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)

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.


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.  However, 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 continues, with the early majority, late majority, and laggards adopting based on interactions within the social system (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)

The graphic above reflects three different types of innovation “outcomes”:

  1. Initiation of an innovation adoption process that results in an organization making a decision to adopt an innovation. See Post 015
  2. Implementation of the adoption decision, which entails planning, change management, and redefining/restructuring and clarifying the innovation in the field so that it delivers its intended benefits. See Post 015
  3. Adoption Success, which presumes success in both initiation and implementation.

This is Part III of a three-part series on innovation in organizations.  In Parts I and II (Posts 015 and 016), we discussed how multivariate regression models are built around an “outcome” we care about, such as organizational innovation.  These models give us insight on how to influence the likelihood of the outcome. In turn, these insights become of the basis more effective strategies and interventions.

The graphic above, however, reveals a difficult organizational challenge.  Centralized management decision-making impacts the three innovation outcomes differently.  During the initiation phase, centralization has a strong negative correlation with the outcome (Panel 1). During implementation, the relationship is moderately positive (Panel 2). The two opposing effects are then netted out in Panel 3.  The result is a statistically weak and moderately negative relationship between centralization and overall adoption success.

So what does this mean?  If we want more innovation in our organizations, we need to forgo one-size-fits-all approaches to management in favor of a staged approach.

Post 017 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.

The staged approach is necessary because several factors in Rogers organizational innovativeness model, introduced in Part I and reproduced below, have this peculiar flipping effect between initiation and implementation: (i) Centralization, (ii) Complexity, and (iii) Formalization.  This is one of the primary reasons that Rogers model has relatively low predictive power.  See Part I (Post 015) (“The predictive power of Rogers’ organizational innovativeness model is much lower than the Post 008 rate of adoption model.”).

Yet, we can adjust to these limitations through the application of our reasoning ability. See Part II (016) (noting that models are just guideposts for strategic thinking).

Consistent with the staged strategy discussed above, this Part III analysis assumes that organizational innovation requires successful initiation (agenda setting and matching) and successful implementation (redesigning/restructuring, clarifying, and routinizing). See Figure in Part I (post 015). This approach results in clear prescriptive guidance on how to increase successful innovation adoption in legal organizations. To the extent possible, this analysis uses specific legal industry examples.

Using the Rogers Organizational Innovativeness Model

Rogers models focus on applied research. This means we mine empirical models for usable insights while taking careful note of their constraints and limitations.  Thereafter, we use the resulting superior knowledge as part of a reasoning process to solve practical problems. See Post 001 (explaining difference between applied and academic research).

Below is the superior knowledge provided by Rogers’ organizational innovativeness model.

I. Individual (Leader) Characteristics — Champions

The first category of variables that influences organizational innovativeness is the presence or absence of innovation champions. An innovation champion is “a charismatic individual who throws his or her weight behind an innovation, thus overcoming indifference or resistance that the idea might provoke in the organization.” Diffusion of Innovations 414 (5th ed. 2003).

The champion could be a leader in formal position of authority (president, vice-president, manager, etc), but not always.  As Rogers notes, “The general picture of an innovation champion emerges not as a particularly powerful individual in the organization, but rather as someone particularly adept at handling people” (p. 415). Roger cites research showing the effective champions (1) tend to occupy a “linking” position in their organization, (2) possess analytical and intuitive skills in understanding various individuals’ aspirations, and (3) demonstrate well-honed interpersonal skills in negotiating with others.

It is easy to imagine how a smart, well-connected person with high EQ could be very effective in rallying enthusiasm during initiation and managing conflict and mediating solutions during implementation. Although the presence of such champions does not guarantee organizational innovativeness, Rogers suggests that their absence likely forecloses it, particularly in cases involving non-incremental change.  “The new idea either finds a champion or it dies” (p. 414, quoting Donald Schon, “Champions for Radical New Inventions,” 41 Harv Bus Rev 77, 84 (1963)).

II. Internal Characteristics of Organizational Structure

Various internal characteristics of organizational structure comprise the second category of variables that affect organizational innovativeness.

1. Centralization (-)

Centralization is “the degree to which power and control in a system are concentrated in the hands of relatively few individuals” (p. 412). As noted in Parts I and II, higher levels of centralization tend to have a negative impact on initiation. Yet, if centralized management can nonetheless manage to adopt an innovation, centralized decision-making can aid its implementation. The overall net effect, however, is negative. This is because senior organizational leaders tend to be too far removed from operational-level problems to identify relevant and workable innovations.

In my work with law firms, I have been surprised to find several examples of law firms that flourish economically because leaders have adopted a strategy of “letting partners do what they want.” In most cases, the resulting innovations take the form of specialized practices where partners command premium rates for providing fast, high-quality solutions that solve difficult client problems. The entrepreneurism consists of playing close attention to how substantive legal issues are impacting clients’ business needs and being the first to create a novel legal solution.  Although this decentralized approach can result in a sizable collection of lucrative niche practices, it likely undercuts potentially important firm-wide innovations such as project management and process improvement.

To cite another legal example, the decentralization of faculty governance in legal education results in many symposia to generate new ideas. However, we are completely lacking in effective central mechanisms for coordinating implementation. Hence our reputation for being stuck in the past.

2. Complexity (+)

Complexity is “the degree to which an organization’s members possess a relatively high degree of knowledge and expertise, usually measured by the members’ range of occupational specialities and degree of professionalism (expressed by formal training)” (p. 412). Rogers notes that a highly educated workforce is more likely to grasp the value of innovations. However, the higher levels of complexity make it more difficult to reach consensus on implementation.  Thus, the net effect of complexity on organizational innovativeness is positive but not particularly strong.

Below is a graphic that shows the complexity relationship by phase.

In law firms and legal departments, there is a strong movement to hire allied professionals trained in a wide range of useful disciplines.  This mixing of professional perspectives is bound to raise the quality of innovative thinking.  Translating these new ideas into effective action will be the core challenge of the next generation of legal professionals. See Post 005 (discussing growing size and complexity of corporate legal departments and the rapid growth of CLOC). To cope with the Panel 2 complexity challenge, the legal industry is undoubtedly headed into an era of standard-setting and standardization. This is going to produce a cultural sea change within the organized legal profession.

