Law firms are seeing no shortage of AI demonstrations. Vendors can summarize documents in seconds, draft correspondence, identify contract provisions, build chronologies, answer questions, and produce outputs that appear remarkably sophisticated. Many of these demonstrations are genuinely impressive and showcase capabilities that would have seemed unrealistic only a few years ago. That is exactly why they attract so much attention from firm leadership and innovation teams. The challenge, however, is that impressive demonstrations can create excitement around technology before organizations have fully considered whether they are prepared to use what they are seeing.
The purpose of a demonstration is to show a product at its best. Vendors understandably focus on speed, capability, and future potential because that is the nature of the sales process. Most firms therefore enter these sessions asking a relatively straightforward question. They want to know whether the technology can perform a particular task or solve a particular problem. While those are certainly important questions, they often shouldn’t be the first questions asked.
A more important question may be whether the organization itself is ready to use the technology successfully. The distance between an AI demonstration and meaningful AI adoption is often much larger than firms initially expect. Many organizations later discover that technology was not actually the primary obstacle. Instead, the larger issues involved workflow design, governance, organizational readiness, or change management. Before attending the next AI demonstration, firms should pause and consider three important questions.
What specific problem are we trying to solve?
Many firms begin evaluating technology before clearly defining the problem they are trying to address. The result is often a familiar sequence of events. Multiple vendors are invited to present solutions, leadership becomes excited about various capabilities, and pilot programs begin to emerge. Eventually, someone asks what success should actually look like and discovers that no one established the answer beforehand. Organizations frequently discover they launched a technology initiative before clearly defining a business objective.
Rather than beginning with technology, firms should begin with the work being performed. Leadership should ask which processes create the greatest friction and which activities consume disproportionate amounts of time. Teams should identify tasks that are repetitive, structured, and measurable. Firms should also define how improvement would be recognized if it actually occurred. The objective should not be implementing AI simply because AI exists. The objective should be improving a measurable business outcome.
There is an important distinction between saying, “We want to use AI,” and saying, “We want to reduce the time required to prepare first draft deposition summaries.” One statement focuses on technology while the other focuses on outcomes. Outcomes are easier to measure, easier to communicate, and easier to operationalize. Technology should support business objectives rather than become an objective itself. Firms that reverse that order often struggle to achieve meaningful adoption.
Do we have the processes and data needed to support this?
AI rarely fixes disorganized processes. In many cases it simply exposes weaknesses that already existed. Organizations often discover that before they can scale AI effectively, they need more consistency around workflows, document organization, knowledge management, and accountability. These issues may have existed for years without creating obvious pain. AI simply brings those weaknesses into sharper focus.
Demonstrations often present information moving through clean and highly structured workflows. Real environments rarely resemble that scenario. Documents may live in multiple systems, naming conventions may vary, ownership may be unclear, and work processes may differ from one team to another. For instance, discovering that executed contracts live in three different systems with inconsistent naming conventions and that no one owns the process of organizing them is not an AI problem. It is a data readiness problem that AI may expose but cannot solve on its own. Firms should ask where information currently lives and whether it is accessible and organized consistently. They should also ask who owns the process and what operational changes would be required to support adoption.
If the same work is performed differently across teams or practice groups, the challenge may not involve AI at all. The challenge may involve operational maturity. Technology generally performs better when built on top of repeatable and consistent processes. Organizations that skip this step often become disappointed because the technology did exactly what it was designed to do, but the surrounding environment was not prepared to support it.
Who will own adoption after implementation?
Most firms can name the vendor they selected. Far fewer can name who owns adoption six months after go-live. Technology deployment and technology adoption are not the same thing. Purchasing a product and making it available to lawyers does not guarantee that it becomes part of normal work behavior. Sustainable adoption requires ownership and ongoing support.
Someone ultimately needs to own the program after implementation occurs. That individual or team must train users, establish practical guidance, measure outcomes, support users, monitor risk, and reinforce usage over time. Without ownership, AI often becomes another interesting pilot that generates enthusiasm during launch and gradually fades into the background.
This is where organizations often realize that AI is not solely a technology initiative. AI adoption is equally an operational initiative. Success depends on leadership, governance, process design, and organizational discipline. The firms creating the greatest value from AI are increasingly recognizing this distinction.
From AI Capability to Organizational Readiness
None of this suggests that firms should stop evaluating AI tools. The most effective organizations increasingly approach vendor discussions through a readiness lens rather than a capability lens. They shift the conversation from what a tool can do to what would be required to use it successfully. The firms creating the most value from AI are not necessarily purchasing the most technology. They are building organizations that are prepared to use technology effectively.
