“Everything should be made as simple as possible, but not simpler.”    — Albert Einstein

The members of the Delta Model working group imagine a world, not too far off, where law schools, legal employers and clients all share a common touchstone for lawyer development.  For the last two years, we’ve been working on such a touchstone, which we call the Delta Model.  Our current version is expressed in the graphic above. Continue Reading The Delta Model: simple, accurate, versatile (125)

Click to enlarge / Match the data-enabled deliverable, service or product to stakeholder needs.

How should legal teams get started with data?  Here’s a prescription, along with a #RealTalk diagnostic.

In Post 066, I shared that law firm and law department leaders often ask me how to get started with data analytics. I also shared that I usually respond by asking about their most important strategic objectives.

For today’s post, I will play doctor and take a cut at a framework to serve as a prescriptive roadmap. The above graphic is the result. It visualizes a practical framework to think structurally about high-value applications of data.

To Design a Value Proposition for Data Analytics, Start with Clear Thinking Around Needs

As with any new idea, tool, service or product, the key to designing a successful value proposition is to think deeply about the needs of the intended end-user.

Decision support, when applied to sufficiently high-stakes contexts in both the business of law (e.g. new business models for legal service delivery) and practice of law (e.g. litigation finance), likely offers the highest probability of generating material economic returns or a strategic leap forward for the sponsoring organization. Of course, identifying the decision opportunity and shaping the analytics approach requires a high threshold of domain knowledge as well as technical expertise across both data engineering and data science competencies — hence the need for a multi-disciplinary team.

Execution support. That said, for the time being, the most widespread data efforts in the legal market probably still focus on fairly familiar products around business intelligence, operational dashboards and financial reporting.  While there is plenty of opportunity here for useful deployment of data-informed decisions and actions, the pace of innovation here is unlikely to hit a drastic inflection point due to the heavy burdens around process improvement and change management for data handling, on an organization-by-organization basis.

Persuasion. Broadly in the world, the art of storytelling has gotten almost as much adoration as data science in recent years, but this is one area that hasn’t gotten much traction in legal. (Let’s see if 2019 brings us a bit more ✨ sparkle.)

Common Complaints, Pain Points & 😱💀 Horror Stories 👻🤡

As with many other topics about new and evolving capabilities, dialogue about data in the legal industry can be confusing and contradictory. There is a material level of hype (Big 📊 Data 🤯 disrupting 🤯 Big ⚖️ Law!!) – paired per usual with the requisite cynicism and skepticism (LOL 😆 lawyers can’t speak data 😖😰🙄).

In the abstract, I 💖 data. Full stop. 😍

Out in the real world, however, my adoration for data has many disclaimers, disclosures, and exceptions — particularly when it comes to the legal industry.  In this section, I address a few of my misgivings about the current state of data usage in the legal vertical, drawing not only on my own experiences but also the most common complaints I hear in my travels.

1. We Have the Data Sets We Deserve (but Not Always the Ones We Need 🦇)

This is often how it starts. Because most legal organizations are in possession of some data assets, and because data-informed or data-driven methods are all the rage these days, many organizations embark on directionless and costly exercises to squeeze insights from the data they already have.

Common scenario, full of traps.

Conversations that begin this way are hugely concerning to me.  What I hear in this exchange is “sure, I’d love to see something interesting or cool because it sounds like it’s free.”  That’s not a cost benefit equation with a high probability of a happy ending, for two reasons:

  1. Overvaluing existing data assets without regard to data quality or sufficiency (more on this a bit)
  2. Underestimating the cost and effort required to extract business value from those assets

On the business of law side, both data sufficiency and data condition are material concerns. A point worth noting here is that much of the existing business data in the legal industry oriented around cost to the buyer: most of the readily available data sets across law firms and law departments tend to come from time-and-billing systems and provide the history of transactions, essentially as accounting events. Of course, these data sets often suffer from material quality issues that require time and money to resolve.

Very few law firms and law departments have comparable depth or volume of data oriented around value delivered. In other words, there are precious few readily available and reliable systems of record that capture the history of legal events through which the service provider created and delivered value to the buyer of legal services. On a per-organization basis, more practice management or experience database solutions are emerging to help create this record, and in very few cases correlate those events directly to billing data. However, in 2018, such efforts are relatively nascent, particularly relative to the time-and-billing systems that have served law firms as their primary mission-critical system for many decades.

In such instances, the upside potential of the project often has a hard ceiling — yet issues with data condition can mount, not just in hard dollar expense but also time lag and overall drag on the organization. Particularly in projects without a sharp focus on the end-goal, those costs and subsequent adverse impact on the organization can be significant: not least, the potential for change fatigue and mounting resistance to future endeavors involving data.

In short, designing an analytics program at scale represents a material investment of organizational resources.  To secure sufficient ROI on such an effort, analytics must meet a higher bar than simply being interesting — analytics must be useful. And the overall utility and value proposition of the data effort should, whenever possible, be articulated at the outset, not while a costly effort is in flight.