3. Formalization (-)

Formalization is “the degree to which an organization emphasizes its members’ following rules and procedures” (p. 412).  This internal organizational attribute has an impact that is very similar to centralization — strongly hindering initiation, aiding implementation, and overall having a net negative impact on successful adoption.

In the legal industry, we see the highest levels of formalization among the managed service providers. In this context, new entrants come on the scene with a core competence in designing and following process.  The high level of formalization results in legal work with fewer errors, lower cost, and faster delivery time.  Yet, the emphasis on process also enables more predictable schedules and greater work-life balance. This is a valuable differentiator to attract and retain talent. See Post 010 (“In addition to a professional wage, a collegial work environment, and freedom from business development pressures, lawyers in the managed service sector can refuse work outside the bounds of a 40-hour workweek.”).

4. Interconnectedness (+)

Innerconnectedness is “the degree to which the units in a social system are linked by interpersonal networks” (p. 412). The more interconnected the interpersonal networks, the greater the organizational innovativeness. This is because interpersonal networks tend to be very influential channels for sharing information, as trust and credibility levels are high. An organization can broaden and deepen these networks through the architecture of its office space and investing in regular inter-office meetings.

Interconnectedness is probably an attribute that legal service organizations tend to undervalue, wanting to avoid the lost time and expense of bringing professionals together for learning and socializing. Yet, I was recently surprised to learn that one of the major benefits of Milbank@Harvard, an intensive annual business training program for Milbank associates that lasts for several years, is that associates in the U.S., Europe, and Asia offices get to know one another in ways the spur trust, collaboration, and innovation. Ironically, these benefits were not part of the original business case for the program. They are just a welcomed second-order effect.  For additional information, see “An Update on Milbank’s Big Bet,” LWB, Nov. 13, 2013.

5. Organizational slack (+)

Organizational slack “is the degree to which uncommitted resources are available to an organization” (p. 412). The greater the organizational slack, the higher the level of organizational innovativeness, “especially for innovations that are higher in cost” (Id.).  Rogers speculates that larger organizations may be more innovative because the aggregate levels of downtime are bound to be greater. To use a sports metaphor, more shots usually result in more baskets.

Companies like 3M, Google, and HP have all adopted innovation strategies based on unstructured free time for knowledge workers. However, in most of the legal world, 100% utilization is the perennial holy grail. Exceptions are hard to find.

That said, the law firm Bryan Cave is an interesting accidental example. In the late 1990s, John Alber, the firm’s longtime innovation partner, returned to the firm after the sale of his logistics company. After fixing the firm’s failing IT system, Alber assisted on a client request for an expert system on international trade regulations (albeit no one called it that at the time). Although Alber had no formal staff, he found someone in the IT department with free time to help. The client was very happy with the resulting technology-based solution, thus starting a John Alber/Bryan Cave winning streak that lasted 17 years and resulted in numerous industry awards for innovation.  The IT staffer with free time was Chris Emerson, who went on to get an MBA. Emerson now runs Bryan Cave’s renowned Practice Economics Group (or PEG).

Another law firm example (from India, not the US) is Nistith Desai & Associates (NDA), a firm with numerous FT Innovative Lawyer awards in the Asia-Pacific bracket.  See long list .  Founded in 1989, the 200+ lawyer firm is based on the principle of continuous learning. Every lawyer, including the firm’s founder, is expected to be involved in the firm’s daily hour-long educational programming, both as a student and content provider. NDA essentially mandates slack time in service of creative solutions.  While virtually all law firms are reactive to client problems, NDA’s model is based on the proactive anticipate / prepare / deliver model show below. Not surprisingly, NDA uses value-based billing.

In late 2017, NDA will unveil a new R&D facility on a four-acre, state-of-the-art campus located on the outskirts of Mumbai. The new facility is referred to as the Blue Sky Thinking Center.  The founder of the firm, Nistith Desai, claims to have built NDA based on a composite of the very best professional services firms, including Wachtell Lipton. For an interesting discussion of the firm’s origins and operating principles, see Nistith Desai, “Management by Trust in a Democratic Enterprise: A Law Firm Shapes Organizational Behavior to Create Competitive Advantage,” Global Bus & Org Excellence (Sept/Oct 2009).

6. Size (+)

Part II of this series (Post 016) focused on the relationship between an organization’s size and organizational innovativeness. Roger viewed size as mostly a proxy or surrogate for other important factors, such as overall resources, complexity, and organizational slack.

Although increased size means additional layers of bureaucracy and higher communication overhead, the benefits can often outweigh the costs.  The highly innovative Corporate Legal Operations Consortium (CLOC) was certainly enabled by the size and scale of modern legal departments. See Post 005 (observing that many legal departments have become “the equivalent of a specialized law firm embedded inside a large corporation”). Likewise, Part II (016) presented compelling evidence that larger firms are ahead on AI and other practice management innovations.  This is almost certainly the result of more resources.

To drive home this point, imagine a firm allocating 2% of revenues to invest in people, process, technology, and data. In a firm with $1.7 billion in revenues (the average of AmLaw 1-20), that amounts to $34 million.  In a firm of $100 million (the average of AmLaw 181-200), 2% equals $2 million. Whatever the benefits of being smaller and more nimble, smaller firms are not well-positioned to attract and retain a critical mass of specialized talent. See, e.g., Update from Baker & McKenzie’s Chief Strategy Officer in Germany (during a day of onboarding, welcoming a “diverse group of lawyers, paralegals, business professionals, economists, data analysts, data visualizers, digital marketing experts”).

Yet, in my experience, size very much interacts with firm scope.  Specifically, when a firm narrows its areas of substantive practice, the innovation quotient can skyrocket despite not having AmLaw 1-20 revenues.  Littler Mendelson (labor & employment), Fragomen (immigration), and Chapman & Cutler (financial services) all fit this profile. Higher levels of innovation are enabled by focus and partner alignment — the firm rises and falls by its dominance in a single practice area. Cf. “Fragomen to Launch Unique Tech Development Center in Pittsburgh,” Leg Intelligencer, July 3, 2017 (suggesting that all companies, including law firms, are destined “to become a tech company in some capacity”).

