difficult to defend if challenged. For a GC, that is not a technology problem. It is a liability one.
The question isn’t whether to use AI – it’s how to keep control of the process while you do
Most legal and compliance teams aren’t looking to slow things down. They want the speed AI delivers. But they also need confidence that outputs have been properly validated, that privilege has been handled correctly, and that there is a clear audit trail if any decision is ever scrutinized. What’s missing in most deployments isn’t the technology. It’s the operational layer that sits around it – the people and processes that make sure AI is being used properly, not just being used.
This is what “LDM in the Loop” means in practice
LDM Global has built review teams across the full evolution of the industry – from traditional managed review through technology-assisted review and into AI-driven workflows including platforms like aiR for Review. That experience has produced something that goes beyond platform familiarity. It has given us a clear picture of where AI performs reliably, where outputs need validation, and where human judgment is simply required.
Being “in the loop” is not about adding a layer of oversight after the fact. It means our teams work within the workflow itself – applying structured QC, managing escalations, and bringing subject matter expertise to bear at the points where the model needs it most. The result is review that moves at the pace AI makes possible, while remaining consistent and defensible throughout.
Getting the workflow right today creates capability for tomorrow
When AI-led review is run properly, the benefits extend beyond the immediate matter. Teams develop a clearer understanding of how their tools are performing. Processes become more repeatable. And the operational knowledge built on one matter carries forward into the next. Your end clients see faster turnaround and fewer surprises. Your internal teams get a model that scales without a proportional increase in headcount or risk.
Two things worth taking away:
- Access to AI is no longer the differentiator in document review. Knowing how to operationalize it – with the right structure, validation, and expertise built into the workflow – is what separates teams that are in control from teams that are managing problems they didn’t anticipate.
- The human layer in AI-led review is not a step backward. It is what makes the whole thing defensible.