2. Mostly Neglected Everywhere: Assessments of Data Sufficiency

My favorite question format on the GMAT is unique to the exam: data sufficiency. (Yes, it is weirdly 🤓🤓🤓 to admit I have a favorite question type on standardized tests. For inquiring minds, I 💕 logic games on the LSAT and analogies on the SAT.)

I provide an example below:

Source: PrepScholarGMAT

For everyone whose eyes glazed over, that’s OK. Data sufficiency enjoys a flavor of notoriety even among the b-school crowd. The question doesn’t directly test for quantitative problem-solving aptitude; the provided problem (in the example above, the ratio of full-time to part-time employees in Division X and Company Z) is usually asinine and beside the point.

Rather, data sufficiency questions test aptitude for metacognition: how to 🤔 think about 🤔 thinking. Correctly answering a data sufficiency question is at its core an exercise in logical reasoning, in addition to a test of content understanding of number properties and statistics. More precisely put, this question format demands rigorous and structured thinking about the underlying method of problem-solving, and most importantly the ability to correctly identify the factual inputs required to generate insights relevant to the problems or objectives at hand.

This represents hard work for our brains that feels unnatural, because the exercise of evaluating data sufficiency requires that we focus on the white space — something beyond reacting to or dealing with the facts and numbers that are right in front of us. This question asks us to think categorically and descriptively about whether we might need facts and numbers that are not readily available.

This is a very specific type of thinking that we don’t often practice in the legal industry. We ought to.

3. Data Sets Have Origin Stories, but We Often Ignore Them

Most data sets don’t simply appear out of thin air via immaculate conception. In some way shape or form, most data sets are generated by people.

In most brick-and-mortar businesses and product-heavy sectors, sensors do much of the work around collecting and gathering data, often unobtrusively in the background. The same is true for high-tech platform plays where data on user behavior turns out to be the core product to be monetized.

That said, the legal services market is still a services-first category, and our industry still relies heavily on manual data inputs. As a result, busy and stressed people are clicking buttons or filling out fields to generate many of our data sets, particularly in intra-organizational initiatives in knowledge management and practice data maintenance. This type of data work demands accuracy and precision, which are two areas where humans tend not to excel. These tasks also tend to comprise the more tedious and soul-crushing components of anyone’s job: data upkeep/maintenance is usually the “one last thing” on the checklist or to do list before quitting time, and on most days, these tasks probably don’t get done. 🤷‍♀️

Particularly because we tend to function in environments that produce low-fidelity data, understanding the means and mechanics of data collection is a critical prerequisite to defensible analysis and interpretation. Too many teams working with data in law firms and law departments function as database (or spreadsheet) administrators, overwhelmed with the mechanics of data collection and cleaning without a serious attempt to engage with the content of the data sets — i.e. what the data says and what it all might mean.

The Root Cause Diagnostic: New & Different Methods Require More & Different Skills (but Not in One New Super-Lawyer)

Out in the real world, using data to actually solve real problems is much 😓 harder and more 😩 effortful than a day of GMAT prep, and the requisite skills are both valuable and rare. The ability to identify the needed inputs and assess the best method of obtaining them are skills we don’t associate directly with data analysis. These upstream competencies in fact-finding and fact collection are skills taught in research, investigation and intelligence work — across academic, enforcement, military and corporate traditions.

In practice, work to assess and achieve data sufficiency tends to look both non-linear and messy, because it is. In addition to a firm grasp on the mechanics of research design, the team must bring to bear some depth of domain knowledge (content understanding of the specific facts and data points relevant to the problem at hand) and environmental familiarity (the ability to navigate the available universe of sources and to assess each information source for reliability). Lastly, this type of work benefits from a few specific attributes: intellectual curiosity, resourcefulness, and tenacity.

Often, generating original and useful insights requires multiple cycles of hypothetical reasoning followed by factual investigation. Formulating smart and specific questions to explore requires creative and open-ended thinking rooted in meaningful understanding of the problem, and actually going out to verify what is happening out in the real world requires a willingness to engage in legwork.

All this is a tall order. In 2011, McKinsey predicted that “by 2018, the U.S. alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”  See “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute, June 2011.  Fast forward to this year: big data and analytics has topped the skill shortage list for the 4th year running in the annual Harvey Nash/KPMG CIO Survey, and two-thirds of IT leaders say this shortage is “preventing them from keeping up with the pace of change.” See “Big Data Skills Shortages – and How to Work Around Them,” Computer Weekly, June 2018.

A 2017 analysis by PwC echoed McKinsey’s 2011 prediction, noting that the overall talent need will be “mostly for business people with analytics skills, not just analysts.” See “What’s Next for the Data Science and Analytics Job Market?,” PwC Research and Insights, January 2017.

