Mark Ross Principal, Deloitte - Legal Business Services and formerly headed Integreon’s Contracts, Compliance and Commercial (CCC) business unit, with accountability for the P&L, solution development, and delivery across the U.S., U.K., India, South Africa, and the Philippines. 

    Mark is a recognized thought leader in the Legal Process Outsourcing (LPO) field. He is a former partner at the first U.K. law firm to offshore legal work, and is the only person to have been invited to address the ABA, the Law Societies of England & Wales and South Africa, The Solicitors Regulation Authority, and the International Bar Association on the topic of LPO.

    Mark pioneered the development of the collaborative law firm and LPO delivery model for end-to-end contract management and led the integration of artificial intelligence into Integreon’s contract review services. He also developed the first State Bar minimum continuing legal education (MCLE) and continuing professional development (CPD) accredited courses on the ethical implications of outsourcing legal work.

    He has been interviewed by numerous publications, including The New York Times, Wall Street Journal, and Time magazine, and has also been invited to speak as a leading authority on LPO by organizations that include: Financial Times, U.C. Berkeley School of Law, Northwestern University School of Law, Stanford Center for the Legal Profession, and the International Legal Ethics conference.

    Mark is on the editorial board of Outsource Magazine, and is on the Advisory Boards of Suffolk Law School’s Institute on Law Practice Technology and Innovation, and Northwestern University Law School’s Center for Practice Engagement and Innovation.

    In September 2016 Mark was inducted as a Fellow of the College of Law Practice Management.

Vince Neicho, formerly a U.K. legal industry veteran, is an expert legal solutions consultant with a focus on law firms and corporate legal departments engaging in e-disclosure, e-discovery, and document review. Neicho leverages his expertise in the litigation support field to help Integreon clients design and plan highly efficient processes, establish flexible and scalable resourcing models, and utilise the latest innovative technologies, including predictive coding and other types of artificial intelligence systems.

   Neicho was previously Litigation Support Senior Manager at Allen & Overy, a global Magic Circle firm, where he introduced the concept of outsourced document review. At A&O he amassed years of experience working with the firm's extensive corporate and financial institution clientele on hundreds of matters, designing and managing a wide variety of litigation support solutions and technologies.

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 Even before the onset of the global financial crisis in 2008, in-house legal departments and their outside counsel were under considerable pressure to do more with less. The Great Recession exacerbated this pressure and led to a surge in the exploration of innovative legal services delivery models. Two key questions came to the fore for the in-house legal department: 1) Can we reduce or eliminate the need to undertake certain legal services? 2) For the services we must consume, how can we do so as cost-effectively as possible?

Stemming from these questions, a new legal ecosystem emerged in which, alongside in-house resources and outside counsel, legal process outsourcing (LPO) began to play a crucial role in the efficient delivery of legal services. Initially, the LPO industry’s raison d’être was the labor arbitrage benefits available from outsourcing certain routine legal tasks to lower-cost locations such as India, South Africa, and the Philippines. The class of providers augmenting the work of in-house departments and outside counsel has since matured and grown significantly — to the point that the “LPO” terminology is not inclusive enough to capture them. Today, a robust group of companies more accurately described as alternative legal services providers (ALSPs) are handling sophisticated work at a level we could not have anticipated even a few years ago.

Much of that transformation is because of ALSPs increasingly leveraging technology, particularly artificial intelligence (AI). This article will discuss the impact of ALSPs (and the AI that some of them employ) on the legal landscape. It will then address the journey law firms have navigated in their use of ALSPs to date and what to expect in the future.  

Artificial Intelligence: Does It Play Well With Others in the Legal Industry?

To appreciate how rapidly the ground has shifted in the legal industry, consider the question we raised in the first edition of this text just three years ago. We noted then, as we did above, that LPOs (what we might now think of as first-generation ALSPs) were powered by human labor; they leveraged the low cost of labor in remote locations to drive down the cost of document review. It seems a quaint concern now, but it is understandable that many wondered at the time whether the introduction of technology into the field of legal services would threaten the existence of LPOs. In a passage that has stood up well, we addressed the concern as follows:  

While some might argue that technological advances represent a competitive challenge to LPO, nothing could be further from the truth. It is technology that led to the advent of LPO, enabling offshore locations to interact with clients thousands of miles away, and it is the LPO industry that has since continued embracing and incorporating technology into virtually every element of its legal services delivery offerings, including assisting and advising corporations and law firms on the selection and implementation of enabling technologies. It is the LPO industry that now pushes the envelope to redefine the art of possibility in the legal field, providing expert consultants who can weave together advanced technologies as an integral thread in overall legal process transformation.

In retrospect, we were correct to note that technology made LPOs — or first-generation ALSPs — possible in the first place, and even more correct to emphasize that future ALSPs would “continue embracing and incorporating technology” into legal services. This has proved emphatically true. The leading ALSPs of today are almost exclusively thought of as companies pioneering the utilization of enabling technologies, and correctly so.

In fact, the question being asked now is almost a complete reversal from the one we discussed in the first edition. It is not whether technology will kill ALSPs, but whether ALSPs powered by technology — specifically, artificial intelligence — will kill law firms. Again, our answer is no.

It is indisputable, of course, that technology-assisted document review, legal research, deal rooms, e-billing software, data analytics, knowledge management, and document assembly have eliminated the need for firms to devote man hours to certain tasks. It’s also true that the application of artificial intelligence is only making the tools of automation more powerful. But the ways in which ALSPs are applying technology to various areas of legal practice are illuminating, revealing that technology tools remain complements to human legal practice, not a replacement for it.

