In writing up the week 4 summary of “How Innovation Diffusions in the Legal Industry,” I discovered that it is near impossible to write about Axiom without referencing a larger change narrative.

Founded in late 1999, Axiom was likely the legal industry’s first venture-backed start-up.  Now, 18 years later, with over 2,000 employees in 17 offices in the US, Canada, Europe, and Asia, nearly 50% of the Fortune 100 as clients, and $300 million+ in annual revenue with continued double-digit growth, Axiom has become the leading exemplar of the NewLaw sector.  Indeed, in the graphic above, which is used by Axiom professionals to explain the evolving legal market, the orange in the bar on the right is what makes the “New Model” new.

Yet, here is the rub: 18 years is a long time for something to be new. And that says more about the legal industry “social system,” see Post 004 (innovation diffuses through a social system), than it does about Axiom. It also makes Axiom a great diffusion theory case study.


For summary of Week 2 guest lectures (Pangea 3, Practical Law Company, Hotshot), see Post 032. For week 3 (consultative sales at Thomson Reuters), see Post 034.

Tom Finke’s story

For the week 4 guest lecture, we were very fortunate to have Tom Finke, Axiom’s Managing Director of West Region Operations.  Tom has a JD/MBA from Northwestern, where he teaches a course called “The Evolving Role of the Law Department in the Modern Corporation and Legal Industry.”  Prior to joining Axiom in 2008, Tom spent five years as an associate at Sidley Austin LLP before switching into a series of business roles in the online media space.

Note: this is really a story about how Tom developed a very novel mindset and perspective — a combination of strategy, sales, operations, and law — and how this rare mix of talents is used by a shop like Axiom.  For those interested in having challenging work they believe in, this is not a trivial narrative.

Used cars

Tom Finke is very funny and self-deprecating, attributing much of his career to lucky breaks, starting with a summer stint as a 17-year old used car salesman in Phoenix, Arizona.  Since Tom knew very little about cars, he had to fall back on simple questions like, “what are you looking for?” After that, his only tool was listening.  Eventually he realizes that if you’re sincerely trying to be helpful, a reasonable number of customers will talk themselves into a sale.  Indeed, there are few better ways to qualify a customer than their willingness to walk around a car lot in 110 degree heat.  You just need to walk with them.

Tom’s first big break in law comes with his job at Sidley.  He interviews in the fall of his 1L year. Fortunately, the partner he interviews with loves the used car stories, and Tom gets an offer — before 1L grades come out and anyone from Sidley can review his less-than-Sidley first-semester transcript. Another break was getting into the MBA program at Kellogg, as Tom applied as the law school and Kellogg were expanding the joint program.  As the years unfolded, the training and connections of the dual degree enabled Tom to credibly wear both a business and legal hat.

“It’s hard to escape law”

After five years as an associate at Sidley, Tom decides to transition to a business role.  After a year of searching for high quality opportunities, he discovers that “it’s hard to escape law,” as the corporate world has a limited appetite for experienced lawyers working in business roles.  By then, it’s 1998 and internet is exploding as a new business platform with companies like Yahoo, AOL, and Excite.  The tight labor market creates an openness to less conventional sources of talent, and Tom finds an opportunity with an online classified ads company called Classified Ventures, a joint venture of major U.S. newspaper companies.  He joins as Director of Business Development, not as a lawyer. Later, he becomes president of a separate business unit focused on online auctions.

Repeating advice he received as a young lawyer, Tom tells the class that the early part of your legal career is about “brand building.”  Credentials and reliably good work are what matter for developing a reputation at the firm and with clients.  Yet, when Tom leaves Sidley, a firm client pulls him aside and says, “Now that you are in the business world, it’s all about track record.” In other worlds, to steadily advance, Tom has to put up outstanding numbers over a period of years.

After serving as CEO of an online business that fell victim to the Internet crash, Tom takes a job at the Tribune Company right before 9/11.  Despite the business upheavals of the early 2000s, the Tribune continues to do well as a newspaper publisher and broadcast conglomerate.  Moreover, Tom’s unit, Tribune Interactive, enjoys explosive growth that eventually reaches more than 30% year-over-year. With the passage of time, however, the decline of print journalism accelerates. These challenges coincide with a plan to turn the publicly held Tribune Company into one of the world’s largest ESOPs.  That transaction ultimately puts a crushing debt burden on the company’s balance sheet.

As the entire economy drifts into a tailspin in the fall of 2008, Tom sees the writing on the wall and contacts one of his best friends from Kellogg, who is running the Chicago office of McKinsey & Company. The colleague passes along a tip that a company called Axiom was looking for someone to start their Chicago office.  Tom applies and in December of that year gets the job. A week later, the Tribune Company files for bankruptcy.

The early days of Axiom Chicago

When Finke joins the Chicago office of Axiom in December 2008, the office had two full-time employees — one attorney along with a junior analyst — and roughly $10,000 in booked revenues.  His second day is the office holiday party, which includes 15 attorneys on Axiom’s “wait list” — i.e., approved for assignment to Axiom clients but without a current match.  Ironically, the sole actively engaged Axiom attorney is working onsite in Des Plaines (a suburb of Chicago) and hence couldn’t attend.