The grid above sets forth their recommended skill inventory mapped to jobs across two categories: analytics-enabled business roles and more technical data science roles. This is an instructive model for legal practitioners and legal educators alike: increasingly, there is a need to rethink the boundaries of the legal practitioner role, either as “data-driven (legal) decision makers” or “data-enabled (legal) analysts.”

I particularly favor this PwC model because it sidesteps a couple of familiar traps. Once in a while, Law Twitter revives the debate over whether lawyers should learn to code (strong opinions abound). I’ve never been fond of how this question is framed. Firstly, I’m not sure it’s helpful to debate whether all lawyers should learn to code. Secondly, I think the question lacks nuance and specificity about what level of conceptual literacy or practical prowess “learning to code” would or should comprise. (As for my two cents on the “learn to code” debate, I agree 🙌 with Jason Barnwell’s hilarious tweet below.)

What the PwC skill grid accomplishes in one visual is to remind us that the full complement of analytics skills modern organizations need must be spread across several jobs. Analytics is a team sport. What’s more, the PwC grid also communicates very effectively that each distinct role demands a differing mix of skills.

Click to enlarge / Don’t try this at home alone 😱

Modern law, too, is increasingly a team sport. Legal teams are particularly challenged to field a high-performing team that brings together the full complement of necessary skills, for several reasons. In the current state, the vertical not only has a skills shortage but no real pipeline strategy to attract high-caliber analytics talent. See Post 066 (talent shortage as structural barrier to innovation).  That talent shortage is exacerbated by the extreme fragmentation in the industry. See Post 051 (key graphic). Indeed, as a mental exercise, I’d be willing to lay 10-to-1 odds that there simply aren’t 200 qualified candidates to lead serious data analytics initiatives at each of the Am Law 200 firms.

The industry relies on pockets of brilliance for thought leadership (Dan Katz of Chicago-Kent & LexPredict and Evan Parker of LawyerMetrix come to mind). However, advancing analytics-enabled thinking at scale will require a much larger talent pool and/or a more creative market-making solution to help scale the limited supply of talent to a broader swath of demand.

A New Hope, Always: the Legal Industry Isn’t Actually That Far Behind the Curve

Before I close, though, let’s take a slightly different look at the state of data analytics in legal — because tech products represent one pathway to scaling the promise of analytics on a one-to-many model.  See Susskind, The End of Lawyers? (2010) (introducing one-to-one and one-to-many terminology). Certainly as of late, more and more legal tech and content players are focused on data applications with the potential to arm legal practitioners and business stakeholders to make better legal and business decisions faster: these efforts currently coalesce around natural language processing, from extraction to categorization and reasoning. Some of these component technologies are already baked into mainstream products in legal research and document intelligence categories:

Click to enlarge / Partial view of the Research and Document Intelligence categories

Many of these products are still likely enjoying success in early markets, but both categories seem poised on the cusp of penetrating mainstream markets. The Research category demonstrates an unsurprising level of consolidation given the longstanding oligarchy of incumbent content publishers. The document subcategories are still fairly crowded, particularly in the diligence engine subcategory: whether a clear winner will emerge, it is too early to say.

One last noteworthy point is that the legal vertical may represent a fairly optimal lab environment to test frontier technologies in the more advanced NLP subcategories like machine translation. In that sense, the perpetual notion that the legal industry is perennially behind everyone else may be a bit fatalistic — and signals in the marketplace suggest that there is likely sufficient interest from both capital and the buying market to drive those experiments forward, particularly in the upmarket segment of Big Law:

  • Eigen Technologies, which works closely with both Linklaters and Hogan Lovells, raised a $17.5m Series A round in June of this year, with Goldman Sachs as lead investor. See “Eigen Technologies Raises £13m,” finextra.com, June 11, 2018.
  • Luminance raised a $10m Series A round in late 2017 with Slaughter and May on its investor roster. See “Slaughters Ramps Up Luminance Investment in $10m Round,” The Lawyer, November 29, 2017.

So, is the legal industry really a decade behind everyone else in how we use and consume data to make decisions? While it probably feels that way to many market participants, I take a slightly more positive view. The advance guard is certainly charging forward. As for most of us in the middle of the pack, I’m here to tell you that many of our frustrations and complaints are commonly heard outside the legal industry.

What I do think we need to improve might be our attitudes: more willingness to invest in attracting new technical talent and more attention to developing the talent we already have by way of education and training would probably go a long way.

What’s next?  See Introducing contributor Dan Rodriguez (076)

Photo by Louis Reed via Unsplash / Microsoft is bringing the scientific method to legal innovation.

Microsoft is pushing legal buy and provider engagement to the next level and asking their primary firms to come along. Here’s why it matters: they’re thinking bigger, committed for the long haul, and bringing a STEM mindset to legal innovation.

Continue Reading Huge, If True: How Microsoft’s Big Ideas Could Transform Legal Buy (069)

Among the many impressive finalists for this year’s ILTA Innovation Awards, the submission for the Telstra legal department stood out as a compelling change management story.  By enabling the right kind of collaboration among its lawyers, the Telstra change initiative reduced the internal workload on the 220-lawyer department by 40,000 hours. Further, by returning time to overburdened lawyers, the department created a culture that is much more supportive of change efforts.

