“AI enabled document review” has fast become the most overused and least defined phrase in the industry. With promises of faster timelines, substantial cost savings and maintained accuracy, it’s a hot topic for a reason.
Alternative Legal Service Providers (ALSPs), eDiscovery companies and many law firms claim to offer “AI Review.” On the surface it sounds like a shared capability. The reality is that the phrase has increasingly become a catch-all marketing label used to signal innovation, attract attention and capture market share, despite often describing fundamentally different workflows, technologies and levels of human involvement.
Legal teams should absolutely be embracing AI. As data volumes continue to increase alongside mounting pressure on legal budgets, AI plays an essential role in modern document review and eDiscovery workflows. However, when every proposal relies on the same language and buzzwords, the operational nuances that actually determine cost, risk, and defensibility are often lost.
The result is a growing gap between what legal teams believe they are purchasing and how document review is ultimately being delivered.
The Workflow Matters More Than the Label
There is no single way AI is integrated into document review workflows and these differences extend beyond the technology itself. Operational differences change how work gets done, how human judgment is applied and how results are defended.
One of the most significant differences between these models is the checks and balances integrated into the workflow alongside the quality and role of the “human-in-the-loop.” AI must be trained, its decisions must be calibrated and its output must be validated if the final work product is to be accurate and defensible.
At one end of the spectrum, AI is used to support large scale human review workflows by prioritizing documents, surfacing potentially relevant material or accelerating reviewer decisions. At the other end, some providers are moving toward highly autonomous workflows with minimal human intervention beyond exception handling or final quality control. Neither approach is wrong, both have value depending on the circumstances of the matter in hand. The issue is they carry very different implications for cost, speed, defensibility and risk.
The key distinction is how human judgment is incorporated into the workflow, who is making those decisions and how the outputs are validated. That distinction becomes particularly important when facing a challenge from opposing counsel, regulators or the court. A provider relying on automation without human oversight may struggle to explain the logic behind a production. In contrast, models that integrate human judgment at key intervals provide a much stronger narrative for the process, turning a technical output into a defensible legal position.
The question is no longer simply whether AI was used. It is how the workflow was designed, executed, governed and documented.
The Risk of Assuming Equivalence
Highly automated review models may appear more cost effective on paper because they reduce the number of human review hours involved in the process. In the right circumstances, that efficiency can deliver significant value. However, lower upfront review costs do not automatically translate into lower overall risk or lower total cost.
Treating different models as equivalent creates significant risk. Teams often fall back on surface level comparisons or guess what the service actually does, leading to “expectation gaps” where a legal team assumes a level of human oversight that isn’t actually part of the provider’s workflow.
If an overly aggressive workflow results in over production, inconsistent privilege determinations, missed context or the need for substantial downstream re-review, the initial efficiency gains can quickly erode. In some matters, the operational cost of correcting errors or defending review decisions may outweigh the savings generated by increased automation. This is why legal teams can no longer evaluate document review models based solely on speed, reviewer counts or headline cost reductions.
Selecting the appropriate workflow requires a clear understanding of how AI is being applied and how human judgement is being integrated:
- How is AI used at each stage of the review?
- Where does human oversight sit within the process?
- How are decisions validated and documented?
- What audit mechanisms are in place?
- How does the process stand up under scrutiny?
These are not new considerations in document review but they are more important now that AI is at the centre of the work. Transparency, governance and defensibility are more critical than ever.
Beyond the Labels
There is no universally “correct” workflow but understanding these distinctions is the only way to select the right approach for each individual matter. The goal should not be to maximize or minimise the use of AI for its own sake. The goal should be to apply the right level of automation to the right matter in a way that aligns with legal, commercial and risk priorities.
At present, there is no universally accepted industry definition of what constitutes ‘AI-enabled review,’ which makes direct provider comparisons increasingly difficult. The legal teams that will navigate this shift most successfully will be those able to look beyond the marketing labels and evaluate the underlying workflow, governance and risk model behind the technology, making the ability to look past the “AI” label one of the most valuable skills a legal professional can have.