Litigation

The days are gone in which huge teams of attorneys reviewed hundreds of thousands — or millions — of unfiltered documents. And it is no longer relatively inexpensive, remote labor that performs the task of document review. The leading ALSPs have long been proselytizers of technology-assisted review (TAR) and have been constantly developing, testing, and refining their workflows to deliver smarter and less costly review processes. ALSPs deploy these technologies in a variety of ways, from supporting a quality control process to leveraging artificial intelligence to perform predictive coding.

Likewise, many ALSPs have created platforms to apply natural language processing, machine learning, and artificial intelligence to the task of legal research. Entrants such as Casetext and Ravel Law offer AI-backed research capabilities and other features. Ravel Law’s Judge Analytics, to cite one example, allows litigants to view a judge’s entire history of decisions in different types of cases. Meanwhile, Allegory, a recent Integreon acquisition, uses what it calls “augmented intelligence” to automate litigation management. Allegory makes it much easier for trial lawyers to access relevant information and makes formerly cumbersome litigation tasks like creating evidence binders a painless process.

Contract Management and Review

In corporate departments, AI has led to impressive developments in the areas of due diligence, contract extraction, and contract data analytics. As with litigation, ALSPs have been at the forefront of these developments. Often triggered by the implementation of a CLM platform, an acquisition, or an audit, corporations can be faced with the need to locate, review, and extract information from thousands of contracts. ALSPs offer technological tools available that can support such an engagement. Integreon, for example, uses Kira’s machine-learning-based technology to assist clients with contract extraction, due diligence, contract analysis, and lease abstraction.

Automated metadata extraction, categorization, and related technologies greatly reduce the cost of a contract-by-contract review performed by lawyers. Even when performing those tasks, however, ALSPs frequently support their technological tools with a review by legally trained personnel; it is this combination of human- and technology-driven analysis that provides the most effective end-to-end solution.

Legal Spend Analytics

Legal spend analytics is another area in which technology is being used to achieve more cost-effective legal services. By analyzing data from legal invoices, corporate legal departments can benchmark historical charges from outside counsel and vendors for a variety of legal services. ALSPs in this area use technology to produce reports that not only track outside counsel spending (broken down by firm, practice areas, timekeeper), but also include savings opportunities, progress against budgets, and other key metrics.

Knowledge is power, and these technology-driven legal-spend analytics tools allow corporate legal departments to revisit their entire relationship with outside counsel — from how they select firms, to how they manage them, to when they cut ties with them — from the position of power. The end game is one in which resource allocation is optimized, using the right legal professionals and technology for the jobs to which they are best suited

The Impact of AI

The takeaway from the above should not be that ALSPs, and the artificial intelligence they sometimes employ, is encroaching meaningfully on the territory of law firms. Instead, in each area, the theme is the same: While technology solutions are automating certain tasks and offering lawyers new insights, the human element remains as important as ever in delivering legal services. AI tools still require that lawyers perform a quality-control check, as they routinely do for contract- and document-review solutions. Lawyers are also needed to provide the input that trains the AI tools to become more powerful.

Most importantly, even with the introduction of artificial intelligence in the legal industry, the heart of a lawyer’s work — legal reasoning, crafting strategy, negotiating with counter-parties, arguing in court, and more — remains largely untouched by technology. Indeed, technology-driven ALSPs are not replacing law firms, but rather: 1) reducing the cost of certain services and 2) allowing them to make more informed strategic decisions. In this sense, ALSPs have advanced beyond LPOs, which were initially aimed exclusively at the first goal. But in another sense, the introduction of AI is similar to the arrival of LPOs: It is a disruption to which some law firms will react better than others. The history of law firms’ reaction to ALSPs, which follows, shows us as much.

Redefining the Law Firm Delivery Model: a Journey of ALSP Acceptance

In order to survive in today’s economy and to thrive in the future, many law firms are actively rethinking their business models. This rethink frequently includes an embrace of ALSPs and a reexamination of the traditional pyramid structure as the usual modus operandi for legal services delivery.

Although some believe ALSPs will increasingly contract directly with corporate clients, it is important to consider that they do not practice law and therefore cannot replace law firms entirely. A more natural fit for ALSPs is to supplant the base of the law firm pyramid. This is not to suggest the only benefit of ALSPs is labor arbitrage. As discussed above, we have ample proof this it is not. What ALSPs are doing is leading the way in incorporation of technology into legal services delivery.

Kicking and Screaming

In or around 2006, it was not law firms but corporate legal departments that were the first proponents of ALSPs. Back in these early days, a cocktail of incredulity with a dash of disdain was the tipple of choice for many a law firm partner when confronted with the ALSP elevator pitch. Big Law executives would protest that ALSPs were win-win-lose: win for the firm’s clients, win for the ALSP, and yet lose for the law firm.

This viewpoint presupposes the adequacy of two hypotheses that simply do not hold water any longer: the zero-sum game (the more the client loses, the more the law firm wins) and that every penny of revenue generated by an ALSP is a penny of revenue lost by the law firm.

In any event, these first couple of years can be characterized, perhaps somewhat harshly, as the phase where law firms were dragged “kicking and screaming” into the arms of ALSPs.

On a case-by-case basis, in-house counsel started to advise their outside counsel that in order to retain their business, the firms must begin to use ALSPs. In fairness to BigLaw, this phase has largely passed and did so fairly quickly. Whether the Great Recession forced them to adapt quickly or merely coincided with a change in attitude is a debate for another day.

