The AI-Enabled Lawyer: What Modern Legal Practice Now Demands

Hey everybody. Welcome back to another episode of the Dominate Law Podcast, where we empower attorneys as entrepreneurs. I'm your host, Ashton Rogers, and today we are tackling the conversation that every firm is having internally, but very few are having, well, the AI enabled lawyer, what modern legal practice now demands. Before we dive in, a big shout out to our sponsor and growth partner, <inaudible> Marketing. Here's what runs underneath today's entire conversation. Your firm can have the smartest AI workflows, the cleanest contract intelligence, and the strongest governance in the world, but none of it matters if the right clients cannot find you and trust you before they ever pick up the phone. Qua specializes in exactly that for over 15 years. They have helped law firms across six countries, including the USA uk, Canada, and Australia, build the visibility, trust, and intake systems that turn modern legal services into actual consultations.

They are offering listeners a complimentary marketing strategy session worth $900 with AI ready client positioning, a visibility and trust review, and a practical growth roadmap tailored to your firm. Go to www.dominatelaw.com/msm to book it now into today's deep dive across five critical pillars, the growing role of AI in everyday legal practice, preserving legal judgment, accountability, and client trust, the impact of AI on drafting, review and workflows, managing risk, governance and responsible use, and how lawyers stay competitive as client expectations continue to evolve. To guide us two of the sharpest minds in legal operations and legal tech. Jen McCarran, co-founder and CEO of contracts.ai. Jen built and scaled legal operations at Netflix, Spotify, and Cisco, and previously served as president of the board of the Corporate Legal Operations Consortium. She brings a powerful lens to contract data, workflow design, and revenue leakage. And Francis Dupree, president success manager at AFT Francis focuses on workflow improvement, technology adoption, and helping legal teams operate more efficiently in a data-driven environment. Let's get into it. All right, guys, now we will be moving on to the main segment, which is the q and a. So my first question goes out to you, Francis. Where is AI already changing everyday legal work inside firms in ways lawyers may miss until client expectations fee pressure or staffing limits shift?

So AI is already changing legal work long before organizations formally, um, approve of it as an enterprise because individually, most legal teams use AI to, um, summarize meeting notes for drafting emails, for creating SOPs for also accelerating research training materials. And it starts individually before it becomes, um, adopted as a whole. And then once it becomes adopted as a whole, the first thing people think is we just want everything done faster. And unfortunately, you can't just do everything faster, even though you're using ai, you can do it a little bit more efficient and it can maybe time, but it won't necessarily be a super fast pace. But, um, AI is already adopted. Everyone uses it individually and then it goes out enterprise wide within their companies.

Mm-hmm <affirmative>. Mm-hmm <affirmative>. Alright. That's a great answer. And what is the first quiet sign that AI is already changing the work, even if leadership has not formally named it yet? There is a bit of a follow up question for, uh, attendees to get a bit of deeper insight into this.

I'd say already changing the work because it seems times, like I said, on research, um, you're able to use the ai, for example, if you're working on a contract and there's been some changes in regulations or rules, and you quickly wanna identify those, you can use the, um, AI to search the clause for you and you can go directly in and make the change that needs to be made.

Mm-hmm <affirmative>. All right. See many different things that you can work on with ai. And the next question is aimed at you, Jen. So when contract data sits in static documents, what breaks first for legal or finance teams facing a client question under deadline pressure from leadership,

Time breaks, productivity breaks, a lot of those questions, especially under a deadline, have urgency to them and must be manually mined out of the contracts. Um, and what it leads to for finance is revenue is not recognized instantly. It's it's dependent on a very large manual process or extraction needs human validation. Um, finance can't trust forecasts because they have to manually build everything. Uh, and legal, legal becomes a bottleneck for every contract related question ever. Even, even the easier ones that you would hope by now would be answerable by technology, but haven't been

Mm-hmm <affirmative>. Mm-hmm <affirmative>. All right. And for some actionable advice for the attendees we have here, what is one contract data point leaders should stop leaving buried in documents.

Um, all of them, uh mm-hmm <affirmative>. Every contract data point. So so much of what's written in that contract powers workflows around the business, including a lot of finance workflows. Mm-hmm <affirmative>. And if you can get one mm-hmm <affirmative>. You can get them all, uh, with the right technology tech, like, uh, what I'm focused on building at contracts ai. So I think it's, it's not a matter of just finding one, it's flipping the paradigm and turning the entire contract into data.

