Legal Tech for Transactional Lawyers Newsletter: #4
Fixed fees on transactional matters, prompt engineering tips, goals for using GenAI in 2024, article links and more!
Happy New Year and hope you had time to recharge with family and friends over the holidays!
In this edition I debate whether fixed fees could be used effectively in transactional matters, provide links to some solid legal tech articles and explain why we should dive into using GenAI with more specific use cases in 2024.
Hopefully you find it useful and interesting. Please feel free to share with anyone that may be interested and have a great weekend!
If you find this content valuable, please connect with me on LinkedIn.
Leveraging Tech in Transactional Matters
📗 Are Fixed Fees on Transactional Matters Possible?
Could law firms drop the billable hour for transactional matters and close financing transactions on a fixed fee basis? Would clients prefer this model?
How could this work? What about all the stuff the billable hour is good for like unknowns and wrinkles that pop up on complex transactions?
It isn't that easy or simple, but how about this:
💰Start with a fixed fee based on a fair baseline budget that legal team can consistently hit.
🔢Lawyers flag budget changes as they come up in the deal (heavy negotiations, unique structuring or property level documents, anything else out of the ordinary that drives costs up).
☠️Dead deal fees based on the % of the project that was completed.
This would result in very pure motivation to increase efficiency that has always been a friction point for adoption of legal tech tools that reduce billable hours. 💖
The new deal team might look like this:
🤵Lawyers handle negotiations, complex drafting, strategy, managing the deal, etc.
💻Tools operated by tech forward lawyers, paralegals and just smart people at law firms can handle the time consuming and mundane aspects of deals (summaries, first drafts, assembling docs, simple changes to docs).
⚡ALSPs and vendors can be engaged for specific tasks that can’t be done with law firm personnel because it’s too expensive (lease summaries, SNDAs / estoppels, diligence review, etc.). Tasks that cannot be profitable for a law firm but maybe could be for someone else.
Why it’s better for law firms?
➡️If done right, lawyers can get paid a premium if peak efficiency is reached.
➡️Associates would be happy and maybe less burnt-out chasing hours requirements.
➡️The highest compensated associates would be the folks that are the most efficient and do the best work, not the ones that bill the most.
➡️Instead of just being able to increase firm profits by getting hours and billable inventory up, firms could make more money by getting creative re: efficiency.
Would clients like it? I think so, other than in matter types that really need hourly pricing.
➡️No need to spend time at the end of deal getting everyone to approve the legal bills.
➡️Speed and efficiency should improve as firms are economically motivated to focus on it.
➡️Legal fees probably won't decrease but they will be known and consistent.
Articles and News
👨💻 Email is Not a Project Management Tool
This white paper by Dashboard Legal does a great job highlighting the need for better project management tools in the legal space. Transactional lawyers have long relied on Word or Excel checklists, but true project management tools can really move the needle in improving efficiency and organization in transactional matters. They are spot on that Teams and email are not the answer to managing complex projects!
⚡ALSPs as a Value Add for Law Firms
Check out this solid post by Alex Su that shares some predictions for where things are heading in 2024 in a quickly changing legal ecosystem. Totally agree that as GenAI tools become more prevalent there will be an increase in ALSPs and technology vendors partnering with law firms to improve their delivery models.
📚 OpenAI Publishes Prompt Engineering Guide
This prompt engineering guide was recently published by OpenAI and lays out some effective tactics to keep in mind when writing prompts. We have used many of these approaches when writing prompts for use in our apps and they definitely are effective (although not always explainable).
🚬The Carbon Cost of Running AI Models
This study by Melissa Heikkilä at MIT provides an eye-opening reminder of the carbon emission costs of running AI models to perform tasks. According to the study, generating 1,000 images has the same carbon impact as driving 4.1 miles in a gas powered car! As we get further along in the journey of using AI tools it will be increasingly important to focus on efficiently using the resources (both from a cost and energy standpoint). Right now we are all just trying to figure out good use cases that produce reliable results.
🔨 Let's Dive to Specific Use Cases with GenAI in 2024
This is another quote that I always like to refer back to when debating whether to try to do something difficult or new.
"It is not the critic who counts; not the man who points out how the strong man stumbles, or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat." - Theodore Roosevelt
Also, check out this interview with Sam Altman where he notes that "History belongs to the doers."
As we move past the hype around LLMs that consumed most of 2023, hopefully 2024 is a year where we dive in head first and apply Generative AI very carefully to specific use cases. It won't be easy and some use cases won't work after trying them in practice, but I am confident there will be a handful of really good applications that can be created for each law firm practice area.
When thinking about use cases, there are four key functions LLMs are good at that can be applied to different steps in a process you are trying to automate:
💡 Summarizing text
🏞️ Extracting information in a structured format
🧠 Searching documents or text to locate relevant information
✔️ Generating or revising text
Check out my recent blog post on what LLMs are good at and why legal teams will be instrumental in developing and implementing impactful applications built with LLMs.