This article discusses potential impacts of Artificial Intelligence (AI) on the legal profession. Specifically, I discuss (i) WHY you should care about AI, (ii) WHAT AI means, (iii) WHERE we are now, and (iv) HOW should you prepare. I suggest three perspectives as you consider AI impacts: (1) the legal practice, (2) individual legal professional roles, and (3) businesses, organizations and society (which necessarily drive the first two). This is not a survey of all AI developments associated with the business and practice of law. Instead, I provide you with resources to appreciate the trajectory and make your own decision on what to do and when.

Fair warning: this article is long. I was going to split this into two or three separate articles to make it easier to digest. However, I include purposefully over 200 footnotes, offering a flow of articles, research papers, video and audio files, and books for you to explore. Since it will take time to absorb the material regardless of the presentation, I offer it in its entirety. To ease your journey, consider pairing this with a very nice glass of wine.[1]

I’m an AI Luddite. Every day I read more about Artificial Intelligence (AI) and how technology is changing our world. Videos demonstrating the future of construction[2] and truckers grappling with automation[3] demonstrate the power and potential of robots and their impacts on businesses and society. Machines play creative Hold ‘Em poker,[4] while Google Translate’s machine learning is reinventing what we know about computing.[5] It appears that humans are not the only ones thinking creatively, ultimately to be surpassed when we reach Omega/the Singularity.[6]

The legal profession is not immune to these changes. Machine learning software is getting to the point where it may drastically improve a judge’s ability to determine if a criminal defendant poses a flight risk, potentially reducing crime and the number of people stuck in jail awaiting trial.[7] Law firms and departments are also pursuing AI, impacting the way legal services are staffed and delivered.[8] J.P.Morgan’s COIN machine learning platform is parsing financial deals that once kept legal teams busy for thousands of hours.[9] The deployment of “Software Robots”[10] continues the trend as more law firms and departments reimagine the business and practice of law.[11] In time, legal writing may join truck driving as an endangered skill, while paralegals, legal assistants, and legal secretaries[12] remain perched in the high risk category in the near term. [See interactive Tableau dashboard to explore where machines could replace humans.[13]]

I enthusiastically embrace technology and change. I enjoy looking around corners, designing integrated process, staffing, and technical solutions to business challenges, and enhancing legal service delivery.[14] Why do I call myself a Luddite? Although this term has come to mean a person opposed to new technology,[15] dig into the foundations of Luddism and you will realize it was something very different.

Luddites were not fearful of technology; they were skilled artisans who relied on technology. They were experiencing business process, staffing, and economic disruptions associated with the use of technology. The years leading up to the initial Luddite protests in 1811 included multiple, failed, non-physical efforts to secure more equitable pricing and better wages.[16] The Luddite destruction of machines with Enoch hammers[17] was the only remaining tactic available.[18] Simply put, their actions were intended to call attention to workforce impacts associated with the use of technology. (See Appendix A for additional perspective about the Luddites.)

There are similarities between now and the Luddite times, including accelerating economic inequality,[19] business process and staffing changes, a hollowing out of the middle class,[20] the prospect of large shifts in employment, and a continuing diverging economic path for skilled versus unskilled workers.[21] However, the current AI-enabled transition is different: we are shifting from machines being tools that increase worker productivity towards an environment where machines will be the workers themselves.[22]

Technology historically generates cultural anxiety, worries of technological unemployment (the loss of jobs caused by technological change)[23] and anxiety over the moral implications of technological progress on human welfare.[24] People react to changes brought on by technology in different ways, including wielding a hammer, protesting, and voting. Reactions depend, in part, on economic circumstances, future employment opportunities, government (in)action, and skills development and education opportunities. Reactions also depend on the ability to accept and guide the use of technology, an ability to work with technology as opposed to against it.

Fortunately, the only Enoch hammer listings on Amazon at the moment relate to music and books. I prefer not to wait to experience all the disruptive associated with technology disruption before I take action. I am an AI Luddite because I want to discuss and plan ahead for inevitable workforce impacts as AI changes our legal profession, organizations, businesses, and society.

I. Prelude

Legal professionals continue innovating legal practices through thoughtful business process analysis, legal project management, staffing optimization, and adoption of increasingly powerful technology. In order to manage near- and long-term opportunities, leaders need to discern both the possibilities and the impacts:

To get ready for automation’s advances tomorrow, executives must challenge themselves to understand the data and automation technologies on the horizon today. But more than data and technological savvy are required to capture value from automation. The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations.[25]

Towards this end, I structure this discussion as follows:

  • WHY you should care: why AI matters now and why you should pay attention.
  • WHAT is AI: an interpretation for discussion.
  • WHERE are we now: an overview of current developments.
  • HOW should you prepare: take action to position yourself and your organization.[26]

My goal is not to convince you that you are wrong or right to believe that AI will/will not (i) replace many workers or your legal job; (ii) present new opportunities to deliver pertinent business and legal advice, (iii) offer you anything of immediate value, (iv) require you to merge with technology to survive, or (v) be something you can leave to others after you retire. Rather, my goal is to elevate you from your daily business and practice demands, enabling you to pause and reflect about AI.[27]

II. Why You Should Care

We are at an inflection point in the history of our economies and societies because of digitization.[28]

Legal professionals should focus on AI now because they need to:

  1. Understand that we are experiencing new ways of working and thinking: AI is changing the way our organizations function and the way we think and go about our work.
  2. Manage the changing models of legal service delivery: AI transformation is occurring in parallel with ongoing changes to the ways we deliver legal services. Legal professionals should proactively harmonize these parallel developments to design efficient, scalable teams and solutions to meet the challenges of today and tomorrow.
  3. Control the speed of change: Given the speed of AI change, you need to start planning and implementing now, even if you will not activate an AI solution next Monday.

New Ways of Working & Thinking

Although the individual concepts are indeterminate at best, together AI, Big Data, and the Cloud are enabling systemic transformations to the way we work. The confluence of these concepts represents a broader “digital transformation” of our organizations.

Digital transformation is the process of shifting your organization from a legacy approach to new ways of working and thinking using digital . . . and emerging technologies. It involves a change in leadership, different thinking, the encouragement of innovation and new business models, incorporating digitization of assets and an increased use of technology to improve the experience of your organization’s employees, customers, suppliers, partners and stakeholders.[29]

During this transformation, AI will present dynamic legal issues for our clients. Legal professionals should grasp the changes to anticipate issues before advice is requested. By way of near-term example, legal professionals will need to appreciate fully the impacts of data and the ways organizations should think about ownership and management. “Who owns the data is as important a question as who owned the land during the agricultural age and who owned the factory during the industrial age. Data is the raw material of the information age.”[30] It is time to raise your digital quotient.[31]

In The Inevitable (Understanding the 12 Technological Forces That Will Shape Our Future), Kevin Kelly describes how we are entering into a new, third computing age, “the Flows.”[32] His brief history of our experience with computers provides some perspective on new ways we think and work:

  • The First Era (batch mode): Our experience in the initial computing age replicated our office environment. Our screens offered a desktop, folders and files. In this first era, we processed information in regular stages. For example, we received our bills via U.S. mail every 30 days, often processing them in batches. [33]
  • The Second Era (daily mode): In the second digital age, we shifted to organizing content on the web. Pages were linked, accessible in a browser, structured not by folders but in a connected web. E-mails ruled and we expected replies the same day! Cycle times jumped from batch to daily mode.[34]
  • The Third Era (real-time mode): In our current transition into the third age, pages are less important. The basic units are now flows and streams – Twitter and Facebook posts, communications accessed via Slack or IM, streams of photos, music, movies and RSS feeds. Tags replace links while our processing shifts from a daily mode to real time. “[I]n order to operate in real time, everything has to flow.” [35] “It is a world of the now.”[36]

Many legal professionals still think and manage work using the desktop/folder/file paradigm. More seasoned professionals still live and suffer with e-mail, while neoteric generations[37] live in their IM or Slack account. Your organization’s Flows are (hopefully) not tweets or Facebook posts. Instead, you likely receive a steady stream of client requests amid reminders of team and project tasks (via messages, e-mails, etc.) as you jump from meeting to meeting.