Despite the stark imbalance between qualified attorneys and paid client work, Tom remembers going home that night and telling his spouse, “I think this company has a chance.”  Why? Because he is blown away with the quality of lawyers/people that Axiom has managed to recruit.

Tom comments, “I was very lucky to start in 2008, as general counsel were looking for something different.  Because of the financial crisis, they had budgetary pressures and no ability to hire additional in-house attorneys.” Relatively quickly, the office added three powerhouse Chicago clients: Accenture, Baxter, and Wrigley.  “Because our attorneys did a great job for them, they allowed us to use their name as a reference client.  I often joke that I said the names of those clients more often than my children’s names in 2009 and 2010, but it might be true.”

Obviously, this is a key diffusion theory point, as these clients were viewed by in-house peers in Chicago as early adopter/opinion leaders, see Post 020, signaling that Axiom is a credible supplier of high-quality legal talent.

Tom is very direct on this point. “When you have no brand of your own [like Axiom in Chicago in 2008,] you have to leverage off of someone else’s.”  In diffusion theory, this connects to “cultural compatibility” factor for innovation adoption.  See Post 008 (discussing key factors related to rate of adoption). Axiom attorneys had the same educational credentials and work experience as a law firm associate, yet they were 40-50% less expensive and had in-house experience. By the end of 2010, sales for the office exceed Tom’s long-term projections by several million dollars. Indeed, Axiom total revenues as a company went from $25 million in 2007, to $50 million in 2008, to more than $300 million in 2017.

Axiom’s evolving business model

As we make our way through life, most of us want to conserve our mental energy by putting things into familiar boxes. Because Axiom doesn’t neatly fit within any established box, accurate categorization has long been a challenge for the company, albeit the effect is often an underestimation of the company’s capabilities, growth, and client base.

Since its founding, Axiom has curated a highly credentialed and experienced legal workforce that can be used to cost-effectively manage peaks, surges, or temporary gaps in corporate legal departments. This is the Axiom’s secondment (or talent platform) model. It continues to generate significant revenues and growth.  However, since just after the financial crisis of 2008, Axiom has been building out large teams of lawyers and other professionals in several “centers of excellence.”  For this workforce, which focuses on large-scale specialized projects and managed service engagements, the value-add for clients comes in the form technology, process, and data analytics that drive up quality, predictability and transparency of the delivery of legal services while driving down per-unit cost.

Depending upon the engagement, the talent platform and service delivery models can be paired together.

An example: The Kraft/Mondelez spinoff

To illustrate how the key pieces of the business work together, Tom picks up a grease marker and begins diagramming a corporate transaction.

A publicly held company — in this case, Kraft Foods, Inc. —  wants to spin off approximately 1/3 of its business into a new publicly-traded entity that focused on the North American grocery store business.  But here’s the problem — to enable this transaction, Kraft Foods has thousands of contracts with customers and suppliers that need to be identified, organized, and evaluated so the in-house lawyers can develop a game plan for assignment, termination, buyouts, and renegotiations, etc.  Kraft identifies 40,000 documents that are potentially relevant to the transaction. For cost reasons, having a large law firm manually review and abstract the contracts is off-the-table.

Looking for a solution, the Kraft legal department contacts Finke at the Chicago office of Axiom. By 2011 (the year the transaction got underway), Axiom had developed expertise in process-driven document review for litigation.  Drawing upon the resources and capabilities of its service delivery center in Chicago, Axiom retooled its Relativity platform so it could efficiently and reliably identify and eliminate duplications and other extraneous documents. After the service delivery unit does its portion, the 40,000 documents yields 10,000 contracts. Then, leveraging process and project management skills, attorneys in the delivery center review the 10,000 contracts to determine the impact of the spin-off.  The final step in the project is to obtain consent from counterparties and re-negotiate many other counterparty contracts, which is legal work  completed over a period of months by more than 10 Axiom lawyers from the talent platform.

The combination of Axiom’s talent and service delivery platforms was a significant enabler of the Kraft/Mondelez spinoff and subsequently became the basis for Axiom receiving a 2013 ACC Challenge Award. It is worth noting that Kraft’s strategic counsel for the transaction was Cravath Swaine & Moore.

Where things are going

The Kraft/Mondelez transaction was a major milestone in Axiom’s history, as it marked the beginning of a new line of business to enable major corporate transactions. This new area of emphasis in 2012/2013 substantially coincided with a decision to get out of the litigation document review business, which Axiom’s leadership concluded would need a massive investment in technology to remain competitive.

During class, Tom shows a slide that summarizes of Axiom’s recent deal work:

  • 80+ corporate transactions completed over the last two years
  • Specific examples of M&A support, spinoffs & divestitures, reorganizations, and joint ventures for an impressive list of corporate clients
  • $400 billion in transaction value over the past four years
  • 500+ Axiom contract specialists and M&A lawyers

Axiom is also growing, likely at the expense of other service providers, particularly law firms.

With this information in mind, it is worth putting side-by-side Axiom’s evolving legal service delivery model with the Post 013 evolving litigation model created by Alan Bryan, Walmart’s head of legal ops and outside counsel management. [click on graphic below to enlarge.]

