Why reasoning models are the missing link for transforming legal
For the last two years, lawyers have been told that AI is "almost there". Yet, in private, the reality has been different.
For the last two years, lawyers have been told that AI is "almost there". Yet, in private, many admitted that for complex, multi-step workflows, the models just weren't reliable enough. The accuracy might have been 90% on a single task, but if you strung five tasks together, the failure rate became inevitable.
That changed in 2025 with the mass adoption of reasoning models (like o1, DeepSeek, and upgrades to Gemini and GPT).
Pausing to think
The defining characteristic of these new models is the use of "hidden tokens" to simulate a thought process. Unlike standard LLMs, which rush to predict the next word in a sentence, reasoning models pause. They generate internal, invisible text to:
- Plan the approach: They map out the necessary steps to answer a query before writing the answer.
- Discern Intent: They ask themselves, "What is the user actually asking for? Which jurisdiction applies here?" rather than assuming the context.
- Self-Reflect: They can critique their own logic before presenting it to the user, significantly lowering the rate of hallucination.
The role of expertise
Prior to reasoning models, AI lacked "expertise." It couldn't define what "good" looked like because it couldn't plan or govern its own quality parameters. It required a human expert to constantly check if the output was hallucinated or legally sound.
Reasoning models introduce a form of synthetic expertise. They can autonomously select the right tools, identify the relevant context to focus on (and what to ignore), and adhere to a "definition of done". This capability is what allows us to finally remove the human from the loop for straightforward, low-complexity tasks.
Why this matters for Legal Ops teams in enterprise
This isn't just about faster document processing. It is about building autonomous legal systems that can be trusted. Because these models can plan and reason, Legal Operations can now deploy them to execute complex, end-to-end workflows – like orchestrating a contract review and negotiation from initial request to final signature – with a level of reliability and compliance assurance that "non-reasoning" models simply could not achieve.