If you and your organization are having difficulty keeping up with the Flows now, you will find it harder as AI disruption continues apace. Understanding AI now will help you get into and manage your Flows at work.[38]

Changing Models of Legal Service Delivery

Law departments and firms continue to explore how to do more with less, (re)considering staffing and service delivery models. In-house professionals shift work internally and implement convergence programs because it offers comparative savings when compared to law firm sourcing. Law firms, in turn, hire career associates,[39] appoint innovation officers,[40] hire directors of analytics,[41] and invest in legal project managers to diversify the business model.[42] “Alternative” providers like Axiom, Elevate, Integreon, QuisLex, and UnitedLex offer additional, cost-effective legal sourcing and practice delivery solutions. New variants emerge on a regular basis, such as LexPredict’s partnership with Exigent to provide advanced contract management solutions.[43]

Outside the legal vertical, other developments alter what it means to be a worker. Reliance on full-time employees shifts to contractors and outsourced workers. “TVCs” stands for temps, vendors and contractors. At Alphabet, they wear red badges, while employees wear white badges.[44] Amazon’s Mechanical Turk, in turn, offers an on-demand, scalable workforce that can take on specific HITs (human intelligence tasks) for fixed prices.[45] This agile workforce now includes approximately 54-68 million independent workers in the U.S., perhaps 25-26% of the workforce in Spain and the UK.[46] How (when?) will this model apply to the legal industry? What elements are already changing your organizational or legal team staffing strategies?

Consider the optimal mix for your organization to take advantage of near- and long-term AI opportunities. Should you implement a contract management solution tied to the way you work now, the way that you can work with alternative contract management providers today, or the way you will be able to work and automatically extract data now or next year?[47] Should you spend time, resources, and effort to code time entries and analyze legal fees when third party platforms can do this for you?[48] Does your team include “new collar” workers who can explain AI options to deliver concrete solutions? [49] Plan carefully because AI may serve as your new “quasi-employee.”[50]

You invest today based on the way you work and think about your work. As you invest, you need to know what is possible today and in 1, 2, 3 and 5 years with AI, lest your investments yield negative returns over time. Retain a consistent legal service mindset, but remain agile enough to deliver that service strategically.[51] Change is going to come.[52]

The Speed of Change

Technology moves fast. References to the doubling power of Moore’s Law[53] raise questions about where we are on the curve of technological change.

[54] Human change is slow. Change management leaders know that change is hard and even harder to hasten. The disparate speed of change between technology and humans is critical for organizations and legal professionals to manage as they plan for the future.[56] Change management continues to play a critical role as legal professionals consider with their clients how AI will enhance legal service delivery.


Today, legal services become products.[57] Products also become services and processes (e.g., the automobile becomes a transportation service).[58] “The spindle (the hallmark of the first industrial revolution) took almost 120 years to spread outside Europe.[55] By contrast, the internet permeated across the globe in less than a decade.”[59] Given accelerated computing capability, the connectedness of all our devices, and the amount of data we are capturing in the Cloud, AI innovations will come faster and faster.[60] Throughout, we will rely more on technology to process the Flows, lest we drop down to “people speed.”[61] Change is coming, fast.


An AI Concept for Discussion

AI Graphic. See See Artificial Intelligence May Change the Face of Business - See more at:

AI is one of those terms that seemingly means everything but conveys nothing. Look to the dictionary and you will find unhelpful references to systems or programs that have some human qualities. [62] AI practitioners, researchers and developers may define AI as “that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.” [63]Other AI descriptions provide context through references to branches of AI exploration and activities: natural language processing, neural networks, machine learning[64], deep learning, expert systems, robotic process automation, etc. [65] If you are ill-disposed to definitions containing terms that require further definition, an alternative way to explain AI might be to look what companies in the 2017 AI 100 are doing.[66]

Within the legal industry, there are also helpful materials to provide additional insights into broader market developments, as well as specific legal industry examples. [67]AI Landscape

Given daily references to “AI” and “robots,” you might be tempted to think about ongoing technology developments into these general groupings. You would not be alone. In Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence, Jerry Kaplan distinguishes between “synthetic intellects” and “forged laborers:”

  • Synthetic intellects: the amalgamation of machine learning, neural networks, big data, cognitive systems, and genetic algorithms.[68]
  • Forged Laborers: systems that see, hear, feel and interact with their surroundings (otherwise referred to as “robots”).[69]

Although this distinction may help you consider near-term AI impacts, in the long term it likely safer to assume that “forged laborers” will possess “synthetic intellects,”[70] perhaps with formal “personhood status.”[71]

For purposes of this discussion, I suggest thinking about AI as delivering human-like mental or physical capabilities that are now being enabled by the accelerated connections between the Data (e.g., “Big Data”), the Cloud, and Programs (machine learning, predictive analytics, deep learning, etc.). I suggest you view these proposed, synergistic elements expansively.


Data is the sword of the 21st century. Those that wield it are the samurai.[72]

Each day we use Captcha to log into sites while we train image recognition software. Local or outsourced team members classify documents for eDiscovery production as they train the system that might take over their jobs: “if you find yourself working with smart software, it is a good bet you’re training the machine to replace you.”[73]

When you think of data, your thoughts may shift to pricing and legal spend, predictive case outcomes, privacy, GDPR, eDiscovery & predictive coding, social media communications, contract terms, and, perhaps, legal process data (e.g., the cycle time to complete a contract review or file a motion to dismiss). Regardless of the context, recognize that the full power of data comes in the way that it can relate to other data, combining in ways that we do not easily grasp. “Bits want to be linked; the more relationships a bit of data can join, the more powerful it gets.”[74] That power increases when you blend various internal and external data sets.

Contrast structured data (i.e., a spreadsheet of people on your team with hire dates, locations, titles, etc.) with unstructured data (e.g., all those contracts sitting on the shared H drive or in your e-mail program). It is relatively easy for humans to follow and process the former, more inefficient and difficult to access and process the latter. (Big) data now enables us to automate a growing range of non-routine cognitive tasks.[75] It is now much easier to spot trends and identify opportunities within and across both structured and unstructured data. For example, data exploration, business intelligence, and data analytics software like Tableau[76] and Qlik[77] enable organizations to derive business insights from their disparate data sources. Although not AI, these tools demonstrate the value of data.[78] The potential to process large amounts of data opens up new possibilities that we are exploring, in much the same way we are exploring and starting to recognize the promise of quantum technology on the atomic and subatomic level. [79]


Your view of the Cloud may simply be that it is a place where software or a service is offered online, rather than on your computer or enterprise server.[80] Your focus is on ethics, security, client data, and cost savings associated with system delivery and support.[81]

As I suggest with other AI elements, observe the Cloud from a higher altitude:

The union of a zillion streams of information intermingling, flowing into each other, is what we call the cloud. Software flows from the cloud to you as a stream of upgrades. The cloud is where your stream of texts go before they arrive in your friend’s screen . . . . The cloud is the seat where the intelligence of Siri sits, even as she speaks to you. The cloud is the new organizing metaphor for computers.[82]

The Cloud is where we all freely offer and store our data, avoiding traffic on the way to work, getting recommended books when we shop online, and tracking what our colleagues are reading on LinkedIn. We will add 34 billion internet-enabled devices in the next 5 years to stream data,[83] reaching a total of 50 billion or more.[84] 100 billion times per day we click on links, teaching the cloud what is important. [85] It is a place where we upload our brains[86] and where robots connect with the network of other robots.[87]


Expert systems, machine learning, and predictive analytics are some of the most current, visible ways we think about Programs. Conceptually, it may be more helpful to think of the Program element by looking at neural networks and deep learning systems that are modeled on neural networks in the human brain.[88]

We typically evaluate AI in terms of its ability to replace humans. For example, discussions about AI impacts in the legal market center on automating routine tasks, but carve out bespoke legal advice from the types of work that will be automated.[89] In the near term, this may a comfortable and appropriate approach.[90] Over time, this distinction may be missing the point.