Checking the Box

Law firms have many constituencies, but their clients always come first. Large firm clients are, by and large, cost-sensitive in-house counsel. Firms can gain both a perception and actual advantage with clients by making clear they understand and are responding to the cost pressures facing their clients.

In-house counsel muscle-flexing manifested itself not only in ad hoc requests that their outside counsel use an ALSP, but also in the increasing prevalence of requests for proposals (RFPs) asking outside counsel whether they had relationships in place with ALSPs.

Law firms responded in turn by undertaking selection processes of their own to choose one or more preferred ALSPs. The end result was that when asked the question in an RFP, law firms could respond in the affirmative. This is the “checking the box” phase. Many of the firms during this phase were simply looking to place a check in the box, and once a master services agreement was put in place between the firm and the ALSP, it was considered a job well done with no further action required. Many firms today are struggling with how to navigate the transition from the “checking the box” phase into the phase that follows: “strategic collaboration.”

Strategic Collaboration

In 2011, our employer Integreon commissioned research tracking the adoption of ALSPs among law firms and in-house counsel. While a minority of firms seemed to worry that using an ALSP might send clients the wrong signal, the results of the research showed such fear to be unfounded. A significant majority, about 75 percent, of both in-house and law firm lawyers believed using an ALSP did not “diminish the brand.” Rather, those that embraced ALSPs were perceived as cognizant of the cost, efficiency, and quality demands of their clients, and consequently appeared to gain a competitive advantage. Today, a significant number of innovative law firms now publicly acknowledge their relationships with ALSPs. These firms are at various stages of the journey that can be termed as “strategic collaboration.”

The end of this journey, one that arguably no firm has yet reached, is when ALSP solutions are so closely integrated into the firm’s overall value proposition that they are simply viewed as part of a suite of solutions that the firm provides to its clients across all of its practice groups. This requires firms to embrace ALSPs at a strategic level, welcoming them into the firm, lifting open the hood, and working with the provider, as Professor Richard Susskind would say, to “decompose” legal functions, map out “as is” workflows, and then reengineer the processes to incorporate ALSP best practices, lower-cost labor, and technology.

The theory behind strategic collaboration is not rocket science. The premise is that the whole is greater than the sum of the parts. Contrary to early concerns that ALSPs would compete directly with law firms, it has become abundantly clear to those firms embracing strategic collaboration that the most effective legal services delivery model is a symbiotic one in which law firms and ALSPs help each other thrive.

ALSPs do not practice law and so are not true alternatives to law firms. Neither ALSPs nor law firms can individually deliver the holistic, end-to-end services corporate clients are now demanding. While one could argue that law firms with captive ALSP units can do so, the fact is that running a captive center, especially offshore, requires a scale that only the largest law firms possess. Even with respect to those few firms, ALSPs offer several other advantages over a captive. These include better capacity utilization by aggregating demand across many clients; conversion of fixed to variable costs; ongoing investments in technology and continuous improvement; and, of course, business continuity assurance with multiple delivery locations.

A common misconception held by proponents of captives is that working with a third-party ALSP means loss of control. This is not the case. Control is more about governance than ownership. For example, some captives are out of control because they have not been properly set up with service level agreements (SLAs). Conversely, a proper SLA and governance structure can give the law firm more control over a third-party ALSP than they might typically have over their own staff.

Law firms that strategically collaborate with ALSPs, meanwhile, can expand their offerings and deliver a complete, end-to-end approach, efficiently providing the appropriate level of legal services required for each type of work product.

Bifurcated Ownership

Unrelenting cost pressure, deregulation, disaggregation, globalization, and technological advances were the genesis of the ALSP sector. Today, the challenge and the opportunity are for ALSPs and law firm clients to develop new service delivery models that will drive even greater innovation. One can either shape the change or be shaped by it. It is incumbent upon all the key constituent stakeholders in the legal services industry to find better ways of working together.

In coming years, there is no doubt we will see even closer collaboration between law firms and ALSPs, with the lines of ownership of the legal services delivery model becoming increasingly blurred as these stakeholders invest in and enter into joint ventures with one another. This can be called the “bifurcated ownership” phase.

How long will it be before an ALSP acquires a major law firm in the U.K. now that external investment in law firms is permitted via the Legal Services Act? Hardly a week goes by without the rumor mill spinning a story about this law firm or that law firm seeking to monetize either their captive ALSP operation or their high-volume practice group. For many of the reasons cited above, it is likely that the majority of those law firms with captive ALSPs today will look to divest these operations in the coming years. Global ALSPs are the most logical acquirers of these entities.

As time progresses, there is a growing optimism about and enthusiasm for reshaping the way legal services are delivered. The new bifurcated model is inevitable. The end result of the journey to this final fourth phase is a seamlessly integrated delivery model, with clients of all kinds benefiting from better, faster, more readily accessible, and cheaper legal services.

What’s Next?

We remain more bullish than ever about the prospects for ALSPs. The industry has matured and transformed over the last 10 years, but that journey, that evolution, has in reality still only just begun.

Out of all of the different stakeholders providing legal services within the new legal ecosystem, it is the ALSPs that have the deepest experience deconstructing and reengineering legal processes using Lean and Six Sigma techniques, applying best practices and flexible resources, and optimizing the mix and utilization of technology, including AI. It is the ALSPs that have the demonstrated expertise to analyze which aspects of the work done by lawyers, paralegals, and support staff can be complemented through automation or the application of AI. As the rollercoaster of legal services innovation and technological advances continues to pick up pace, not only will ALSPs be
along for the ride, but they’ll also have front row seats.

