One of the biggest stories over the summer of 2017 was an open letter from 25 general counsel announcing that they are working together to test industry assumptions about the legal market. Although the composition of this group is very impressive, it is also not random. Each company is a member of AdvanceLaw, a network of buyers and suppliers of legal work that seek to drive value by sharing quality metrics and creating data-driven best practices.

Fortunately, Legal Evolution readers are about to get the benefit of some of AdvanceLaw’s insights. Over the next three days, AdvanceLaw Managing Director Dan Currell will post a three-part series on law firm convergence and preferred provider networks — basically, theory versus practice (029), how to build a panel that can deliver value (030), and the necessity of active client management (031).

Over the last 10 to 15 years, many large corporate clients have attempted to use convergence to rein in their legal costs, typically through a process that reduces the number of outside law firms, often from two or three hundred to twenty or fewer”preferred provider” law firms. Convergence is controversial because, among other things, it disrupts longstanding (and often comfortable) relationships between in-house lawyers and established law firms. Also, because the process is run by risk-averse corporate counsel who are winnowing firms for the first time, the results tend to favor the “safe” choice.

Notwithstanding these problems, we are going to see more — and better run — convergence in the future, as clients have a strong incentive to fix the underlying design and execution issues. The state-of-the-art is definitely going to improve.

Dan Currell is uniquely qualified to write on this topic.  Prior to joining AdvanceLaw, Currell spent more than a decade running the General Counsel Roundtable for the Corporate Executive Board (CEB). Between his time at CEB and AdvanceLaw, Dan has spent more time listening to challenges of senior in-house lawyers than virtually anyone in the legal industry.  Further, Dan and his colleagues at AdvanceLaw are now in a position to help shape the future.

I hope you enjoy these posts. WDH.

What’s next?  See Part I on Convergence: Why Theory Falls Short of Practice (029)

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.

Results

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.

Do academics and practitioners believe they have much to learn from each other?  If we look for evidence of meaningful exchange — shared conferences, the prevalence of journals that appeal to both groups, or just the quantity and quality of listening that occurs when both are in the same room — the answer appears to be “not much.”  Why is that?

Part of the reason likely turns on status.  The academy and practice have different reward systems, with little reserved for plowing the middle ground. Yet, what happens when two groups of smart people working on the same problem set effectively tune each other out, not necessarily out of disrespect, but just so they can finish what they perceive as their real work?

This post (026) offers some insight into this question. Post 026 also completes a three-part series on “Crossing the Chasm” and the “Hype Cycle” (two well-known practitioner frameworks) and is the final post in Legal Evolution’s foundational series on diffusion theory (something likely perceived as academic).


Posts 024-026 are the final installment of Legal Evolution’s foundational series on diffusion theory. Readers seeking to influence innovation within the legal industry will be more successful if they obtain and apply this background knowledge. Care has been taken to make this information non-technical and accessible.

In Part I (024), I wrote that it is important to understand Crossing the Chasm “from the perspective of Moore and his audience — i.e., as practical business advice being dispensed to entrepreneurs.” Now it’s useful to explore the full origin of these ideas.

Origins of Crossing the Chasm

During the 1980s, Geoffrey Moore was a partner at Regis McKenna, Inc., a Silicon Valley marketing firm. In the 15 years prior to Moore’s arrival, the firm’s legendary founder, Regis McKenna, had provided counsel to an extraordinary roster of technology start-ups that went on to become industry giants (e.g., Apple, Compaq, Intel, Lotus, Microsoft, National Semiconductor, Silicon Graphics, and 3COM).

According to the account given by Moore in Crossing the Chasm, the dominant business framework relied upon by the Silicon Valley start-up crowd was the Technology Adoption Life Cycle (see top graphic above).  Although there’s no reason to doubt Moore when it comes to Silicon Valley terminology, the Technology Adoption Life Cycle is, in fact, the Rogers Diffusion Curve (see second graphic above).

Although Everett Rogers is not cited anywhere in the first edition of Crossing the Chasm (or in the third edition published in 2013), Moore apparently had some vague knowledge of the model’s origins.  In the first chapter, Moore writes, “People are usually amused to learn that the original research that gave rise to this model was done on the adoption of new strains of seed potatoes among American farmers.”  (The underlying research involved mostly corn farmers, see Rogers’ 1958 article.) Moore continues, “Despite these agrarian roots … the model has thoroughly transplanted itself into the soil of Silicon Valley” (p. 11).

Ironically, the core thesis of Moore’s book is that the Technology Adoption Life Cycle model (aka the Rogers Diffusion Curve) contains a serious flaw.  Moore writes, “The basic flaw in the [Technology Adoption Life Cycle] model … is that it implies a smooth and continuous progression across segments over the life of a product, where experience teaches us the opposite” (p. 56).  Hence, Moore’s insertion of the chasm to create the “The Revised Technology Adoption Life Cycle” model.  See figure below.

In making this change, Moore was not the slightest bit burdened by the decades of empirical research that backed up the original model. We know this to be true from the acknowledgements at the beginning of the first edition of Crossing the Chasm.  Moore, who has a PhD in English Literature, writes:

Prior to the world of high-tech, I was in English professor. One of the things I learned during this more scholarly period of my life was the importance of evidence and the necessity to document its sources. It chagrins me to have to say, therefore, that there are no documents or summary of evidence anywhere in the book that follows. Although I routinely cite numerous examples, I have no studies to back them up, no corroborating witnesses, nothing. [p. xv]

If Moore has no awareness of the original source material, how was Rogers’ work transmitted to Silicon Valley? In fact, the most likely route is a textbook example of Rogers own theory in action.

In 1975, Everett Rogers joined the faculty of Stanford University, where he stayed for approximately a decade. During this time, Roger became interested in how the distinctive high-tech culture shaped the region’s business and academic norms.  Thus, Silicon Valley got incorporated into Rogers’ research. See, e.g., Rogers & Larsen, Silicon Valley Fever: The Growth of the High-Technology Culture (1984); Rogers, The High Technology of Silicon Valley (1985).

What seems likely is that the basics of diffusion theory, including the diffusion curve, were shared with some of Rogers’ research subjects and other professional acquaintances.  In turn, some — likely the innovators and early adopters —  applied Rogers’ ideas to the problems of high-tech marketing.  Because the diffusion curve proved to be quite useful, it was shared throughout Silicon Valley’s “social system” as the Technology Adoption Life Cycle, a title that fit its purpose.

Several years later, Moore, reflecting upon his experience and desiring to communicate a strategy that (a) his clients could understand, and (b) would cause them to avoid financial ruin, came upon the chasm as a better description of his clients’ core dilemma.  Ironically, this adaption of Rogers’ own ideas is what diffusion researchers call “re-invention.”  See Diffusion of Innovations 180 (5th ed. 2013) (defining re-invention as “the degree to which an innovation is changed or modified by a user in the process of its adoption or implementation”).

Eventually Moore’s re-invention came to Roger’s attention. In the fifth edition of Diffusion of Innovations, Rogers writes:

The five adopter categories … are ideal types, conceptions based on observations of reality that are designed to make comparisons possible. … Pronounced breaks in the innovativeness continuum do not occur between each of the five categories, although some scholars claim that a discontinuity exists between the innovators and early adopters versus the early majority, late majority, and laggards (Moore, 1991). Past research shows no support for this claim of a “chasm” between certain adopter categories. On the contrary, innovativeness, if measured properly, is a continuous variable and there are no sharp breaks or discontinuities between adjacent adopter categories[.] (p. 282).

As an academic, I understand that the chasm is not supported by data.  Yet, as someone who spent several years in a data analytics start-up company, I know there is a second question worth asking–is there benefit in having the team believe there is a chasm so, in an effort to avoid it, we adopt a laser-like focus on endusers very different than us?  The answer, of course, is yes. See Parts I-II (024025).

Theory and Practice

As I describe the origins of the Technology Adoption Life Cycle and the chasm framework, I hope it is obvious that I am not passing judgment on Geoffrey Moore or his Silicon Valley peers. In fact, the opposite is true.

Crossing the Chasm has sold 300,000+ copies because it addresses an important problem — generating sufficient sales before start-up funds are exhausted — in a lucid, non-technical way that is loaded with industry context.  It is noteworthy that solving important problems in a simple, culturally compatible way is the precise advice that flows from Roger’s empirical work. See Post 008 (listing high relative advantage, low complexity, and cultural compatibility as key factors in rate of adoption). In fact, the guidance provided by Crossing the Chasm is remarkably consistent with Diffusion of Innovations.  This is a testament to Moore’s powers of observation and his effectiveness as a business counselor.