Yet, what is most significant about this story is that virtually any legal organization could replicate this success by taking a few simple steps.

The business challenge facing the Telstra legal department

Telstra is an Australian telecom company that was formally a state-run utility.  Shortly after completing a phased privatization in the mid-2000s, the 2008 financial crisis forced the company into downsizing mode. 10% annual budget cuts were implemented for all parts of the business, including the legal department.

Like many successful change initiatives, this one began with false starts and disappointment.  As the cost-cutting pressures continued to mount, in 2013 the legal department created a long list of key pain points that needed to be addressed for the group to be successful. Recalls Mick Sheehy, Telstra’s General Counsel of Finance, Technology, Innovation & Strategy, “we thought the list was so important we made it everyone’s shared responsibility, including our senior legal leaders, which meant ultimately it became no one’s responsibility.”

A Process to Prioritize, Plan, Implement, and Repeat

With the department struggling to gain significant traction, in 2015 Sheehy attended a design thinking course at Harvard Law School.  Impressed with these ideas, Sheehy returned home and ran a design thinking workshop with a group of his own lawyers, receiving some expert facilitation from a team at Herbert Smith Freehills. Cf. Post 015 (noting key determinants of organizational innovativeness are leadership’s attitude to change and openness to external perspectives).

After once again creating a laundry list of the department’s biggest pain points, the group limited itself to the top four.  Thereafter, they used design thinking techniques to construct potential solutions for each problem and to implement them through an eight-week “sprint.” (Borrowing from the world of software development, a “sprint” is a discrete time period — usually two weeks to two months — where a team creates a working prototype or an updated version of a product. See Agile Glossary.)

Below is the simple process each Telstra work team used evaluate and improve each change initiative:

What makes the Telstra process different that other change initiatives is that it is iterative and enables the group to learn from implementation.  Thus, a decision to continue is also a decision with much better information and a higher likelihood of success.  Likewise, a decision to kill an initiative is less a failure than a prioritization of limited department resources to support the highest impact projects.

Notes Sheehy, “We ran the sprints and we came back to another workshop and we looked at what we achieved and were so enthused and excited that we decided to do the whole thing again. And we haven’t stopped. This is now an embedded process in Telstra legal and we recently ran our 8th Telstra innovation workshop.” Cf. Post 008 & Post 011 (noting simplicity and trialability as among the keys to successful adoption).

Telstra rotates lawyers through the innovation program, known internally as the Legal Innovation Forum, or LIF.  As of August 2017, 35 Telstra lawyers have participated in the program.


Thus far, four “streams” have left the Forum, having achieved their core objectives.  Although Sheehy notes that none of them are particularly exciting on their own, “collectively they’re telling a great story.”  Here are the four streams.

  1. Self-Service NDAs (5,300 hours saved).   Most non-disclosure agreements are standard and low-risk.  By embedding the key decision points into an automated workflow, the number of lawyers hours per annum dropped from 6,425 to 1,125, resulting in an 82% time savings.
  2. Less Legal Report Generation (2,250 hours saved).  The equivalent of two lawyers were producing a weekly report for the CEO that he was not regularly reading. So the reports going to the CEO were cut by nearly 2/3, reducing the time commitment from 3,750 hours to 1,500, resulting in an 60% time savings.
  3. Fewer Internal Meetings (31,500 hours saved).  Throughout the legal department, the numbers of internal meetings was widely viewed as excessive. As part of a LIF initiative, internal meeting where categorized as either “decision making” or “information sharing” meeting. For decision making meetings, organizers were told to only invite people they needed and to make the decision points explicit in advance. For information sharing meeting, each attorney was limited to 2.5 hours per week. Across the 220-lawyer department, this resulted in a drop in internal meeting hours from 60,180 to 28,680 (52% reduction).
  4. Reduce Legal Review of Internal Communications (1,008 hours saved).  A careful triage of the type of internal communications subject to legal review revealed that a substantial volume of review was unnecessary.  Better workflow criteria resulted in reduction of attorney hours from 3,470 to 2,462 (29% time savings).

Telstra’s internal time saving target for these four initiatives was 27,000 hours per annum time.  Yet, they overshot the mark by achieving more than 40,000 hours.  This is the type of ROI available when lawyers use people, process, and technology to “do less law.” See Ron Friedmann, Do Less Law — A Taxonomy of Ideas, June 11, 2015.  It was also enough for Telstra to win the 2017 ITLA award for legal department innovation.

