The Pilot Should Have a Purpose
The first mistake many organizations make is running a pilot with no clear goal. They let a small group use the tool and then ask whether people liked it. That is too vague. A real pilot needs a defined purpose. You need to know what problem you are trying to solve.
Maybe the goal is faster first drafts. Maybe it is better summaries of long records. Maybe it is stronger issue spotting, cleaner claims notes, faster contract review, or more consistent internal communications. Pick a few use cases that matter. Pick work that repeats often enough to measure. Pick work where improvement would have real value. If the pilot tries to test everything at once, it will teach you very little.
A good pilot asks a simple question. Does this tool improve speed, quality, consistency, or insight in a way that matters to our work. Everything else should flow from that question.
Choose the Right Test Group
The second mistake is picking the wrong people. Do not build the pilot around only the most enthusiastic users or only the most skeptical ones. You need a balanced group. Include people who are curious, people who are cautious, and people who do the kind of work the platform is meant to help.
The group should be small enough to manage and large enough to test different work styles. In many organizations, five to ten users is a good start. You want a mix of senior and junior people. Senior professionals know what good looks like. Junior professionals often adapt to new tools faster and can show where training gaps exist. Together, they give you a better read on whether the platform works in practice.
This is especially true in legal and claims settings. Senior lawyers and claims leaders know when output sounds polished but lacks judgment. They know when something misses nuance, distorts risk, or sounds better than it is. That perspective matters because AI often produces material that feels impressive on first look but weakens under scrutiny.
Set Rules Before You Start
A pilot should not begin with open ended experimentation. Set rules first. Decide what users can upload, what data they cannot use, what matters stay off the platform, and what review steps apply before anything generated by AI leaves the building. Those rules protect the organization and improve the test.
That means deciding early whether the platform may be used with live files, sample files, or sanitized material. It means deciding whether users may rely on the platform for summaries only, first drafts only, or more advanced uses. It means deciding who reviews the work and how errors get flagged. Without clear guardrails, the pilot turns into a loose experiment with uneven results and avoidable risk.
The pilot also needs a baseline understanding of confidentiality, accuracy, privilege, data handling, and human review. No one should use the platform without knowing those ground rules. Training should come before use, not after a problem appears.
Train Users to Think Before They Prompt
The best pilot programs do not just teach the buttons. They teach the method. Most AI failures come from weak prompting, poor judgment, or blind trust in the output. So before users begin, teach them how to think through the task before they ask the platform to help.
That means asking basic questions first: What is the goal? Who is the audience? What facts matter? What tone fits? What should the output include? What should it avoid? The user who thinks through those issues will get better results and will also be more likely to catch errors.
Training should also teach users how to test the output. Compare it to source material. Check whether it missed an issue. Ask for alternative approaches. Push back on weak answers. Rewrite the text in your own voice. The point is not to let the machine do the thinking. The point is to help the user think better and faster.
Measure the Right Things
A pilot that relies on impressions alone will not tell you enough. You need actual measures. That does not mean building some massive data project. It means tracking a few metrics that show whether the platform creates real value.
Measure time saved on specific tasks. Measure whether the first draft improved or got worse. Measure how much editing the output needed. Measure whether users spotted recurring errors. Measure whether the tool made users better at seeing issues, trying new approaches, or organizing complex information. Also measure adoption. A platform that works in theory but that no one wants to use may not be the right fit.
Do not focus only on speed. Fast bad work is still bad work. The better question is whether the platform helps people produce stronger work in less time while keeping judgment intact. That is the standard that matters.
Create a Feedback Loop That Actually Works
Pilot programs fail when the feedback is casual, vague, or too late. Build a real feedback loop. Ask users to keep short notes on where the platform helped, where it failed, what surprised them, and what kind of prompts worked best. Meet with the group at set intervals. Review examples. Compare experiences. Look for patterns.
Those conversations often teach more than the raw numbers. One user may discover a prompt structure that improves output for everyone. Another may spot a recurring weakness in summaries, citations, or analysis. A senior user may explain why something that sounds good on paper would fall apart with a client, judge, or business leader. That is the kind of insight you want before a wider rollout.
The pilot should also include examples of failure. Those examples are useful. They show where the tool breaks. They show where training must improve. They show where the platform may not belong. A pilot should not be a sales exercise. It should be an honest test.
Refine the Use Cases Before You Expand
Once the pilot runs long enough to generate real examples, step back and refine the use cases. You may find that the platform is excellent at first drafts but weak at nuanced analysis. You may find that it helps with summaries but not with strategic recommendations. You may find that one department gets major value while another sees very little.
That is exactly what a pilot should reveal. Not every tool fits every task. Not every user should use the platform in the same way. The goal is not to prove the platform is magical. The goal is to identify where it helps, where it needs limits, and where it should not be used at all.
This is also the stage where organizations should refine training materials, create approved prompts, build review checklists, and develop practical guidance based on what the pilot actually showed. The pilot should leave behind a playbook, not just opinions.
Expand in Phases, Not in One Leap
After the pilot, do not jump straight to a full launch. Expand in phases. Start with the groups and tasks most likely to benefit. Give them the refined guidance, the sample prompts, the lessons from the pilot, and the guardrails that proved necessary. Let those groups build experience before the tool spreads more broadly.
That approach reduces risk and improves adoption. It also helps create internal champions who can teach others. People trust the platform more when it is explained by colleagues who tested it, learned its strengths, and know its limits. A phased rollout builds credibility because it grows from actual use, not abstract enthusiasm.
Expansion should also come with ongoing review. AI changes. Platforms update. Users get more ambitious. Risks shift. The work does not end after the pilot. The pilot simply gives you a smarter way to begin.
Leadership Must Stay Involved
A strong pilot needs leadership, but not in the form of slogans. Leaders should stay involved enough to define the purpose, support the training, insist on honest evaluation, and resist the urge to declare success too early. The worst rollout happens when leaders want proof that their AI investment was right and the pilot becomes a performance.
Good leaders ask harder questions: Where does this platform actually help? Where does it create risk? What should we ban? What should we teach? What should we measure? What should we fix before this expands? That mindset turns a pilot into a real decision making tool.
Senior professionals should also stay close to the process because they can provide context that newer users may miss. They know when output lacks sound judgment. They know the difference between something useful and something merely fluent. They can help make sure the organization adopts the technology without lowering standards.
The Goal Is Not Fast Adoption. The Goal Is Smart Adoption
Organizations feel pressure to move quickly with AI. That pressure is real. But there is a difference between moving quickly and moving wisely. A pilot program gives you the chance to test the platform in a controlled way, teach people how to use it properly, measure the right outcomes, and build a rollout based on evidence instead of hope.
That is how smart organizations will do this. They will not ignore AI. They will not worship it either. They will test it, train around it, challenge it, and build from what works. They will use a small group to learn the tool, sharpen the process, and create a framework that others can trust.
That is the value of the pilot. It gives you room to learn before you scale and room to think before you commit. In a moment when so many organizations want to move fast, that discipline may be the smartest move of all.
Frank Ramos is a partner at Goldberg Segalla and practices in Miami in the areas of commercial, products, and catastrophic personal injury.