Mm-hmm <affirmative>. Mm-hmm <affirmative>. Alright. Thank you for that answer. And the next question is for you once again, Francis. So how do legal teams decide between useful experimentation and firm wide controls when individual AI use is already happening inside daily workflows across practice areas?

So I think that the goal should be to make it a safe space because it's already being used. And, um, I think that legal struggles between innovation and governance, and they are both actually needed. So teams should identify low, low risk use cases where AI can provide immediate value, such as drafting contracts, um, reviewing invoices, summarizing calls, pulling out tasks that you can do. But at the same time, companies need to make sure that they have correct guard rails when it comes down to the AI because it's being used everywhere and there's no stopping it. It's just a matter of how it's gonna be used and the way that the company uses their security to make sure that it's used, it's being used the right way and nothing is being leaked that shouldn't be

Mm-hmm <affirmative>. Mm-hmm <affirmative>. And for some more deeper insight, what kinds of experimentation should be encouraged instead of shut down?

I think that it just depends on the business needs at the moment, because every business need is different. Some companies may need it immediately to read their invoices, some may need it immediately for their contract. So it just depends on the business need and that's something that the stakeholders have to come to an agreement on.

It's pretty surprising. Alright. So the next question that I had on the line was for you, Jen. So what decision rule helps leaders judge when AI belongs inside contract workflows under pressure from volume speed, or revenue leakage across business teams daily?

Yeah. My, uh, the first rule I would suggest is if humans are manually searching anything in A PDF, AI belongs there. If humans are paid to manually search and tag the PDF humans, uh, AI belongs there. Um, those are two huge areas that, uh, we're experiencing a paradigm shift in right now with the advent of newer AI contract tech and those are no longer needed. And then from a, the perspective of revenue, um, if missed terms create any kind of risk, financial risk, you can put AI there. If answering a simple business question requires reading through hundreds or thousands of pages of documents, you should put AI there. The, the AI can definitely help. It's really, um, I, I come back to the manual piece. There's really, we've been, we've accepted the status quo of doing a lot of manual work review, searching, control, f-ing, extracting, copying and pasting into spreadsheet. It's been normalized. We need to, uh, break that and not accepted any longer 'cause it's truly unacceptable.

Mm-hmm <affirmative>. Great advice right there. And what makes a contract workflow a good candidate for AI support? I know you touched on this, but if you could go deep into it for attendees,

For AI support, what makes, what's a good candidate for a workflow? A contract workflow? Mm-hmm

<affirmative>. Yes.

Um, if it impacts revenue, if it is, uh, has renewals obligations to be managed, if there's compliance, uh, aspects that need to flow down to other teams to be managed, um, forecasting anything, deal financial related, uh, those are good for AI contract workflows because we can now mm-hmm <affirmative>. Get those deal financials read, understood, structured, tagged and ally flowing out into other workflows or systems, et cetera. So, um, and, and then the old rule of legal ops is if it's a highly repeatable and lower risk contract, those are ripe for workflow all day.

Definitely a good deal. Amazing. And now once again, my next question will be for Francis at Matter kickoff. What conversation sets the boundary between approved AI support and work requiring direct lawyer view before tasks are assigned to staff?

The most important conversation is not the technology itself, it's about accountability. Mm-hmm <affirmative>. At matter kickoff teams need to define what tasks AI may assist with, what information is being entered to approved tools, what outputs require human validation, who's responsible for the final judgment? And when attorneys need to review mandatory before advice, recommendations, or, um, communication is being done, um, AI has certain CAP capabilities, but it's not a decision maker. And since AI is not the decision maker, the prep work has to be done and legal ri uh, legal professionals remain accountable for legal analysis, RI risks, negotiations and final decisions. So establishing those boundaries and those expectations ahead of time is the conversations that need to happen.

Mm-hmm <affirmative>. All right. And to address a challenge, what boundary is most often unclear to staff until a mistake happens? Like, what have you noticed?

