What to Decide Before Your Firm Touches an AI Tool

Your firm is already running on AI. Most managing partners have no idea how much.

That is the argument Kathy Serenko, Amy Adams, and I made during our June 3rd webinar on “Three AI Conversations Every Law Firm Needs to Have”, hosted by the Law Firm Profitability Group on LinkedIn. Not which tools to use, but what your firm needs to have decided before it touches any of them. The recording is here.

Kathy Serenko is the founder of AI Efficiency Labs, and Amy Adams is the founder of Gaia Allies and AIReady™.

Kathy led with the governance perspective. Her opening question: Are you already exposed? Her answer, based on what she sees in firms daily: almost certainly yes.

AI enters your firm in ways leadership cannot see. A paralegal uploads 300 pages of medical records to a personal ChatGPT account under a deadline. The summary looks clean. Three months later, the error surfaces in a settlement discussion. A vendor tool your firm approved last year has AI inside it you never examined. The tools you signed off on produce errors that reach binding deliverables before anyone reviews them. AI is designed to produce plausible output, and it will not tell you when it is filling in blanks.

Kathy’s second point: having an AI policy is not the same as having governance. Less than 40% of law firms have a policy at all, and a policy is only a statement of intent. The missing layer is the day-to-day controls built into your actual workflow. Without those controls, the policy and the reality of what is happening in your firm will not align.

Governance means a domain expert, not IT, is the authority over AI output in each practice area. Someone who reads a research memo and recognizes when the content is wrong. The errors appearing in court sanctions are content errors, and the people catching them are judges and opposing counsel. They are domain experts. Your oversight authority in each practice area needs to be one too.

Amy made the workflow case. Her rule before recommending any AI system: map the workflow first. She recently spent 90 minutes with a family law partner doing exactly that. In your firm, that work probably has not been done. If the process exists only in someone’s head, the AI system inherits every gap in it. The tool scales what is already there.

Before the workflow is designed, the economics need to be settled. If a matter that used to take 10 hours now takes five, and your firm is still billing hourly, you may be cutting your revenue in half for that work. Pricing needs to be decided before the workflow is built. Firms that build the workflow first and sort out the pricing later often discover they cannot make money at it.

Everyone in your firm is asking the same question in different ways: what happens to me? The concern is real at every level, and dismissing it does not make it go away. What is actually changing is what each role does. Paralegals are running AI systems that used to require associate hours. Associates who used to produce first drafts are now verifying AI output and building judgment through that review. Partners who used to do the work are moving toward designing the systems that do it. The firms handling this well are having that conversation before the tools arrive.

Leadership does not get to delegate this responsibility. You cannot hand AI governance to a technology committee and check back in six months. You have to understand where the exposure is and whether your compensation structure supports what you are asking of your partners.

Your partners who put their expertise into an AI system will watch their billable hours fall as the system takes on that work. That contribution needs to be reflected in how they are compensated. If your firm still pays only for billable hours, you will not get the cooperation needed to build something that works.

The firms moving ahead well on this have answered the harder questions before reaching for tools.

Three AI Conversations Every Law Firm Needs to Have

I am not going to tell you AI will transform your law firm.

Transformation will not begin until you address three priorities most firms have yet to discuss. And order matters.

Business model first. Workflow second. Guardrails throughout.

On June 3, I’m moderating a webinar with two people who think about this for a living.

I’m a law firm management consultant with more than 35 years of experience in the legal industry. The emergence of AI is already transforming how law firms operate and compete, and this disruption will only accelerate.

The goal of the webinar is to highlight the key strategic and operational issues you need to address in order to implement AI successfully in your firm.

We will discuss:

Business Model: I’ll dig into pricing, leverage, partner compensation, and the decisions firms need to make. Unless they want AI to make them by default.

Workflow: Amy Adams, Gaia Allies + AIReady™, will unpack where AI fits into the actual work, and why adoption stalls after the champion stage.

AI Guardrails: Kathy Serenko, AI Efficiency Labs, will lay out what’s allowed, who’s accountable, and how risk is contained.

Sixty minutes, three perspectives.