To date, we have programmed computers to take specific steps. We write the code. What happens if that changes? Deep learning and other AI developments are still in their early stages but it may not be long before AI software learns to make AI software.[91] Hyperbole but hopefully you get the point. Deep learning systems will increasingly make sense of data on their own, essentially learning from their experiences.[92] The goal is to build machines that truly understand.[93] In the interim, the algorithms behind these systems provide the instructions, delivering great value for business but also raising issues for employees and society.[94]

As you consider Data, the Cloud, and Programs together, consider both the broad and narrow. The broad — what impacts will AI have on organizations, business, and the way everyone interacts? The narrow — how will AI support my legal practice? Given the daily pressures of client requests and deliverables, your tendency may be to adopt a narrower, practice-specific perspective. It is important to remember that broader changes (by organization, industry, government, society) ultimately drive to your practice demands.

Finally, as you frame your understanding of AI, recognize that computers will think differently.[95]

[I]t is no longer necessary for the rules that these machines follow to resemble the rules that human beings follow.[96]

Put another way, “[t]o demand that artificial intelligence be humanlike is the same flawed logic as demanding that artificial flying be birdlike, with flapping wings. Robots, too, will think different.”[97]

IV. Where Are We Now?

A Near Horizon

[B]y the time the current generation of infants legally reaches adulthood, they will have done so in a world where nearly half of our current jobs may be automated.[98]

By 2035, assuming current technology, most occupations will see some partial automation, with perhaps 47% of all work activities being automated.[99] Current estimates suggest up to 80% of jobs earning less than $20/hour might be replaced by AI, along with one third of those making $20-$40/hour.[100] Although only 5% of high-paying jobs might be impacted by 2025, automated knowledge worker tasks replaced by that time will be equal to 110 million to 140 million FTEs.[101] Among other things, we can also expect to see one trillion sensors connected to the internet, robotic pharmacists, and AI-performed corporate audits.[102] The numbers for high-skilled workers will look very different if researchers can create sentient AI that can do the same things humans can do.[103]

A Near Perspective

We likely won’t run out of work, but we might run out of jobs.[104] Assuming that AI offers a potential future of job displacement, where are we now? Although reasonable minds may reach different conclusions regarding the correlation between the timing and specific impacts, change is here. Let’s take a look.

Blue Collar

There’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.[105]

SAM is a bricklaying robot. Sam stands for “Semi-Automated Mason” that can lay bricks 3 times faster than a human.[106] Sam does not replace humans (yet), but works with them. Interestingly, SAM can perform some actions that objectively might seem to require some domain expertise in the field, such as correcting for inconsistencies between a building specification document and reality.

Statue of John Henry outside the town of Talcott in Summers County, West Virginia SAM’s story reminds me of the legend of John Henry and his race against a steam-powered hammer.[107] Folklore suggests John Henry was a “steel-driving man” – someone tasked with creating holes for explosives to blast rock for railroad tunnel construction. He won a race against a steam-powered hammer but lost his life from the stress as a result. [108] (For the audio of John Henry listen to Bruce,[109] Johnny,[110] or Ernie.[111])

SAM’s story is not unique. Foxconn workers are being replaced by robots,[112] as other manufacturers race to perfect “the machine that makes the machine.”[113] Up to 45% of current supply chain work could be automated,[114] while milking a cow will be left to the cows and machines.[115] Within the next decade, robots will share “all the information they need” and figure out more things on their own.[116] During the transition, people may present more problems from a business and legal standpoint: “[a]s human beings are also animals, to manage one million animals gives me a headache.”[117] The long-term solution may be to merge with machines lest humans become useless.[118]

According to a recent PWC study, nearly half of surveyed CEOs around the world fear that automation and robots in factories and offices will prompt distrust among workers, investors, and the general public.[119] The Economist recently offered a video focusing on changes to the trucking industry[120] suggesting this fear is well placed. Although the title of the story is “Truck drivers grapple with automation,” the interviewed driver does not directly address technology, automation, or the looming impacts to his industry (the Economist suggests that in the prelude). Rather, he describes how “more educated” folks in California (and New York) are chasing trucking companies (and manufacturing) out of business. “They want the money, they don’t want us.”[121]

Legal professionals should be able to issue spot how these changes and similar changes may lead to client requests for advice. The friction we read about today involves technology ownership. [122] Future frictions will continue to evolve and involve the workers — truckers, bricklayers, and other professionals.

White Collar

[I]t’s not just low-skill, low-wage work that could be automated; middle-skill and high-paying, high-skill occupations, too, have a degree of automation potential. As processes are transformed by the automation of individual activities, people will perform activities that complement the work that machines do, and vice versa.[123]

Libratus[124] is an AI poker-playing machine. Relying on 3 different types of AI, Libratus learned some scenarios while playing and randomized its actions to beat some of the best poker players in the world. Robots developed by Google show us how robots can learn and accelerate the development of autonomous robots.[125] Outside the robot realm, Google’s enhancements to its translation service in November suggest that Google’s AI is developing its own interlingua (a system learning a common representation in which sentences with the same meaning are represented in similar ways regardless of language).[126]

If these developments are too abstract from the legal industry perspective, look at what JPMorgan is doing with COIN (Contract Intelligence). The COIN machine “parses financial deals that once kept legal teams busy for hundreds of thousands of hours, … reading and interpreting commercial loan agreements” and learning patterns by ingesting data. Historically, this work involved 360,000 hours of lawyer and loan officer work each year but “[n]ow it’s done in seconds and with fewer errors by a system that never sleeps.” The bank is exploring other ways to deploy the technology, perhaps using it to help interpret regulations and analyze corporate communications.[127] It took several years for the legal industry to change the eDiscovery process. Consider a similar, but more rapid shift across broader swaths of the legal practice to get a better sense of where we are headed.