III. External Characteristics of Organization — System Openness (+)

This category of variables is very simple conceptually: Does the organization proactively open itself to new ideas that could solve or mitigate important strategic problems? Compared to other industries, legal service organizations score low on this dimension.

Roger writes, “[m]ost organizations engage in an opportunistic surveillance by scanning the environment for new ideas that might benefit the organization” (p. 422). When it’s working well, “Answers often precede questions” (Id.) What Rogers is getting at is awareness-knowledge, defined as information that an innovation exists(p. 173).  Awareness-knowledge is obviously impeded by closed systems.  Lawyers are disadvantaged here on several fronts:

  • Ban on outside investment. The Rule 5.4 prohibition on non-lawyer investment means that lawyers cannot co-venture with other professionals, thus cutting lawyers off from valuable perspectives and learning.
  • Culture of immediate productivity.  The legal industry, particularly in the US, is strongly oriented toward production. As a result, eclectic reading, conference travel, and sustained high-level training and programming is often viewed as extravagant, as budget targets are high and the time is non-billable. Unfortunately, this ethos carries over to many legal departments. In-house counsel are largely firefighters. All too often, they lack the time, resources, and mindset to prevent fires.
  • Lawyer-centricity. All too frequently, lawyers refuse to accord legitimacy to the views of people who don’t possess a JD (and hence are “non-lawyers”). This is a recurring theme among allied professionals who work in the legal industry. Pros: high pay. Cons: routinely ignored or dismissed by lawyers.

If a legal organization wants to be more innovative, it can change some of these factors through enlightened leadership. In the long run, lower levels of innovations are ruinous to entire organizations and industries. A fiduciary cannot responsibly ignore these issues.

Finally, whatever I’ve just written about law firms and legal departments (the topic is organizations) applies to legal education.  To this day, I am struck by the lack of academic participation in organizations and events on the front lines of change.  E.g., CLOC, ILTA, LegalWeek. The economic rules of modern practice are poised to get rewritten. Once this happens, a lot of cheese is going to get moved.

Relative Importance of Rogers Organizational Innovativeness Model

Assuming you’re an innovator or early adopter who wants to use Rogers’ models to improve your organization, the following question is relevant: “What is the relative importance of the organizational innovativeness model (analyzed above) compared to the rate of adoption model in Post 008 [see thumbnail to right]?”

We don’t have systematic empirical data to answer this question, but we do have one article worthy of mention.  In a study of 25 hospitals that were adopting 12 new technologies in a midwestern city,  the dependent variable (outcome) was a nine-point scale ranging from “staff being awareness of an innovation (1 point) through adopting and using the innovation regularly (8 points) to expanding and upgrading the new technology (9 points)” (p. 414).  In effect, the scale is measuring the progression through the entire innovation adoption process, see Figure in Part I (post 015), from the early stages of initiation to complete implementation success. This is an ideal dependent variable.

The study authors found:

  • 40% of the variance explained by the perceived attributes of the innovations,  with observability, low risk, and low complexity being key.
  • 11% of the variance explained by organizational innovativeness factors, with CEOs as innovation champions and larger hospital with more aggressive marketing strategies being the most influential attributes.

(p. 412, citing Meyer & Goes, “Organizational Assimilation of Innovations: A Multi-Level Contextual Analysis,” 31 Acad of Mgmt J 897-923 (1988)).

What’s my advice? In both models, systematically explore cost-effective ways to influence every variable in the direction that will make success more likely.  This is how applied research works.

What’s next?  See Legal Operations Skills During Your 1L Summer (018)

The graphic above, adapted from Rogers, Diffusion of Innovations (5th ed. 2013), shows the distribution of innovativeness among 324 German banks.  The innovativeness scale is a count of innovation adoptions from a universe of 12 interactive telecom innovations that were diffusing through the German banking sector during the early 1990s. To help distinguish the early adopters, more recent innovations were weighted more heavily.  The distribution is a textbook example of the Rogers Diffusion Curve. The long innovators tail exists because innovators are typically 2+ standard deviations from the mean on innovativeness.

Rogers uses the German bank study to illustrate numerous factors associated with higher levels of organizational innovativeness.  One factor is size.  Specifically, the largest German banks accounted for a large proportion of the innovators and early adopters.  In Rogers’ dataset, the correlation between innovativeness and total assets was a remarkable .75 (p < .01). Likewise, the correlation between innovativeness and employee headcount was an equally stunning .70 (p <.01). If readers are wondering why I am surprised, it is because the results are contrary to the standard trope that larger, more mature, and more financially successful companies — be it manufacturing, pharmaceuticals, or technology — struggle with innovation.  Indeed, this is the very problem Clayton Christiansen is trying to solve in the Innovator’s Dilemma (1997).

Post 016 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.

This post is Part II of a three-part series on innovation in organizations. See Post 015 (Part I of series).  The goal in this post (016) is to unpack the counterintuitive relationship between size and innovativeness, as the strategic takeaways are far from obvious.

Deft Minds and the Size Effect

Everett Rogers had a remarkably deft mind that could puzzle through seemingly contradictory data and, with enough time and reflection, derive the most plausible causal story. Chris Zorn, my fellow co-founder at Lawyer Metrics (now LawyerMetrix), has a similar rare ability, which is to say I have some hands-on experience in this area. Drawing upon this experience, let me gently set reader expectations: What is important in Posts 015-017 is analytically subtle in a way that is not intuitive for most lawyers.

Let’s start with the size effect, which is present in Rogers’ study of German banks.  The size effect is relevant to lawyers because (a) there is credible, recent evidence that size is correlated with innovativeness in law firms; and (b) as Rogers acknowledges, the higher levels of innovativeness are, in most cases, substantially driven by the “covariants” of size, rather than size itself.  It is this second point (b) that requires the deft researcher’s mind, albeit it can definitely be grasped by patient smart people. So hang in there.

(a) The Altman Weil Law Firms in Transition Survey

The recent credible evidence of the size effect comes from the Altman Weil Law Firms in Transition 2017 survey, The survey polled managing partners and chairs at 798 US law firms with 50 or more lawyers. The response rate was 48%, including 50% of the NLJ 350 (ranking based on lawyer headcount) and 50% of the AmLaw 200 (ranking based on gross revenues).