Wednesday, June 3 | 11 AM PDT | 2 PM EDT – Webinar hosted by the Law Firm Profitability Group on LinkedIn. Register: https://us06web.zoom.us/meeting/register/N9SV1dEcRC6X5GXwdRxO0g

Graphic announcing a webinar titled 'Three AI Conversations Every Law Firm Needs to Have' hosted by Law Firm Profitability Group. Features panelists Kathy Serenko, founder of AI Efficiency Labs, and Amy Adams, founder of AIReady™, with moderator Colin Cameron, founder of Profits for Partners. Includes date and time: June 3, 2026, at 11 AM PDT and 2 PM EDT.

What Changes When Intelligence Becomes Cheap?

Mark Alcazar opened our recent Law Firm Profitability group session, “AI Agents for Law Firms,” with this question (See session recording here.) The evidence shows that AI is able to handle large parts of what law firms do. A significant portion of the cognitive scaffolding for legal work, including routine drafting and document formatting, is already handled at near-zero cost. Your judgment and your client relationships are not going anywhere, but the production layer around them is another matter.

Most of the law firms I speak with are using AI for narrow, low-stakes tasks, if at all. The session I hosted with Mark Alcazar and John Fitzpatrick from Apex Velocity Catalysts was designed to change that. Everything they showed was live. Tools they are building and using in their own work today. What those demonstrations showed is worth your attention.

Building Without a Developer

The first thing they demonstrated was how fast something useful can be built without a software developer. Mark built a working new-matter intake form in roughly five minutes using Claude Cowork. He wrote a detailed prompt describing what the form should do, and Claude returned working HTML: a form that populates a table and exports to a spreadsheet. Not production-ready, but a great starting point. The distance between “we need a tool for this” and “here is a working prototype” is now measured in minutes, not months. Cost and technical complexity used to keep custom tools out of reach, but both have dropped considerably.

Improving the Output

The second thing they showed was how to improve the AI’s output. Most of you are already using Claude or ChatGPT for some part of your work, and the output quality varies based almost entirely on how the prompt is written. Two techniques made an immediate difference. The first is context: a bare prompt like “draft an engagement letter” returns something generic. Adding jurisdiction, matter type, client background, and the purpose of the letter produces something that resembles actual work your firm would do. The second is more interesting. Instruct the model to create a scoring rubric, score its own output against it, and iterate until it meets a defined threshold. In the demonstration, Claude scored its first draft at 86 out of 100 and kept improving. The mechanism works, and the results are noticeably better.

Agents vs. General AI

The third area was the distinction between general AI and purpose-built agents. General AI is versatile but unfocused. An agent is built for a single defined workflow, such as your NDA process or conflict check. Because the scope is narrower and the instructions are specific, agents are measurably more reliable for the workflows they support. John demonstrated a full NDA workflow: intake, drafting, multi-agent review, negotiation with version tracking, client approval, and e-signature integration. Early indicators from firms adopting this kind of focused approach suggest the gains come precisely from that focus. One workflow done reliably outperforms a broad tool used inconsistently.

Before You Deploy

Before any of this goes into practice, certain things need to be settled deliberately.

What can the AI do without human approval? That is a decision your firm has to make deliberately, not by default. Mark’s own agent is instructed never to send an email on his behalf, even though it is technically capable of doing so. He set that limit, the system did not.

How does AI-generated work get verified before it reaches a client? Whether that is a second agent reviewing against a rubric, or a structured checklist process, the principle is the same: one step produces, another step evaluates.

Where is your client data going? Consumer versions of Claude and ChatGPT carry different protections than enterprise accounts. If your firm is using consumer tools for client-related work, that needs to be resolved before any other adoption decision.

Where to Begin

On the question of where to begin: every firm has what Mark called “drag.” It is the repetitive, error-prone work that consumes time without generating strategic value. It is spread across every role and usually invisible because everyone is too busy doing it to examine it. The exercise is simple. Identify the drag and put a number on it: hourly rate multiplied by time spent each week. Prioritize based on what automation would recover.

Then pick one workflow and make it work before touching anything else. Forward-thinking firms are not trying to transform everything at once. They are proving value in one place and expanding from there.

Colin Cameron is President of Profits for Partners and founder of the Law Firm Profitability group on LinkedIn. The session was presented by Mark Alcazar and John Fitzpatrick of Apex Velocity Catalysts.