This is the third & final installment in the AI & The Business of Law series. The first post, which addressed the business case for AI in law, can be found here. The second post, which explored the use of AI-based applications to augment basic law firm strategies such as cross-selling and succession planning, can be found here.

There is little doubt that technology has become a key aspect of the legal profession, critical to both the buyers and sellers of legal services. In many situations, the focus is often on the practice of law, or the business impact of technology on the practice of law. Law firms and legal departments have long utilized technology to increase administrative efficiency, from document automation to e-billing to proposal automation and much more. But can AI be used to assist the administrative function in law firms and legal departments to operate more efficiently?

Let’s start first with Pricing. Historically, pricing legal services was fairly basic, as the attorneys charged an hourly rate for their services, and the client paid the invoice. The routine unit of measurement within the legal profession became the “billable hour”, and it became the cornerstone of both compensation structure for many law firms and matter duration metrics for legal departments. The 2008 recession was quite impactful, as clients began pushing back on the rapidly rising billable hour rates, and to maintain profit margins, Law firms reacted by reducing their operating costs where possible, in part to retain and attract talent. With pressure from the business increasing to reduce legal department costs, many buyers of legal services began requesting fee structures that were an alternative to the billable hour, including fixed- and flat-fee engagements, putting the onus on law firms to improve their efficiency. Rather than adjusting compensation structure to incentivize efficiency and profitability, many firms instead invested in process improvement and project management, which, while helpful, still did not fix the problem. The problem, as my colleague Michael Mills expertly addressed in his “AI in Law: The State of Play” presentation at the 2016 International Legal Technology Association Conference, is that of “constant cost” for law firms. Reducing hourly rates, which became the strategy for many buyers of legal services, simply reduced the cost per unit of output for the law firms. Many firms then doubled down, opting to increase the number of producers via mergers and lateral acquisitions, in an effort to grow revenue through volume. Except that, as Mills noted in his address, because law firms have constant cost, when quantity rises, total costs rise at exactly the same rate. Every unit of output costs the same as the last one, especially with repetitive tasks. Again, the problem of constant cost.

Harvard Business Review recently published an article in their September 2016 issue that tackled the issue of productizing aspects of professional services organizations that have historically dealt with the challenge of constant cost. From the HBR article, authored by Mohanbir Sawhney:

Technology offers professional services firms a way out of their predicament. By leveraging the power of algorithm-driven automation and data analytics to “productize” aspects of their work, a number of innovative firms are finding that, like Google and Adobe, they can increase margins as they grow, while giving clients better service at prices that competitors can’t match. Productivity rises, efficiencies increase, and nonlinear scale becomes feasible as productized services take over high-volume tasks and aid judgment-driven processes. That frees up well-paid professionals to focus on jobs that require more sophistication—and generate greater value for the company.

This makes complete sense, and its why professional services companies like McKinsey, Deloitte, Littler, and more were featured in this article for implementing products into their traditional services. It is well documented that the legal profession is largely both change- and risk-averse. But, if the last 8 years have taught us anything, it is that law firms do care about what their clients tell them to care about. So, as clients started pushing back on hourly rates, matter staffing, and more, law firms smartly turned to business professionals for guidance.

Which brings us to Pricing and Artificial Intelligence. The pricing of legal services is as much more art as it is science, built over years (or decades) of experience by pricing directors who routinely navigate the multitude of various outside counsel guidelines and internal resources to deliver per-matter pricing that addresses both the client’s requirements as well as the firm’s necessary margins. This is a process that should (and often does) start with Business Development before seamlessly entering into the pricing and proposal phase. Included among the myriad of questions that should be asked during the business development phase is asking the client about both their needs and wants, and intently listening to their answers. For example, it’s important to ascertain not only their concerns around matter pricing, but also the metrics the business is using to measure the in-house attorney, legal department and outside counsel performance. Is the client looking specifically for cost certainty on this matter? Is there an appetite for shared risk, and is this a matter that potentially offers a good opportunity for a success fee? Is the client uncomfortable with fixed fees, but is still worried about higher partner rates? Do the outside counsel guidelines restrict the use of certain levels of timekeepers in specific fee arrangements? Will client require a budget, and if so, is it matter or phase-based?

For many larger, decentralized firms, this can create a significant amount of work for pricing officers. Many firms already have decision trees in place to guide their professionals along a desired path, but they are often in Excel or PowerPoint. This is a great example of where applications created on expert systems can be particularly powerful. By developing applications that guide attorneys and business development professionals through a question flow established on a rules base featuring if/then mappings that develop into decision trees, pricing directors can ensure that their expertise, judgment, process, and strategy is being followed without their constant involvement in every pitch, proposal, and RFP response regarding the fee arrangements that should be offered. It is the exact business case Sawhney presents in his HBR article, except the focus in this example is on internal clients – the lawyers in the firm. Can law firms scale, productize, and automate some of their internal services? The answer is yes, and as firms continue to grow, it will be a necessity.

Beyond Pricing is the question that is a core aspect of any organizational strategy. My longtime friend and trusted advisor Allen Fuqua, the Chief Marketing and Business Development Officer at Winstead, is famous for always reminding his firm that “the test of any strategy is the ability to say no”. Law firms are often inundated with Requests for Proposal (RFPs) that have become so complex and thorough that the staff time to respond to a single RFP often exceeds 100 hours, and the success rate for most firms is below 20%. As a result, many firms have established various “Go / No-Go” frameworks to assess the RFP or Proposal request and make a determination on whether or not to utilize the already reduced internal firm resources to proceed. Can AI methodologies be utilized to help firms make smarter decisions and increase internal efficiency? Absolutely. Earlier this summer, Neota Logic showcased an example focused on a moderately complex real estate transaction where machine learning and expert systems were combined to assess both risk as well as projected revenue. You can see the brief demonstration here, where RAVN, HighQ, and Neota Logic were quickly aligned and rapidly deployed to solve a problem in way that significantly reduced internal costs. Imagine leveraging products like RAVN, Kira Systems, KM Standards, or others to quickly read RFP Documents while platforms like Neota Logic are used to develop applications that can identify criteria or clauses that will nudge the overall RFP risk assessment up or down based upon how the firm had tuned the weighting algorithm. Whether the output is a score, or something more visual like a RAG (Red > Amber > Green) display, the firm is able to scale their interpretation and analysis of RFPs by practice, industry, and office location in a consistent, efficient manner. This should result in the more focused use of internal resources for opportunities with higher probabilities for success along with the corresponding reduction of internal costs and resources on lower probability opportunities.

Finally, as lateral recruiting and movement continues to be strong, the onboarding of new attorneys is critical. Again, most firms have established practices and processes already in place, but few are utilizing expert systems to guide the onboarding process and workflow. By developing an application that can be deployed internally with if/then scenarios and decision trees based upon practice groups, industries, client teams, office locations, committee seats and more, firms can standardize and optimize how their significant investments in new attorneys are being onboarded to the firm.

To date, much of AI has been focused on improving efficiency in the practice of law, or in developing client self-service applications that generate new revenue streams. But, as noted earlier, one of the effects of the recession was the significant reduction of non-practicing staff by the law firms. The staff is constantly being asked to do more with less, which creates a perfect opportunity for artificial intelligence concepts like machine learning and expert systems to be widely adopted and utilized.