Client intake summaries and contract first drafts for a 22-person regional firm (12 attorneys + 10 paralegals).

You run a regional business law firm with 12 attorneys and 10 paralegals. Your bread and butter is commercial contracts, employment law, and real estate for SMB clients.

Your billing coordinator uses ChatGPT for invoice cover letters, and your marketing assistant uses it for newsletter drafts. But more concerningly, you suspect junior associates are informally using consumer AI to draft research memos without a firm policy on client-confidential content.

The firm's growth is bottlenecked at the front door. Synthesizing intake notes into matter summaries and conflict packets takes 2 to 3 hours per new client.

Because the engagement letters are document-heavy, partners are stuck in a review bottleneck. It routinely takes days to get a simple engagement letter out the door, frustrating new clients and delaying billable work.

Enterprise legal AI platforms exist, but they are priced for Big Law. Using cheaper, general-purpose AI feels like a professional responsibility nightmare.

You are paralyzed by concerns over client confidences and your malpractice carrier's reaction. You assume that your bespoke matters simply cannot be trusted to an algorithm.

Margaret, your Managing Partner, and David, an Associate Practice Lead, realize that ignoring AI won't stop shadow usage—it will only hide it.

They decide the firm needs a defensible process, not just a software subscription. They use a structured assessment to identify the ethical questions, review gates, and policy decisions that need partner attention.

Looking at the workflow, they select AI-Assisted Client Intake Summaries as the hero opportunity. The sample objective is to reduce intake synthesis time, but the assessment treats that as a target the firm would have to validate.

To evaluate this responsibly, they record a narrow scope: AI may draft internal matter summaries and engagement outlines from structured inputs. Associates and partners remain responsible for edits and client communication.

The assessment highlights that while the intake-to-engagement process is well documented, "Risk & Security" needs serious attention.

Using AI around client matter summaries and engagement materials requires strict vendor evaluation. The firm acknowledges that informal associate ChatGPT use is a major liability that must be addressed immediately.

The firm records strict rules. No client-identifying information can be placed in external AI without partner approval and vendor confidentiality terms.

They also record that all client-facing documents require partner review, and that litigation matters are excluded from the sample scope until the firm completes deeper policy review.

The sample assessment narrows the initial concept to commercial contract intake using de-identified fact patterns.

It records proposed participants, partner-review gates, and stop conditions for confidentiality incidents. The story shows how the firm frames the decision; the product does not provide legal advice, carrier approval, or guaranteed implementation work.