It is obvious that both graphics are signaling the identical future — one where law firms are called upon for strategic and exceptional events and the balance of the run-the-company work is split between in-house departments and outside service providers based upon efficiency and value.

A changing talent market

According to Finke, the evolution of the legal market over the last decade has created significant industry-level pressures on talent.  Since 2008, major law firms have hired significantly fewer entry-level associates, which in turn impacts Axiom’s traditional talent pipeline.  Although Axiom’s flexible work model and blue-chip client base remain highly attractive for many law school graduates, higher student debt-loads affect the timing of when lawyers can make the jump.

Tom notes that over the last decade, in-house lawyers have become “the owners of core operating functions” and that “BigLaw is competing for marketshare with their clients’ legal departments and losing.”  Cf. Post 003 (showing rapid increase in in-house lawyering over last 20 years). At present, over 70% of the lawyers on Axiom’s talent platform have in-house experience, which clients generally find more valuable than law firm-only experience, at least for work that supports a company’s business units. Thus, in recent years, consolidated legal departments following a corporate merger have become an important source of talent for Axiom. Yet the market overall is tightening for the right kind of experienced lawyers.

The key takeaway is that the traditional law firm apprentice model is breaking down. The incoming numbers are lower; and from the client perspective, the law firm skill set has become less valuable.  Ultimately, these economic realities impact law school applications and enrollment, particularly as student debt loads remain at historical highs.  Tom noted this was a industry-level problem with no easy or risk-free solution.

An focus on technology

Recent additions to Axiom’s leadership arguably signal the company is positioning itself for a future where technology will be a major differentiator.   In the fall of 2016, Axiom’s co-founder and CEO Mark Harris recruited Elena Donio, former CEO of software giant Concur, to replace him.  Furthermore, Axiom recently hired a chief technology officer, Doug Hebenthal, who formerly served as Director of Engineering at Amazon and held numerous technical positions at Microsoft.

Referring to Hebenthal, Finke observed, “If someone had told me in 2008 that Axiom would one day hire a CTO of that caliber, I doubt I would have believed them.  But our business has evolved in response to a changing market. And tech-enabled delivery of legal services is clearly where things are headed.”

Diffusion theory takeaways

The methodology of the class is take in take a deep dive into examples of legal industry innovations — always a combination of people and organizations — and to examine relative successes and failures through the lens of diffusion theory.  In most cases, we are referencing Everett Rogers’ rate of adoption model, which was covered in foundational post 008 and summarized in the figure below [click on to enlarge].

Within this model, the “Perceived Attributes of the Innovation” category tends to be the most important.  Without a sufficient quantum of these factors, the social system adoption process will not get triggered.

Applying the rate of adoption model to Axiom’s 18-year track record of growth, the combination of three factors appears to be key:

  • Relative advantage: 50%+ cost savings over law firms.
  • Cultural compatibility: work done by attorneys with BigLaw training and in-house experience.
  • Trialability: giving Axiom small, low-risk projects until the client obtains confidence in the lawyers’ ability.

The 50% cost saving by itself would have been insufficient for Axiom’s adoption. Further, the financial austerity created by the 2008 financial crisis was a key factor in changing the relative advantage calculus. 50% saving post-2008 was a lot more valuable than 50% pre-2008. Cf. Post 032 (David Perla also acknowledging that the financial crisis was a major accelerant for Pangea3).

Likewise, Axiom invests heavily in “Efforts of Changes Agents” by fielding a large team of consultative salespeople.

In the fall of 2016, I had the opportunity to participate in a meeting of Axiom’s Western Region sales team. Basically, to handle sales in the Midwest (Chicago, Minneapolis, Detroit, St. Louis, and Ohio), Axiom employed 15 full-time sales professionals.  Of the group, the vast majority were MBAs; only two had law degrees, and only one had practiced law.  I asked why Axiom had built out the sales team in this way.  Tom acknowledged the advantage of the JD credential.  Yet, experience revealed that it was easier to get an MBA to acculturate into the legal world (such a Rebecca Thorkildsen from Week 3) than to get a lawyer to (a) feel comfortable providing pure business advice and know-how to prospective clients, and (b) deal with the frequent rejection that comes with a sales role at a company seeking to disrupt the industry.

By necessity, law is ceding ground to various allied professionals. Because this brings new perspectives, this bodes well for future innovation.

What’s next? See The Decline of the PeopleLaw Sector (037)

Week 3 of my “How Innovation Diffuses in the Legal Industry” class focused on the crucial role of consultative sales and established distribution channels in the diffusion of innovation.  The success was entirely due to our guest lecturers from Thomson Reuters, pictured above.

The value of this class, however, will not make sense without first providing some real-world context. So let’s start there, circling back to diffusion theory and how Borstein, Thorkildsen, and Stroka are, in fact, “change agents” as defined in Post 020.


For the Week 2 wrap-up, see Post 032.