Ron Friedmann in his post “Law Firm Profitability + Service Delivery: What the Altman Weil Survey Says,” Prism Legal, June 21, 2017, conducted a masterful secondary analysis of the survey results. One of the survey questions asked law firm leaders, “Technology tools that incorporate artificial intelligence (AI) and machine learning — like Watson and Ross — are beginning to be adopted by some law firms.  What is your firm’s stance on the use of legal AI tools?”  From the respond data, Ron generated the chart below:

This graphic shows a very strong relationship between law firm size and the use of AI.  Over 50% of the 1000+ lawyer firms claim to have begun adoption. Further, the effect is clearly linear, with the level of use and exploration steadily declining with firm size. Drawing upon what we learned in Part I (015), the orange bars reflect the “initiation” phase and the blue bars reflect “implementation.”  (Keep in mind that the implementation phase is fraught with difficulties and often ends in failure. See Post (015).)

Further, the relationship between size and innovation is not limited to AI.  We observe the same size/innovation effect in results that show the linkage between alternative fees and changes in how work is being staffed and delivered.  The graphic below is also courtesy of Ron Friedmann:

[Query: Why is linking AFAs to staffing and service delivery so innovative? Because the real value of alternative fees is to incentivize a re-design of workflow that (i) increases quality, (ii) speeds up delivery, and (iii) decreases cost. Otherwise, alternative fees become either a price discount or a gamble with poor or unknown odds. Stated another way, there is no point in hiring a pricing specialist unless you’re also going to hire specialists in project management and process improvement.]

(b) Covariants to size, not size itself

The graphics above reveal a clear and meaningful relationship between size and innovativeness. Yet, it does not necessarily follow that increasing size will increase innovation.  Correlation, as they say, is not causation.  Instead, it may be the case that other actions or activities need to be taken to improve or enable innovation; and for a variety of reasons, those actions or activities are more likely to occur in a larger firm.

To illustrate, let’s return to Rogers’ Organizational Innovativeness model, which is reproduced below:

In category II, Internal Characteristics of Organizational Structure, there are six factors (i.e., independent variables) listed, with size being number 6.  In Diffusion of Innovations, Rogers asks the question, “Why do researchers consistently find that size is one of the best predictors of organizational innovativeness?”  The first reason, writes Rogers, it that size is easy to measure with precision and thus “is included for study in almost every organizational innovativeness investigation” (p. 411).  Rogers continues:

Second, size is probably a surrogate measure of several dimensions that lead to innovation: total resources, slack resources (defined as the degree to which an organization has more resources than those required for ongoing operations), employees’ technical expertise, organizational structure, and so on. These unidentified variables have not been clearly understood or adequately measured by most studies. These “lurking” variables may be a fundamental reason for the common finding that size and innovativeness are related (Id.).

These “lurking” variables are covariants — i.e., attributes that generally move in a linear relationship with one another, either positively (height and weight) or negatively (age and memory).  Thankfully, when we have a lot of potentially meaningful variables that are correlated with one another, we can sort out what matters, by direction of effect and magnitude, through multivariate models.  In this case, factors 1-5 in Rogers’ model reveal the more valuable insights. We will carefully review those factors in Part III (017). But before we do that, let’s make sure we know enough about the underlying statistical models to avoid very serious errors in judgment.

Size is not a strategy

In the quoted paragraph above, Rogers is describing what statisticians call “omitted variable bias.”  This occurs when we leave something out of a multivariate model — like factors 1-5 above — and get a result that inflates or deflates the predictive power of the remaining variables (like size of firm).  The risk here is that folks running and interpreting the models are not sophisticated enough to know that they might have left something out and, if so, how to correct for it.  In turn, they drawn incorrect inferences that could form the basis for disastrously wrong strategy.

An example of the subtleties at play here can be seen in a law firm profitability model that Evan Parker (Managing Director of Analytics at LawyerMetrix) and I published in The American Lawyer. See “Playbook: Top 5 Strategies of the Most Successful Firms,” January 2017. As a first-cut analysis, size is positively correlated with average partner compensation: .53 with revenues, .28 with headcount.  And among many lawyers, there is a reflexive view that bigger is better, particularly in uncertain economic times.  See, e.g., Laurence Simons, “Number of US law firm mergers rockets,” Oct. 14, 2015. Yet, many other factors are bound to matter as well, such as (a) geographic presence in particular markets, (b) geographic concentration, (c) specific practice areas, (d) practice area concentration, (e) client concentration in specific industries, and (f) measures of reputation in lucrative financial services markets.

The graphic below shows the results of a model that includes all the factors just listed.

When reliable measures of these additional factors are included in a multivariate model, size (in this case, headcount) becomes a negative predictor of profitability at a level that is statistically significant.  In other words, when all these other levers of strategy are factored into the analysis, more lawyers means lower average partner compensation.  Further, the model explains approximately 80% of the variation in average partner compensation, which makes it highly unlikely that many firms can thrive using strategies significantly at odds with the model’s results. (For the prescriptive advice that flows from this model, please read the full article.  H/T Chris Zorn and Erik Bumgardner, both PhD social scientists, who helped build the model. )

I show the profitability model to make a simple but important point:  The use of statistics to guide strategy and operational decisions only becomes valuable when deep contextual knowledge of an industry and its problems is combined with quantitative competence and the ability to effectively communicate results. By dint of his deft mind and an amazing work ethic, Everett Rogers developed this ability in the realm of diffusion theory.  Outside my former colleagues at LawyerMetrix and a few others, this combination remains all-too-rare in the legal field.  Over the next generation, this gap is destined to close.  In the meantime, beware of charlatans selling into a naive and fearful market.  Also, don’t expect an internist to perform surgery.