Tony Williams is a principal at Jomati Consultants LLP, a U.K.-based international management consulting firm for law firms, lawyers and in-house counsel that specializes in strategic expansions, reorganizations, and client strategies. Before founding Jomati Consultants, Tony was worldwide managing partner of Andersen Legal and head of its U.K. practice, where he developed the firm’s international strategy. Prior to joining Andersen Legal, Tony was managing partner of the world’s largest law firm, Clifford Chance. He was with Clifford Chance for almost 20 years and prior to his managing partner role he was a corporate partner in London, Hong Kong and the managing partner of the firm’s Moscow office. For his role in the orderly and controlled dissolution of Garretts following the Enron crisis, he was named “Partner of the Year” by The Lawyer Magazine in 2002. Tony is also a founding member of Halsbury’s Law Exchange, an independent and politically neutral legal think tank that contributes to the development of law and the legal sector.

Jack Newton is the founder of Clio and a pioneer of cloud-based legal technology. Jack has spearheaded efforts to educate the legal community on the security­, ethics­ and privacy­ issues surrounding cloud computing, and has become a nationally recognized writer and speaker on these topics. Jack also co-­founded and is acting President of the Legal Cloud Computing Association (LCCA), a consortium of leading cloud computing providers with a mandate to help accelerate the adoption of cloud computing in the legal industry.

How satisfied are your clients? The data indicates most lawyers don’t know. When we surveyed nearly 2,000 lawyers in the U.S., we discovered 37 percent don’t collect client feedback at all — and for the firms that do, 42 percent collect feedback casually and informally in person, meaning they may only be hearing positive feedback affected by courtesy bias. The implication of this is that poor client satisfaction could be the most critical blind spot for today’s law firms.

Poor satisfaction is what ruins businesses. If your clients aren’t satisfied, there’s little chance they’ll hire you again or recommend you to someone else — and they may even deter others through word of mouth or negative online reviews. It’s a bad prospect for any law firm that wants to succeed.

On the other hand, the types of businesses that thrive in today’s digital economy are the ones obsessed with customer satisfaction. The internet has leveled the playing field, and your competitors are just a click away for your prospective clients; more than ever, your clients need to see a clear reason to hire you over another firm. Law firms that earn the satisfaction of their clients are the ones that see significant momentum in the future success and profitability of their business.

The State of Client Satisfaction in the Legal Industry

To better understand the state of legal services in the 21st century, we set out to assess client satisfaction on an industry-wide scale by using a metric known as a Net Promoter Score (NPS). Research has shown that NPS is one of the most reliable predictors for business growth, and it’s based on more than just satisfaction or loyalty — it’s based on the likelihood to recommend. Given that lawyers typically rely heavily on referrals, NPS should be considered especially salient for law firms.

How satisfied are your clients? The data indicates most lawyers don’t know. When we surveyed nearly 2,000 lawyers in the U.S., we discovered 37 percent don’t collect client feedback at all — and for the firms that do, 42 percent collect feedback casually and informally in person, meaning they may only be hearing positive feedback affected by courtesy bias. The implication of this is that poor client satisfaction could be the most critical blind spot for today’s law firms.

Poor satisfaction is what ruins businesses. If your clients aren’t satisfied, there’s little chance they’ll hire you again or recommend you to someone else — and they may even deter others through word of mouth or negative online reviews. It’s a bad prospect for any law firm that wants to succeed.

On the other hand, the types of businesses that thrive in today’s digital economy are the ones obsessed with customer satisfaction. The internet has leveled the playing field, and your competitors are just a click away for your prospective clients; more than ever, your clients need to see a clear reason to hire you over another firm. Law firms that earn the satisfaction of their clients are the ones that see significant momentum in the future success and profitability of their business.

The State of Client Satisfaction in the Legal Industry

To better understand the state of legal services in the 21st century, we set out to assess client satisfaction on an industry-wide scale by using a metric known as a Net Promoter Score (NPS). Research has shown that NPS is one of the most reliable predictors for business growth, and it’s based on more than just satisfaction or loyalty — it’s based on the likelihood to recommend. Given that lawyers typically rely heavily on referrals, NPS should be considered especially salient for law firms.

Calculating NPS starts with asking a cohort of clients a standardized question: On a scale of 0 to 10, how likely are you to recommend your lawyer to a friend or colleague? You then calculate the percentage of respondents who answered within the 0 to 6 range (known as “Detractors”) and subtract that from the percentage that responded with a 9 or 10 (known as “Promoters”). Those responding with 7 or 8 (known as “Passives”) neither subtract nor add to an NPS. What you’re left with is a minimum score of -100 (100% Detractors) and a maximum score of 100 (100% Promoters).

For perspective, some of the most successful businesses in the 21st century have achieved incredible growth based significantly on highly favorable NPS. These companies (and scores) include Amazon (62), Netflix (68), Apple (76), Starbucks (77), and Costco (79).^[1]

Where does NPS net out for the legal profession? After collecting data from a cohort of over 1,300 American consumers on their experiences working with a lawyer, we calculated a benchmark NPS of 25 for the legal profession as a whole. This is based on a breakdown of 48% Promoters, 30% Passives, and 23% Detractors. While nearly half of clients would highly recommend their lawyer, nearly a quarter of all clients would actually dissuade others from their lawyer.