 February 2014: Meet-up of legal start-up entrepreneurs

At a meetup of legal start-up entrepreneurs convened in the shadow of ALM’s 2014 LegalTech show, David Perla, Josh Kubicki, and Rob Saccone dispensed advice to the standing-room only crowd.

One comment I never forgot came from David Perla [Week 2 guest lecturer], who scoffed at the notion that Thomson Reuters, Lexis, Wolters-Kluwer and other serial acquirers were not a significant part of the innovation ecosystem. Perla stated emphatically that some of the smartest business people in the legal industry worked inside Thomson Reuters. “They definitely have some brilliant people that understand how business works; how people make decisions; how to lever off brands and established customer bases to build up dominant businesses.”

Perla, who was long gone from Thomson Reuters by then, was warning the audience not to get arrogant, overestimating our own creativity and underestimating the acquirers’.  As the co-founder and active operator of a legal industry start-up, see Post 004, I had, by February 2014, consumed enough humble pie to not want any additional helpings.  I didn’t understand Perla’s observations at a deep level, but I stored them away in my mind for possible future use.

March 2016: Chicago Legal Innovation & Technology Meetup

Fast forward to the spring of 2016, where Dan Linna, Dan Katz and others are running another iteration of the Chicago Legal Innovation & Technology Meetup (this one in the shadow of the ABA TECHSHOW).  I’m on a panel with Joe Borstein, who is running sales for Thomson Reuters legal managed services unit (formerly known as Pangea3).  I remember saying to myself, “This Borstein has better intel on the legal start-up market than anyone I’ve ever met. And where in the world did he come up with these slides? They’re gold.”  In addition to being funny, Joe also had a knack for simplifying the complex. People liked listening to him.

July 2017: Chicago/Milwaukee Regional Meeting of CLOC

Fast forward again to July of 2017. I’m attending the Chicago/Milwaukee regional meeting of CLOC. Paul Stroka, Director of Legal Solutions at Thomson Reuters, was paying for lunch and arranged for some educational programming, including an overview of emerging legal technology from his colleague, Rebecca Thorkildsen.

Two things stuck in my mind from that meeting:

  1. Paul Stroka had a wonderful light touch, doing whatever he could to facilitate a higher-value meeting for CLOC members, never once engaging in anything that felt like a sale pitch.
  2. Paul’s colleague, Rebecca Thorkildsen, was the single most knowledgeable person on legal technology that I had ever met, providing useful framework after useful framework for understanding the bewildering arraying of technology that was now coming into the marketplace.

And then the lightbulb goes off — “This is what Perla was talking about. These are extremely knowledgeable professionals who are growing business units at Thomson Reuters.”  Thus, before the meeting ended, I asked Paul and Rebecca to come to my “How Innovation Diffuses in the Legal Industry” class at Northwestern Law in the fall, and, if possible, include Joe Borstein.

Consultative Sales — what it is and when and why it works

In the anecdotes shared above, the common theme is consultative sales.  Borstein, Stroka, and Thorkildsen are subject matter experts who are sincerely focused on listening, educating, building relationships, and problem solving.  This is a relatively expensive “long-game” approach. Yet, its underwriter is Thomson Reuters, a legal information giant that deeply understands the economics of sales and distribution through decades of selling books, practice guides, and online subscription services.

To boil it all down, consultative sales works best when (a) prospective clients are struggling to understand their own business challenges, often due to significant or rapid industry changes, and (b) your products provide the best solutions to a reasonable subset of those challenges.  Although your educational and problem solving efforts will sometimes point prospective clients toward another vendor, they are sure to come back to you when they need your specific product. Moreover, they will refer you and your product/service to their industry peers.

It should be obvious that this wonderful long-view approach is unavailable to fledgling legal start-ups who need sales and reference clients before they run out of cash.  In many respects, today’s legal industry is similar to the automotive industry circa 1905:  There are hundreds of small car builders who rightly believe that cars are the future — it’s just not going to be their car.  This is because the industry inevitably must consolidate into a smaller number of dominant companies that can simultaneously focus on both quality and cost while building a sales and distribution network that can handle the complexities of warranties, service, parts, and repairs, etc.

As Thomson Reuters knows as well as Ford or General Motors or Chrysler did back in the day, in addition to projecting stability to your client base because of your size and client base, there are tremendous economies of scale to selling.  That is why Thomson Reuters can afford to field an A-team of seasoned lawyers (Borstein and Stroka) and MBA consultants (Thorkildsen) to educate the market.

Example of educating the client

I specifically asked the Thomson Reuters crew to walk the class through materials they use when interacting with prospective clients.  Below are three key slides presented by Rebecca that beautifully illustrate the value and power of consultative sales, particularly in a crowded and confusing marketplace like legal circa 2017.  Note the three slides below have a contract/transaction focus. The next section touches on litigation.

Here’s the basic set-up: Imagine you are a legal operations professional working in the legal department of a large global company.  The goal of the company is to grow and prosper economically — and for 365 days a year, that requires the company to form and execute contracts.  Obviously, because of the scale of the business, those contracts become an enormous management challenge.

In 98% of all cases, the legal department lacks the time (and often business training) to understand their challenges within a broader system framework.  Note how Slide 1 divides the world into legal and business drivers (left side) and provides an lifecycle framework for overall contract management (right side).