For perspective, here are some examples of how AI is changing aspects of white collar work:

  • Journalism: Heliograf is changing the way news is produced.[128]
  • Financial management: technology automatically generates portfolio commentary and shareholder reports, replacing people and the days they may take creating commentary. [129] The machine can learn your data and write summary reports in plain English.[130]
  • Compliance: AI is being used for AML compliance,[131] certain employment decisions,[132] and proactive marketing compliance.[133]
  • Insurance: AXA teamed up with ABS to “deliver legal and risk advice” powered by IBM Watson. “AXA customers “will have their legal questions answered either at the touch of a button or simply by asking ‘Grace’ a verbal question” [134] Separately, a Japanese insurance company is replacing workers with a Watson-based system that can calculate payouts to policyholders.[135]
  • Litigation+: AI is being used to value cases,[136] proactively identify litigation risks,[137] and assess litigation, attorneys, judges, parties, and other aspects of the legal practice.[138]
  • Due diligence: Fenwick, DLA Piper, and others are using AI to alter the way due diligence is staffed. [139] These approaches are impacting the India legal market. Expect that to grow.[140]
  • Legal advice: Neota Logics and Kim enable legal professionals to embed their legal know-how into expert systems and otherwise automate processes to exchange of expertise, workflow, and documents with clients.[141]

These developments suggest the extent to which tasks that are presumed to be exclusively the province of skilled, educated professionals are vulnerable to automation.[142]

Professionals tend to distinguish between “routine” and “non-routine” tasks, feeling immune to technological displacement because “computers are incapable of exercising judgment or being creative or empathetic, and that these capabilities are indispensable in the delivery of professional service.”[143] Unfortunately, research suggests these assumptions may be misplaced.

[W]hen professional work is broken down into component parts, many of the tasks involved turn out to be routine and process-based. They do not in fact call for judgment, creativity, or empathy.[144]

Of greater import, professionals may be miscalculating what is “non-routine” because that is not the way machines will think in the long term:

… in thinking about automation, the appropriate criteria is not whether a task is ‘routine’ or ‘non-routine’ from the standpoint of a human being. . ., but whether [the task] has features that make it more or less routinisable from the standpoint of a machine. If a task is routinisable, a routine can be composed that allows a machine to perform it – but that routine may not necessarily reflect the way in which a human being performs the task.– Daniel Susskind[145]

If believe you add value because you can outthink computers, “you are on the John Henry track.”[146]

Augmented Intelligence (“AuI”)

Despite claims that machines are intelligent and thinking for or beyond us now, it is more appropriate to state that we are currently experiencing changes associated with Augmented Intelligence (“AuI”), as opposed to “Artificial Intelligence.” That is, AuI is augmenting the way legal professionals do their work, taking over specific tasks for humans (i.e., conduct due diligence, predict court results, etc.)[147] Simply being a good lawyer is “no longer enough.”[148] The combination of machine learning and human curation and intuition “produc[es] an outcome greater than the sum of its parts.[149] In certain respects, this approach was used to predict the Gorsuch nomination to the Supreme Court.[150] In the near term, legal professionals will gain efficiencies and deliver value by work alongside machines, focusing on task automation as opposed to job automation.[151] How does one determine which tasks are ripe for automation?

Deskilling & Process Analysis

I’m a terrific bundle of experience. – Buckminster Fuller.[152]

In the times leading up to Luddite protests, an important feature of 19th century changes to the manufacturing technologies was that they were deskilling. Jobs are deskilled when technologies are introduced in ways that no longer require workers to have formerly necessary skills, enabling semiskilled and unskilled workers to take their place.[153] “[T]he best way to use new technologies is usually not to make a literal substitution of a machine for each human worker, but to restructure the process.”[154]

Arguably, the legal profession is already on this path. We are disaggregating our work and unbundling our services — breaking down tasks associated with legal service delivery.[155] Through increased, appropriate focus on legal project management, design thinking,[156] and business process analysis,[157] we are deconstructing legal service into discrete tasks, intelligently creating more efficient processes and delivering more value at lower costs.

Some of the deconstructed work is shifting to alternative service providers.[158] The overarching goal is to allocate work from a skilled artisan to lower cost, less-skilled workers; shift work to the right timekeeper for the right cost. As one firm recently suggested, these process changes enable legal service providers to “automate repetitive tasks so that lawyers and administrators can focus on higher-value work.”[159] As they consider the mix of tasks that constitute legal service delivery, legal professionals should start thinking about adding robotic process automation (RPA) to the list of options on the horizon.[160] We can expect (and should plan for) deskilling to accelerate as computers take on more knowledge work tasks.[161]

To prepare for additional AuI disruption, start by analyzing your legal service processes through process mapping, design thinking or other approaches to collect data about your legal service operations. Follow intelligent process automation and how it will drive change in your organization and your legal team.[162] In this way, leaders can strategically anticipate and manage deskilling opportunities. Individuals can also prepare.

V. How Should You Prepare?

Mind the Gap[163]

Legal professionals should monitor these broader developments to provide prescient business advice, recognizing that AI will take on more “routine” work for their organizations and colleagues. As the power of AI increases, “what gets counted as routine expands to include more and more of the tasks we do.”[164] Assuming continued AuI-driven change, how should individuals plan for the gaps that will appear in their work?

In Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, [165] Thomas Davenport and Julia Kirby suggest five approaches to plan for and manage AuI: (1) Stepping up; (2) Stepping aside; (3) Stepping in; (4) Stepping narrowly; and (5) Stepping forward.

Step Up

Individuals who step up offer big picture advice and insights that are too unstructured for computers, to provide the vision and managerial skills to lead the transition.[166] They “will translate the evolving pivot points in their business models into specific implications for work, looking beyond jobs, and recognize the transformative role AI can play.”[167] Where can AuI be used and how does it fit into the overall business/institution legal service delivery process? These individuals will use a mix of technical skills and knowledge of real-world problems to answer these questions.[168] They get the big picture,[169] have the ability to lead change initiatives, and are comfort with technology.[170] Today, these skills likely reside with your legal operations and KM professionals.[171]

Step Aside

Individuals who step aside can focus on non-decision-oriented work that computers are not good at, such as motivating and sympathizing with people, and more generally mastering EQ skills.[172] They possess skills like empathy, humor, creativity, design thinking, and thinking outside the box — capabilities that artificial intelligence has trouble replicating.[173] As AI develops, individuals desiring to step aside should focus on emotional intelligence (EQ) skills in order to differentiate themselves.[174]

[N]urture and invest in these [emotional intelligence] abilities the same way that you have the more technical parts of your career.[175]

These skills will not be automated[176] and will be critical to succeed in the next era.[177]

Step In

Individuals that can step in are facile with AI. Their roles will be to interpret, monitor and improve computer decisions.[178] The relevant skills are those associated with “new collar” workers[179] — data translators and purple people[180], who appreciate both how computers work and the legal services to be automated.[181] Data translators help bridge the gap between “Big Data” results and the business.[182] Purple people, in turn, possess technical skills plus communication skills, business acumen, and political know-how to connect people with technology, translating for legal professionals the decisions a system makes.[183] “Without this talent set, organizations will continue to struggle to both create the right mindset for strategic change and drive the process forward.”[184]

Step Narrowly

Individuals that step narrowly focus on aspects of the legal practice that are so narrow, they will not be automated.[185] You need to be a passionate, self-taught expert about the topic.[186] In the near term, organizations will need specific legal skills, particularly around the ownership and use of data — privacy, security, cybersecurity, and the nuances of cross-border transactions.[187]

Step Forward

Individuals that step forward develop new systems and technology.[188] This is likely the place for data scientists and others with deep technology expertise.[189] These individuals possess the technical skills for future that help break down problems into small parts, solve them, build a system around them with an understanding of how they interconnect.[190] “If you can synthesize a massive, complex system into something that is essential that you can articulate it in a very crisp way, that’s exactly what programming teaches you.[191]

Across all approaches, employers may need to prioritize adaptability over expertise: “[t]he valuable worker won’t necessarily be the one who knows everything, but who can adjust if and when another job gets turned over to machines.”[192]

VI. Take Action

It had long since come to my attention that people of accomplishment rarely sat back and let things happen to them. They went out and happened to things. – Elinor Smith[193]

As AuI and AI continue to advance, “[w]e are, and will remain, perpetual newbies.”[194] AI will continue to challenge our understanding of how organizations, people and technology best align: how people relate to people, how people relate to business, and how businesses relate to each other.