While an NPS of 25 isn’t entirely bad, it’s nothing to celebrate. Other industry benchmarking studies put this number at the same level as airlines (26), banks (26), wireless carriers (25), and credit card companies (23)—none of which are known for exceptional service.^[2] The deeper implications imply that poor client satisfaction is hindering growth in the average law firm.

The Power of Satisfaction in Driving New Business

In speaking with countless lawyers and other legal professionals, one of the most common sentiments they share is that managing a business is incredibly difficult. Most wish it were easier.

One of the most difficult challenges for lawyers is generating business for their firm. Many see this as an uphill battle, where every step is a part of a grind that never gets easier. The problem with this mind frame is it fixates on effort as a burden. But effort is only one side of the equation, one that’s focused entirely on inputs. On the other side are your business outputs. Unlike inputs, which are a limited resource, the work put into maximizing outputs can have exponential returns. In other words, rather than putting more effort into your business, think about how to get more returns from the effort you already put in.

The business researcher and consultant Jim Collins has written and spoken prolifically about the concept of “the flywheel.” The idea behind the flywheel is that it takes a sustained and constant effort to build momentum; but once that wheel starts to turn, it serves as an energy store, and maintains much of its momentum even once force is no longer applied to it. The wheel wants to turn of its own volition, and any ongoing effort only speeds it up. The effect is a perpetual compounding of return-energy for every bit of continued effort.

The key to developing a flywheel effect for your business is to (1) understand what components will build momentum and then (2) aligning your efforts in the right direction.

Which brings us back to NPS. Client satisfaction, reputation, referrals, and repeat business are all components that build off each other to create momentum and drive law firm success. A positive client experience will drive more repeat business and referrals, which in turn will bring more clients to your firm, who in turn will drive more repeat business and referrals. Success begets more success. This is the client experience-driven flywheel in motion, and it's one of the most powerful growth levers law firms have at their disposal.

What contributes to positive client experiences? Our NPS research demonstrated a strong connection between NPS and factors that were intrinsically related to the ease (or difficulty) of working with a law firm: ease in understanding case expectations, bedside manner, and overall responsiveness to communications. Since the growth prospects of any law firm rely so heavily on client satisfaction (as measured by NPS), focusing on client experiences should be how lawyers think about their future success. 

Forecasting the Future of Success in the Legal Industry

The legal industry is transitioning in a way similar to any other during times of rapid technological change. The key to weathering these changes — and prospering through these times — is to recognize where problems lie and what efforts will have the most returns in solving them.

Today, technologies are unlocking new opportunities for lawyers to get more from their efforts. With these opportunities, lawyers have more options for how they structure and deliver legal services, and in how they design client experiences. Technology also has the potential to provide better data in understanding what’s working in today’s law firms and what needs improvement.

It’s an exciting time to be practicing law, but it’s also a time that will require firms to focus on client experience in order to be truly successful.

Rees W. Morrison is Guru for Online, Law-Related Surveys and was  a principal of Altman Weil, Inc. He has more than 25 years of experience advising law departments on cost control, department structure, process improvement, outside counsel management, performance benchmarking, and other key issues. He also specializes in data analytics for legal organizations.

   Before joining Altman Weil, Mr. Morrison consulted independently for five years and held partnerships at several legal consulting firms, including an earlier tenure at Altman Weil from 1998 to 2002.  He has had several in-house positions including Business Manager for Google’s law department and Consulting Assistant to the General Counsel of Merck. Earlier in his career he was vice president of two software firms, and an associate at Weil Gotshal & Manges and two other New York law firms.

   He has written extensively on law department management, including nearly two-hundred articles, six books, and a well-known blog on law department metrics. For two years he wrote a bi-weekly column, Morrison on Metrics, for InsideCounsel. Among his books are “Law Department Benchmarks: Myths Metrics and Management”; “Client Satisfaction for Law Departments”; and “Law Department Administrators: Lessons from Leaders.”
   He is a Certified Management Consultant (CMC), a member of Scribes (The American Society of Legal Writers), a fellow of the College of Law Practice Management, and has been on the Board of Advisors of legal publications Corporate Counselor, Law Department Management, and Metropolitan Corporate Counsel. A Life Fellow of the American Bar Foundation, he has participated in the ABA's Law Practice Management Section and ACC’s Law Department Management Committee. 

   Mr. Morrison graduated from Harvard College in 1974, earned his law degree from Columbia Law School (1978) where he was a law review editor, and received an LLM from New York University Law School (1984)

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Day after day, law firm partners and managers confront operational problems, think about them, and make choices about what to do or not to do. In other words, they decide something. They would make better decisions if they took into account the data available to them. If they collect metrics and weave them into their deliberations, the outcomes will be both sounder and easier to explain.

Two subtler and broader advantages from decision-making that incorporates numbers should be emphasized. First, it encourages a different way of thinking about decision-making than traditional approaches. Make it a practice throughout the firm to ground arguments in data and to present arguments buttressed with numbers, or else accept that a resolution rests on power, values, or ideology more than quantifiable evidence.  

Second, being mindful of data is being mindful of what you do. This is a deeper benefit arising from a law firm’s receptivity to data. A general awareness of metrics helps lawyers and others in law firms take stock of their processes, describe them and their output in more tangible, numerate terms (“15 10Ks reviewed this month” rather than “Lots of 10Ks”). They become more aware and reflective about what they are doing and how they might do better.

But let’s consider our claim — “data helps decision-making” — and ground it by walking through a scenario for a management decision. Assume that three partners want to figure out whether to hire another paralegal. Further, assume that the partners disagree. Their decision will improve if they marshal relevant data, analyze it effectively, and apply it to their discussion. How can numbers help them objectively think through the problem and potential solutions better than they would have without? Here are five ways.