Yet, does Moore’s example prove that practitioners have little to learn from academics? Or, stated another way, that the most valuable lessons have to be learned in the trenches and communicated as business lore? I am skeptical of this claim, particularly as it applies to lawyers.  If the slow pace of innovation is now threatening the viability of our organizations and the legal profession as a whole, we don’t have time to sort out whose “more practical” ideas to follow or, for that matter, whether any of them really work.  Instead, we need to seek out valid, reliable data.

The attenuated connection between Rogers (a university academic specializing in applied research) and Moore (a marketing practitioner) illustrates a tension experienced by those of us “in the field” doing either applied research or working as change agents.  Applied research is generally not esteemed by university colleagues, primarily because it’s viewed as problem-solving (what practitioners do).  University professors are supposed to create knowledge.  See Post 001.  Yet, among practitioners, the work of applied researchers is often perceived as too academic and a distraction from keeping a paying client happy. As a result, the middle ground tends to be pretty barren.

It’s a long journey from pure university research to innovations that can be packaged and sold to demanding private sector clients at a profit. That journey is made longer, however, because people in different camps are reluctant to invest the time to listen to one another, as it takes effort to overcome communication and cultural gaps. See Post 020 (discussing challenges of change agents).  The great psychologist Amos Tversky once quipped, “The secret to doing good research is always to be a little underemployed. You waste years by not being able to waste hours.” Michael Lewis, The Undoing Project (2016) (quoting Tversky).

Innovation is advanced when disparate social systems–like Rogers’ and Moore’s respective professional networks–remain connected with one another. Although the information exchanges will tend to be more cognitively taxing than exchanges with peers, the resulting insights justify the effort. See Post 020 (noting that innovation travels through “weak ties” on the social system’s periphery with innovators and early adapters serving as connectors). In the case of Rogers and Moore, the contact was incidental rather than planned. Nonetheless, the power of the underlying ideas was sufficiently great to leave an indelible mark on the high-tech industry.

Nothing left to chance

As this is the last post in Legal Evolution’s foundational series, I’ll reinforce what I hope is an obvious point–in the year 2017, none of this needs to be left to chance. There is a well-developed science of innovation diffusion. As we struggle with the many problems created by lagging legal productivity, see, e.g., Post 006 (discussing  how lagging legal productivity is affecting court systems and the demand for law grads), we can use diffusion theory — and Geoffrey Moore’s brilliant metaphorical conceits — to accelerate the adoption of innovation. Further, we can do it at a lower cost and with significantly less risk.

The price of admission is investing the time to learn a seeming academic theory. Many of your colleagues will think this is a dumb and impractical use of their time, albeit they don’t see the world through the eyes of an innovator or early adopter. As a result, these difficult problems / opportunities fall to people like you.

Back to the Hype Cycle

I’d like to end the foundational series by looking at emerging technology not from the perspective of an entrepreneur trying to turn a technology into a successful business, but as a buyer evaluating a confusing landscape of emerging technology and trying to sort out what is strategic (potentially affects my company’s survival) versus operational (potentially affects my bonus). This is what IBM used to call the FUD factor — the fear, uncertainty, and doubt that surround high-stakes decisions on relatively new and unproven technology.

Managers and executives struggling with the FUD factor have long looked to Gartner’s annual Hype Cycle of emerging technologies.  See Part I (024).  As shown in the figure to the right, the Hype cycle is divided into five stages, which Gartner describe as follows:

  1. Innovation Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.
  2. Peak of Inflated Expectations: Early publicity produces a number of success stories — often accompanied by scores of failures. Some companies take action; many do not.
  3. Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.
  4. Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.
  5. Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off.

What makes the Hype Cycle so tricky for law firms is that some of the technology coming online is not operational IT that can be safely put off until stage 5. Rather, it’s “discontinuous innovation” that has the potential to fundamentally change how legal problems are solved–hence the growth in the number of legal start-ups and NewLaw companies who see the opportunity. This suggests that there are real consequences to arriving late to the party.  These dynamics move law firms closer to their clients in terms of needing to continuously innovate. See, e.g., Fragomen to Launch Unique Tech Development Center in Pittsburgh, Corp. Counsel, July 3, 2017 (quoting legal industry expert, “Every company is going to become a tech company in some capacity. That ultimately is going to be true of professional service firms and law firms as well.”). This is a sea change that is steadily gathering force.


This is the end of Legal Evolution’s foundational series on diffusion theory.  I hope you have found it a valuable use of your time.  Going forward, Legal Evolution’s commentary will be much more focused on examples.  To the extent we need theory, we’ll have these foundational posts to refer back to.

Bill Henderson, Editor, Sept 2017

What’s next?  See A Successful Legal Change Management Story (027)

In Part I (024) of this series, I introduced Geoffrey Moore’s Crossing the Chasm framework.  In Part II (025), the goal is to apply it to a contemporary example of a high-tech company selling to legal departments. Part II then finishes the chasm framework and discusses some of the special challenges of applying it to the legal industry.


Posts 024-026 are the final installment of Legal Evolution’s foundational series on diffusion theory. Readers seeking to influence innovation within the legal industry will be more successful if they obtain and apply this background knowledge. Care has been taken to make this information non-technical and accessible.

The pre-chasm challenge

Imagine that we are part of a legaltech start-up that has developed a machine-learning AI capability with the potential to be a best-in-class solution for many time-consuming and important activities inside a large legal department.  We’ve made a few sales to some visionary legal innovators/early-adopter types, but the work has mostly been custom.  As yet, we don’t have a turn-key solution that is scaleable. Further, none of us has focused on the humdrum details of successful implementation.  In fact, we have no reference customers that would satisfy a pragmatist buyer. In short, we are a pre-chasm company.

To keep the team believing in the cause and to avoid running out of cash, we have three short-term objectives:

  1. Dramatically reduce our sales cycle
  2. Limit the amount of customization (ideally to zero)
  3. Obtain a base of satisfied pragmatist clients.

Following Moore’s chasm playbook from Part I (024), these three objectives are only possible by overwhelming a niche market segment with our commitment to their problem set, making our company “the only reasonable buying proposition” (p. 110).

Thus, the task on our plate is to correctly identify the right niche market and, through intense focus, successfully deliver a whole market solution. Otherwise, we are going to fall into the chasm.

Which market niche?

As noted in Part I (024), the only tools we have to cope with our “low data, high risk” environment are imagination and empathy.

We start by developing composite profiles of characters working inside our typical buyer and evaluate as objectively as possible how our product positively and negatively affect each of their lives. If the buyer is a legal department, the cast of characters would likely include the GC, the Director of Legal Ops, line in-house counsel, paralegals and admin staff, CEO and CFO, etc.

If we are like other founders and technical types, we’re likely very self-satisfied regarding the versatility of our technology, claiming it can solve many problems well. That may be true, but what product application is going to have the biggest impact across multiple internal stakeholders? If we can deliver a whole product solution in that specific niche, the resulting word-of-mouth buzz will create the enormous tailwind we need to get to the other side of the chasm.

We identify the starting point by building a matrix of stakeholders and applications and scoring each combination on a 1 to 5 scale. Using Moore’s scoring system,  1  = “not usable” and 5 = “must have.” See Figure 6 to right (numbering continued from Part I (024)).

What are some the applications for machine-learning AI?  Based on what I’ve seen at CLOC, ILTA, the ACC Legal Ops meetings and general networking within the industry, there are many.  Each of the applications in Figure 7 below reflect real use cases currently being pitched to large legal departments. In other words, the fate of numerous pre-chasm companies hangs in the balance. The assigned numbers are based on the composite sketches of how the application would impact the daily lives of specific personnel.  Following Moore’s methodology, we are always looking for “must haves.” Thus, 5’s are highlighted in yellow.

Note that the scores inevitably vary based on the stories we construct, albeit we want to construct the most balanced and plausible story possible.  Indeed, the entire point of the exercise is to prime the right side of our brains so we can see the world through the eyes of prospective customer stakeholders and end users and accurately identify who would most benefit from our product. Once identified, we’ll do everything in our power to adapt it into something they must have.

For example, regarding the first application, M&A due diligence, a corporate acquisition can be a heavy burden on in-house corporate counsel and paralegals. Thus, they might welcome the automation of a large volume of boring scut work.  Yet, how much internal juice do they have?  If, however, the company is a serial acquirer where the typical targets involves complex IP or environmental issues that warrant the extensive use of outside counsel, then the score assigned to the GC, Director of Legal Ops, or the CEO/CFO might reach a 5, particularly if the whole product solution reveals a large quality advantage (i.e., the machine makes fewer mistakes than people; the machine aids corporate integration). This has become Kira System’s value proposition.