Strategy that works = models + reasoning ability

For lawyers, there is a silver lining to all of this complexity: High quality empirical models — like the Rogers’ rate of adoption model (Post 008), the organizational innovativeness model, or the profitability model above — are nothing more than signposts that communicate what is more, or less, likely to matter. They don’t, by themselves, produce fully baked strategy.  Rather, strategy based on models requires the application of additional reasoning ability, of which lawyers have no shortage. Fortunately, sophisticated quantitative analysis is itself an innovation that is starting to diffuse through the legal industry social system.  See Post 004 (“Rogers’ core insight – one that is absolutely foundational for Legal Evolution readers – is that the diffusion of innovation is a process that occurs through a social system” (emphasis in original)).

In Part III (017), we can finally dive into the Rogers organizational innovativeness model with confidence that we can draw the right inferences and, in turn, use the model to set and execute sound organizational strategy.

What’s next? See Innovation in Organizations, Part III (017)

Every legal innovatorearly adopter and change agent shares a common, unifying desire: To speed up the pace of innovation within their organization.

This statement is true whether the context is a law firm, legal department, government agency, bar association, or law school. Over the years, I have commiserated with them all. Although they don’t know it, their disappointment is rooted in the fact that organizations are much harder to influence than individuals. See foundational posts 007 and 008 (discussing complexity and challenges of successful organizational adoption). For better or worse, organizations are everywhere within the legal ecosystem. Thus, it would be extremely useful to understand what levers to pull that can make them more innovative.

Post 015 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.

Rogers Organizational Innovativeness Model

The model above, drawn from Everett Rogers’ Diffusion of Innovations Ch. 10 (5th ed. 2003), summarizes several factors that positively or negatively affect an organization’s level of innovativeness.  The model aggregates the results of numerous empirical studies that utilize multivariate regression analysis. However, just like the “rate of adoption” model discussed in Post 008, Rogers conveys the key findings using words rather than numbers. This is because, as an applied researcher, Rogers wants his analysis to be understood and used by a smart lay audience. See Post 001 (explaining difference between applied and academic research).

To illustrate, a multivariate regression model has some number of “independent” variables that predict some outcome we care about. We call that outcome the “dependent” variable. In the graphic above, the left side lists several independent variables while the right side contains a single dependent variable.  Thus, it can be said that the level of organizational innovativeness depends upon the values of several specific independent variables. In very practical terms, the model tells us what categories of change we should focus on to increase innovativeness within our organizations. And, by implication, it tells us what not to do.  It is very hard to overstate how useful this is. In the early days of any innovation, Rogers’ models (above and in Post 008) are both map and compass. It is just plain foolish not to learn how to use them.

That said, to have a fair chance of success, readers need additional background knowledge on the challenges of organizational innovativeness.  Thus, I am breaking this topic into three parts. Part I (Post 015) reviews the reasons why organizations tend to become bottlenecks for innovations that are crucial to their long-term survival. Part II (Post 016) discusses a very counterintuitive fact — that organizational innovativeness is strongly correlated with size, even in law firms. With this background information in place, Part III (Post 017) dives into the details of Rogers’ innovativeness model (above) with special emphasis on how it applies to legal service organizations.

Brief Review of Diffusion Theory

Innovators and early adopters are very interested in speeding up the rate of adoption of innovations. Everett Rogers’ rate of adoption model in Post 008 sets forth many factors that positively or negatively influence this outcome. The model groups these factors into five distinct categories: (I) Perceived Attributes of Innovation, (II) Type of Innovation-Decision, (III) Quantity and Quality of Communication Channels, (IV) Nature of Social System, and (V) Efforts of Change Agents.

As noted in earlier foundational posts, the first category, “Perceived Attributes of Innovation,” contains the most biggest levers for change. This is because the five attributes identified in the research — higher relative advantage, lower complexity, greater compatibility, use of pilot trials, and increased observability for prospective adopters — explain the majority of variation in rate adoption.  With sufficient quantities of time, money and effort, innovators, early adopters and change agents can alter these factors in the right direction. See Post 008 (urging those favoring innovation to “focus your attention on these five factors”); Post 011 (explaining “slow innovations” based solely on these five factors).

Yet, for those of us working in the legal industry, “Type of Innovation-Decision” is equally important. This is because Type of Innovation-Decision is essentially distinguishing between individual and organizational adopters. And the latter are (a) much more common and economically influential within the legal industry, and (b) more likely to result in adoption failure, particularly in the absence of significant planning and intervention.

Innovation in Organizations

As noted in Post 008, there are three types of innovation adoption decisions: (1) optional, (2) collective, (3) authority.  If the adoption decision is optional, it’s akin to market forces: individuals are free to take it or leave it (think Smartphone, Uber, or wearables). In contrast, when an organization is the adopter, either collective or authority adoption decisions apply.

Collective is the most problematic decision type, as a collective adoption decision requires some level of group consensus (think law firm partnership or law school faculty).  Authority adoption decisions are, in theory, easier because a single authority can decide (think CEO or GC). But successful implementation still depends upon overcoming the opposition of the laggards and late majority. See Post 007 (defining adopter types).  Indeed, “massive passive resistance” (MPR) awaits the executive who underinvests in team buy-in. See Post 008 (defining MPR and discussing its pervasiveness in corporate legal departments).

In summary, if you work in the legal industry and want to bring about beneficial change, your success largely depends upon your ability to work with, or within, organizations.  This is because good ideas, unsheltered by a well-informed sponsor, are no match for the strong anti-change headwinds created by organizational decision making. This is a structural feature of the industry that consistently impedes organizational innovation, albeit innovation is never foreclosed — not unless you and others give up. For this ultramarathon journey, Rogers’ models are essential survival tools.

That said, an important caveat is in order.  The predictive power of Rogers’ organizational innovativeness model is much lower than the Post 008 rate of adoption model.  One of the main reasons for the lower predictive power is that factors that make an organization more likely to innovate are simultaneously factors that tend to undermine successful implementation.  Specifically, the likelihood of an organization deciding to adopt an innovation is positively correlated with (i) lower centralization of authority, (ii) higher complexity of work, and (iii) less formalization of procedures. Yet, these three attributes are negatively correlated with successful implementation.

Obviously, very few organizations have the level of self-awareness necessary to make appropriate mid-stream adjustments.  Instead, leaders try to power through obstacles with a one-size-fits-all management approach. In legal organizations in particular, when an innovation fails, we place the blame on lawyers’ contentious, skeptical, autonomy-loving nature. This is a bogus uninformed analysis.  Fortunately, this pathetic cycle can be broken through careful planning and leadership.

