AI Implementation Roadmaps
How to Run a Legal AI Pilot That Produces a Go-or-No-Go Decision
By Anthony Villanueva ·
A well-designed AI pilot produces a clear go-or-no-go decision with data to support it. Most pilots fail this test because they were not designed with the decision in mind. This guide covers the design principles for a decisive pilot.
A legal AI pilot has one purpose: to produce enough information to make an informed decision about whether to proceed with full deployment. A pilot that ends with "it showed promise" or "we learned a lot" has failed as a decision-making instrument, even if the technology performed well.
A pilot that ends with "we deployed the system to 12 attorneys, they used it for 847 contract reviews over 8 weeks, the AI correctly identified 94% of non-standard provisions, and attorney review time dropped by 40%" has succeeded — regardless of whether the go/no-go decision is yes or no, because the decision is now data-driven.
Define success criteria before piloting
Designing for a decisive outcome requires starting with the decision criteria before designing the pilot. What performance level would convince a skeptical practice group partner to deploy the system firm-wide? What attorney adoption rate would demonstrate that the workflow is sustainable? What cost-per-matter change would justify the technology investment? Write these criteria down before the pilot begins, get agreement from the decision-makers, and design the pilot to produce measurements on each criterion.
Scope pilots for representative results
The pilot scope must be narrow enough to produce measurable results in the pilot timeframe but broad enough to be representative of real production conditions. A pilot that tests the AI on a curated set of easy contracts will overstate performance; a pilot on highly unusual matters will understate it. The representative sample design is an engineering judgment call that requires understanding both the AI system's capabilities and the distribution of work in the firm's practice.
Apply NIST evaluation framework standards
NIST's AI Risk Management Framework's guidance on AI system evaluation provides a useful structure for pilot design — specifically, the guidance on defining evaluation criteria before system deployment, testing against representative inputs, and documenting evaluation results for accountability. This guidance was written for enterprise AI contexts, but it maps well to legal AI pilots. Firms that use NIST's evaluation framework as a pilot design template produce pilots with more rigorous measurement than those that design pilots informally.
Frequently asked questions
What specific criteria should be defined and agreed upon by decision-makers before starting an AI pilot?
Designing for a decisive outcome requires starting with the decision criteria before designing the pilot. What performance level would convince a skeptical practice group partner to deploy the system firm-wide? What attorney adoption rate would demonstrate that the workflow is sustainable? What cost-per-matter change would justify the technology investment? Write these criteria down before the pilot begins, get agreement from the decision-makers, and design the pilot to produce measurements on each criterion.
Why is designing the scope of a pilot a difficult engineering judgment call?
The pilot scope must be narrow enough to produce measurable results in the pilot timeframe but broad enough to be representative of real production conditions. A pilot that tests the AI on a curated set of easy contracts will overstate performance; a pilot on highly unusual matters will understate it. The representative sample design is an engineering judgment call that requires understanding both the AI system's capabilities and the distribution of work in the firm's practice.
What NIST AI Risk Management Framework guidance is applicable to the design of legal AI pilots?
NIST's AI Risk Management Framework's guidance on AI system evaluation provides a useful structure for pilot design — specifically, the guidance on defining evaluation criteria before system deployment, testing against representative inputs, and documenting evaluation results for accountability. This guidance was written for enterprise AI contexts, but it maps well to legal AI pilots. Firms that use NIST's evaluation framework as a pilot design template produce pilots with more rigorous measurement than those that design pilots informally.