Note how the search for “must haves” in the example above has the effect of narrowing the niche market — to serial acquirers with due diligence that is voluminous and legally complex.

The second AI application, outside counsel selection, can also be narrowed.  For example, if legal is a significant cost in a thin-margin business (e.g., insurance, retail, transportation), the GC and CEO/CFO scores might reach the must-have level. This might compensate for the fact that lawyers and staffers who work regularly work with outside counsel aren’t going to like the disruption of changing firms.

Likewise, for the fifth AI application, automated legal review, there are products entering the market that score the legal risks of a proposed contract against desired terms in the company’s playbook, essentially doing the reading and analyzing normally done by lawyers. In most legal departments, this will score a 3 or 4, as it adds no strategic value and the AI machine might make a mistake that will make decision makers look bad. Yet, in complex industries where in-house staff is already at 100% capacity, automated first-level legal review of low-risk, high-volume contracts may be a better long-term solution than more FTEs. Thus, this might become a “must have” for a GC or Director of Legal Ops who needs more lawyer bandwidth focused on high-value company legal work. I know this because Cisco’s legal department is experimenting with this technology in conjunction with Kim Technologies.

The above exercise can be uncomfortable for those of us in the technical crowd who helped build the generic product. We wonder, “why can’t they see what we see?”  Thus, reflexively, we tout data and the technical features of our product, often repeating ourselves. Yet, if we can endure the discomfort of getting inside the head of people very different than us, we’d see how our offering is often a mixed bag when second- and third-order effects are factored in. Cf. Post 020 (reporting “client orientation” and “client empathy” as key attributes of effective change agents).

To boil it down, if this exercise is faithfully performed, we dramatically increase our odds of locating a niche mainstream market where a specific application of our product is a must have. But all-too-often, the temptation is to double-down on sales. “We don’t have time for theories. We don’t have time for books.” Cf. Moore at 68 (“The consequences of being a sales-driven during the chasm are, to put it simply, fatal”).


The above exercise is based on Chapter 4 “Target the Point of Attack” of Geoffrey Moore’s Crossing the Chasm (1st ed. 1991). The original exercise, now more than 25 years old, used a pen-based laptop as the innovative new technology.


How to position (i.e., describe) our product

Buyers have different agendas than sellers, particularly in the mainstream market.  As Moore notes, the lead buyers in the mainstream are pragmatists who want to make a safe choice that will enable them to look good and hit their numbers. Pragmatists also have other things on their plate besides making a purchasing decision.  Thus, to save time and avoid mistakes, the’re going to categorize our product based upon their current frame of reference.

According to Moore, this will be done by placing us within a competitive bracket based upon other vendors and products.  Such categorization takes mental work.  If we leave all of this work to the pragmatist, the comparisons will be too simplistic and unfavorable to us.  Thus, as much as possible, we’ll pre-package a comparison to aid our prospective customers.

Moore calls this “positioning” and offers the following plug-and-play formula to make sure we get it right. Moore instructs the reader to “just fill in the blanks”:

  • For (target customer)
  • Who (statement of need or opportunity)
  • The (product name) is a (product category)
  • That (statement of key benefit–that is, compelling reason to buy)
  • Unlike (primary competitive alternative)
  • Our product (statement of primary differentiation). [pp. 160-61]

How useful is this? Moore offers the following example of Microsoft’s positioning of Windows 3.0 in the early 1990s:

For IBM PC users who want the advantages of a Macintosh-style graphical user interface, Microsoft Windows 3.0 is an industry-standard operating environment that provides the ease of use and consistency of a Mac on a PC-compatible platform. Unlike other attempts to implement this type of interface, Windows 3.0 is now or will very shortly be supported by every major PC application software package. (p. 162, emphasis added)

In a profoundly concise format, this positioning statement give the pragmatist everything he or she needs to make a purchasing decision.

By proper positioning, we boil everything down so we can pass what Moore calls “the elevator test.” Specifically, if our product can’t be easily described in the time it takes to travel from floor to floor in an elevator, then our product will never get the enormous tailwind of a word-of-mouth campaign within the mainstream market. Cf Moore at 159 (“Since we have already established that word of mouth is fundamental to success in high-tech marketing, you must lose [if you can’t pass the elevator test]”). Until we get this distillation right, we’re stuck with an impossibly long sales cycle and the likelihood that our competitors will do our positioning for us.

What our customers say about us

When crossing the chasm, there is (a) the positioning statement we communicate to our target customers before the sale, and (b) what our customers say about us after they’ve experienced our product.  The subtitle of Moore’s book may lull lawyers into believing that Moore is only talking about (a) — how to position the product. Yet, Moore seems no less worried about (b). Moore writes:

In the simplified [whole product] model there are only two categories: (1) what we ship and (2) whatever else the customers need in order to achieve the compelling reason to buy. The latter is the marketing promise made to win the sale. The contract does not require the company to deliver on this promise – but the customer relationship does. Failure to meet this promise in any business-to-business market has extremely serious consequences. As the bulk of the purchases in this marketplace are highly reference-oriented, such failure can only create negative word-of-mouth, causing sales productivity to drop dramatically. (p. 115).

A careful reading of Moore reveals that the “big fish, small pond” strategy is as much about conserving bandwidth and resources by not overpromising as it is finding a market segment with a must-have customer need.

Ironically, as difficult as it is to enter the mainstream market — have a great generic product, pick the right market niche, position the product so it’s easy to buy, and then deliver on the whole product solution — the rules seem to operate in reverse once a company gets to the other side of the chasm.  Moore notes, “the more you spend time with mainstream customers, the more you see how relentlessly they pursue this conspiracy to sustain market leaders” (p. 75). Thus, crossing the chasm is a one-time event that permanently alters the financial fortunes of a company — a game that is very much worth the candle.

Selling and law firms as distribution channels

Returning to our AI-enabled legaltech start-up, what’s our sales plan?

Most of the context of Crossing the Cross is based on enterprise-level technology solutions sold to large corporate clients — that is, the same posture as most legaltech start-ups. Moore lists out several options for making sales along a spectrum of “demand creators” (a direct sales force using consultative sales) to “demand fulfillers” (retail outlets).  The more novel and innovative our product, the more we’ll need a direct salesforce to prime the pump.

The problem is that direct sales is expensive. Moore notes, “To support a single consultative salesperson requires a revenue stream of anywhere from $500,000 to several million dollars [in 1991 dollars], depending upon presales and postsales support provided” (p. 173).  As good as a direct sales team can be at educating prospective customers and creating demand, Moore argues that a direct sales force is probably not viable unless the minimum sales is at least $50,000 — again, in 1991 dollars.

As a more cost-effective alternative, Moore suggests a “selling partnership” with another company that already has a business relationship with the target clientele.  Here, law firms come to mind, either as a bundled offering with the firm’s consultative legal services or as a preferred vendor when the firm cannot get the work without adding an external capability that the client is demanding. Under this approach, law firms could become an invaluable distribution channel.  Although Moore acknowledges that this approach may dramatically cut into pricing power — “he who owns the customer owns the profit margin and the future of the product” — he nonetheless endorses it as a way to reduce risk and avoid the grief of managing a salesforce not fit for purpose (p. 175).

For many a legaltech and NewLaw start-up, this approach sounds good in theory but has seldom worked well in practice.  Perhaps the reason can be found in the must-have value proposition that mainstream pragmatist buyers find most irresistible. According to Moore, this is a product offering that “radically improves productivity on an already well-understood critical success factor” (p. 103).  No disruption; just a quantum improvement in what we already known. Unfortunately, so often the business opportunity of legaltech and NewLaw is reducing the inefficiencies and quality constraints of the traditional practice of law billed by the hour.

I know several start-up founders who wish they could get back the thousands of hours invested in trying to strike a deal with law firms. Whether it’s short term self-interest or the consensus decision making of law firm partnerships, see Post 008, law firms have yet to see the benefits of being a distribution channel for new products or services that could significantly help their clients.  Unfortunately, this is a major bottleneck to innovation diffusion within the legal industry.

What’s next? See “Crossing the Chasm” and the “Hype Cycle”, Part III (026)

The two figures above reflect frameworks that are widely used within the technology industry to grapple with the treacherous nature of high-tech product development.

