A Checklist to Evaluate AI Case Management for PI

Here is a checklist to evaluate AI case management for a personal injury firm. Nine points, each one a question with a right and a wrong answer. Score every vendor on plain, specific, numeric responses and penalize adjectives. The tool that scores well runs your intake, moves your cases, proves what it did, and lets you leave whenever you want. The tool that scores badly is a filing cabinet with a chatbot on it. This list is how you tell them apart before you sign.

I build in this category, so this is the checklist I would actually use, written for a buyer.

Intake and speed

1. Does it run intake on its own? When an inquiry arrives at midnight with no human around, does the software respond, qualify, and schedule, or does it just create a record? Storage is not intake. Speed at intake decides which cases you get, which I covered in how personal injury firms lose cases to slow intake. Make them show a median response time from real customer data.

2. Does it qualify, not just capture? Capturing a lead is table stakes. Running the screening conversation and returning structured, ranked cases is the value. Ask what facts it collects and how it decides what to escalate.

3. Does it cover nights and weekends? Injuries do not keep office hours. If the system only works while your staff is in, it misses the exact window where the good cases arrive.

Case operations

4. Does it move cases or just track them? Ask whether it drafts and chases records requests, flags stalled treatment, and assembles demands, or whether it only shows you statuses. The bottleneck in a PI firm is running cases, not watching them, which I argued in case ops, not case management, is the PI bottleneck.

5. Is the AI native or bolted on? Was the product built around the model or was a chatbot stapled to an old database? This changes what it can reliably do. Learn to tell them apart using AI-native vs AI bolted on: how to tell the difference.

Trust and safety

6. Does it log every action? For legal work, an unrecorded action is a liability. Every automated step should leave an inspectable trail. This is non-negotiable, and I explained why in how to add audit trails to AI systems. If logging is an afterthought, the AI is not ready for client files.

7. Does it escalate when unsure? The dangerous system guesses confidently. The trustworthy one flags the case for a human at the edge of its competence. Ask exactly what happens when the model is not confident.

8. Does the vendor overclaim? Watch for capability claims with no limits attached. An honest vendor states where its AI stops. Overclaiming in the deck predicts corner-cutting in the product.

Ownership and switching

9. Can you get your data out, and what does onboarding take? Where does client data live, who sees it, does the vendor train on your clients' information, and can you export everything cleanly if you leave? Then: who runs the migration, how long until you are live, and what breaks during the switch. Portability is leverage. Onboarding is where deals quietly fail.

How to score it

Give each point a plain pass or fail based on a specific answer, not a vibe. A vendor that passes all nine with numbers and straight talk is worth trusting with your firm. A vendor that hedges on more than one or two is telling you what the product is really like once the demo ends.

This checklist is specific to PI case management, but the underlying discipline applies to any AI vendor, which I generalized in how to evaluate an AI vendor before you depend on it. The point is the same everywhere: buy on proof, not on polish.

We built CaseSolo to pass all nine points, because a personal injury firm that gets this evaluation wrong pays for the mistake on every case. Run the checklist on every vendor you consider, including us. The right tool has nothing to hide from it.