Initiation versus Implementation

Below is a graphic that summarizes the five stages of an innovation adoption process in an organization. Notice that the adoption decision is made only after a period of agenda-setting (Stage #1) and matching (Stage #2). Thereafter, the painstaking work of implementation begins.

Note also that the model above essentially assumes that the innovation process is managed by an existing bureaucracy, ostensibly just one of many managerial duties.  The process begins with “Initiation,” which consists of “all of the information gathering, conceptualization, and planning for the adoption of the innovation, leading up to the decision to adopt” (pp. 420-21). After the leadership makes the adoption decision, the organization commences the “Implementation” phase. This consists of “all the events, actions, and decisions involved in putting the innovation to use” (p. 421).  When the innovation is so integrated in the organization that it becomes routinized, it “loses its identity” as something new. In essence, the innovation has merged into the status quo.

As noted above, several organizational attributes that support successful initiation become sources of weakness during implementation. This should be very humbling to legal innovators and early adopters who likely excel at initiation but are prone to underestimate the hardships and complexities of successful implementation. This tendency is explicitly discussed in the Silicon Valley classic Crossing the Chasm by Jeffrey MooreMoore’s solution is simple: when the time comes, replace the innovator/early adopter management team with more mainstream operators whose skill set is execution rather than ideation. For the opposite situation — when an organization is very good at setting and following procedures but struggles to innovate — Rogers suggests a skunkworks as a potential solution.

Unfortunately, there is good reason to believe that law firms, the longstanding cornerstone of the legal industry, reflect the worst of both worlds. The partnership structure hinders both successful initiation and implementation, not to mention making a timely adoption decision. Cf. Bruce MacEwen, Tomorrowland: Scenarios for Law Firms Beyond the Horizon (2017) (discussing at length the business liabilities of governing a law firm as a partnership; suggesting that the partnership model will become a source of numerous law firm failures). Yet, this is less a reason for hopelessness than cause for careful study and preparation, at least among those who intend to stay in the industry beyond the short to medium-term.  Society has many hard problems. This one belongs to lawyers.

There is more to unpack in Parts II (016) and III (017), which I’ll post shortly.

What’s next? See Innovation in Organizations, Part II (016)

The global law firm Gowling WLG has just launched a platform that automates document production for a private placement offering.  The video above does a remarkably good job of explaining how the product (called Smart Raise) works.  Far from scary and technical, the innovation comes across as simple and inviting. Quite an accomplishment in two short minutes.

Gowling WLG is a global law firm that operates in Canada, the UK, Continental Europe, the Middle East, and Asia. Although it doesn’t have a US office, US firms ought to take notice, as the Gowling’s rollout is a good illustration of two trends starting to take hold in the legal ecosystem:

  1. The use of complex technical sales methods that include market preparation activities as part of a long-term strategy;
  2. New pricing models that connect together commodity and bespoke offerings in ways that thin out weak competitors.

Complex Technical Sales

This sounds like an oxymoron, but complex technical sales is about simplification. For example, when a new innovation is launched, prospective clients don’t understand its technical aspects. Thus, they have a reasonable fear of making an expensive mistake.  Closing this knowledge gap is costly, particularly in the B2B space, because it takes time and cognitive effort.  Showing the product in action in often the best way to reduce this load. Cf. Post 008 (explaining how lower complexity and higher trialability and observability increase innovation adoption).

In contrast to the rest of Law Land, Gowling WLG has head start. Its Leader of Innovation Initiatives is Mark Tamminga (pictured right), a long-time partner who pioneered the use of practice automation tools in building the firm’s Recovery Services practice. Through automation efforts that began over a decade ago, Mark and his colleagues built a series of products and services that captured the Canadian market. The practice has become very profitable and highly defensible.

One of the things that Tamminga and his colleagues understand is that you start the sales process by emphasizing simplicity and ease of use rather than technical prowess. This is hard for innovators because it requires extra steps.  The natural tendency is to jump to the most advanced features in an attempt to impress prospective clients. The result is typically confusion. Yet legal entrepreneurs make this mistake over and over again. See Post 008 (discussing how immersion in technical details makes it difficult to see the world through the eyes of the end user).

Finally, a short video can be an extremely effective sales tool because it is asynchronous and puts the viewer in control. If it’s done well, qualified buyers find you.  I first watched the video via a LinkedIn post.  In the year 2017, LinkedIn is a very important “communication channel.”   See Post 008 (discussing knowledge awareness and the adoption decision; discussing how more and better communication channels speed up adoption).


Smart Raise reduces the volume of hourly work. It then seemingly compounds the financial hurt by showing the ease of the new process. Skeptical lawyers are bound to ask, “How does this support revenue production?”

The answer is that it probably doesn’t, at least not directly or in the short-term.  Instead, it signals expertise.  If Gowling WLG is smart enough to automate a substantial portion of the private placement offering process, it’s likely they’re experts on the remaining complex issues.  “Perhaps we would give them a call.”  This is a market positioning strategy based on a realistic assessment of where the legal market is headed.  As Susskind has written, “If [cannibalization of legacy offerings] is going to happen, you should be one of the first to the feast.” See Tomorrow’s Lawyers 128 (1st ed. 2013).

What’s next?  See Innovation in Organizations, Part I (015)

glasses_diffusionAre rapidly adopted innovations more valuable and important than innovations that take a long time to take hold? Not necessarily.

Post 011 is part of LE’s foundational series on diffusion theory.  Here’s the key point:  Speed of adoption is not a reliable guide for an innovation’s importance. In fact, competitive advantage is much more likely to lie among slower ideas where innovators focus on several key factors to accelerate the rate of adoption.

It is difficult to accept an insight this counterintuitive. Thus, we need an illustration. Continue Reading Fast versus Slow Innovations (011)

Below are two beliefs I carried with me for many years.