Figure 1 is the 2017 Hype Cycle, which is published by Gartner, a large international research company that helps CIOs and other IT professionals understand and evaluate emerging technologies. The Hype Cycle has been published every year since 1995, always with the same shape and the same five stages, beginning with the “innovation trigger” and ending with the “plateau of productivity.” The only changes are the technologies and their relative placement on the Hype Cycle.

At the “peak of inflated expectations” for 2017, we see several types of artificial intelligence that are now widely discussed in the legal press, often with headlines that foreshadow the replacement of lawyers by machines. If Gartner is right, such a threat is premature, overblown, or both.

The second figure (Figure 2) looks like a Rogers Diffusion Curve, see Post 004, but with a large gap between the early adopter and early majority segments. The gap is the “chasm” discussed in Geoffrey Moore’s Silicon Valley classic, Crossing the Chasm (1st ed. 1991).  Figure 2 is titled “The Revised Technology Adoption Life Cycle” because Moore added the gap to reflect the crucial transition from early adopters to the early majority. (Prior to Moore’s book, the high-tech community relied heavily upon the unrevised model — i.e., a model identical to the Rogers Diffusion Curve.)

Moore’s core thesis is that most high-tech ventures fall into the chasm and die because they fail to grasp the many moving parts that must be fashioned and coordinated to move from early market to the mainstream market — i.e., to cross the chasm.  Moore’s book provides very detailed prescriptive advice. Once on the other side of the chasm, the company has a much higher probability of ascending the upward-bound S-curve and thus producing a large financial return for company founders and investors.

Moore’s book is essentially a practitioner’s manual, far removed from academic theory or empirical validation.  Yet, it is now in its third edition and has sold more than 300,000 copies.


Posts 024-026 are the final installment of Legal Evolution’s foundational series on diffusion theory. Readers seeking to influence innovation within the legal industry will be more successful if they obtain and apply this background knowledge. Care has been taken to make this information non-technical and accessible.

The Roadmap

This is Part I of a three-part final installment in Legal Evolution’s foundational series on diffusion theory, albeit the topics covered — “Crossing the Chasm” and the “Hype Cycle” — are not part of diffusion theory, at least not directly.  Nonetheless, these two frameworks have long guided decision making in the tech industry; and tech is indisputably where the legal industry is headed. Therefore, those working in legal innovation need to understand how these concepts fit together.

  • Part I (024) summarizes the key features of Geoffrey Moore’s widely used and powerful chasm framework.  It is very important for readers to understand these ideas from the perspective of Moore and his audience — i.e., as practical business advice being dispensed to entrepreneurs. Surely practicing lawyers ought to be sympathetic to this approach.
  • Part II (025) applies Moore’s framework to a real-world example of high-tech companies selling to legal departments.  Part II then finishes the chasm framework and explains the special challenges of applying it to the legal industry.
  • Part III (026) reveals the unusual intellectual origins of Crossing the Chasm and how those origins illustrate several of the key concepts of diffusion theory in ways that are surprising and ironic. Part III also returns to the Gartner’s Hype Cycle as a lens for viewing technologies at the industry rather than the company level.

More so than any other foundational posts, Posts 024-026 speak to the specific challenges of legal start-ups and technology companies. Law firms are discussed, albeit primarily as part of a “distribution channel” that controls valuable client relationships.  For a variety of reasons, most law firms will be reluctant partners. It is unclear whether in the long run that will prove to be a wise strategy.

The Whole Product Solution

An entrepreneur “crosses the chasm” by turning a fledging start-up into a high-growth business built around a new technology. Much to the chagrin of technology inventors, the technology itself is not sufficient to reach this goal. Instead the technology must to become part of a “whole product solution” that anticipates and overcomes a wide range of potential obstacles to adoption.

Building a whole product is less a technical feat than an extended exercise in commercial empathy. Several market competitors may have similar technology.  But according to Moore, the first to build and communicate a whole product solution will be the first to cross the chasm and capture the mainstream market.  See p. 114 (“[W]inning the whole product battle means winning the war”).

Below are two graphics that reflect the whole product solution.

Figure 3 is what Moore refers to as the Whole Product Model.  At the center is the Generic Product, which is “what is shipped in the box and what is covered by the purchasing contract” (p. 111).  However, end users make buying decisions based on the “expected product”, which Moore describes as “the minimum configuration of products and services necessary to have any chance of achieving the buying objective” (Id).  For example, if the generic product is an IBM PC equipped with a Microsoft DOS operating system — a product on the market when Moore was writing the first edition of Crossing the Chasm — Figure 4 is a reasonable depiction of the ancillary products and services that make up the expected product.

A company has crossed the chasm when it has become a market leader in a niche portion of the mainstream market.  Although the foothold is not large, it is the ideal place to develop the “augmented” and “potential” products (the third and fourth rings in Figure 3).  If the expected product was the IBM PC shown in Figure 4, the augmented product might include a customer hotline, advanced training, accessible service centers, and expanded software offerings. Likewise, the potential product might include what we see today — a platform for streaming entertainment and playing complex, interactive games.

The Early Market versus the Mainstream Market

According to Moore, its not the underlying technology that separates winners and losers.  Rather, it is a failure of both entrepreneurs and investors to realize that the market is divided into two parts — an early market, where a promising generic product is sufficient; and a mainstream market, where customers will not buy without overwhelming evidence that they are, in fact, buying the whole product. See Figure 5 below.

Success in the early market comes in the form major sales to visionary clients who see the technology’s breakthrough potential. This market evidence is often sufficient for venture capitalists to provide another round of financing in anticipation of rapid growth. This is because VCs — more so in the days before Moore’s book — bought into the smooth continuous shape of the Technology Adoption Life Cycle (Figure 2 above, but without the chasm).  All-too-often, however, the S-curve adoption never materialized.

The recurring mistake, according to Moore, is that customer expectations are dramatically different in the early versus the mainstream market.  In the early market, “visionary” early adopter customers are drawn to the potential breakthrough implications and have the imaginative capacity to envision the whole product as something real and inevitable.  Yet, visionary buyers are relatively rare. But Moore argues that the problem runs deeper than that — these “visionary” early adopters are unsuitable as reference clients.

Moore refers to the early majority (the leading edge of the mainstream) as “pragmatists” because their buying requirements are so practical and stringent.  “When pragmatists buy, they care about the company they are buying from, the quality of the product they are buying, the infrastructure for supporting products and services and systems interfaces, and the reliability of the service are going to get” (p. 43).  When the company’s sales team points to visionaries, the pragmatists are unimpressed, distrusting the visionary’s risk-taking, flaunting of convention, excessive attention given to early stage technology, and the “overall disruptiveness” they impose on others. See p. 58-59.

If a tech start-up is not aware of the chasm, it runs the risk of being lulled into complacency by a few large sales to visionary clients.  But more problematic is the resulting “catch-22.” As Moore notes, “The only suitable reference for an early majority customer … is another member of the early majority” (p. 21).  This is the problem that Crossing the Chasm is trying to solve.

How does a company cross the chasm?

A company transitions from the early market to the mainstream market — i.e., crosses the chasm — by focusing on a niche market where, by sheer dint of preparation and focus, it becomes “the only reasonable buying proposition” (p. 110).  The narrow focus is necessary to conserve limited resources and increase the odds of delivering a whole product solution.  This “big fish, small pond” strategy enables the start-up to “secure a beachhead in a mainstream market — that is, to create a pragmatist customer base that is referenceable” (p. 68).

Moore provides tremendous guidance on how to find the right niche market.  The difficulty is that a technology start-up only has a few significant clients in the early market, none of whom are representative of the mainstream.  Because better information is time and cost prohibitive, pre-chasm companies operate in a “low data, high risk” environment.  Thus, the only option is to engage the management team’s imaginative faculties to anticipate the needs, preferences, and objections of the various stakeholders employed by your target clientele.  This is the extended exercise in commercial empathy mentioned earlier — and its extremely difficult, particularly for left-brained technical types (lawyers as well as engineers).

In Part II (025), I will illustrate how this is done by applying Moore’s methodology to a contemporary legal industry example.

What’s next?  See “Crossing the Chasm” and the “Hype Cycle”, Part II (025)

Legal Evolution PBC is pleased to announce our first public event.  On Tuesday, October 10, author and researcher Randy Kiser of DecisionSet® will give a lecture and Q&A session based on his recently published book, Soft Skills for the Effective Lawyer (Cambridge University Press 2017).