There are many changes in store for our organizations, industries and workforce. We don’t need to battle the robots,[195] nor should we Ned Ludd them with a hammer at our next encounter.[196] Instead, we should expand the way we think and, more importantly, know that computers can help us think in new ways while they think differently.

Although AI in the legal industry is still in its relative infancy, I take a near- and long-term view about AI impacts on legal teams and organizations. Regardless of whether we have arrived at the inflection point, our window to experience and manage material impacts in the legal practice may be relatively short. 10-15 years? Sooner?

To prepare for the inevitable change, legal professionals should:

  • Recognize that change is happening.[197]
  • Energize technology and innovation initiatives.
  • Analyze legal & business processes.
  • Centralize legal spend with providers leveraging technology.
  • Temporalize a technology strategy into a workable timeline.
  • Optimize legal and business processes to enable (robot) process automation.
  • Rightsize staffing strategies to deliver near- and long-term value.
  • Prioritize efforts to capture more data about legal operations and service delivery.
  • Emphasize how AI will lead to near- and-long term impacts for you and your clients.
  • Rationalize application portfolios[198] to ensure optimal use of technology.
  • Internalize the value of technology into your institutional fabric and culture.
  • Synthesize staffing and practice strategies that align with your business vision.

Legal professionals should start their AI journey now not only to deal with the daily Flows, but also to identify the right mix of people, process and technology for today and tomorrow. At a minimum, your strategy should contain a thoughtful data capture, ownership and management approach to deliver meaningful, actionable intelligence about your operations, legal service opportunities, and competition.

Hopefully, the foregoing exploration of AI convinces you to consider, plan for, and perhaps work with AI rather than against it. If so, you are an AI Luddite colleague. Whatever path you take or call yourself please consider AI carefully but do not call it a game changer.[199]

This article originally appeared on LinkedIn on December 30, 2016. Interested readers are invited to (1) use a link below to share this article on LinkedIn, Facebook, Titter, etc. and/or (2) visit the article on LinkedIn to share the article or their comments.

©2017 Peter Krakaur

VII. Additional Reading

  • The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies[200]
  • The Fourth Industrial Revolution[201]
  • Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy[202]
  • The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future[203]
  • Only Humans Need Apply: Winners and Losers in the Age of Smart Machines[204]
  • Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence[205]
  • Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will[206]
  • Rise of the Robots: Technology and the Threat of a Jobless Future[207]
  • Average Is Over: Powering America Beyond the Age of the Great Stagnation[208]
  • Industries of the Future, Alec Ross[209]
  • The Great Convergence: Information Technology and the New Globalization[210]
  • The Future of the Profession, How Technology Will Transform the Work of Human Experts[211]

Appendix A — A Luddite Perspective

Resolved – That Cut-up Goods (except Breeches and Waistcoast Pieces) wrought in the Plain or Robbed Branches of our Trade are reducing those extensive and valuable Branches to the lowest ebb of Degradation; and, if persisted in, would inevitably throw those lucrative Parts of British Manufacture into the Hands of Foreign Artisans.[212]

Although there is a question whether Ned Ludd even existed, here is an amalgamated version of the story. In the 1790’s a poor, framework knitter apprentice[213] named Edward Ludd lived near Leichester, England. His neighbors called him Ned.Irritated by his boss and/or work, he broke a stocking frame with a hammer.[214] By the end of 1811, whenever astocking weaver was upset (by the loss work, poverty set upon by bad wages, etc.), they might say when speaking of their frame “I have a good mind to Ned Ludd it,” meaning to break it. “The frame-breakers called themselves Luddites, and signed some their proclamations Ned Ludd, sometimes adding Sherwood Forest.”[215]

Machine smashing was not new; the practice had been taking place for over 100 years.[216] By time of the early reported instances of Luddite activity in 1811, framework artisans faced economic upheaval and widespread unemployment that was caused by a variety of factors, including poor harvests, impacts of the Napoleonic Wars, stoppage of trade with America,[217] and changes in fashion (knee-breeches and fitted stockings were going out of fashion being replaced by long trousers.)[218]

In addition to their broader economic challenges, including income inequality,[219] Luddites were challenging business process and staffing changes associated with the use of technology. One evolving practice was making ‘cut-ups:’[220]

Instead of weaving the entire stocking around, they’d produce a big sheet of hosiery and cut it up into several stockings. “Cut-ups” were shoddy and fell apart quickly. . . . [221]

This practice undermined the wages of the skilled frame and other textile workers. Additionally, Luddites protested the way the machines were staffed by “colts” – workers who had not completed their full 7-year apprenticeship. This further drove down the cost of labor, impacted standards of living, and further diminished the value of the skilled frame workers.[222]

Through the Luddite efforts, wages rose slightly in the short-term but there was disruption in the nature and scope of their roles in the evolving workplace of the 19th century. The Luddites focused on specific issues — economic challenges (and associated income inequality), business process changes, and workforce changes resulting from new ways of integrating technology into their business. Although we are in different times, our current economic and workforce landscape offers relevant parallels to our Luddite past.

This article was first published on March 22, 2017 on LinkedIn. One goal of this article is to start a conversation. Interested readers are invited to (1) use a link below to share this article on LinkedIn, Facebook, Titter, etc. and/or (2) visit the article on LinkedIn to share the article or their comments.

