AFAs in the age of AI

This is the third of a four-part series on hourly rates and benchmarking. Links to parts one and two are included below. Part four will continue on the future of hourly billing. 

Recently, I have written about why managing hourly rates is still important. I also pointed out that however much corporate law departments like to pedestalize AFAs—70% of which are flat fees of one kind or anotheras the solution to all their spend management problems, many of those same organizations have barely scratched the surface on things they could be doing right now to mitigate the currently hourly situation. Now, I want to raise a far more provocative issue: What if artificial intelligence (AI) makes flat fees seem behind the times? 

Before I get a tomato thrown at me, let me emphasize that I am just playing “what if?” My crystal ball is cloudy, and I cannot see into the future. Nor do I necessarily believe that these scenarios will come true—but they are worth considering to better recognize the limitations of flat fees. So, here are some AI “what if” scenarios that might undermine the case for flat fees. 

What if hourly billing became painless? 

Everybody hates hourly billing. Outside counsel hates it because they went to law school to do legal work, not fill out invoices. They often procrastinate on it for weeks, then try to reconstruct what they actually did in the last month by looking at their emails and other notes—a situation that law firms claim results in lost revenue and corporate law departments claim results in overbilling. Then, the billing partner has to drop everything he or she is doing, review the invoice, write down unnecessary work, and send it to the administrative people. 

Law firm administrators have a difficult time with billing, too. They must learn to use multiple e-billing systems, as required by their clients, and conform to the outside counsel guidelines of each client or risk rejection due to non-compliance. Oftentimes, the invoice will have to be submitted more than once because of the sheer chaos of attempting to comply with a crazy-quilt of guidelines. 

What if all of that just went away? What if AI-assisted timekeeping applications fulfill their potential and present outside attorneys with automatically-created timesheet entries at the end of every month? What if AI helps pre-scrub those invoices so they comply with billing guidelines and eliminate all those headaches? 

What if invoice review became painless? 

After the law firm people get done hating billing, then in-house people get to hate it. They often used to work at law firms and still have nightmares about doing those timesheets. Now, they have nightmares about reviewing them, viewing the task as “not what they went to law school for” and basically an afterthought. In fact, internal Wolters Kluwer research has indicated that many law departments are writing down fewer than 1% of line items. Sometimes, they get so frustrated they do what most invoice reviewers do and “just hit approve.” 

AI is already making this situation go away. LegalVIEW® BillAnalyzer Expert Service from Wolters Kluwer’s ELM Solutions uses AI to score individual line-items for whether they need human review and combines that with a service where we have our trained team of professional bill reviewers review and negotiate down individual line-items for you. We also recently launched LegalVIEW BillAnalyzer Data Service, which brings that same power to your internal bill review team if you have one. But things are going to get even better as our AI gets smarter and the kinds of analytics around the activity occurring in individual invoices become more and more sophisticated.

In the future, what if it takes only a few seconds to review some very powerful visualizations of the activity described in an invoice, identify just those charges that were unnecessary, and write off a couple hundred thousand dollars?

What if hourly billing data became a goldmine for both clients and law firms?

This one is a bit of a misnomer because that data already is a goldmine. However, currently, to get at that gold, you actually have to “mine” it—hard work that is expensive, time-consuming and often gets put on the back burner, if not forgotten altogether. What if AI does all that hard work for you and brings those insights to the surface in neat, interactive visualizations that allow you in just a few seconds to identify quick and easy ways to save thousands and thousands of dollars? What if more and more corporate law departments use products like LegalVIEW Predictive Insights, which predicts outside counsel fees and cycle times down to the phase level, to negotiate favorable budgets and control costs?

What if AI could automatically classify matters into a standardized, industry-recognized taxonomy like the SALI standards that would allow for apples-to-apples benchmarking against like legal matters in huge benchmarking databases, like our LegalVIEW Data Warehouse? What if AI automatically classifies not only legal matters but also different phases, tasks and activities occurring in those legal matters and goes further to enhance them with various metatags—allowing lawyers to see awesome visualizations that show them a spread answering questions like, “How many hours should it take to write a motion to dismiss a 10b-5 action in front of Judge X?” What if someday you could basically say, “Alexa, I’m looking for a 6th year associate in an AmLaw 20 firm who works in Atlanta and has spent over 10,000 hours researching and writing about HIPPA litigation?” 

Don’t you want to be able to do all this cool stuff? Well, it isn’t going to happen if accurate hourly billing data isn’t available. In my next post, I’ll detail some additional ways that AI could incentivize hourly billing and wrap up my thoughts on the future of the hourly model. 


About The Author

Nathan Cemenska

Nathan Cemenska, JD/MBA, is the Director of Legal Operations and Industry Insights at Wolters Kluwer's ELM Solutions. He previously worked in management consultancy helping GCs improve law department performance and has prior experience as a legal operations business analyst.

In past lives, Nathan owned and operated a small law firm and wrote two books about election law. He holds degrees from Northwestern University, Ohio State University, and Cleveland State University.