This event is graciously hosted by Chapman and Cutler LLP, 111 W. Monroe Street, Chicago, IL. The event will run from 6 to 7:15 pm in the building’s 8th floor auditorium (doors open at 5:30), followed by a reception at the firm. There is no charge for this event. However, advanced registration is required. You can register online here.



Why attend this event?

If you’re a lawyer or law student, the opening paragraphs from Kiser’s book, excerpted below, provide a powerful answer:

Anthony Sonnett, Ford Motor Company’s trial attorney, had nearly completed his cross-examination of Barry Wilson. After listening to Mr. Wilson describe how he showers, catheterizes, and frequently repositions his paralyzed wife, following an accident in which her Ford Explorer rolled over and fractured her spine, Mr. Sonnett posed his final question to Mr. Wilson: “The silver lining, to the extent that there could be one, it has brought you and Benetta [Mrs. Wilson] and the family closer together?” Mr. Wilson responded: “I think where we were together before, we are together after. I don’t think it’s done more for us. I think it’s — I don’t think it’s a benefit or a plus in any way. I am sorry, I don’t think I can see it that way.”

The jury returned a verdict against Ford for $4.6 million in economic losses, $105 million in noneconomic losses and $246 million in punitive damages. Reviewing the verdict on appeal, the California Court of Appeal honed in on Mr. Sonnett’s “silver lining question” and noted, “This question implied that the family should find a silver lining in what befell Mrs. Wilson. It may very well have been viewed as callous by the jury and might explain, in some manner, the actions of the jury in rendering a verdict so out of line with the amounts requested by the Wilsons’ own counsel.” The question, the court stated, “might well have inflamed the passions of the jury.” Concluding that the award against Ford was excessive, the court reduced the noneconomic damages award to $18 million and lowered the punitive damages award to $55 million.

The silver lining question, according to Adam Liptak, the Supreme Court correspondent of The New York Times, “was a legal classic that has echoed through the appeal of the case.” “The Wilsons’ case,” he opines, “suggests that a lot can turn on little things, including flat-footed lawyers and stupid questions.”

Attorneys can argue endlessly about the appropriateness and impact of the silver lining question. That argument obscures the fact that judgment calls like the silver lining question permeate a lawyer’s daily existence and are not resolvable by statutes, rules, regulations, appellate court opinions or practice guides. These judgment calls are invariably subjective and inherently dangerous; they tend to be more personal than rule-based, more intuitive than empirical. They require a broader set of skills than technical legal knowledge and analysis and necessarily implicate “soft skills” like sensitivity, discernment, empathy, perspective taking and foresight.

In making these judgment calls, whether cross-examining a witness or negotiating contract terms, attorneys rely heavily on their personal experiences and their sense for people. They ask themselves imponderable questions like: How am I coming across to everyone else in this room? Do they trust me? What do they expect of me? Have I realistically assessed this challenge? Am I adequately prepared? Is my sense of what is happening here affected by how I feel about something else today? What will I do if I fail here? This book is about these types of questions — how we pick the questions to ask ourselves about ourselves, how accurately we answer them and how we can improve the soft skills that are ignored in educational testing but turn out to be dispositive in life.

Over their fall break, current Indiana Law 2L and 3L students are eligible to enroll in Randy Kiser’s 1-credit course based on Softs Skills for the Effective Lawyer. We are very fortunate to have Chapman and Cutler LLP also host the class in their Chicago office. I am proud to have played a role in organizing the Kiser Soft Skills course and the Kiser public event.

More about Randy Kiser

Randy Kiser was a very successful practicing lawyer for 20 years before pursuing graduate studies in psychology and developing a second career as a researcher and consultant on lawyer decision making.  Although Kiser has no permanent academic affiliation, his work on the legal profession is among the most rigorous, engaging, and important currently being published, with wide-ranging implications for society, practicing lawyers, and legal education.

Kiser’s 2008 article “Let’s Not Make a Deal” in JELS (the leading peer-reviewed empirical journal on legal topics) documents the pervasiveness of errors among lawyers in the decision to reject a final settlement offer and proceed to trial. Although plaintiffs’ lawyers were twice as likely to make a mistake (determined by getting a judgment less than the final settlement offer), defense counsels’ errors were vastly more expensive for clients — the average costing $43,000 on the plaintiff side versus $1.1 million for the defense.  See 2008 NY Times article on Kiser’s research.

The message that comes through Kiser’s research is that all of these mistakes are fixable by becoming aware of a wide range of inherent human biases and taking appropriate corrective actions. Nonetheless we persist in our decision making ignorance, with clients suffering the economic consequences and lawyers wondering why their careers are stalling.  For the last several years, I have integrated portions of Randy’s seminal book, How Leading Lawyers Think (Springer 2011), into both my Deliberative Leadership and Legal Professions course at Indiana Law. The book does extensive qualitative research on plaintiff and defense counsel who consistently make wise evaluative judgments and thus obtain consistently better client results when going to trial.

You can learn more about Kiser at his DecisionSet® website.

What’s next? See “Crossing the Chasm” and the “Hype Cycle” (024)

The graphic above is a breakdown of the 76 sessions at the 2017 CLOC Institute. Since there were seven concurrent tracks, it was impossible to attend more than a small fraction of the total programs.  Nonetheless, if one wants to understand the mindset and priorities of corporate legal departments, there is hardly a better window than a careful review of the various problems that the CLOC sessions are trying to solve.

The sessions are grouped into eleven subject matter categories (HT to research assistant Seth Saler for his help).  The numbers inside each unit reflect specific sessions (session titles can be accessed here). Below is a brief discussion of the content of the top categories.

Inside the Client’s Head

The biggest category is Legal Department Design, which suggests that the top priority of legal ops professionals is designing, building, and upgrading the legal department of the future.  It is both high-level and strategic in orientation.  Topics in this category include legal department budgeting, KPIs, using metrics to calculate ROI, data analytics, workflow design, and building and deploying internal dashboards. A common theme in all of this is doing more with less.

Continuing this theme, the second biggest category is Outside Counsel Management.  This includes convergence, AFAs, e-billing systems, legal project management, applied technology, outside counsel guidelines, rate evaluations and benchmarking [internal methodologies and tools, not sharing of industry data], litigation budgeting, outside counsel selection, client/law firm collaboration, using metrics to drive alignment, and law firm scorecards and evaluation. At most law firms, strategic planning takes the form of annual revenue targets by practice group. Judging from the CLOC sessions, it’s going to take some innovative thinking to get greater wallet share from these clients.

Professional Development and Tools & Technology tie for the third biggest category, with nine sessions each. Professional Development focuses on personality assessments (overview plus an applied workshop), improving teamwork and collaboration, workplace generational shifts, and networking. Tools & Technology includes technology platform selection, workflow automation, data security, technology roadmaps, how to create dashboards, and process design.

Note that Artificial Intelligence in its various forms appears in several session titles, but always as part of specific use cases. At least at CLOC, AI is no longer an introductory, freestanding topic.

The Professionalization Project

One relatively large category that I was not expecting to create was Legal Ops Professionalization. Instead, it emerged from the data.  The six sessions in this group focus on legal ops core competencies [click on CLOC figure to the right to enlarge], creating a legal ops function in your company, review of the legal operations maturity model {detailed multi-level model created by CLOC members], and salary negotiations for legal ops professionals.  Session title 62 says it all: “Control Your Destiny: How to Assess and Develop Your Legal Ops Skills.”

History is replete with examples of workers coming together to “professionalize” their craft through the creation of a common language and set of standards. This same process is now fully in motion in the emerging field of legal operations.  Although still a few years away, it will eventually culminate in a system of credentials and certifications to help the market identify and allocate legal operations talent. Such a system helps organizations hire the right person for a very important, high-stakes role.  As a second order effect, it also helps legal ops professionals increase their economic power and influence.

It is my view that legal ops is not, strictly speaking, a career path within legal departments.  Instead, legal operations is field that focuses on systems and controls for managing legal problems and complexity.  Under this broader definition, there are legal ops professionals inside progressive law firms, see Post 021 (categorizing law firms based on innovations in people, process, and technology), and legal managed service providers, see Post 010 (noting how managed service model requires “remarkably tight systems for project management and process improvement”). Although buyers and suppliers of legal inputs will always have slightly different perspectives, their underlying knowledge and skills are on a convergence path.

We are still very much in the early days of the legal operations movement.  This is a key part of solving the lagging legal productivity problem.  See What is Legal Evolution? (001) (discussing importance of solving lagging legal productivity); see also Six Types of Law Firm Clients (005) (discussing rise of CLOC).