The purpose of Slide 1 is to help the client identify, organize, and ultimately prioritize their internal pain points.  The legal professionals, after all, want to impose order on their massive workload and feel like they are delivering consistent value and quality to their business unit.  It is a good sign when a prospective client asks for your slide deck, as it means you’ve connected with a real problem.

Again, if you are a legal department operations professional, you are constantly being pitched by a bewildering array of technology vendors. Invariably they ask themselves, “What do all these company do?  And how do they relate to one another?”  Slide 2 places the vendors into categories based on functions and features.  With this one slide, you know where to focus your time.

What is remarkable about Slide 2 is that Thomson Reuters products are mixed in with competitors but not in a way that makes them identifiable.  Let the customer react and talk and, over time, a large number of good fits will be revealed.

Slide 3 uses a similar approach.  However, now it’s organized in a way that maps onto the department’s legal functions and workflow.

If Rebecca Thorkildsen spent an hour or two with you, sharing company materials and helping connect your problems to potential solutions, most professionals will reciprocate by buying from Thorkildsen’s company when the need and product(s) are a strong match.  Why? Because the problems never end and they want access to her expertise in the future. That’s the Thomson Reuters’ long game.

The careers of two ex-litigators tell an important story

While Thorkildsen has a tremendous command of legal technology, particularly in the contracting space, Joe Borstein and Paul Stroka shared some personal experiences from their time as lawyers that were (a) useful in understanding the arc of the broader legal market, and (b) the personality and mindset of someone who is likely to be good at consultative sales.

Borstein told the story of cutting his teeth as a litigation associate at a major law firm where he was put in charge of managing a team of several dozen professionals on a massive document review for a bet-the-company case.  Although the work was generating tens of millions of fees for the firm each year for several consecutive years, the process was highly inefficient and plagued with quality control issues. “From the inside, it was obvious that the system was broken.  Friends of mine at other firms were drafting business plans for new ventures. There was no way the status quo was going to last.”

Then came 2008, which had a massive effect on the client-law firm relationship.  If you recall from Post 032 (featuring Pangea3 co-founder David Perla), the financial crisis was Pangea3’s breakout moment. By the time Borstein joined in early 2011, the company had a marquee list of large corporate clients.  Although the entire sales team had, by then, written off law firm customers as a “hopeless cause,”  Borstein persuaded Perla to let him try.  “I was convinced there had to be people who had my experience — who were worried about quality, meeting deadlines, and damaging the firm’s reputation.  Further, it wasn’t the right kind of work for brilliant people from great law schools.”

Much to the surprise of his peers, Borstein’s approach ended up cracking the code for law firms.  “It’s still a tougher sale,” said Borstein, “but law firms are now an established part of our client base, and it’s growing.”

Like Borstein, Paul Stroka’s experience inside BigLaw made him skeptical of the business. “As an entry-level associate benefiting from the salary wars of the mid-2000s, I couldn’t understand why my salary kept going up even though I didn’t know anything useful yet.”  As he switched firms and focused on labor & employment litigation, he was troubled that success as a partner looked like having a base of clients who got sued a lot with cases that lasted a long time. “Litigation is usually a miserable experience for clients on both a cost and emotional level,” recalled Stroka. In his ninth year of practice, Stroka concluded that “I needed to be in a place where I was working to make some of those problems go away.”

At the time, Pangea3 didn’t have an open sales position. But Borstein was impressed by the persistence of a Chicago litigator who keep messaging him on LinkedIn and requesting a meeting.  Eventually Borstein flew Stroka to New York City to see if opportunity might be knocking. “It is very hard to find people who are good at this job,” said Borstein. “We make rain by listening to problems and finding good fits.  Paul has that very rare skill set.” Interestingly, Stroka commented that he has become a much better problem solver since joining Thomson Reuters, because that has become the primary focus of his job.

I pressed Borstein on the rainmaking point. He explained that outstanding BigLaw rainmakers use this same problem solving sales approach. However, they are constrained because the clients end up wanting them to also do the work. “In these big cases, the big-producing partners are pulling all-nighters with the associates. Law is the only business where the salesperson actually has to do the work. That is an enormous constraint of their model.”

An “ugly” but important slide

My original impressions of Borstein, Stroka, and Thorkildsen were strongly reinforced by their visit to my class. They have a tremendous grasp of the market, owing in part to their lengthy legal industry work experience, but more importantly because of the time they devote to visiting clients, reviewing data, reading industry press, and connecting with others in the industry.  This enables them to produce work product that dramatically simplifies a very complex and rapidly changing industry.

A good example is Slide 4 below, which Borstein described as “ugly but profound.”

Slide 4’s key takeaway is that the historical bi-lateral relationship between law firm and client is now being supplanted by a collaboration among multiple parties.  Obviously, Thomson Reuters has positioned itself as a technology and alternative legal service provider (ALSP), see Post 010 (discussing the rise of managed services), but that doesn’t make the graphic any less useful for understanding industry change. And note, Thomson Reuters senior leadership likely saw this more than a decade ago.