  1. In all human endeavors, incentives exert a powerful effect on behavior
  2. Within the legal industry, the billable hour is the primary impediment to innovation and efficiency

efficiencyengines2Belief 1 still stands. But belief 2, which I viewed as a corollary of 1, recently fell like an oak tree. This shift in worldview happened during my research for Efficiency Engines, a story on rise of legal managed services.  During visits to several managed service facilities, I witnessed quantum leaps in legal productivity for relatively sophisticated legal work.  And in each case, the work was priced and sold by the hour.

The biggest value of visiting an innovator is the possibility of learning something that disconfirms one’s own belief system.  A character in a John le Carre novel once quipped,  “A desk is a dangerous place from which to view the world.” That’s good advice for those seeking to understand today’s legal industry.

A better theory

After the fall of belief 2, I needed a theory on innovation and efficiency that incorporated my new learning. Here is what I came up with:

The billable hour can be harnessed as a powerful tool for innovation and efficiency.  This can be accomplished by: (a) selling work with a clearly stated budget, (b) paying your workforce by the hour, and mostly crucially (c) managing quality and the risk of cost overruns through world-class project management and process improvement.  A service provider who reaches scale and establishes a brand based on both quality and price can lock-in a profitable business with a long-term competitive advantage.

In a nutshell, this is the business logic of the legal managed service model.  The private equity/venture capital crowd have found it sufficiently compelling to fund it with hundreds of millions of dollars.

This post (010) situates the managed service model within a more fundamental theory of professional service firms.  It also explains how the managed service model is successfully pulling on the levers of diffusion theory, particularly compatibility, to accelerate its growth. Cf. Post 008 (adoption more likely if innovation is compatible with existing practices and norms). Post 010 then offers a few thoughts on the adoption of managed service methods, which is something separate from the managed service business model. Regardless of who wins and who loses, the methodology pioneered by managed service providers represents an innovation that is diffusing throughout the legal industry.

Balancing three goals inside two markets

The fundamentals of the professional service business model are explained in David Maister’s Managing the Professional Service Firm (1993) (chapter 1).  As shown in the diagram below, firms operate in two markets: clients and talent. For the owners of the firm, profitability is certainly the ultimate goal. Yet, this outcome is entirely derivative of (a) providing outstanding service to clients (market 1), and (b) offering high-quality career opportunities to a talented workforce (market 2).  Thus, under Maister’s theory, the professional services firm is always balancing three goals in the context of two markets.


Although this model looks simple, it’s easy to get wrong.  Firms that fail to understand their own cost structure are at risk of substituting revenues for profits.  As recently observed by Bruce MacEwen, this is why so many firms overpay for lateral partners.  See Opinion: It’s time to re-think the price war on talent.  Likewise, in the client marketplace, the growth of national and international businesses has reduced the importance of client relationships based on geography and increased the importance of best-in-class talent in specialized areas. On some level, law firms know this because they have jettisoned the “general service” descriptions that were so common 20 years ago.  However, there remains considerable internal resistance to closing down out-of-scope practice areas and offices. Cf. Henderson, “How to Take Market Share,” American Lawyer (Oct. 2015) (discussing ruthless focus needed to become market leader based on quality).

Perhaps its obvious that the client marketplace has become differentiated. Yet, differentiation is also occurring in the legal talent market — a point I did not fully grasp until the belief 2 oak tree fell.  Beyond preferences based on practice area, some lawyers are drawn to BigLaw for reasons of money and prestige.  But other lawyers — with the same level of intellect and pedigree — are looking for a different type of work environment.

In the course of writing Efficiency Engines, I asked Jane Allen, Founder and Board Member of Counsel on Call, how the company selected its talent.  Jane described a behavioral interview process that focused on a profile very different than a BigLaw rainmaker:

  • Loves the technical aspects of law
  • Wants to be part of a team
  • Does not need to be in charge
  • Is comfortable learning new technology
  • Places a high value on work-life balance

In a large law firm, a lawyer who fits the above profile would likely be diverted to an “off-track” position. Yet, in a managed service firm, these same lawyers are the core building blocks of the business.

Using the billable hour to balance the legal talent market

The managed service model is tapping into the large segment of the legal talent market that wants work conditions very different from BigLaw.  At the top of the list is work-life balance.  In addition to a professional wage, a collegial work environment, and freedom from business development pressures, lawyers in the managed service sector can refuse work outside the bounds of a 40-hour workweek (or, in some cases, pre-negotiated shorter weeks).  Further, if the managed service firm needs additional hours, the lawyers are paid for their time. These constraints force managed service providers to build remarkably tight systems for project management and process improvement.

In large law firms, the incentives run in the opposite direction.  Once the firm locks in its labor costs in the form of high guaranteed salaries to associates, it benefits financially from hours that are billed during nights and weekends. Work that is unplanned, unpredictable and urgent is often the most profitable.  It is any wonder that firms would be reluctant to invest in technology and systems that would make client work more transparent and thus more manageable?  Some legal work will always be unplanned, unpredictable and urgent. But not all legal work. Clients are slowly figuring this out.

In all three of the managed service facilities I visited (Counsel on Call, United Lex, and Axiom Law), I encountered the same thing: lawyers who enjoyed working in a team-based environment where efficiency and innovation were valued.  Indeed, being part of a continuous improvement process appeared to be a key component of their job satisfaction. They enjoyed the challenge of exceeding their clients’ expectations. In turn, their employers benefited through higher volume that included a built-in profit margin.  Note the careful balancing of Maister’s three goals.

Leveraging cultural compatibility

One of the reasons that the legal managed service market is growing at 15-20% per year is that most managed service providers have packaged their offerings in a way that is culturally compatible for legal department clients.  As noted in Legal Evolution’s foundational posts, particularly Post 008, compatibility is an important driver of innovation diffusion.  When an innovation is highly compatible with a social system’s existing practices and norms, adopters can obtain the benefits while staying within their comfort zone.

In the managed service model, there are two major touchstones of compatibility.  First, the work is still being done or supervised by lawyers with large firm experience and strong academic qualifications — lawyers just like the in-house buyer. Second, and perhaps most crucially, the work is sold in billable hour units, which is the only system of measurement that all lawyers understand.  Pricing by the hour invites a direct cost and quality comparison with law firm associates, which casts the managed service provider in a very favorable light.