Lessons learned

As noted above, as of August 2017, Telstra had eight workshop/sprint iterations, which is the basis for an enormous amount of organizational learning. What are the key lessons?  Sheehy offers several:

  • Data.  “It’s critical to measure your baseline and know your starting point so you can tell a data driven story so people can understand all the effort you’re putting in is driving results.”  Cf. Post 008 (data makes innovation more observable and thus more likely to be adopted by others).
  • Not reinventing the wheel.  “The problems we’re solving are not unique to Telstra legal department and may be faced by other law firms and departments in the company. Having an outward focus rather than an inward focus is critical.” Cf. Post 017 (noting openness to external ideas and influence as key determinant of organizational innovativeness)
  • Not waiting for perfect; avoiding options paralysis. “We have a tendency to overthink problems when we sometimes just need to get started. Jeff Bezos had a great point when he said that if you’re waiting for more than 70% of the information to make a decision you’re probably waiting too long, and getting something wrong is less expensive than being slow.”
  • Communication.  “All of this has a degree of behavioral change and behavioral change is really hard. We had to focus on the communication. The reduction in meetings was difficult and to get people to think differently on that – a lot of it was down to communication.”

Below is the last graphic from Telstra’s ILTA presentation.  Note that in its original form it was a series of sticky notes generated by team members during the Forum debriefs. In other words, a simple low-tech process is the engine that is powering tremendous organizational efficiency and learning.   Per Sheehy and his Telstra colleagues, the blocks in red are particularly important.

A special thanks to Mick Sheehy and Ali Caldicott of Telstra for making the ILTA slides and presentation script available to me.

What’s next? See Currell on Convergence and Preferred Provider Panels (028)

Rogers Figure 6-1

If you have been readings the foundational posts for Legal Evolution, this installment (Post 008) will reward you with something of clear, practical value: An empirically grounded model that identifies specific factors that influence the rate of adoption of an innovation.

What is the specific practical value?

  • If you are an innovator, this model can be used as a functional checklist to assess whether your innovation is ready for market; and if so, where to focus your limited bandwidth to maximize the odds of successful adoption.
  • If you are an early adopter, this model helps you assess whether you want to cast your lot with a specific innovation or, instead, hold your powder until the innovation is more developed or another innovator produces something better.

bookdiffusionsofinnovationsThe graphic above is adapted from Chapter 6 of Everett Rogers, Diffusion of Innovations (5th ed. 2003). As noted earlier, this is one of the most cited books in all of the social sciences.  Although the graphic does not look quantitative, it is actually a user-friendly presentation of a multivariate regression model.

The left column of the graphic lists five groups of variables that influence the rate of adoption of an innovation.  The rate of adoption is the dependent variable, which is listed in the right column. The rate of adoption is a dependent variable because its value depends on the value of the other variables. In the parlance of statistics, the other variables are called “independent” or “predictor” variables. The five groups of variables on the left have been shown by Rogers and others researchers to be valid and reliable predictors of the rate of adoption of an innovation.

If you’re investing a lot of time and money in an innovation, this is a profoundly useful model.

I. Perceived Attributes of the Innovation

Among the five categories of predictor variables, the most important is the first category, the “perceived attributes of innovation”.  Rogers reports that between “49 and 87 percent” of the variance in the rate of adoption can be explained by five attributes: (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5) observability (p. 221`).

Note that this is a list of perceived attributes. Perceived by who?  The target adopter.

There are many ways to fail as an innovator, but one of the most common is failing to adopt the perspective of the end user.  Rogers begins Chapter 6 with a telling quote: “If men perceive situations as real, they are real in their consequences” (quoting W.I. Thomas Florian Znaniecki, The Polish Peasant in Europe and America 81 (1927)).  Adopting the perspective of the end user is an exercise in empathy. This can be very difficult for the innovator, who is often deeply immersed in the technical workings of the project. He or she is at grave risk of falling in love with features that are of little practical value to the target end user.  Cf. Curse of Knowledge (cognitive bias that afflicts experts).

Rogers distinguishes between “objective rationality” relied upon by the expert who carefully reviews data and “subjective objectivity as perceived by the individual” (p. 232). The latter is what is relevant to adoption. Most of us try to generalize based on what makes sense to us. Instead, we need to spend all of our time studying someone very different and seeing the world through their eyes.  Acquiring this skill set take effort, self-awareness and humility. What you think or I think literally does not matter.

Here is a summary of each perceived attribute.

1. Relative Advantage

Relative advantage is “the degree to which an innovation is perceived as being better than the idea it supersedes” (p. 229). The advantage could take the form of economic benefit, an increase in social status, or both.

It is worth reinforcing the user perspective here.  I have seen numerous legal start-ups struggle and fail because the founders were pitching efficiency to law firms.  Although clients complain about high legal bills, the law firm that makes a large capital investment in efficiency has a very difficult time capturing a reasonable portion of the value created. See Henderson, The Legal Profession’s ‘Last Mile Problem”.  When a salesperson makes the efficiency pitch, they are generalizing from their world, not the world of the prospective law firm adopter.  Quality, on the other hand, has a much stronger appeal to lawyers, primarily because it is associated with lower risk. We’ll go deeper on this point in a future post. See also the discussion below regarding trialability and Practical Law Company’s successful entry into the US legal market.

