Many teams end up judging tools based on demos, but actual legal work is often more complex and way messier. Tools need to be evaluated in an environment that reflects real world conditions. That means considering a realistic scope, the complexity of data, the actual questions being asked (for example, real world criteria for a privilege call), and a statistical analysis of the results. Demos are often constructed using a data set selected to produce the best results. Tests should be conducted using data that is as close to the real world as possible.

AI can perform very differently depending on the type of documents and workflows, and not every team has time to test it against their own data. It’s easy to overlook how well a tool actually fits into existing systems and processes, which can make adoption harder than expected. Many AI tools require a specific environment to run in, such as a particular cloud environment, which will need additional work to build adequate workflows into and out of existing data stores. Things like data security, privilege, and auditability are critical but can be difficult to evaluate thoroughly without a structured approach. Too often, these items are left until the end of evaluation (or even until implementation) when they become difficult to address and can affect the defensibility of the entire process. Teams don’t always have clear success metrics upfront, so it can be hard to measure whether a tool is really delivering value. “Good” is often a relative term, and defining the expectations of an AI-enabled process is instrumental in building a solid evaluation of any technology.