It near impossible to overemphasize the importance of cultural compatibility.  I have witnessed other highly innovative legal services providers who have refused to share the amount of lawyer effort needed to produce their outstanding work product. Instead, they hold to a pricing model based on terabytes of data or overall scope of work.  Most in-house lawyers lack the time and interest to get into the weeds of a cost and quality comparison.  An easier sell is pedigreed lawyers working by the hour in a process-driven environment.  Cf. Post 008 (discussing crucial importance of relying on the buyer’s rather than the innovator’s perspective).

NewLaw is not as new as we think

Managed service companies like Counsel on Call, Axiom, UnitedLex, Pangea3, and Elevate are often put into the NewLaw bucket.  I don’t dispute the categorization, except to point out that it’s our awareness of alternative models that is new, not the companies themselves.  Axiom and Counsel on Call both launched in 2000. Pangea3 was founded in 2004 and sold to Thomson Reuters in 2010.  UnitedLex opened in 2006.  Although Elevate is a relative newcomer (opening in 2011), its CEO, Liam Brown, founded another NewLaw company, Integreon, in 2001. Further, he began in the NewLaw space nearly 20 years ago, starting Conscium in 1998, which pioneered virtual deal rooms for lawyers and investment bankers.

Sally_crab_labelWhy does “new” takes so long in law?  Like the Galapagos Islands at the time of Charles Darwin’s landing, the legal sector operates in peculiar and isolated ecosystem that bears only a distant resemblance to the economic mainland. Under the ethics rules, lawyers can’t co-venture with professionals from other disciplines. If you can’t co-venture, opportunities to share knowledge, know-how, and perspective are inadequate for the challenges being faced. As noted in Post 008, this dramatically slows the diffusion of innovations within the legal social system.

Yet, let’s not confuse slow change with no change.  Very few lawyers appreciate the amount of outside investor money that has arrayed itself to profit from the inefficiencies of law firms. These capitalists are waiting longer than usual for their desired return (7-10 years are the usual outer limits for PE and VC investors).

However, they are also learning more about the legal industry and normalizing their involvement in the legal supply chain.

I first interviewed Mark Harris, founder and then CEO of Axiom Law, back in 2013.  At the time, I had the mindset that Harris was running a legal start-up.  But by then, Harris was 13 years in.  Axiom was less a start-up than an ultramarathon. (In late 2016, Harris became Axiom’s Executive Chairman, handing over the CEO reigns to Elena Donio, formerly President of Concur, a $1 billion business software company.)

The long game

During that 2013 interview, Harris laid out the long game analysis — the same analysis he was then using to sell services to Fortune 500 general counsel.

On a whiteboard, Harris drew a set of diagrams that segmented legal work into three groups: extraordinary events, experienced demand, and efficiency demand. In the past, corporate clients viewed virtually all legal work as risky and complex. Thus, they outsourced virtually everything to law firms. Over time, as in-house lawyers took over the role of buying legal services, they began unbundling legal work and insourcing some of it to themselves. This fueled the rapid growth of corporate legal departments. See Post 003 (showing the 20-year grow trajectory of in-house legal departments). In its early stages, this was pure labor arbitrage. Yet, as in-house lawyers develop more sophisticated sourcing strategies, Harris explained, the extraordinary events and experienced demand segments will shrink while the efficiency demand grows.  See diagrams below.


A huge part of moving work from the top of the pyramid to the bottom is introducing systems and process that can partially supplant the clients’ deeply held faith in elite brands and credentials. This challenge is truly an ultramarathon, and only a handful of managed service providers are running it.  If Harris and his investors are correct, and they play the long game correctly, they will be among a relatively small number of competitors who divide up a large portion of the green triangle above.

How does this end?

One scenario is that extraordinary events work consolidates with a small number of firms that attract brain surgery-type talent. Think the Wall Street elite plus Latham, Kirkland, Gibson Dunn and a few others.   A tranche of complex and strategic work remains in-house. See Post 003 (reporting that more than 105,000 lawyers work in-house).  And a large portion of the remaining pyramid goes to large managed service providers who specialize in high-volume process work. These companies could go public or be owned by the BigFour accounting firms. Think Accenture. (See Efficiency Engines for how this is possible under the existing ethics rules.)

Yet, there are alternative scenarios that are likely to co-exist with the ambitions of the managed service sector. For example, it is likely that some US and UK law firms will emerge as global “general contractor” firms. They will earn a premium for their supply chain expertise, from captive LPOs to AI-enabled automation to high-stakes court room practice. Think Allen & Overy, Herbert Smith Freehills, Baker McKenzie, Hogan Lovells, and a few others.

In the labor & employment space, Littler Mendelson and Ogletree Deakins have already incorporated many managed service methods into their businesses. They tend to get ignored by the legal press because their profits per partner are lower than the rest of BigLaw.  Yet, managed service methods are the equivalent of a moat around the franchise. Would you rather have $2 million this year, or $500,000 per year for 20?  This isn’t a lottery hypothetical. It’s a strategy choice made by lawyers with large L&E practices.

Likewise, many elite US law firms have relatively large alternative staffing operations that are deployed in massive M&A deals and Big Game litigation. To protect their brands, these firms seldom publicly tout these capabilities. Yet, within the confines of a client pitch, the existence of these capabilities helps protect work at the top of the pyramid. In this context, bundling is likely more valuable to the client than fine gradients of cost and quality.  Although currently run primarily as support units for high-end work (at least for now), many of these operations are quite profitable based on their own P&Ls. Thus, in some segments of the market, the rich are only getting richer.

How does this end?  Obviously, the work methods of managed service providers are innovations that are diffusing throughout the legal industry.  Large scale disruption of the legal industry by managed service companies seems less likely that successful co-existence with law firms who adopt similar work methods. The buyers, after all, are mostly lawyers working in legal departments.  That said, a lot of law firms are far behind on the methods front.  It’s a difficult slough for law firm leaders because the required investments don’t produce higher profits this year or next. Instead, the payoff is long-term relevance and survival, a topic many senior partners put in the same category as global warming.

At some point, legal education will begin to grasp the import of managed service methods in helping law grads obtain high quality employment.  But that is a topic for another day.

What’s next?  See Fast versus Slow Innovations (011)