There are legions of high-IQ people in law, but the vast majority of them are very busy trying to hit this year’s revenue targets, leaving precious little white space for fact-gathering and reflection. This dynamic gives a company like Thomson Reuters an enormous advantage in seeing and understanding the big picture.

Borstein and Stroka commented that what has happened in the litigation realm over the last decade — with, for example, predictive coding, legal process outsourcing, and the rise of managed services — is about to happen in the transactional realm.  That’s too big a topic to cover here. If you want the same education, invite them in for talk.

Thorskildsen is a lawyer, right?

It’s time to wrap this post up and connect it to diffusion theory.

Rebecca Thorkildsen is often asked where she went to law school.  She replies, “I didn’t go to law school.  I have an MBA.” Fortunately, when that question gets asks, it is usually because Thorkildsen has impressed someone with her command of complex, law-related materials or her excellent, organized communication style.  During class, Borstein observed that it shouldn’t matter whether a professional working with lawyers has a law degree, “but often it still does.”

Thorkildsen explained that after graduating from business school in 1995, she joined Arthur Andersen as a consultant. “One of my first assignments was to help implement an email system inside a law firm. The next assignment was with a corporate legal department.  In the consulting world, that can quickly turn you into an industry specialist.”  Thorkildsen subsequently joined Baker Robbins, a technology consulting firm focused on legal clients, which became part of Thomson Reuters in 2000.

The connection to diffusion theory

As noted in foundational posts 020 (on change agents and opinion leaders) and 024 (crossing the chasm), consultative salespeople are often the key change agents within an industry.  In most cases, two personal attributes are threshold requirements for effectiveness: (a) cultural similarity with clients (referred to as homophily) and (b) credibility in the eyes of clients.  The observations and experience shared by the Thomson Reuters team certainly corroborate the theory.

Yet, this insight has deeper significance for the legal industry.  Specifically, it suggests that crucial non-legal innovations related to legal productivity, such as data, process, and technology, will tend to diffuse faster when the communication channels are lawyer-to-lawyer, even when the underlying content is entirely non-legal. Some might call this snobbery or prejudice, but according to diffusion theory, it’s just a recurring feature of any social system, including law.

Finally, below is a diagram from Post 020 that ticks off the factors that enable a change agent to be more effective at accelerating innovation adoption. Isn’t it obvious that the consultative salespeople at Thomson Reuter hit them all?

For additional analysis for these seven factors, see Post 020.

What’s next?  See Mark Chandler Speech from January 2007 (035)

As the above syllabus excerpt suggests, there is now a law school course on how innovation diffuses in the legal industry.  This new ground is being tilled at Northwestern Pritzker School of Law, where I am visiting this fall.  It is one of the few courses at Northwestern Law that enrolls both JD and Masters of Science in Law (MSL) students.  This enrollment is ideal, as the diverse educational backgrounds and professional experiences of the MSL students are a terrific complement to the 2L and 3L students who have already internalized a surprisingly large amount of legal culture.

The class started last Monday (10/16) and runs for eight classes.  As diffusion theory is part of an applied research tradition, see Post 004, we spent exactly one class on the underlying theory and the legal industry before moving to examples.

The examples are supplied by legal innovator and early adopter guest lecturers.  For Week 2 (10/23), we had the pleasure of hosting David Perla, co-founder and former co-CEO of Pangea3, and Ian Nelson, who was part of the original US sales team of Practical Law Company (PLC) and more recently co-founder of Hotshot, a tremendously innovative e-learning company focused on legal professional development.

NewLaw and legaltech start-ups are now widely covered by the legal press. But that was hardly the case during the booming mid-2000s when all the focus was on soaring BigLaw profits and salaries.  I wanted to start our guest speaker series with David and Ian because during this hey day period, both quit top-of-the-food-chain jobs to pursue obscure and speculative business opportunities (David in 2004 and Ian in 2006).  At the time, the future we are now living in was far from obvious.  Yet, when their respective companies sold to Thomson Reuters a few years later at valuations and multiples on-par with highly successful Silicon Valley start-ups, it became clear that NewLaw and legaltech were sectors with enormous opportunity for the innovative and ambitious.

Perla’s story

Over the years, I have heard several renditions of Pangea3 founder stories.  But Monday’s edition provided a new twist, as David focused on the preeminent importance of professional relationships and how, in hindsight, the long game is the only game that really matters.

David went through a long litany of examples of how a decade’s worth of professional contacts accumulated since law school (by both he and Pangea3 co-founder Sanjay Kamlani) were crucial to opening doors or indirectly supporting the fledgling start-up.  From getting free access to 1,200 Indian lawyer resumes from Monster.com so the duo could stand-up a work team in India over the course of a few days (David had just quit the GC position at Monster to launch Pangea3); to several months of office space at Katten Muchin so Pangea3 could signal a midtown Manhattan address (David was a former Katten associate); to an initial investment by a prominent Indian-American lawyer who had credibility in both US and India venture capital and legal circles, thus greasing the skids for everything that followed (this came through Sanjay’s tenure at PWC and OfficeTiger, a first-generation business process outsourcer in India), each story illustrated the tremendous importance of relationships. Cf. Post 020 (discussing crucial role played by “weak-tie” relationships in the diffusion of innovation).