1.   Sidestep Cognitive Fallacies. 

Data can counteract many of the cognitive biases that afflict decision-makers. Often we are unaware of the gremlins in our minds that attack what we believe to be our clear-headed, balanced evaluations. Consider four well-known cognitive fallacies and how data might correct for them:

Framing: An antidote to framing could be benchmark data on paralegals per lawyer in firms.

Salience: To blunt its potential impact, someone could gather articles that report average lawyer/paralegal ratios based on surveys of many companies.

Confirmation bias: Perhaps the partners’ practice group submitted the mixed evaluations on a survey of paralegals, which would be data that challenges a one-sided view.

Risk aversion: A risk-averse partner may argue for more paralegals because the group never wants to be over-extended; past data on large bumps in hours might dispel the concern.

2.      Uncover and Query Empirical Assumptions.

When people make decisions, they often neglect to articulate the factual assumptions on which they base them. Worse, they may not even realize that they have been motivated by unstated (and usually untested) beliefs about how common something is or how much there is of something measurable. For example, one partner might accept on faith that lawyers will delegate work to paralegals, while another could trust without verifying that it will be easy to find, hire, and retain capable paralegals. If underlying assumptions such as these are not identified and if there is no data either way, decisions will likely be weaker (and take longer).

3.     Disrupt Entrenched Convictions.

As data becomes available for decision-makers, they should incorporate it and change how they view the probability of being correct. In a Bayesian view (a fundamental technique of statistics), new data changes what are called priors and helps make predicted outcomes more accurate. New findings and facts should cause thoughtful people to recognize and reconsider their animating beliefs.

4.     Delay Premature Conclusions.

When making a decision, it is crucial not to seize upon the first plausible solution to the problem. Much better, instead, is to keep exploring alternative possibilities. Data helps stimulate new possibilities to address a problem or to encourage managers to think about the problem longer.

5.     Counter Peer Pressure.

In groups, solid data can serve as a talisman against the fancy (or loutish) talker, the zealot, or the high-ranking executive. A partner who disagrees with her colleague might be more inclined to back them if some metrics bolster the point.

This is not to claim that good data automatically means good decisions. It is to claim that operational data can frequently help steer managers to reach a sounder decision. To be sure, the toughest decisions tend not to have decisive metrics. But even the gnarliest decisions — those that entangle personalities, tradition, long-term visions, or fights over fundamental values — can benefit from whatever dollops of data are available.  

1.       What Data Do Law Firms Have That Can Contribute to Better Decisions?

Analytic tools require data, and law firms have data sets aplenty. From this author’s recent book, “Data Graphs for Legal Managers,”[1] the table below shows a wide array of firm data.

A.     Types of data law firms have.

Starting with such internal data, firms can mix in information from other sources, possibly within the firm or from data outside the firm. As for client information, supplemental information might include whether it is publicly traded, whether it has an in-house law department, its revenue, the number of employees, SIC codes, and more. For example, you might want to combine time and billing data or input information from your HR system. Years of experience or academic degrees of timekeepers could be mixed in from the firm’s HR database. 

B.     Repositories of data.

All this potentially useful data lives in paper files, people’s memory, time and billing software, customer relationships software (CRM), marketing records, personnel files, exhaust (data that is created by someone doing something else, like making phone calls or scheduling conference rooms), general ledgers, status reports, surveys, and counts of all kinds of events. A law firm that wants to capitalize on data needs to extract and store it in databases, spreadsheets, in the cloud, or wherever it can best make use of it.

C.      Data cleaning.

            A key part of data analytics is the unglamorous slog of grooming it for analysis.  Software can help users correct the data, such as when there are missing or highly unusual values, the latter being what data analysts call outliers.

Clean data also cannot have too many missing values or different styles in cells of the same column. If the fees column has cells with a dollar sign and other cells without the sign, for example, the software might stumble. If some cells in the spreadsheet show “NA” when data is not available, but others show “—“, you need to clean that. Clean data is also reasonably accurate data (it was not a data entry error that some associate billed 4,299 hours last year!) and not pockmarked with bizarre values.

Still, you can actually do useful analyses and, more fruitfully, make predictions with modest amounts of data. For example, with a spreadsheet having details on 50 or more closed cases or matters of a similar type, you can infer a great deal.

2.     What Are Potential Uses of Data Analytics by Law Firms?

Metrics and their exploration can benefit law firms everywhere that partners contemplate management decisions. The next section sketches several decisions that could benefit from data analysis and metrics.

A.     Increase Revenue.

The recent surge in law firms collecting, analyzing, and visualizing information aims —quite understandably — to increase firm revenue. Why, managing partners ask, should we invest the time and money to do predictive analytics (AKA machine learning) if we don’t expect to hear the cash register ring? That goal of increased fees (or improved profitability) makes sense. It also orients firms to focus analytic tools on substantive legal analyses. Much can be done to transform the straw of data into the gold of profitable clients, practice groups, or billing arrangements. Analyzing cost drivers of lawsuits to make more money on fixed-fee arrangements would be an example.

B.     Retain and Wisely Promote Associates. 

One benefit of data is when the firm is hiring lawyers. When firm ambassadors make their pitch to hire associates or lure lateral partners, they deserve to be able to describe the firm glowingly and convincingly. Solid, impressive numbers on growth, revenue, quality, and associates, not to mention clients, persuade recruits, especially when made clear with effective graphs. Or they will let machine-learning software loose to study who makes partner and why, or to tackle attrition in terms of which desirable associates are at risk of leaving the firm.

