You can be confident that over the next few years both law firms and law departments will increasingly include in their decisions the various results from machine-learning software. Machine-learning software takes in data and applies sophisticated algorithms to produce numerical predictions or nonnumeric classifications. The suite of machine-learning algorithms that law firms and corporate legal departments deploy may be the same, and there are many to choose from, but the circumstances vary significantly. Firm and department motivations and resources are quite different.
Among those differences, it is instructive to compare law firms with law departments on characteristics that relate to their use of predictive analytics – another term for machine-learning software. These include (in the order discussed below) managerial control, the availability of data, information technology support, cost centers or revenue generators, culture, vendor relations and psychology.
General counsel can tell their direct reports what to do with far more authority than managing partners or the heads practice groups can muster when speaking to their partners. A general counsel normally has the power to fire someone, dole out bonuses, change assignments or shift titles. Managing partners rely more on persuading, inspiring with vision and mobilizing collective direction. Law firm partners are more likely to be equals, autonomous economic agents, than obedient corporate employees who fall into line with a leader’s directives.
To the degree that this distinction holds true on the spectrum of command and control, a law department can pivot to exploit predictive analytics more assuredly and briskly than a law firm. A law department with a true-believer general counsel as its drummer will march to the beat of machine learning more readily than a law firm.
Machine-learning software devours data, the more the better. Law firms of heft–say, more than 100 lawyers–tend to have more matter-related information available for such software than law departments. They counsel multiple clients on many projects in whatever areas of law or types of litigation they excel at, and they can combine the data they accumulate into a set large enough to illuminate patterns and assay predictions.
The majority of U.S. law departments are small – five lawyers or fewer – and they don’t encounter enough matters of a similar kind to provide good fodder for algorithms. Even if the particular issues they face frequently would lend themselves to software exploration, departments rarely seem to track data about those matters. (A general counsel could request that the department’s primary law firms contribute key data on specified matters, enabling the department to assemble an adequate data set. But such a request would likely be resisted by law firms, if only due to concerns about attorney-client privilege.)
When we compare law firms and law departments on technology support, it’s hard to give a definitive advantage to either. On the law firm side, selection of software, programmers and use of consultants is within its sole control. A firm sets priorities and funds as it sees fit, but its infrastructure is meager compared with that of companies, and it might be hard-pressed to hire first-rate machine-learning programmers. After all, if truly big data beckons elsewhere and the owners of it are not technophobes like many law firm partners, why would a crack coder work for a law firm?
On the law department side, the muscle of the corporate IT function is theoretically at its disposal, with deep talent, experience and purse. But despite that chimera of support, law departments rank low on IT’s priority list, and a general counsel’s ability to mobilize corporate IT resources to the department’s tiny and specialized ends is problematic.
Cost Center Versus Profit Generator
Law firms want to increase revenue (and distributable profits) so if machine-learning software gains appreciation as a tool to boost revenue, partners will climb on board. The return on that investment and the lure is fatter bonuses. The law firm is the business, whereas a law department supports a business.
By contrast, law departments want to reduce costs (or increase productivity), so if machine-learning software can help achieve those ends, they will spend. Cost reductions, however, bump into limits that revenue growth doesn’t. For example, the rewards for slogging through outside counsel reductions (and the attendant disruptions) are less personal, less cash value than bigger bonuses.
Half of the total spending by U.S. law departments goes to law firms. Thus, when machine-learning software makes inroads in law departments, the analysis will lean toward reduction of law firm costs. Law firms, by contrast, will focus on data associated either with client matters or with the effective deployment of their own lawyers and staff. They won’t regard their spending on vendors as nearly as vital as matter productivity, investment and outcomes. Or they will let machine-learning software loose to study who makes partner and why, or to tackle attrition in terms of which desirable associates are at risk of leaving the firm.
In a broad generalization, companies value and rely more on numbers than law firms do. The sheer size of companies, their range of activities, publicly traded securities and auditors, among other factors, have pushed executives to immerse themselves in data. Law firms orient themselves differently. They espouse running themselves profitably, but they share a stronger culture of professionalism, client service, intellectual prowess, partnership and the importance of words (legal concepts, cases, statutes, agreements).
Data can actually be feared as conspiring against the humanistic values of the partnership. Many partners in law firms shy away from data analytics because the findings invite divisive comparisons. All data discriminates, we must acknowledge. Moreover, many partners don’t really want their clients thinking about performance metrics and costs.
The same could be true of general counsel: Don’t scrutinize our budget and staffing – just approve it. General counsel, having grown up in law firms and having been inculcated with the same beliefs, may still share that mistrust of metrics and data analysis, but CEOs and CFOs will hardly agree. Business executives value data, and they will increasingly want their in-house lawyers to collect and analyze it.
The Big Question
So, where will machine and software take root, in the data culture of companies or in the values culture of law firms? To the degree that this cultural dichotomy accurately applies to data-driven companies (trickling down to the enclave of law departments) and the values-driven law firms, it would seem that machine-learning software will find more fertile ground in companies. Law departments have the force of management authority, a corporate culture that prizes quantification, and less psychological and cultural opposition.
Or will law firms be more likely to adopt machine-learning tools because they have more data, directable programmers and software, and a stronger motivation to persevere? Never underestimate the power of profits.
Either way, watching these complicated forces and drivers play out will be fascinating to be part of!