Our Take
Contract review is real work for AI, but the bottleneck is not the model—it's whether your team has documented its own rules clearly enough to teach a system what to accept.
Why it matters
Legal departments are piloting AI contract review but most lack the operational groundwork to scale beyond isolated experiments. The gap between capability and deployment is organizational, not technical.
Do this week
General Counsel: audit your internal contract templates and negotiation playbooks this month so you know what you'd need to codify before an AI tool can apply it consistently.
Contract review is the near-term use case for legal AI
AI adoption in law is accelerating, and contract review has emerged as one of the most immediate and impactful applications. Legalfly is hosting a webinar on 11 June featuring senior legal solutions engineer Stephanie Adriaens to discuss how legal teams can build the operational foundations for successful AI-assisted contract review.
The session will cover three practical areas: identifying which contracts are suitable for AI review, organizing and centralizing internal templates, and creating playbooks that encode an organization's preferred clauses, fallback positions, and negotiation strategy. Rather than treating AI as a technology problem alone, the discussion frames implementation as an operational challenge that requires teams to articulate and standardize their own decision-making first.
The real work is making your own processes legible to a machine
Most legal teams run contract review through unwritten custom practices: senior lawyers carry the firm's preferences in their heads, junior lawyers learn through osmosis, and consistency varies by reviewer and deal context. When a legal team tries to hand this work to an AI system without first documenting it, the model cannot apply stable rules because none are written down.
The webinar's framing identifies this gap directly. Effective AI review depends not only on technology, but on the quality and consistency of the legal knowledge underpinning it. Teams experimenting with AI tools in isolation discover quickly that one-off reviews do not scale. Moving from isolated experimentation to repeatable, trusted workflows requires the organization to codify what it actually does and why.
This is not a knock on the tools. It is a statement about organizational maturity. Contract review looks like a straightforward AI win until you realize that success depends on having clear internal agreements on what counts as acceptable, which is often the thing legal teams have never had to write down.
Start with process mapping, not tool selection
Before licensing an AI contract review platform, audit your existing playbook. If you cannot articulate your fallback positions, your preferred clause variations, or the escalation criteria for flagging a contract, you do not yet have the operational foundation an AI system needs to be useful.
Centralizing templates is the prerequisite. Once templates are in place and shared across the team, you can begin documenting the decision logic that lives around them: which clauses are non-negotiable, which have standard alternatives, which require partner review. This is the actual work. The AI tool becomes valuable only after that documentation exists and is consistent.
The webinar is aimed at legal professionals looking for grounded guidance they can apply immediately, rather than abstract discussion of AI's potential. The focus on practical realities—how to prepare teams and processes, not just tools—reflects the actual constraint most in-house legal departments face when trying to move contract review from experiment to operation.