An anonymous real example
An operations team was tired of waiting for legal reviews. They put contracts into Copilot and began reviewing them themselves. The speed was exciting. They believed they could get the job done without the delay. Then things started to go wrong. They had taken on work whose complexity they did not fully understand, using a tool whose limitations they did not understand either. Removing the wait had also removed the specialist judgement the handoff was meant to provide.
The assumption worth revisiting.
Legal may still own the policy and carry the expectation of oversight. Yet the actual contract work can move into Operations without a formal transfer of responsibility. An approved tool does not make that a controlled process. Generating a convincing review is different from understanding the commercial context, the interaction between clauses and the consequences of getting them wrong. AI can support that work; confidence in its output cannot stand in for the expertise needed to assess it. The exposure can arrive long after the apparent time saving: a commitment accepted, an exception overlooked, or a negotiation position weakened. A faster review count says little about whether the organisation made a better decision.
Has demand fallen—or have people stopped asking?
Give the capacity a purpose.
Give Legal and Operations time to design a route that is fast enough to use, with clear boundaries around what colleagues can do and when specialist judgement must enter. A rule that only says ‘come to Legal’ will struggle if the original bottleneck remains.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Explore this question without assuming our first interpretation is right: Has demand fallen—or have people stopped asking? Offer competing explanations, identify assumptions and suggest evidence we could obtain from the people doing the work.
Treat the answers as possibilities to test with people and evidence.
Go deeper into the evidence.
Judiciary of England and Wales · Published judgment
The judgment addresses false legal citations and the responsibility of legal professionals to verify research and authorities.
Specific proceedings, not a general error rate for AI legal tools. The practical application here is a review workflow, not advice on a particular dispute.
video · 4 min
AI lawyers are here – can you trust artificial intelligence for legal help?
University of Sheffield
A short introduction to the opportunity and limits of AI-supported legal help, connecting customer empowerment with responsibility for accurate decisions.
Listen or watch for: What can a better-prepared customer now question, and where will they still need expert support?
About this resource
University and legal-support practitioner perspectives, including student tests; not evidence of a measured increase in successful legal claims.