2. Compatibility

Compatability is “the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters” (p. 240),  The phrase “disruptive innovation” undoubtedly helped Clayton Christensen sell hundreds of thousands of copies of his famous book, The Innovator’s Dilemma.  However, it not a phrase that will endear you to the vast majority of adopters who have zero interest in having their livelihoods disrupted. The touchstone here is familiarity. The closer we hew to what is known and accepted, the lower the levels of perceived uncertainty.  That is a zone where your innovation has a chance of getting adopted.

To illustrate this point, Rogers notes that care should be taken in naming an innovation, as the name often carries influential connotations that can undermine relative advantage (pp. 250-51). Note that compatibility is often treated as an empirical question. “Positioning” research looks for optimal associations with accepted products or services in the adopters’ environment. Likewise, “acceptability” research seeks to identify factors that tend to make or break an adoption decision. Compatibility research is quantifying the emotional, subjective reactions of potential users.  The only thing close to this in law are focus groups designed to simulate juror reactions. The best trial lawyers use this methodology in preparation for trial. (E.g., Fred Bartlit once told me he used eight separate mock juries for case he was trying. No surprise — he won.)

3. Complexity

Complexity is “the degree to which an innovation is perceived as relatively difficult to understand and use” (p. 257). Whereas relative advantage and compatibility exert a positive influence on adoption, complexity has a negative effect. The higher the perceived complexity, the lower the rate of adoption.  Thus, it is not surprising that successful tech companies obsess over user experience (UX) and user interface (UI).  Design thinking often adds value by removing unnecessary and cumbersome complexity.  See, e.g.,  Design Thinking Comes of Age, HBR (Sept 2015). The graphic below illustrates this point.  The product on the left was designed for the end user; the product on the right stayed too much within the perspective of the engineer.


For the curious, the iOS Human Interface Guidelines are published online here.

4. Trialability

Trialability is “the degree to which an innovation may be experimented with on a limited basis.” Rogers continues, “New ideas that can be tried on the installment plan are generally adopted more rapidly than innovations that are not divisible” (p. 258).

Several years ago, the original US sales team of Practical Law Company (PLC) shared with me how they successfully established their US operations. PLC sells annotated forms and practice guides for sophisticated corporate work.  Although PLC had a complete lock on the UK market, they had no US customers when they landed in New York in 2007. Through trial and error, they soon discovered that the single best way to overcome the skepticism of US lawyers was to put them in front of a computer and let them use the PLC product.  After experiencing the product’s immense utility, subscriptions were relatively easy to close. By the time PLC sold to Thomas Reuters in 2013 (for a price between $300-450 million), PLC had 700 legal departments and 86 percent of the AmLaw 200 as customers.  See Thomson Reuters to Acquire Practical Law Company.

Trialability was certainly relevant to PLC’s rate of adoption.  However, the PLC product line also had a huge relative advantage over the incomplete, out-of-date, and unannotated internal forms they were replacing. Trialability enabled perspective adopters to experience the quality difference. To enable high quality decision-making, it is important to keep analytically distinct each of the five perceived attributes of an innovation. Trialability is different than overall relative advantage, though both levers are important.

5. Observability

Observability is “the degree to which the results of an innovation are visible to others.”  Observability is very much related to relative advantage and trialabilty. If an innovation is trialable for early adopters, its relative advantage can be more easily observed by other parts of the social system. See foundational posts 004 and 007.

The importance of observability is documented in an early and influential diffusion study that focused on adoption of hybrid seed corn in two communities in Iowa. See Ryan and Gross (1943). What drove adoption for the vast majority of farmers was not the technical sales pitch made by college-educated agronomists. Rather, it was the observably better corn growing on their neighbor’s land. The technical pitch was primarily relevant to the innovators and early adopters in the social system, who set the adoption cycle in motion. The average time between “knowledge awareness”  and the “adoption decision” (technical terms of art in diffusion research) was a fairly lengthy six years. See chart below.


I believe the above chart is very relevant to all the hype regarding how artificial intelligence is going to revolutionize the legal field.  AI does not have a relative advantage that is easy to observe. Mere efficiency (an obvious and potentially observable advantage) is not good enough for many lawyer-adopters, as efficiency currently creates collateral business problems that most clients fail to acknowledge. See Henderson, The Legal Profession’s ‘Last Mile Problem”. AI is also very complex.  These perceived attributes are going to impede AI’s rate of adoption in law.  Many smart people in legal start-ups are trying to use design principles to solve or mitigate these issues. Yet, the best of them know they are climbing a very steep mountain.

Summary of perceived attributes

As noted earlier, the five factors above explain 50% or move of the variance in adoption rates. Stated another way, if you have an innovation you would like others to adopt, focus your attention on these five factors. This simple, empirically derived piece of guidance is one of the reasons that applied research can be so powerful.