What’s next? See Public Event: Soft Skills for the Effective Lawyer (023)

Earlier this week came some unexpected good news for the legal ecosystem.  Dan Linna of Michigan State Law unveiled the Legal Services Innovation Index, which provides some very interesting and compelling measures of innovation by: (1) country, (2) practice area, (3) type of innovation, (4) firm size / global segmentation, and (5) individual law firm.

The Legal Services Innovation Index is a project of MSU Law’s LegalRnD, which is a mission-based research center focused on innovation in legal services.  The Director of LegalRnD is Dan Linna, a legal education doer with a multi-faceted background.  Prior to joining MSU in 2014, Linna was an equity partner at Honigman (a Michigan-based AmLaw 200 firm); and before law school, he worked for several years as an IT manager and consultant.  Over the last few years, Linna has been instrumental in organizing legaltech meet-ups in both Michigan and Chicago.

What I admire most about Linna, however, is his ability to mentor young people so they have the confidence and focus to build great legal careers.  See, e.g., LegalRnD’s application of Lean principles to student employment outcomes. The Legal Services Innovation Index is justifiably going to get a lot of attention from the entire global legal services industry — and remarkably, it was substantially built by law students working under Dan’s direction.

Zero to One

In the project Overview, Linna goes to great pains to explain that the Legal Services Innovation Index is a “Phase I, Version 1.0” release that should be viewed as preliminary.  Linna writes:

I’m releasing Phase 1, Version 1.0 of this index to add to and improve legal-industry discussions about legal innovation and technology. My inner perfectionist–a voice empowered during my journey to equity partner in an Am Law 200 law firm–would prefer that I conduct far more research and complete Phase 2 and Phase 3 before releasing anything. But this type of perfectionist thinking is itself a barrier to legal-services innovation. Instead, I will follow the Lean Startup innovation process [example online here] … striving to continuously improve our legal industry discussions about innovation and technology.

Linna notes that the project was inspired by LSC President Jim Sandman’s speech at the 2016 CodeX FutureLaw Conference. Sandman argued that if law firms were ranked and assessed by their use of technology rather than just revenues and profits, we’d find ourselves in a virtuous competition that could potentially redound to the benefit of those who lack access to legal services. (Even if the connection between BigLaw tech and PeopleLaw access is attenuated at best in the year 2017, it’s nonetheless a better vision than pure financial metrics.) When Sandman repeated this call at the 2017 CodeX conference — thus revealing that nothing happened over the past year — Linna committed himself and his Center to the Index project.

Tactically, it’s wise for Linna to be cautious about what the Index data mean — he describes Phase I, Version 1.0 as a “minimum viable product” that will improve with user feedback. Regardless, for the rest of us, it is hard to overstate the importance of this first iteration. Basically, Linna and his students have moved the state of the art from zero to one.  On Monday, conversations about legal innovation took place in a data vacuum.  On Tuesday, we had a system of measurement and classification and corresponding innovation data on 263 of the world’s biggest law firms (that is how many unique firms are in the Am Law 200, Canadian Top 30, and the Global 100).

To their credit, Linna and his crew are trying to frame the conversation as “How can we make this better?”  Regardless, even in its current form, the Index is bound to be extremely influential. Seeing where your firm stands relative to other firms is going to change both conversation and behavior. This is the psychological phenomenon of reactivity, which can be profoundly powerful. See Espeland & Sauder, “Rankings and Reactivity: How Public Measures Recreate Social Worlds,” 113 Am J of Sociology 1 (2007) (discussing law school rankings as an example of reactivity with far-reaching social and institutional effects).

What is the Innovation Index?

The Index in its current form is really two systems of quantification: The Innovation Catalog and The Law Firm Index.

(1) Innovation Catalog

The Innovation Catalog captures legal-service delivery innovations that are currently being implemented by law firms in the AmLaw 200 (US-headquartered firms based on gross revenues), the Canadian Top 30 (based on attorney headcount), and the Global 100 (122 firms on two lists ranked by revenues and headcount).  Innovations are grouped into three categories — products, services, and consulting. The results are presented using Tableau, a popular data visualization tool.

Below is the graphic showing the number of innovation offerings based on the law firms’ home jurisdiction:

Why is the UK in the leader’s position?  Some would argue it is because the UK’s domestic market became saturated 20 years ahead of the US market, forcing deeper strategic thinking on how to adapt and grow.  Another factor might be the residue of lockstep compensation, which tends to create and incentivize a longer time horizon.  Note that these figures have not been “normalized” — i.e., adjusted for the size of the home market.  Because the UK market is much smaller, it makes the UK’s leader position all the more interesting.  Although elite U.S. law firms appear to be running away with the most lucrative financial services work, see Simon & Bruch, “The Strange Tale of How Elite US Firms Surpassed Their UK Counterparts,” Law.com (Aug. 2017) (three-part series), the chart above likely reflects the larger and more contested market for operational legal work.

Below is another Index chart that breaks out innovations by tool or discipline.  The range and diversity of innovations is striking — this is not a market where nothing is happening.

Where the rubber meets the road, however, is a table that breaks down all the data at the granular law firm and innovation level.  See Catalog by Law Firm (last tab). To date, only three firms have one or more innovations in each of the products, services, and consulting categories: Allen & Overy (UK), DLA Piper (US), and DWF LLP (UK).  US-based firms with several innovations include: Duane Morris (7), DLA Piper (7), Fenwick (6), Ogletree Deakins (6), Norton Rose (5), Littler Mendelson (5), Bryan Cave (4), Hogan Lovells (4), and Seyfarth Shaw (4).

One of the most interesting features of the firm-specific table is the inclusion of strategic partners.  These companies are necessary to solve difficult technical problems or resource gaps:

  • Intapp (with Ashhurst (UK))
  • Deloitte (with Allen & Overy) [related to contract management and compliance]
  • Elevate (with Corrs Chambers (AU))
  • HighQ (Corrs Chambers (AU), Norton Rose)
  • Kira (with Clifford Chance and DLA Piper)
  • Neota Logic (with Clifford Chance, Foley & Lardner, Hall & Wilcox (AU), Hive Legal (AU), Husch Blackwell, Littler Mendelson)
  • Thomson Reuters (with Ackerman, Clifford Chance, DWF LLP, Nixon Peabody) [mostly managed service research support]

(2) The Law Firm Index

The Law Firm Index is based on Google Advanced searches for indicators of innovation on law firm websites. The methodology section gives the precise search terms for each category. There’s likely a bias favoring large firms, as they have more lawyer biographies and practice group pages to tout the same innovations.  That said, there are many relatively small firms posting relatively large innovation numbers; and many large firms that are posting relatively small numbers. And for those of us fairly close to this space, there are few if any surprises. To me, they appear face valid.

Here is the breakdown of the average hits per firm website by area of innovation:

This is a very interesting breakdown because the largest category, Blockchain, bears on changes affecting the core business of corporate clients. In some respects, this innovation borders on changes in substantive law and how contracts get formed and enforced. Because this is closer to lawyers’ natural wheelhouse, perhaps it’s unsurprising that lawyers are ready and willing to innovate.  Yet, on service delivery innovations that are directed at reducing billable hours and overall cost, such as automation and process, we see a lot less activity.

The analysis based on country and firm grouping is also interesting and informative:

The big takeaway here is that size + geographic reach appears to be strongly correlated with at least the seedlings of innovation. Cf. Innovation in Organizations, Part III (017) (discussing complexity and size as correlates of organizational innovativeness and explaining why this is likely true in law). Although really large firms have significant challenges with management and communication overhead, there’s no substitute for a critical mass of resources to build new service offerings.

Finally, the Law Firm Index has two breakdowns by individual firm (the last two Tableau tabs) — one based on total Google Advanced website hits and a second based on percentiles.

The Index website repeatedly communicates that these firm-level charts are not a ranking. That said, the differentials among firms are massive, moving from less than 10 mentions of innovations to nearly 15,000.  This raises a very real question for partners — “is it worth trying to get my firm to innovate, or should I take my clients to a market leader firm?”  Cf. Henderson & Zorn, “The Most Prized Lateral of 2015 Wasn’t a Partner,” American Lawyer, Feb. 1, 2016 (discussing media attention given to movement of 4-person process improvement team from a UK Silver Circle firm to a Global 100 UK-Australian firm and predicting that this type of sophisticated capability will eventually attract lateral partners trying to hang onto clients). The failure to invest in innovation may prove to be extremely expensive for late majority and laggard law firms.