Who owns the certain task? Is it legal? Is it procurement? Is it legal operations? Sometimes that is the biggest issue and you don't figure this out until, like you said, there's a problem. So then point fingers trying to have conversations and pull everyone in and figure out who is the, who takes ownership when that conversation should have been done at the beginning.

Mm-hmm <affirmative>. Proactive instead of reactive.

Exactly.

Exactly. And Jen, this question is geared towards you. When AI summaries miss contract context, who feels the failure first during renewal talks with sales, finance, or the client under budget pressure today?

Possibly legal, although I don't know, a lot of places relying on contract summaries that they didn't build themselves by hand. Um mm-hmm <affirmative>. Getting really good ai, AI contract summaries is a very new phenomenon inside the contract technology stack. Um, so yeah, I, I don't know of a lot of folks who worked off of contract summaries, but I think legal would feel at first and potentially, uh, counterparts in finance

Mm-hmm <affirmative>. Mm-hmm <affirmative>. Got it. Got it. And Francis, for you, which observable behavior tells leaders that AI governance is becoming daily practice rather than a policy document in busy legal teams under workload pressure?

Well, I mean, honestly, I think that it already is because I, as I said previously, everyone uses it in daily lives. Whether it's to edit and revise an email, to read a contract, um, some college students use it when they're working on papers. You know, paralegals and attorneys use it for research or if they're citing cases. So this is already everyday use

Mm-hmm <affirmative>. Mm-hmm <affirmative>. Alright. Right. And for some actionable advice, what is one team ritual that keeps governance practical? What would you say?

Hmm. I'd say knowing when and what information to use when you're using the ai, because you have to, when you're using it personally, there's certain information that's up to you if you want to use it or not, but then when you're using it professionally, you have to make sure that you're protecting your company.

Mm-hmm <affirmative>. Of course. Jen, do you want to hop on that answer and add anything onto it?

Uh, like general practical advice?

Yes. General advice.

Yeah. When it comes to, um, solving with ai, uh, start with the problem and define actual business outcomes that you or the department want and need to see first. It's very easy to go shopping and see products and then suddenly align your outcomes to what their options are. And, uh, it's good to kind of take inventory of your own problems first so you can solve, I don't know, correctly or relevantly. Mm-hmm

<affirmative>. Relevantly, they say don't go grocery shopping when you're hungry.

That, that too.

So, and the next question that I had lined up for you, Jen, was which pricing rule helps firms handle AI assisted work when time saved no longer matches value delivered to clients in recurring contract matters at scale?

Yeah. There, there's a long standing debate going on, on, um, the hourly rate that law firms, uh, make money under, uh, that, that financial construct and trying to, legal ops folks have been trying to shift the narrative with legal leadership to more of a fixed fee or non hourly, uh, rate financial arrangement with law firms. Um, AI can compress a lot of effort that law firms charge for and it should, it should compress effort. But how do you reflect that in hourly billing? Uh, it all points to outcomes based pricing. I just don't know that it's happening or happening fast enough. Perhaps AI will put a, will accelerate those conversations, um, and make this hourly billing thing less defensible. But I don't know, I'm not anti hourly billing. I think it makes a lot of sense. I think law firms do that. I, I just do think that, like I, I sell software. I build and sell software now and the most important thing I can do is create value for the customer, demonstrate that, and then give them that value in exchange for money. I think that goes for anyone, including law firms as the most important thing. What business value did you create for their, your clients today? And then how do you have a financial arrangement that reflects that?

Alright. So we did get through most of the questions I had ready for today quite fast. So here's some quick rapid fire questions for the two of you. So the first one is for you Francis. So some staff may be excited about ai while attorneys are more cautious. How do you keep that tension productive instead of turning it into a culture fight?

Um, I believe having conversations, you know, and showing the value in using the ai mm-hmm <affirmative>. And also having a clear understanding that when you're using ai, whatever you put into it is what you're gonna get out. So you have to make sure the information, the prompts, the data, all of that is clean. Uh, make sure you understand your foundation, your workflows, because if you don't already have that, if you can't explain it prior to implementing any type of ai, it's going to fail and it's gonna be a problem. So I don't think, uh, it's necessarily a conversation of who wants to use AI more and who's more cautious, but more so of where are we at as a business when it comes down to our roles, our responsibilities, and our workflows and our data. And once we have that clean than any AI that we implement or we use, is gonna give us the results that we're looking for. So it starts with the background foundation, first

Background foundation. Alright. And for you, Jen, if a firm is still using contracts mostly as PDFs in folders, what is the first contract data win they should chase before buying another tool?