©2017 Peter Krakaur

  1. Lewis Cellars (perhaps Alec or Mason).
  2. Futurism, The Future of Construction: This Robot Can Lay Bricks Three Times Faster Than Humans,
  3. The Economist, Truck drivers grapple with automation,
  4. Cade Metz, Inside Libratus, The Poker AI That Out-bluffed the Best Humans,
  5. Gideon Lewis-Krause, The Great AI Awakening,
  6. Jürgen Schmidhuber, When Creative Machines Overtake Man,, Ray Kurzweil, The Singularity is Near: When Humans Transcend Biology,
  7. Tom Simonite. How to Upgrade Judges with Machine Learning,
  8. See Artificial Intelligence (AI) in Law Departments: Opportunities; Artificial Intelligence (AI) in Law Departments: Staffing; Jeff Cox, AI for Legal Ops and Corporate Counsel – The First Wave, ACC Legal Ops Observer (March 2017),; Jason Koebler, Rise of the Robolawyers,
  9. Matthew Griffin, JPMorgan unleashes artificial intelligence to automate its legal work,
  10. Roy Strom, Seyfarth Shaw Puts ‘Software Robots’ to Use in Automation Push,
  11. See Baker McKenzie Innovation Program to Reimagine the Business of Law; Human-centered Design and the Law: A conversation between Seyfarth and IDEO, Joshua Kubicki,
  12. Carl Benedikt Frey and Michael A. Osborne, The Future of Employment: How Susceptible are Jobs to Computerisation?,, pp. 4, 8, 41.
  13. McKinsey, Where machines could replace humans — and where they can’t (yet),
  14. See Outside Counsel Cost Management: A View Around The Corner
  15. E.g., Oxford Living Dictionary
  16. See J.L. Hammond and Barbara Hammond, The Skilled Labourer, 1760-1832,
  17. Writings of the Luddites, Kevin Binfield,, p.54.
  18. Edith Gardner , Revolutionary Readings: Mary Shelley’s Frankenstein and the Luddite Uprisings (which also offers an interesting analysis of Frankenstein as a depiction of the contemporary economic setting in the time of the Luddite uprisings between 1811-1817); Richard Conniff, What the Luddites Really Fought Against,; See also John Rule, The Labouring Classes in Early Industrial England, 1750-1850, p. 363.
  19. See Erik Brynjofsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies,, pp. 131-134.
  20. Erik Brynjolfsson , AI and the Economy, (also discussing the Great Decoupling and then skill-biased, capital-biased, and superstar-biased technological changes @ 14:00-16:40.); Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, p. 202.
  21. Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, pp.133-141 (see discussion of “skill-biased technical change”).
  22. Martin Ford, Rise of the Robots: Technology and the Threat of a Jobless Future,, p. xii.
  23. See Wikipedia, Technological Unemployment generic description,; Daniel Susskind, A Model of Technological Unemployment, Oxford University Discussion Paper, N0. 819, March 16, 2017, also John M. Keynes, Economic Possibilities for our Grandchildren,; Gary E. Marchant, Yvonne A. Stevens and James M. Hennessy, Technology, Unemployment & Policy Options: Navigating the Transition to a Better World, (discussing 6 types of policy options to address technological unemployment); David Autor, Why Are There Still So Many Jobs? The History And Future Of Workplace Automation And Anxiety,
  24. Joel Mokyr, Chris Vickers, and Nicolas L. Ziebarth, The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?; Future of Life Institute, Asilomar AI Principles for broader ethical considerations associated with AI,
  25. Michael Chui, James Manyika, and Mehdi Miremadi, Where machines could replace humans—and where they can’t (yet), We will also see this at home. See Top Consumer Trends That Will Impact the Digital Workplace in 2025, Gartner,; Jean-Baptiste Coumau, Hiroto Furuhashi, and Hugo Sarrazin, A smart home is where the bot is, (“Homebots” will manage and perform household tasks, perhaps establishing emotional connections with us.)
  26. See Rohit Talwar, Artificial intelligence – 10 questions every CEO should be asking, See also Stuart Russell, Daniel Dewey, Max Tegmark, Research Priorities for Robust and Beneficial Artificial Intelligence,, p.106; Future of Life Institute, Open Letter – Research Priorities for Robust and Beneficial Artificial Intelligence,
  27. See Klaus Schwab, The Fourth Industrial Revolution,, p. 115 (referencing leaders who lament that “they no longer have time to pause and reflect.”)
  28. Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, p. 11; See Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future,, p.51.
  29. David Terrar, What is Digital Transformation? Shahar Markovitch and Paul Willmott, Accelerating the Digitization of Business Processes,
  30. Alex Ross, The Industries of Future,, p.202.
  31. Tanguy Catlin, Jay Scanlan, and Paul Willmott, Raising your Digital Quotient,
  32. Kevin Kelly, The Inevitable,, p.63.
  33. Id. pp. 63-65
  34. Id.
  35. Id.
  36. Klaus Schwab, The Fourth Industrial Revolution, , p. 65 (emphasis in original).
  37. Wikipedia, Generation Z,
  38. See also Daniel Goleman, 3 Ways to Get Into the Flow State at Work,
  39. See Orrick Career Associates,
  40. Gowlings,; Bryan Cave,; Baker Donelson,; Pinset Masons,
  41. Littler,; Paul Hastings,
  42. Roy Strom, Legal Project Managers: The New Rainmakers?
  43. See Michael Bommarito, LexPredict Partners with LPO Exigent to Provide Advanced Contract Management Solutions,
  44. Lauren Weber, The End of Employees,
  45. Amazon Mechanical Turk,
  46. Elaine Pofeldt, McKinsey Study: Gig-Economy Workforce Is Bigger Than Official Data Shows in U.S., Europe
  47. See e.g., kReveal; Kira; RAVN; LawGeex Seal; Brightleaf; eBrevia
  48. E.g. BrightFlag; Legal Decoder; Simple Legal;Sky Analytics
  49. Chris Weller, IBM’s concept of ‘new collar jobs’ could be vital in an automated future, See Artificial Intelligence (AI) in Law Departments: Staffing
  50. Jason Koebler, Rise of the Robolawyers, How legal representation could come to resemble TurboTax,
  51. See John Coleman, The Best Strategic Leaders Balance Agility and Consistency,
  52. See Ajay Agrawal, Joshua Gans, and Avi Goldfarb, The Simple Economics of Machine Intelligence,
  53. Klaus Schwab, The Fourth Industrial Revolution,, p. 172 (if Moore’s law continues at the same pace, CPUs will reach the same processing power as humans by 2025.)
  54. Martin Ford, The Lights in the Tunnel,, p.53 (graph provided to visualize the likely trajectory; it is not based on data).
  55. Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, p.43 (graphic showing growth of hypothetical Tribble population that doubles each day).
  56. See Thomas Davenport and Julia Kirby, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines,, pp. 217-219.
  57. See e.g. ComplianceHR; See also Neota Logic, Technology for Law Firm Leaders: Services as Products,
  58. Kevin Kelly, The Inevitable,, pp. 6-7; Mohanbir Sawhney, Putting Products into Services,
  59. Klaus Schwab, The Fourth Industrial Revolution,, p. 16.
  60. Id. p.8.
  61. See Brian Fung, Elon Musk: Tesla’s Model 3 factory could look like an alien warship,
  62. E.g.,–intelligence (“the capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system”); Cambridge Dictionary, (“the use of computer programs that have some of the qualities of the human mind, such as the ability to understand language, recognize pictures, and learn from experience.”)
  63. Stanford University One Hundred Year Study on Artificial Intelligence, Artificial Intelligence and Life in 2030 Report,, p. 12, quoting Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge, UK: Cambridge University Press, 2010)).
  64. See Dorian Pyle and Cristina San Jose, An executive’s guide to machine learning,; Warren Agin, A Simple Guide to Machine Learning, See also Frank Wammes, AI, robotics and the future of work,
  65. Paul Daugherty, Artificial Intelligence May Change the Face of Business, (graphic from article).See Gil Press, Top 10 Hot Artificial Intelligence (AI) Technologies, (10 current ways to think about AI). See also Stanford University One Hundred Year Study on Artificial Intelligence, Artificial Intelligence and Life in 2030 Report,, p. 14-17 for a review of various AI trends.
  66. CB Insights, The AI 100 2017,