Conclusion

Kudos to Dan Linna and his team at MSU Law’s LegalRnD.  For the foreseeable future, your Legal Services Innovation Index is going to be the measuring stick for law firm innovation.  You have given lawyers, law students, and law faculty a useful, tractable, and relatively comprehensive window into the changing legal services market, at least in the large law firm segment.  Going forward, we can be spared the blanket generalization that lawyers and law firms can’t innovate. That by itself is a tremendous public service.  We all look forward to improvements in the months and years ahead.

What’s next? See Inside the Client’s Head: 2017 CLOC Institute Programming (022)

The chart above, drawn from Everett Rogers, Diffusion of Innovations Fig. 7-1 (5th ed. 2003), shows the adoption of hybrid seed corn by farmers in two Iowa communities. The dashed line on the bottom shows the number of adoptions by year.  The solid line on top shows adoption on a cumulative basis.  The first farmer in the sample adopted hybrid seed corn in 1927. 15 years later, in 1941, the last four farmers made the switch.

The dashed line is a real-world example of a Rogers Diffusion Curve. See Post 004 (discussing curve); Post 007 (discussing adopter types).  Likewise, the solid line is a real-world example of the S-shaped curve. The farmers switched to hybrid seed corn because it was more bountiful, disease resistant, and drought resistant than traditional methods. The chart above is useful because it shows the common diffusion pattern of (1) a prolonged period of slow adoption, even for a highly advantageous innovation; and (2) a short period of rapid adoption. Cf. Post 016 (showing histogram with long innovator tail).

In the case of the Iowa farmers, the prolonged period of slow adoption was not a random event. Few if any farmers would have adopted hybrid seed corn but for agronomists from Iowa universities. The agronomists were necessary to help the innovator and early adopter farmers understand and use this new technology.  When some of the more influential early adopter farmers met with success, they shared their experiences with other farmers.  As the benefits of the innovation were experienced by the early majority farmers, adoption spread like a social contagion through the two Iowa communities.

In this real-life example, the university agronomists were the change agents. And the influential early adopter farmers were the opinion leaders.  This post (020) explains the crucial role played by these two types of actors. It also emphasizes how these concepts apply to the current challenges facing the legal industry.


Post 020 is part of Legal Evolution’s foundational series on diffusion theory. Readers seeking to influence innovation within the legal industry will be more successful if they obtain and apply this background knowledge. Care has been taken to make this information non-technical and accessible.

Awareness-Knowledge

Before the farmers could adopt hybrid seed corn, they needed “awareness-knowledge”, which is knowledge that such a technology exists.  However, there can be a considerable lag between awareness-knowledge and actual adoption of a new innovation.  This dynamic is shown in the chart below, which is based on the same study of Iowa farmers. (This chart was first shown in Post 008 to help illustrate Rogers’ rate of adoption model).

For the typical farmer, roughly six years elapsed between hearing about hybrid seed corn and adopting it.  In addition to inexperience and uncertainty with hybrid seeds, the lag time was due to the sheer novelty of the innovation, which was rooted in laboratory science and at odds with longstanding views regarding how to grow the best corn. See Post 008 (discussing how complexity and cultural incompatibility can impede adoption of innovations).

Yet, in the early 20th century, agricultural production was a matter of national security, as World War I had driven home the importance of a secure and bountiful domestic food supply.  Farmers had also become a formidable legislative lobby. Thus by 1920, there were more than 3,000 agricultural extension workers funded by a mix of federal, state, and county agencies.

Remarkably, despite the benefit of a large and well-financed change establishment delivering an unalloyed benefit to farmers, the uptake was far from rapid.  The key sociological question was “why?” The parallel applied research question was “can the rate of adoption be accelerated?” The answer to the latter question was yes, thus creating foundational research that would eventually result in a general theory for how innovation diffuses.

Early versus Middle-Late Diffusion: The S-shaped Curve

Diffusion theory is part of an applied research tradition that seeks to enable change strategies that work in a controlled and predictable way.  The core insight is that the diffusion of an innovation is a process that occurs through a social system. See Post 004 (discussing Rogers Diffusion Curve). In most cases, the process begins with a need or problem and a desire by some members of the social system to find and implement a solution.

For the purposes of this post, we can divide the diffusion process into two stages: an early stage, characterized by a relatively long period of slow adoption (base of the S-shaped curve that starts with the long innovators tail); and a middle-late stage, characterized by rapid adoption over a relatively short period (the steep portion of the S-shaped curve followed by a plateau).

Between these two stages, the early stage is far more tenuous and fragile. This is because it requires a member of the social system to (1) obtain knowledge of an innovation, (2) evaluate its relative benefits and costs, (3) make an affirmative adoption decision, (4) successfully implement the innovation, and (5) confirm the existence of the desired results. In substance, this is a time-consuming and potentially expensive experiment that could fail.  Obviously, only a sub-segment of any population would be willing and/or able to bear this risk.

In the diagrams above, the early stage would roughly correspond to the 1924 to 1933 time period. Many farmers had heard about hybrid seed corn, but only a handful had adopted it. The early stage typically comes to an end when the social system’s opinion leaders become part of the adopter group and can vouch for the innovation’s effectiveness. Rogers writes, “[T]he [cumulative] diffusion curve is S-shaped because once opinion leaders adopt and begin telling others about an innovation, the number of adopters per unit of time takes off in an exponential curve” (p. 300).

The middle-late stage of diffusion begins with the rapid ascension of the S-shaped curve (1934 to 1941). In Diffusion of Innovations, Rogers discusses the concept of “critical mass”, which is the point at which enough adoption has occurred that further adoption becomes “self-sustaining.”

[T]he heart of the diffusion process is the modeling and imitation by potential adopters of their near peers’ experiences with the new idea. In deciding whether or not to adopt an innovation, individuals depend mainly on the communicated experience of others much like themselves … . The subjective evaluations of an innovation flow mainly through interpersonal networks. (p. 330).

On a micro-level, change is occurring because individuals are observing each other and responding to social proof. Each individual in the social system has a “threshold” of proof needed to spur change. Once the middle-late stage of diffusion is reached — i.e., the steep part of the S-shaped curve — the adoption process become less deliberative and more imitation of people in their close social network. Thus adoption moves like dominoes from early adopters to the early majority to the late majority to the laggards.  Although thresholds operate at an individual level and vary by adopter type, at a system level, their aggregate effect is to create a critical mass that leads to a tipping point.

In the case of culturally novel and complex innovations, critical mass is seldom reached without the participation of opinion leaders. Thus, it is important to understand their characteristics and attributes.

Opinion Leaders

Opinion leaders are rarely innovators and are not necessarily early adopters.  Their relative position among the five adopter types depends upon the norms of the social system.  Within the tradition-bound legal industry, the opinion leaders may be members of the early majority, refusing to adopt change without a very high standard of proof.

Roger defines opinion leadership as “the degree to which an individual is able to informally influence other individuals’ attitudes or overt behavior in a desired way with relative frequency.” Thus, among corporate law firms, Cravath Swaine & Moore is clearly an opinion leader. See, e.g., Cravath Raising Starting Salaries to $180,000, WSJ, 6/6/16 (reporting that “[c]hange is likely to spawn a wave of copycat moves”). Likewise, Harvard Law leads the way in legal education. See Harvard Law is now accepting the GRE. Could other schools follow?, Boston Globe, 3/21/17.  Yet, neither institution is widely viewed as an early adopter. In less conservative social systems, however, the overlap between opinion leaders and early adopters would be significantly larger.  Cf. Post 007 (discussing the influence and sway of early adopters).

A key feature of opinion leaders — and one that usually renders innovators unfit for the role — is their strong conformity to social system norms. Respect for norms is necessary to obtain the trust and allegiance of other adopter types.  Note that the value at play here may be less about innovation than power and influence, as opinion leaders can be disregarded or toppled. Rogers writes:

The interpersonal relationships between opinion leaders and followers hang in a delicate balance. If an opinion leader becomes too innovative, or adopts a new idea too quickly, followers may begin to doubt his or her judgment. One role of the opinion leader in the social system is to help reduce uncertainty about an innovation … . To fulfill this role, an opinion leader must demonstrate prudent judgment decisions about adopting new ideas. So the opinion leader must continually look over his or her shoulder and consider where the rest of the system is regarding new ideas. (p. 319)

On balance, however, opinion leaders tend to be distinguished by several attributes, at least as compared to other members of the social system. Opinion leaders tend to have:

  1. greater connections to the outside world (more “cosmopolite”)
  2. greater exposure to diverse media
  3. higher levels of social engagement
  4. higher socioeconomic status
  5. more innovative than followers
  6. greater exposure to change agents.