Four other categories of variables influence the rate of innovation adoption (II to V in the graphic above). Most of them cannot be significantly influenced by the efforts of innovators, though they are highly relevant because they enable an innovator or early adopter to handicap the odds of market acceptance. In other words, they bear on practical questions like, “should I put more money in?”; “should I sell now?”; “should I fold the business?”;  “how long is adoption likely to take compared to other business contexts?”  Thus, let’s finish the model with a eye toward how it applies to the legal industry.

II. Type of Innovation Decision

At some point after a potential adopter becomes aware of an innovation and weighs its relative advantages, a decision will be made to accept or reject.  There are three types of innovation decisions.

  1. Optional. Basically everyone in the social system is free to decide for themselves. This is market-based.  E.g., smartphones, healthier foods, Facebook.
  2. Collective. Through agreement or strong cultural norms, adoption requires a consensus of the entire group. This mechanism has the most negative impact on rate of adoption.  It is also the mechanism that best describes the typical law firm partnership.
  3. Authority.  One decision-maker makes the decision for the entire social system. E.g., corporate executive; government official. Although authority innovation-decisions are generally the fastest, they run the risk of being “circumvented by members of a system during their implementation” (p. 29).

The type of innovation decision is very relevant to the legal industry.  Back in 2015, I organized a panel of legal innovators for the ABA Center on Professional Responsibility.  One of the panelists was an venture capitalist who was an investor in Modria, an online dispute resolution service that uses an automated dispute resolution methodology similar to those used by eBay and PayPal. As a former associate at a prominent Silicon Valley law firm, the VC helped pioneer some of the early investment in legal tech, albeit not all investments worked out well. In front of an audience of 300 law firm lawyers, the VC stated that he would never again invest in a technology that was designed to be sold to law firms because “law firms don’t made decisions like rational businesses.”

Placed into the Rogers decision framework, the VC was frustrated by the collective decision-making process of law firm partnerships.  From far away, it looks irrational. Up close, however, it’s justified as culture.

That said, it is very easy to confuse the long sales cycle in law with the more fundamental issue of relative advantage. For example, many partners hear their clients clamoring for greater efficiency, and hence are willing to listen to sales pitches. Yet, the partners don’t know to how to honor the clients’ wish because it requires to them to simultaneously (a) pay for, and learn how to use, expensive, complex innovations, and (b) endure a loss in revenues because the clients insist on using hourly production to measure value. Insistence on hourly billing, or shadow billing of AFAs, is a great example of a compatibility restraint that impedes innovation.  The legal profession has a very serious last mile problem.

type 6 clientI am confident that the rise of the legal operations role within legal departments is substantially due to the authority innovation-decision advantages of having a single general counsel who possesses traditional executive perogatives. That authority is increasingly being delegated to legal ops professionals who have a clear directive for better, faster, less expensive. See Post 005 (discussing CLOC and the rise of the Type 6 client).

Yet, in the best of circumstances, change management in legal departments is no cakewalk. My friend Jeff Carr, formerly GC of FMC and now at Univar, acknowledged the challenge of MPR, or “massive passive resistance”, in implementing necessary change.  Having achieved remarkable financial results through his ACES model, Jeff became a fierce proponent of general counsel as leader, a discipline and topic completely foreign to most lawyers.

jeff carrIf you ask Jeff about the key to successful implementation of change — e.g., requiring every in-house lawyer in his department to regularly score outside counsel using a standard grading rubric — he is likely to point to his face:  “See this look. This is the look of me not caring. These metrics are necessary for the functioning of the company. Please do your job.”  Another prominent general counsel who successfully transitioned a large legal department away for the billable hour, and has served as an influential advisor to many general counsel, acknowledged to me that such a transition could easily entail the resignation or dismissal of roughly 30% of the department — that was the volume of turnover in his department and other successful legal department transitions he has observed.  Change is hard, even for highly educated professionals.

Suffice it to say, whether its collective innovation-decisions, or the reluctance of lawyer-leaders to stay the course because we have little training or experience as managers or leaders, the legal industry presents special challenges for innovation adoption and diffusion.

III. Communication Channels

The rate of innovation is positively influenced by the number and quality of communication channels. This is true in two ways. First, early adopters may become aware of an innovation through a new communication channel (e.g., the trade press or an industry conference).  Second, more and better communication channels make innovations more observable to the rest of the social system. Not only does this facilitate economically driven adoption decisions based on relative advantage, it also works to set and reinforce group norms. Thus, a subset of adoption decisions will be socially driven by a desire to fit in or avoid feeling left behind or out of date. Again, diffusion of innovations is a social process; incentives are present, but they are often more social than economic.

Not surprisingly, the advent of new communication channels like print journalism, radio, television, and the Internet have all increased the pace of innovation adoption.  The rise of mass media is one of the most important areas of study in diffusion research.  Following the publication of the first edition of Diffusion of Innovations in 1962, Everett Rogers, who was a sociologist by training, joined faculty of Department of Communications at Michigan State University. At the time, MSU was the leading institution in this fledgling academic discipline.