The most surprising and powerful story was David’s family connection to the head of litigation of a major global bank.  The family friend took David’s call, but said at the outset,  “I am happy to help you in any way I can through mentoring and coaching, etc., but I’m never going to send any documents to India.”  David replied, “I understand.  Is it okay if I check in with you every six months?” The head of litigation said “Sure.”  David foreshadowed that this connection would turn out to be key to the ultimate success of Pangea3.

In the meantime, David and his colleagues were trying to crack the code of the large global banks.  One of their prospective customers broke the disappointing news that “we innovate in our trading strategies, but not in operations or sourcing. For that stuff, we’re a second mover. So if you want us to hire you, go get [list of global banks] as one of your customers. Then we can talk.”

Fast forward a couple of years, the big break for Pangea3 was the turmoil in the financial services market in the fall of 2008 and the resulting global recession.  David fields a call from one of his board members, who buoys his spirits, “Doubledown on sales; recessions are good for outsourcing.”  Shortly thereafter, David takes a call from the family friend / head of litigation at the major global bank. “David, the General Counsel just informed us that our budget has been cut by 25%. I know I said never, but never just happened.  Can you be here this week for a meeting?”

That meeting resulted in Pangea3 landing its largest and highest-profile client, which in turn sped up the sales cycle for several other large banks waiting to go second. David acknowledged that he did not have the benefit of diffusion theory when he was building Pangea3.  Yet, on both the investor and customer side, he could see how certain key early adopters had the effect of making a wide array of disparate pieces fall into place.  David specifically referred to these people as “influencers.”  Cf. Post 020 (discussing how “opinion leaders” within a social system are “able to informally influence other individuals’ attitudes or overt behavior in a desired way with relative frequency”).

Nelson’s story

I first met Ian Nelson in the fall of 2008 at a legal innovation conference–in hindsight, the first of its kind–organized by USC law professor and economist Gillian Hadfield. By 2008, Ian had been working at PLC for two years, initially in content creation but then transitioning to a lead role in sales.  Although PLC had already become a dominant force in the UK, the US was a bigger market governed by different law.  Thus, for all practical purposes, Ian had joined a US-based start-up.

There are two reasons why the PLC model is highly relevant to anyone interested in legal innovation.  First, Thomson Reuters’ acquisition of PLC in 2013 remains the high-water mark for financial success among legal industry entrepreneurs.  See “Thomson Reuters buys Practical Law Company,” Telegraph, Jan. 23, 2013 (reporting the size of the deal between £200 and £300 million, all of it achieved without outside investment). Second, there remains a wide array of activities currently performed non-expertly by law firms and legal departments that could be turned into highly successful businesses by carefully applying the core logic of the PLC model. In fact, this logic is very much at work at Ian’s current company, Hotshot. Thus, let’s briefly review the PLC model.

Practical Law began life in 1990 as a trade journal focused on the UK legal market. Some of the most popular features were practice tips that pulled together and explained the technical aspects of new and emerging methods of finance.  This is not surprising because PLC’s two founders, Robert Dow and Chris Millerchip, began their careers as associates at Slaughter and May, a leading Magic Circle firm specializing in M&A and sophisticated corporate finance.  Quipped Millerchip, “We created the thing that we wanted when we were practicing.” Ross Todd, “Web Practice Tools for Transactional Lawyers,” Legaltech News, Jan. 23, 2009.

With the growth of the web, PLC’s offerings became simultaneously better and easy to access via an online subscription model.  Relatively quickly, firms were being placed in a competitive disadvantage if they lacked access to PLC work product, which included document templates, standard clauses, practice guides, and deal checklists. In theory, firms could create this content on their own.  Yet, the ability to scale across the entire corporate bar enabled PLC to deliver higher quality work product at a much lower per-unit cost. In effect, PLC had become a privately run shared service relied upon by the vast majority of top UK law firms. The economics of a shared services model make it virtually impossible to dislodge a well-run first mover — and PLC fit that description to a tee. Cf. Thiel, Zero to One 97-98 (2014) (“[M]oving first is a tactic, not a goal. …  It’s much better to be the last mover—that is, to make the last great development in a specific market”).

During his guest lecture, Ian recounted his early days as a NYC corporate associate when he first encountered some of the quality gaps later filled by PLC and now being targeted by Hotshot.  The first instance occurred within a few days of hiring when Ian was dispatched to a far-flung city for due diligence on a “reverse triangular merger.”  The supervising partner instructed Ian to review a large volume of contracts and flag anything that “looked weird.”

Despite his law review credentials, Ian had no idea what a reverse triangular merger was, much less the definition of weird. Thus, for the next 48 hours, Ian was thrown into a silent panic, fearing that his legal ineptitude would should be exposed to the world. Yet, what he soon discovered was that neither the partner (and apparently the client) cared about the inefficiency of the process, as Ian’s overinclusive approach to copying “weird” provisions for further study at the firm’s headquarters was all being billed by the hour. Had Ian had access to Hotshot, he would have had on-demand videos and practice guides enabling him to get the answers that the supervising partner lacked the time or interest to provide.