Regarding point #6, below is a bar chart showing the average number of change agent contacts per year for a group of farmers in Brazil. It is drawn from an agricultural diffusion study conducted by Rogers and other researchers.

The key takeaway from this chart is that change agents are sources of innovative ideas.  Rogers demonstrates the empirical connection between the Mark Granovetter’s Strength of Weak Ties theory and access to high-impact information.  In Granovetter’s well-known study of how people found employment, connections to far-flung cliques and social groups, albeit weak, were far more powerful than local networks of friends and family.  Thus, peripheral “weak” ties tend to be more informationally rich than the dense connections at the center of the social system.

Change agents and their ideas enter a social system through these weak ties.  Although change agents find the greatest receptivity with innovators, their success often hinges upon their ability to influence opinion leaders.

Change Agents

A change agent is defined as “an individual who influences clients’ innovation-decisions in a direction deemed desirable by the change agency” (p. 27). Their biggest impact is felt during the tenuous early stage of diffusion.

In the agricultural study, the change agents were government-funded university agronomists who were hired to help farmers adopt new technology. The goal was to boost agricultural production. However, in other contexts, change agents could be public health workers trying to reduce the spread of HIV; teachers introducing new curricula and materials to public schools as part of a broader “new math” movement; or salespeople selling enterprise software to large organizations. Indeed, this last example became the basis for the Silicon Valley classic, Crossing the Chasm (1991), which I’ll discuss in the next and final foundational post.

In cases of complex or novel innovations, change agents are necessary to fill gaps in technical knowledge and know-how.  These change agents typically have a significantly greater technical competence than members of the “client” social system.  Unfortunately, this superior know-how often creates communication and cultural gaps that are difficult to bridge.  This phenomenon is very much present in the legal industry circa 2017 as lawyers and legal educators struggle to learn new work methods grounded in data, process, and technology. The gap is undoubtedly the most visible with artificial intelligence.

The Tradeoff between Information Impact and Communication Ease

Communication and cultural gaps are most likely to occur when change agents are very dissimilar from members of the social system. A straightforward example would be lawyers needing to learn technical information from data scientists, software developers, and process engineers. This dissimilarity is referred to as “heterophily” (the technical term used in diffusion theory).  Although there is an enormous breadth of knowledge in these pairings, and thus the latent potential for high-impact knowledge transfer, communication tends to be slow, arduous, and uncomfortable. Thus, except among innovators and early adopters, persistence in heterophilous pairings is rare.

Conversely, when two individuals are very similar (homophily), such as two lawyers who attended the same law school and work in the same area of law, any communication gap is likely to be small or non-existent.  Unfortunately, that pairing is unlikely to transmit high-impact information, as their base of knowledge is too similar.  Cf. Scott Page, The Difference (2008) (economist demonstrating that diverse teams outperform homogenous teams on tasks requiring creativity and innovation). Thus, in a very real sense, law firms, legal departments, and law faculty cannot be leaders in innovation if their information gathering and strategizing is substantially limited to high-level meetings among lawyers. Remarkably, many will try.

The diagram below illustrates the conundrum.

On the far left side of the diagram, the transfer of high-impact information is impeded by significant communication and cultural gaps between change agents and members of the social system. Simply stated, they are too dissimilar to connect. On the far right side, communication is easy and fluid, but there is little or no novel information to share.  

, when an effective change agent works with innovators and early adopters and eventually receptive opinion leaders, a knowledge-rich exchange is possible (center left). After that, diffusion follows the example of opinion leaders, adopting sequentially early majority, late majority, and laggards (center right). See Post 007 (profiling the five adopter types).

Effective Change Agents

The theory of change agents may seem relatively simple.  However, when the desired change is complex and impinges on social and cultural norms, the change agent’s job is enormously difficult. Rogers observes:

As a bridge between two differing systems, the change agent is a marginal figure with one foot in each of two worlds.

In addition to facing this problem with social marginality, change agents also must deal with the problem of information overload, the state of an individual or a system in which excessive communication inputs cannot be processed and utilized, leading to breakdown. ….  By understanding the needs of the clients, the change agent can selectively transmit to them only information that is relevant. (p. 368-69).

My own interest in diffusion theory was borne of my six years at Lawyer Metrics. See Post 004.  As an applied research company, we created data analytics tools for legal service organizations.  Although the company had PhD social scientists who could build highly sophisticated quantitative models, our biggest challenge was finding ways to present data that lawyers could process, understand, and accept. On many occasions, we quipped that the statistical work was simple by comparison.

As I survey the legal landscape in 2017, I see the same challenges affecting many legaltech start-ups. Most early stage entrepreneurs emphasize the technical features of their product, because they know and love its full range of capabilities. Yet, this perspective places them at a high risk of failure.

Below is a model of change agent effectiveness based on Chapter 9 of Diffusion of Innovations. Suffice it to say, it fully aligns with my professional experience.

The original rate of adoption model in Post 008 listed five categories of variables that influence the rate of innovation adoption. The fifth category was “Efforts of Changes Agents.” The model above provides additional detail for that category. Cf Post 011 (discussing importance of the first category, “Perceived Attributes of Innovation,” to explain the difference between fast and slow innovations, even when the innovations at issue can save human life).

  1. Making contact with clients (+).  Frequent contact builds familiarity and creates opportunities to establish credibility and trust.
  2. Client orientation (+). Is the change agent trying to solve the clients’ problem or trying to advance their own agenda (e.g., make a sale)? If the change agent is listening, they can learn ways to modify and improve their innovation.
  3. Client empathy (+). A change agent is more effective when she or he can see the world through the eyes of the client.
  4. Homophily with clients (+). Can the change agent look and act like an insider? In the legal industry, change agents with law degrees generally have an easier time because of a common experience and background with most clients.
  5. Credibility in the clients’ eyes (+). Can the change agent fluidly answer tough questions? If the client must trust the change agents’ judgment, do the change agents possess the credentials and background to understand the underlying innovation?
  6. Working thru Opinion Leaders (+). Rogers observes, “The time and energy of the change agents are scarce resources” (p. 388). Engaging opinion leaders is the most efficient path to systemwide success.
  7. Improving technical competence of clients (+).  Clients dislike long-term dependency on change agents.  Thus, effective change agents often make education the cornerstone of their efforts, which builds trust and enables clients to make future adoption decisions on their own.

The Legal Productivity Problem

I started Legal Evolution because I believe the legal industry has a very serious problem of lagging legal productivity.  This problem is (a) causing ordinary citizens to forgo access to legal advice; (b) fraying relationships between corporate clients and outside counsel; and (c) causing a collapse in demand for law school graduates.  See Post 001. From a social welfare perspective, this is a very precarious situation.

Solving the legal productivity problem is going to require the uptake of new innovations. If you want to be an effective change agent, perhaps in the cause of your own innovation, you would benefit from learning the basic principles of diffusion theory and deploying them in an analytically rigorous way.

The final foundational post discusses Crossing the Chasm and Hype Cycle, which are topics highly relevant to law in the year 2017.

What’s next? See The Legal Services Innovation Index (021)

If we categorize all of our business conversations into the above four buckets, which bucket is the fullest?

Unfortunately, I vote for bucket 4.  We end up in bucket 4 because we want to be perceived as being fully informed.  Yet, being fully informed takes a lot of solitary, uncompensated effort with no certain prospect of a return.  So in our business conversations with one another, we fudge how much we really know.  First to ourselves and then to others.

Everyone likely agrees that bucket 1 is where we need to be.  Yet bucket 1 is the endpoint.  We start in bucket 2 with something like this opening line:  “Our business relationship is not working as well as it should because we are not making decisions from a solid foundation of shared facts. I would very much like to change this.”  If we’re selective on how and where we begin the conversation, we have good odds at a substantive, ongoing dialogue about information gaps and how to jointly fill them.

During the spring and early summer, I wrote two pieces for Law.com that focused on the legal profession’s Last Mile Problem and Last Mile Solution. They presented examples of unproductive dialogue between clients and lawyers.  The unproductive conversations are no one’s fault, yet they are real and pervasive.  These two articles are now combine in a single PDF.  Below is a copy of a “Last Mile” slide deck that contains all the figures in the articles. Hopefully, a few innovators and early adopters use these materials for a “bucket 2” dialogue.  bucket 2 + time = bucket 1.

What’s next?  See Change Agents and Opinion Leaders (020)