  67. For some examples of current AI in the legal sphere, see Michael Mills, Artificial Intelligence in the Law: The State of Play in 2016, (mind map graphic from article); Artificial Intelligence (AI) in Law Departments: Opportunities (; Daniel Martin. Katz, [a.i. + law] a 6 part primer, (additional context to help translate AI with real-world examples).
  68. Jerry Kaplan, Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence,, p.5.
  69. Id. pp.5-6.
  70. See Kevin Kelly, The Inevitable,, p. 42 (“The mechanical innovations of the Industrial Revolution acted as a substitute for human (and animal) strength as well as dexterity, but the machines of that time could not reason, compare, compute, read, smell, sense, hear, or make snap decisions. However, if artificial intelligence and robotics continue on their present trend, future machines will be able to carry out these human capabilities, at least in certain contexts and to a certain extent.”)
  71. Alex Hern, Give robots ‘personhood’ status, EU committee argues, See Mady Delvaux, DRAFT REPORT with recommendations to the Commission on Civil Law Rules on Robotics,
  72. Jonathan Rosenberg, former SVP of Product Management at Google as quoted by Tracy Wilk, How to Outsmart the Robots and Save Your Job – Tips from Leaders at Google
  73. Martin Ford, Rise of the Robots,, p.123 (discussing sustainability of e-Discovery collaboration where eDiscovery specialists train the machine regarding document relevancy)
  74. Kevin Kelly, The Inevitable,, pp. 265-266.
  75. Carl Benedikt Frey and Michael A. Osborne, The Future of Employment,, p.16.
  76. Tableau, See e.g., McKinsey, Where machines could replace humans,
  77. Qlik,
  78. See also Hans Rosling, The Best Stats You’ve Never Seen,
  79. The Economist, Quantum technology opens up a world of possibilities,
  80. ABA LTRC, Cloud Computing for Lawyers,
  81. See Flying in the Clouds: A Checklist to Help You Navigate Your Way; A.J. Zottola and Alexis A. Martirosian, Understanding the Dark Sides of the Cloud: Top Ten Legal Risks for Cloud Computing Users,
  82. Kevin Kelly, The Inevitable,, p. 65.
  83. Id. p. 252.
  84. See Klaus Schwab, The Fourth Industrial Revolution,, p. 158.
  85. Kevin Kelly, The Inevitable,, p. 253.
  86. See Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, p.253.
  87. Klaus Schwab, The Fourth Industrial Revolution,, p. 26.
  88. Aditya Singh, Deep Learning Will Radically Change the Ways We Interact with Technology, (“deep learning will change the way people interact with technology as radically as operating systems transformed ordinary people’s access to computers.”)
  89. See Lexum, The Future of Law and “Intelligent” Technologies: Prophecies, Technologies and Opportunities – Part 2, (seeing strong impacts on document review; moderate impact on “case administration” (contract review), document drafting, due diligence, legal analysis strategy and legal research; and light impact on all other tasks) – a summary of Dana Remus and Frank Levy, Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law (draft), (exploring claims and predictions about technology and its ability to displace lawyers).
  90. Steve Lohr, A.I. Is Doing Legal Work. But It Won’t Replace Lawyers, Yet.,
  91. Tom Simonite, AI Software Learns to Make AI Software,; Futurism, New AI Can Write and Rewrite Its Own Code to Increase Its Intelligence,
  92. Id.
  93. See Elemental Cognition; Why this company will help change the future of artificial intelligence, Kris Hammond,; Kevin Kelly, The Inevitable,, p.19.
  94. See Bruce Schneier, Replacing Judgment with Algorithms,; Tim O’Reilly, The great question of the 21st century: Whose black box do you trust?; Lee Rainie and Janna Anderson, Code-Dependent: Pros and Cons of the Algorithm Age,; Ceclia Mazanec, Will Algorithms Erode Our Decision-Making Skills?
  95. See Jerry Kaplan, Humans Need Not Apply ,, p.36.
  96. Daniel Susskind, A Model of Technological Unemployment,, p. 10 (emphasis in original).
  97. Kevin Kelly, The Inevitable,, p.51.
  98. Frank Wammes, AI, robotics and the future of work,
  99. See Carl Benedikt Frey and Michael A. Osborne, The Future of Employment: How Susceptible are Jobs to Computerisation?,, pp; 37-38 James Manyika, Michael Chui, Mehdi Miremadi, Jacques Bughin, Katy George, Paul Willmott, and Martin Dewhurst, A Future That Works: Automation, Employment, and Productivity,, (50% work activities automated by 2055, perhaps 20 years depending on various factors).
  100. Brian Fung, Everything you think you know about AI is wrong, (referencing Artificial Intelligence, Automation, and the Economy (December 2016) citing Executive Office of the President, National Science and Technology Council Committee on Technology (Oct. 2016), Preparing for the Future of Artificial Intelligence
  101. McKinsey Global Institute, Disruptive Technologies: Advances that will transform life, business, and the global economy,, p.40 (noting a potential $5.2 trillion to $6.7 trillion impact by 2025).
  102. Klaus Schwab, The Fourth Industrial Revolution,, p. 35, Table 1
  103. Id.
  104. Tim O’Reilly, What’s the Future of Work?, p.87.
  105. Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, p.11.
  106. Futurism, The Future of Construction: This Robot Can Lay Bricks Three Times Faster Than Humans,
  107. Wikipedia, John Henry (folklore)
  108. As with the Luddites, there is more to the story. One historian suggests that John Henry, victimized by Virginia’s notorious Black Codes, was shipped to the infamous Richmond Penitentiary, become prisoner number 497, was forced to labor on the mile-long Lewis Tunnel for the C&O railroad, and died in the process. Scott Nelson, Steel Drivin’ Man – John Henry, the Untold Story of an American Legend,
  109. Bruce Springsteen, John Henry
  110. Johnny Cash, John Henry
  111. Tennessee Ernie Ford, John Henry
  112. IEN, Foxconn to Replace Most Workers with Robots,
  113. Brian Fung, Elon Musk: Tesla’s Model 3 factory could look like an alien warship,
  114. Alexa Cheater, Running on autopilot, The growing role of AI in supply chain management,
  115. Andrew Amelinckx, Rise of the (Cow Milking) Robots,
  116. Amanda Schaffer, Robots That Teach Each Other,
  117. Alex Ross, Industries of the Future,, p.46 (quoting Terry Guo, Foxconn’s founder in 2012 New York Times article; See John Markoff, Skilled Work, Without the Worker,
  118. Arjun Kharpal, Elon Musk: Humans must merge with machines or become irrelevant in AI age,
  119. PWC, Redefining business success in a changing world,
  120. The Economist, Truck drivers grapple with automation,
  121. Id.
  122. Daisuke Wakabayashi and Mike Isaac, Google Self-Driving Car Unit Accuses Uber of Using Stolen Technology,
  123. James Manyika, Michael Chui, Mehdi Miremadi, Jacques Bughin, Katy George, Paul Willmott, and Martin Dewhurst, Harnessing automation for a future that works,
  124. Cade Metz, Inside Libratus, The Poker AI That Out-bluffed the Best Humans,
  125. Cade Metz, Google’s Go-Playing Machine Opens the Door to Robots that Learn,
  126. Gideon Lewis-Krause, The Great AI Awakening,; Mike Schuster, Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System,
  127. Matthew Griffin, JPMorgan unleashes artificial intelligence to automate its legal work,
  128. Joe Keohane, What News-Writing Bots Mean for the Future of Journalism, See Phoenix Kwong, AI penetrates China’s media sector as robot starts writing business reports,; IT News Africa, African startup using AI to produce news stories,
  129. Narrative Science Asset Management; Quill Portfolio Review for Wealth Management,
  130. See Quill Engage; Quill Portfolio Review for Wealth Management
  131. Quill for Anti Money Laundering,
  132. See Compliance HR,
  133. See PerformLine and ChatScout
  134. Neil Rose, AXA teams up with ABS in bid to “disrupt delivery of legal advice” with machine-learning app,
  135. Justin McCurry, Japanese company replaces office workers with artificial intelligence,
  136. Michael McDonald, Fulbrook Using Big Data To Get Ahead In Litigation Finance
  137. See Artificial Lawyer, Intraspexion: the AI System that Predicts and Prevents Future Lawsuits; Jeff Cox, AI for Legal Ops and Corporate Counsel – The First Wave, ACC Legal Ops Observer (March 2017),