A second formative incident occurred a couple of years later when Ian headed to London to work on-site at a UK firm that was co-counsel on a major transaction. During a tour of the office, Ian was shown the cafeteria, the copy room, and the work area for the PSLs.  Ian asked, “What’s a PSL?”  He was flabbergasted to learn that in the UK it was common practice to have “Professional Support Lawyers” who were responsible for, among other things, organizing and cataloguing the firm’s work product so the very best precedent could be quickly located for use on future client matters.  Ian subsequently returned to the US and lobbied his firm for the creation of a similar role, as it would replace the then-common practice of firm-wide emails soliciting documents that could be used as a starting point for current client matters. Upon hearing these ideas, however, the partners shrugged with indifference.

Six years into corporate law practice, these were some of the formative experiences that caused Ian to respond to a job ad for PLC — experiences that really struck a chord with the students. At my request, Ian did a deep dive into Hotshot. However, Hotshot as a business and product offering warrants its own future post.

What diffusion theory insights did we learn?

As I reflect on Week 2, three themes stand out.

  1. There is no innovation without execution. It’s easy to discuss innovation as something conceptual, but until the early adopter end-user receives full value, innovators are just trafficking in ideas.  Although this terrain is covered in LE’s “Crossing the Chasm” and “Hype Cycle” series, see, e.g., Posts 024025, it was made more vivid by Perla’s description of solving funding and operational challenges while also hunting down the early adopter customers. Standing up a quality-first operation in India (the putative innovation) is extraordinarily complex, time consuming and costly. Although Pangea3 was able to hit its ambitious sales targets relatively early, the time span between a signed deal, doing the work, and getting paid — particularly when large corporate clients string out vendors for 45 to 90 days — resulted in a “near death” experience for Pangea3. Suffice it to say, there is enormous risk in translating an idea into an innovation that warrants diffusion. Innovator-leaders like David and Sanjay who can skillfully coordinate the technical talents of others are very rare and very valuable.
  2. There are usually several social systems that matter; not just one.  As I listened to David and Ian, it became obvious that several social systems were interacting with one another.  For example, David describes how inroads with the New York global banks had little to no impact with legal departments at large US tech companies.  In fact, it was an investment by famed Silicon Valley VC firm Sequoia Capital (on less favorable terms that other VC shops, though the discount was worth it) that opened doors with tech companies on the west coast and, in turn, reverberated throughout India, signaling to young Indian lawyers that Pangea3 was the firm to join.  In Ian’s case, the early adopters at Hotshot were all Silicon Valley-based law firms who saw real advantages to having better-trained associates who could actually understand and do the math in venture capital deals.  A credible roster of west coast-based AmLaw 200 firms were eventually enough to open doors on the east coast and in the midwest where the deal flow was more traditional M&A.  Cf. Post 004 (“Rogers’ core insight … is that the diffusion of innovation is a process that occurs through a social system“).
  3. Relative advantage, cultural compatibility, and trialability really matter.  Apparently, for a large global bank, the difference between “never” and “this week” is a 25% budget cut.  Thus, drawing upon the Post 008 rate of adoption model, Pangea3’s big break turned on a sudden shift in the “relative advantage” of legal process outsourcing. Likewise, regarding cultural compatibility, both David and Ian emphasized how their status as former BigLaw corporate lawyers, including knowledge of cultural norms related to speech, dress, and credentials, opened both minds and doors.  Finally, Ian gave examples of how trialability was key to making sales for PLC and Hotshot, while David discussed how small projects resulted in growing sales, including a mandate from a major client that Pangea3 would be used by all outside counsel for all large-scale document reviews.  Apparently, nothing is more convincing than tasting the soup.  If it tastes good, your early adopter customers will set off a chain reaction (within a firm or legal department, or among peer firms or legal departments) that will do the work of an army of salespeople. Cf. Post 025 (Geoffrey Moore noting that word-of-mouth marketing is essential to crossing the chasm).

Week 3 of How Innovation Diffuses in the Legal Industry features three highly accomplished Thomson Reuter professionals:

  • Joe Borstein, Global Director of TR’s Managed Legal Services (formerly Pangea3) and Innovation Columnist at Above the Law
  • Rebecca Thorkildsen, Global Director of Legal Solutions (a person with an amazingly broad and deep grasp of the rapidly expanding legal ecosystem)
  • Paul Stroka, Director of Legal Solutions (a very capable corporate lawyer who has a deep understanding of consultative sales — i.e., selling as a second-order effect of customer problem solving — which is the core skill of a change agent)

I’ll do my best to pass along what we learn.

What’s next?  See The 2017 Forum on Legal Evolution (033)

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.

Awareness-Knowledge

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

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

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

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

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

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

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

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

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

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

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

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

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

Opinion Leaders

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

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

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

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

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

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

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

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

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

Change Agents

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

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

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

The Tradeoff between Information Impact and Communication Ease

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

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

The diagram below illustrates the conundrum.

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

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

Effective Change Agents

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

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

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

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

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

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

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

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

The Legal Productivity Problem

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

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

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

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

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)