C.     Improve Firm Operations. 

A number of benefits of predictive data analytics should be recognized in the domain of law firm operational management. As much as managing partners want to grow or increase profitability and bring in more fees and add more lawyers, they may overlook or discount secondary uses of law firm data for running the firm as leaders focus almost exclusively on the short-term return on investment in business development.

Facilities. Another use of data arises frequently in infrastructure planning. Should we sublet additional space? Should we move to another location or open a branch office? Sometimes there are questions about installing a larger server or rewiring the existing offices. Answers to questions like these, and decisions made thereafter, are wiser when there is data available to support them. 

Proposals. Almost every Request for Proposal that a firm receives asks for data. The law department that issued the RFP wants to know about diversity, or about practice groups and their numbers of lawyers, or about the size of transactions handled recently. It is efficient to have the raw data already compiled and curated in a spreadsheet or database. 

Press Relations. When reporters call, the partner who responds will make points more tellingly if they can rapidly cite reliable facts about the firm or topic. “Almost 40 percent of our clients do business in more than 10 countries” impresses reporters far more than getting back two days later with “Lots of our clients are multinationals.” The first statement, with its impressive precision and prompt delivery, can only be made if the appropriate numbers have been tracked, analyzed, and made available. 

Vendors: Any time a law firm considers buying something, it will make sounder decisions if it precedes the decision with tallies and tracking. Do we need to buy more user seats under a software license? Have people made sufficient use of the expensive subscription? Research into these kinds of questions pays off; research should be captured as data for decisions.

Law firms focus on data associated either with client matters, or with the effective deployment of their own lawyers and staff. They won’t regard their spending on vendors as nearly as vital as matter productivity, investment, and outcomes.

3. What kinds of analytical tools are available?

Some partners in law firms may not be aware of the full panoply of data analytics that their firm might employ. Let’s briefly review eight different analyses that software can produce.

Descriptions and summaries of data.

At the most basic level, software can take data, such as the billable hours of lawyers, and describe with varying degrees of summarization the key numeric features of the data. Software can calculate the average billable hour, the median of the billable hours, or how dispersed it is (usually expressed as standard deviations). Software can pick out the lowest value and the highest, break them into groups (called quantiles), and tell us ranges. Contingency tables can also illuminate the data. Furthermore, software can depict those features of the data in graphics, such as histograms, density plots, scatterplots, and bar charts.

Correlations between variables in the data.

It may be useful for legal managers to see how one variable (an element of data tracked for every associate, client, lawyer, or whatever) moves up or down in relation to the average when another variable changes. Thus, for instance, the software can show the correlation between the number of matters worked on by a lawyer and billable hours reported. A correlation tells you whether there is an association between two variables, how strong it is, and in what direction the variables move. It is a positive correlation if both numbers move in the same direction (such as higher billable hours and higher bonuses); it is a negative correlation if the numbers move in the opposite direction (such as higher billable hours and lower psychological well-being).

Comparisons of averages and differences.

Several statistical tools can detect whether the difference between two or more numbers has significance mathematically. So, for example, there are many techniques to tell whether the average billable hours in a year between two offices of a law firm vary enough for managers to consider intervening and taking some action. These tools, such as ANOVA and the Student’s T-Test, help to determine whether variations are important enough to deserve discussion.

Measures of inequality.

Managers of lawyers may want to assess the quality of a set of numbers, such as bonus distributions. Along with the well-known Gini coefficient, several other measures allow software to put a number on inequality and even pinpoint where in the set of numbers the actual data diverges from theoretical equality. These analytics help managers explain their decisions and make better decisions in the first place, if equality is sought.

Understand influence of variables and make predictions.

A whole family of regression tools goes beyond correlations. If, for example, a firm wants to predict the estimated amount of fees to be paid to it during the coming year, it could run a linear regression. The software would then point out which of the variables was more influential in predicting total fees paid and how much of the total fee paid is accounted for by the variables. One of the best-known techniques is multiple linear regression. It makes some assumptions about the relationships between whatever value is being predicted and the variables that are associated with it (e.g., level of the person retaining the firm, presence of a law department, range of practice groups involved, and years as a client).  

The regression algorithms generate a “model.” Once you have a model, you can extract information from it. A model often takes in data and makes predictions regarding new cases, clients, or matters. Think of a model as the software learning on a “training set” of data that has been labeled, such as settled for less than $10,000 or not) and applying that learning to predict something (maybe total fees) for a new case or example. With multiple regression, naïve Bayes algorithm, or neural nets prediction is a common output. For example, given a few dozen instances of a type of lawsuit, any of those machine-learning algorithms could predict the likely cost of a new matter once sufficient information is available and tell you how probable that cost would be. 

As another example of machine learning, a regression model might explain and forecast how fees and hours devoted to five common litigation tasks are associated with outcomes and therefore can predict the likely outcomes for the next case that can identify the corresponding data. Moreover, the machine-learning software can tell you which of the five tasks underpin the strongest association with the outcomes as well as how confident you can be that your prediction is correct. 

Extracting insights from text.

Words in documents can be handled statistically by software as text mining. When a survey returns free-text comments, for example, software can pick out not only which terms are used most frequently, but also assess the sentiment (the positive or negative vibes of the comments). Even more powerful are the algorithms that can assemble words from the survey comments into topics. A person has to examine the words and identify the actual topic, but the laborious work of parsing all the documents and doing the math can be done quickly by the computer. If you want to show off, mention latent Dirichlet allocation (LDA) as your topic-modeling algorithm of choice! 