  138. See Premonition; Unicourt; Lex Machina See also LexPredict (offering a range of predictive analytics services to the legal vertical.)
  139. DLA Piper; Fenwick; Christina Wojcik, Why can’t due diligence be done in a weekend?
  140. See Sayan Ghosal, How AI may become a game-changer for the Indian legal industry
  141. Neota Logic; Kim See ComplianceHR For additional process, document and workflow options, see ThinkSmart and Onit
  142. See Martin Ford, Rise of the Robots,, p.85.
  143. Richard and Daniel Susskind, Technology Will Replace Many Doctors, Lawyers, and Other Professionals,
  144. Id. See also Ivy Nguyen, Tiffine Wang, Freddy Dopfel, and Ryan Morgan, Robots aren’t automating the jobs we want them to
  145. Wikipedia, Technological Unemployment,; Daniel Susskind, A Model of Technological Unemployment, p.12.
  146. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.29.
  147. See e.g., Jason Koebler, Rise of the Robolawyers,; Edward Fennell, Adapting to artificial intelligence
  148. See e.g., Edward Fennell, Adapting to artificial intelligence (quoting Jason Marty, Baker McKenzie’s global director of operations.)
  149. Sandy Bord, Why AI must be redefined as ‘augmented intelligence,’ See Tyler Cowen, Average Is Over: Powering America Beyond the Age of the Great Stagnation,, p.82; Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.61; Jacob Morse, 3 Key Components of Augmented Intelligence
  150. Michael Bommarito, How FantasyJustice Predicted the Gorsuch Appointment and What we Can Learn.
  151. See James Manyika, Michael Chui, Mehdi Miremadi, Jacques Bughin, Katy George, Paul Willmott, and Martin Dewhurst, Harnessing automation for a future that works,
  152. William Martin, The Christian Science Monitor; Boston, Mass, Sept 23, 1983, Proquest Doc ID 1037913035. The oft-cited quotation from Bucky Fuller is “I am not a genius, I’m just a tremendous bundle of experience.” I am unable to find the primary source for this quote. See Wikipedia, Buckminster Fuller; Buckminster Fuller Institute Perhaps this is another example of the many quotes found on the Web that really were never expressed by the attributed source.
  153. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.5.
  154. Erik Brynjofsson and Andrew McAfee, The Second Machine Age,, pp.138; See The Future of Employment,, pp. 4, 8, 38, 41.
  155. Stephanie Kimbro, Law a la Carte: The Case for Unbundling Legal Services,
  156. E.g. IDEO
  157. See e.g. Connie Brenton, Christina O’Connell and Emily Teuben, How to Automate Business and Legal Processes to Save Time and Money (CLOC subscription required)
  158. See Center for the Study of the Legal Profession at the Georgetown University Law Center and Thomson Reuters Legal Executive Institute, 2017 Report on the State of the Legal Market,, pp. 10, 16-17.
  159. Rob Strom, Seyfarth Shaw Puts ‘Software Robots’ to Use in Automation Push,
  160. Nigel Walsh, Next Big Thing: Robotic Process Automation See Hans Jessen, Will robots change the future of outsourcing? Is Robotic Process Automation (RPA) the new Business Process Outsourcing (BPO)?
  161. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.15.
  162. Federico Berruti, Graeme Nixon, Giambattista Taglioni, and Rob Whiteman, Intelligent process automation: The engine at the core of the next-generation operating model
  163. YouTube, London Tube
  164. Ryan Avent, The world if robots take our jobs, @ 1:57-2:06
  165. Thomas Davenport and Julia Kirby, Only Humans Need Apply,
  166. Id, p.97.
  167. Id, p.76.
  168. Tyler Cowen, Average Is Over,, p21.
  169. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.98.
  170. Id. pp.105-06.
  171. See id, pp.90, 93 (referring to KM). See also Knowledge Management: The White & Case Summit NYC (discussing Law Firm KM professionals focusing on AI).
  172. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.77. See CLOC Legal Operations Career Skills Toolkit for a list of EQ skills that may be relevant to those who might step aside.
  173. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.120.
  174. Id. See Paul Daugherty, Artificial Intelligence May Change the Face of Business,
  175. Megan Beck and Barry Libert, The Rise of AI Makes Emotional Intelligence More Important
  176. Carol Grunberg, Director of Business Development, Head of Channel Alliances | Ant Financial / Alipay as quoted by Tracy Wilik, How to Outsmart the Robots and Save Your Job – Tips from Leaders at Google
  177. Klaus Schwab, The Fourth Industrial Revolution,, p. 120.
  178. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.77.
  179. Chris Weller, IBM’s concept of ‘new collar jobs’ could be vital in an automated future
  180. Tom Davenport, Purple People At The Heart of Cognitive Tech,
  181. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.138.
  182. Chris Brady, Mike Forde and Simon Chadwick, Why Your Company Needs Data Translators,
  183. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.151.
  184. Deloitte, Disruption by Design: Agile strategy for the new world, Today, these skills may also reside with your legal operations and KM professionals. See footnote 171.
  185. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.77.
  186. Id. p.174.
  187. See Id.
  188. Id., p.77.
  189. Id., pp.178; 199-200.
  190. Rob Marrs, Why law students should be thinking tech,
  191. Alex Ross, Industries of the Future,, pp.269-270 (quoting Jack Dorsey regarding the benefits of programming language fluency.)
  192. Thomas Davenport and Julia Kirby, Only Humans Need Apply,, p.174.
  193. Elinor Smith, Aviatrix, p 143. See About Elinor Smith
  194. Kevin Kelly, The Inevitable,, p.11.
  195. Flaming Lips, Yoshimi Battles the Pink Robots Pt 1, .
  196. Cf. Tyler Cowen, Average Is Over,, introductory page (“I would bring a hammer,” quoting chess grandmaster Jan Hein Donner, when asked what strategy he would use against a computer.)
  197. Kevin Kelly, The Inevitable,, p.19-20 (“We have arrived at protopia, neither utopia or dystopia. Protopia is a state of becoming. . . .. It is a process . . . . Protopia is hard to see because it is a becoming. It is a process that is constantly changing how other things change, and, changing itself, is mutating and growing. It is difficult to cheer for a soft process that is shape-shifting. But it is important to see it. ”)
  198. Ramesh Nair and Ajay Nayar, Aligning and rationalizing your business applications: How to simplify the IT portfolio and reduce costs in financial services
  199. Stephanie Mullins, Why I hate the use of the phrase “game changer” in press releases
  200. Erik Brynjofsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies,
  201. Klaus Schwab, The Fourth Industrial Revolution,
  202. Erik Brynjolfsson and Andrew McAfee, See The Economist, Race Against the Machine (book review).
  203. Kevin Kelly,
  204. Thomas Davenport and Julia Kirby
  205. Jerry Kaplan,
  206. Geoff Colvin,
  207. Martin Ford
  208. Tyler Cowen,
  209. Alec Ross,
  210. Richard Baldwin,
  211. Richard & Daniel Susskind,
  212. “To the Framework-Knitters of the British Empire,” address from the United Committee of Framework-Knitters, quoted by Kevin Binfield, Writings of the Luddites,, pp.108-109.
  213. Framework Knitters Museum, About Framework Knitters,
  214. Early reports indicate Ned Ludd was ordered whipped by a magistrate. The destruction of the frame was his reaction. John L. Hammond, The Skilled Laborer, p.259.
  215. Id.
  216. Richard Conniff, What the Luddites Really Fought Against
  217. John L. Hammond, The Skilled Laborer, p.202.
  218. Id. pp.224-226.
  219. Peter Lindhert, When did inequality rise in Britain and America? (English income inequality rose in real terms in the period that might be dated roughly as 1740-1810.)
  220. Clive Thompson, When Robots Take All of Our Jobs, Remember the Luddites,; John L. Hammond, The Skilled Laborer,p.257-258.
  221. John L. Hammond, The Skilled Laborer, pp 226-227. See Steven Jones, Against Technology: From the Luddites to Neo-Luddism,, p.66; Kevin Binfield, Writings of the Luddites,., p.14.
  222. Id.