Diligence is a structured information problem
OpenAI has published a customer account of Ropes & Gray’s proprietary AI tool for transactional diligence. The system examines commercial contracts and prepares a structured first pass for deal teams. In a process where hundreds or thousands of agreements may need review under tight deadlines, consistent extraction and issue spotting can free lawyers to focus on terms that affect negotiation or valuation.
A diligence tool has to do more than summarise prose. It must identify the relevant agreement, distinguish amendments from superseded versions, apply the current review playbook and connect each conclusion to the clause that supports it. A fluent answer without that chain of evidence can create false confidence precisely when a transaction team is moving fastest.
The tool supports lawyers rather than replacing advice
Ropes & Gray positions the system as support for professional work rather than autonomous legal advice. Lawyers remain responsible for assessing materiality, resolving ambiguity and deciding what belongs in a report to the client. This division is important because an unusual change-of-control provision or consent requirement may matter differently depending on the structure of the deal.
The reported benefits are speed and consistency. A machine-assisted first pass can reduce repetitive reading and make it easier to compare treatment across a document set. Consistency, however, should be measured against expert-reviewed matters, including contracts whose scanned pages, tables, cross-references or handwritten changes make extraction difficult.
Evidence and exceptions determine whether speed is useful
Confidentiality is another central constraint. Deal rooms contain commercially sensitive information about companies, employees, counterparties and planned transactions. Firms need clear controls for workspace access, retention, subprocessors and model improvement, along with matter-level separation and reliable deletion when access to a data room ends.
The deployment shows why legal AI is primarily a workflow-design problem. Model capability matters, but the defensible service also needs authoritative source files, versioned playbooks, traceable outputs and accountable review. Firms evaluating similar tools should measure missed issues and unnecessary escalations alongside hours saved, because a faster review is valuable only when its risk remains understood.
What teams should validate before production
For implementation, teams should record the exact model, software, dataset, connector and policy versions behind every result. Representative evaluation must include normal work, boundary cases, deliberate misuse and component failure, with outcomes separated by user role, data sensitivity and consequence. A strong average should never hide a serious failure in a critical path. Changes should pass the same regression set before promotion, and operators need a documented fallback that preserves evidence rather than silently broadening access or lowering quality.
Procurement and planning should account for the human work required to review exceptions, maintain controls and investigate incidents. Published performance or efficiency claims are useful starting points, but production confidence comes from evidence collected in the organisation’s own environment. A named owner, measurable acceptance threshold, cost model and tested shutdown path turn a promising release into a service that can be governed over time. Where decisions affect safety, rights, money or regulated records, accountable professional review remains essential.
Security and operational evidence
Security review should map every identity, credential, network destination, stored artefact and administrative access path involved in the workflow. Logs need enough context to reconstruct who initiated an action, which evidence was consulted, what the system proposed and what ultimately executed. Sensitive prompts and traces should not be retained merely because they are useful for debugging. Retention, redaction and access must follow the underlying data classification, with a practical process for correcting or removing material when policy or law requires it.
Operational monitoring should measure quality and service behaviour together. Latency, throughput and availability matter, but so do unsupported claims, missed constraints, unsafe tool calls and the rate at which people overturn recommendations. Teams should rehearse dependency outages and partial failures before unattended operation. If a model, connector or external service becomes unavailable, the workflow should fail predictably, preserve its state and tell the user what remains unfinished. Clear degradation is safer than an agent improvising around a missing control.
Transparency should scale with adoption
Organisations should explain the scope and limitations of the deployed capability to the people affected by it. Users need to know when automation is involved, how to challenge an outcome and where a human can intervene. That transparency improves feedback and prevents a pilot result from being interpreted as blanket approval for broader autonomy. Expansion should follow demonstrated evidence, not novelty or pressure to match a competitor announcement. The durable advantage comes from dependable integration, careful measurement and learning that survives the next model or product update.
Finally, teams should schedule a review after real usage rather than treating launch as the end of governance. The review should compare expected benefits with observed quality, cost, incidents and user behaviour, then retire workflows that do not justify their complexity. A service that works well can expand through the same evidence-based process. A service that fails should be narrowed or stopped without losing the records needed to understand why. This feedback loop is what turns experimentation into accountable operational capability.