They should put contract intelligence. They should evaluate putting in contract intelligence around those PDFs and folders. Um, you know, for many years what lawyers and law professionals in-house needed was search and the ability to get stuff out of the contracts. And then they were sold lifecycle tools that handle intake and workflow and doc generation and approvals and signature and landing and a repository, and then have a very poor search. Um mm-hmm <affirmative>. The search piece is coming online now in a big way thanks to the generative AI movement mm-hmm <affirmative>. And so I would say don't kill a fly with a cannon. Uh, drill down again to what I said earlier, what is really needed? What's the biggest pain point around contracting? If it is workflow, go get yourself the workflow tool or the lifecycle tool. Uh, if it's repository and search and mass search and intelligence coming out, uh, beyond control f search, consider, uh, the contract intelligence players like us in the market, um, that can help turn p static PDFs into data.

And yeah. We are coming into the final home stretch. I want to ask each panelist for your 62nd golden rule for building an AI enabled law firm without losing judgment, accountability, or client trust. Um, Francis, let's start with you then, Jen. So what do you have to say? Just one sentence for the attendees here to leave tonight.

So, my golden rule, when it comes down to building anything ai, as I mentioned earlier, just make sure everything that you put into it is clean. When you're drafting prompts and you want specific output outputs, make sure that you're as specific as you can be because the AI is not human and you may be looking for a specific answer and it's not gonna tailor it the way you want because you weren't detailed enough. So just be very particular and detailed about all of the information that you're putting in and have realistic expectations. It can't solve every problem and it can't do anything in lightning speed. So you need to have realistic expectations. The bots, they, you know, they make mistakes too because they're not perfect. So just keeping all of that in mind is what I would say. Mm-hmm

<affirmative>. And Jen from you?

Sure. Um, before I put technology in, uh, working at companies like Netflix and other big tech companies, I spent a lot more time understanding the process, uh, current process in place inside that team, that law practice area, the department or cross-functionally. Uh, by the way, I don't know how to build anything in the law firm. I've never worked in a law firm, but I've worked in in-house legal departments inside companies, so I know how to build there. I'm also not a lawyer, so I can't tell anyone how to be an AI enabled lawyer, but hopefully there's some tips that translate from today's session. Uh, but I spent a lot of time focused on defining the process and understanding how information communication data moves and then engineering a new process or the desired process and vis-a-vis, uh, part of that could be a tech solution. So it always pays to get that all down on paper first before you just take AI or any technology and throw it at it a problem and expect it to have a meaningful change or improvement in the workflow.

And what an important conversation that was. A huge thank you to Jen McCarran and Francis Dupree for the practical real world wisdom they brought to today's episode from Jen's Clear rule that if a human is manually searching, A PDF AI belongs there to Francis's Reminder that AI is not a decision maker no matter how capable it gets to the bigger truth that AI is not a technology conversation, it is a judgment, workflow, governance and trust conversation. If there is one line that captures the whole episode, it is the closing thought we kept coming back to. AI is not just a tech topic for lawyers anymore. It is the operational center of how your firm earns judgment, accountability, and client trust over the next decade. The firms that pair AI with strong process, clean data and honest client communication are the firms that will lead. The firms that treat it as a shortcut will be the firms that lose ground.

And that brings me right back to our sponsor, Equa Marketing, because building a modern AI ready firm is one half of the equation. The other half is making sure the right clients can actually find and trust your firm before they ever speak to you. Equa builds the visibility, positioning, and intake systems that make that happen. With SEO conversion focused websites and content that turns searches into consultations, they are offering a complimentary marketing strategy session worth $900 with AI ready client positioning and a custom growth roadmap. Book it at www.dominatelaw.com/msm. If this conversation was useful, share it with one firm leader who is wrestling with these decisions and follow the Dominate Law Podcast. So the next episode lands right in your feed. I am Ashton Rogers, build the AI ready firm, but never lose the judgment that makes you a lawyer. We will see you in the next episode.