Communication channels are important to innovation because they increase the flow of information. Yet, factors that influence total flow are different than the factors that influence the persuasiveness of the information content.  For the latter, relative advantage, compatibility, complexity, trailabilty, and observability remain the touchstones.

curveLegal Evolution is designed to be a new communication channel that will help accelerate the pace of legal industry innovation.  As noted in Post 001, this publication is an experiment in applied research.  To be successful, I need the readership of legal innovators and early adopters — the light blue portion of the curve.  I hope this elite readership enjoys Legal Evolution’s clean layout and the absence of banner ads. If you have the stamina to read a 3,500 word foundational post, these niceties are the least I can do.

By the way, what is the likelihood I could adequately reach my target readership if I published this analysis in a traditional law review?

IV.  Nature of the Social System

In Rogers’ model, the nature of the social system is the fourth category of variables that can impact the rate of adoption of an innovation.

For the legal industry, the nature of the social system generally impedes innovation adoption. The most established, influential, and prestigious portions of the legal profession — large law firms, the federal judiciary, legal academia, and the ABA — tend to be traditional bound and skeptical of change that does not initiate with them.

Part of this conservative ethos may be the product of Rule 5.4, which has been adopted in some form by every state.  Rule 5.4 prohibits lawyers from co-venturing with other professionals in any business that involves the practice of law. If lawyers can’t be business partners with accountants, engineers, software developers, process experts, and data scientists, etc., that’s going to cut down on the opportunities to learn from them. This makes our social system much more isolated from other innovative parts of modern information economy.

Enough said about that.

V. Efforts of Change Agents

Chapter 9 of Diffusion of Innovations is focused on the change agent.  It begins with the following quote:

One of the greatest pains to human nature is the pain of a new idea. It … makes you think that after all, your favorite notions may be wrong, your firmest beliefs ill-founded. … Naturally, therefore, common men hate a new idea, and are disposed more or less to ill-treat the original man who brings it (p. 365, quoting Walter Bagehot, Physics and Politics 169 (1873)).

This is harsh but also has a ring of truth to it.  To avoid a hostile reception, effective change agents seek out individuals more disposed toward their message, a group disproportionately comprised of innovators and early adopters. After the change agent assists this group in obtaining a large advantage that others can observe, the change agent will become more accepted within the broader social system.  But probably not until then.

Change agents can be university field specialists trying to disseminate agricultural best practices for the good of the state economy. They might also be public health professionals seeking to curb a longstanding but harmful cultural practice that is increasing the spread of disease. The biggest challenge facing change agents tends to be “hetereophily” — i.e., they are often conspicuously different than members of the social system in terms of background and technical expertise. Hence, they struggle to communicate effectively with prospective adopters. Successful change agents find ways to overcome this hurdle. Rogers writes, “As a bridge between two different systems, the change agent is a marginal figure with one foot in each of two worlds” (p. 368).

In the legal industry, change agents are most likely to take the form of technical sales people who are trying to get onto your calendar to sell you a new technology or service.  At industry events, these folks are typically called “vendors.”  The connotation associated with vendors is often negative. In my opinion, this is a parochial way of viewing the world that cannot be squared with our poor record on client service, innovation, and access to justice.  In light of these issues, perhaps we should be more gracious and openminded to those offering tools for improvement.

That said, change agents also exist in established law firms and legal departments — they are quixotic lawyers and other professionals convinced there has to be a better way. In turn, they forge ahead without an empirically grounded theory to guide their actions.  As a result, their courage and good intentions are too often wasted.

As editor of Legal Evolution, I’ll acknowledge my own desire to serve as a change agent. After 15 years of study, it is clear to me that traditional methods of legal problem-solving are underserving clients and broader society. See Post 001 (explaining problem of stagnant legal productivity); Post 006 (connecting the breakdown in judicial system with declining legal job market and declining legal enrollment). This systemic breakdown can only be shored up through innovations that improve legal productivity — i.e., combining lawyerly judgment with better people systems, process, data, and technology. Higher productivity will enable more legal output to be afforded by more people and businesses. I realize this entails a value judgment on my part — I generally favor the innovators. But it is also a judgment informed by a lot of data and field research.  I am also motivated by the longterm welfare of my students at Indiana Law. I need to be part of a system that works for them and their clients.

My change agent role at Legal Evolution has a very simple formula. After explaining the basics of diffusion theory — through these foundational posts — I’ll present finely drawn examples of innovations that appear to be working in the field. In each case, I’ll provide as much context as possible, as the goal is to enable the success of legal innovators and early adopters.

Post 008 will be the longest foundational post by a wide margin. But this is the heart of diffusion theory and how it can be used as a tool of applied research.

Related posts:

What’s next? See Online Dispute Resolution Leader Modria Acquired by Tyler Technologies (009)