A second form of machine learning would be at work when text mining software takes thousands of emails, identifies patterns in words such as repetition or proximity to each other, or pores through email messages to tag possible indicators of insider trading.

Classification and clustering.

Whenever a law firm or law department has collected a set of data, it can use a range of software tools to cluster the observations. This means that the software brings together related clients, matters, or law firms based on the information available to it about them. Once the software has clustered the observations, managers can more easily detect patterns and understand similarities and differences. A chart known as a dendrogram can depict the clustering of data and how clusters relate to each other. Somewhat similarly, software can classify observations into similar groups. Both of these types of analytics help partners see patterns that they could not otherwise detect from a massive set of data.

Other models can also classify new observations into the most appropriately fitting group. With several types of algorithms, including K-Nearest Neighbor or Support Vector Machines, you can classify clients or other data. You would be able, for example, to identify publicly-traded clients or clients likely to reach a certain realization level. 

Other varieties of machine-learning software do not require labels. Their models cluster the data into groupings that will reveal something. For example, they might cluster a firm’s clients by profitability. The K-Means algorithm can do this, and with the Principal Components Analysis you can aggregate “variables” to find out which of them is more influential.

Machine learning.

At this time, the most sophisticated data analytics that can help partners resides in a branch of artificial intelligence known as machine learning. The term encompasses a range of methods by which software chews its way through mounds of data and detects patterns. In one broad category, supervised learning, someone has to classify enough of the instances so that the computer can figure out a pattern. In another category of machine learning, unsupervised, the computer “does its own thing,” so to speak. The output can be a classification, or a regression, or other kinds of results. These tools include neural nets, support vector machines, deep learning, and Bayesian tools, among many others. This field is currently a hot spring of innovation.

4.   What Do You Need to Do to Incorporate Data Analytics More into Your Deliberations?

A.       Your firm needs a partner who is influential and exudes enthusiasm to push the initiative.  Ideally, the champion will proselytize for data analytics and secure funding. Sad, but true; you will need to ante up to find out whether and how your firm can take advantage of machine learning. 

The champion ought to be persuasive, eager to learn something about new computational tools, and adept at conveying a vision of how the firm should take advantage of the evolving capabilities of data analytics for legal management.

The champion will need to handle objections skillfully. Data can actually be feared as conspiring against the humanistic values of the partnership. Many partners in law firms shy away from data analytics because the findings invite divisive comparisons. All data discriminates. Moreover, many partners don’t really want their clients thinking about performance metrics and costs.

At this early stage of law firms exploring predictive analytics, it is very important for someone influential to explain what the benefits are and how the firm can achieve those benefits. The domain of data, software, statistics, programming, and algorithms will be mostly unfamiliar within your firm, and explanations will be welcome. A partners’ off-site conference is a good opportunity to raise awareness and attract supporters.

If IT, a practice group, HR, marketing, and a champion all have roles in a machine learning initiative, it will likely either bog down or take far too much time and money. Each group has a different interest. Someone needs to coordinate meetings, decisions, and timelines.  That project management role might fall to a junior person, or the champion might take it on.

       B.      Programming and IT Support. 

Your firm will also need programming, perhaps from a consultant or an employee.  Programmers and consultants aren’t cheap, but they are crucial. Also crucial is that any coding be work for hire, heavily commented so that someone else can follow the steps and logic, and adhere to the tenets of reproducible research.

People who have not written code for a computer to run probably don’t realize how difficult it is to code well. It is challenging to get a computer to do what you want it to do. This hurdle becomes greater as the sophistication of the programming increases, and sophisticated programming is undoubtedly required to command machine-learning algorithms. 

Your firm will need to choose software that can carry out the analyses. Those algorithms exceed the capabilities of Excel, but many other choices exist. This author relies on the open-source R programming language, which has been optimized for statistical analyses and data visualization. Another open-source choice would be Python. Many commercial packages jostle in the market, including SPSS, Tableau, SAS, and Mathematica.

As with most change initiatives, your firm should start with a pilot study and learn from it before you roll out a more ambitious project. A practice group that wants to be able to predict results, costs, or duration of matters from a subset of its past matters would be a good choice. The HR group might also apply multiple regressions on data to reduce attrition or understand better who makes partner.

    C.      Subject Matter Expert.

Your firm will need a lawyer who not only supports the initiative but also qualifies as a “subject matter expert.” A SME can look at the data set and understand the relative importance of pieces of it, what’s missing or odd, and what the firm might learn from it. A SME can translate in-the-trenches reality to the champion and programmer. For instance, looking at a set of information about certain kinds of cases, a subject matter expert could point out that the tenure of the judge — senior, mid-career, newly appointed — seems likely to correlate with the decision. An SME might also say that the duration of a case is not particularly useful because there are long stretches where neither party takes any actions. Even more usefully, an SME could classify matters as successful, unclear, or unsuccessful so that the software can tease out patterns and influential variables. 

Appendix – Source articles

Portions of this chapter came from the articles cited below, albeit with significant re-arrangement and revisions.

Rees W. Morrison, Mind the Machines: Time to Explore the Potential of Machine Learning, InsideCounsel (Oct. 21, 2016).

Rees W. Morrison, The Math Behind AI, as Explained to Lawyers, InsideCounsel (Dec. 26, 2016).

Rees W. Morrison, Drawing ACES, LegalTech News, L12 (Feb. 2017).

Rees W. Morrison, Making the Machine-Learning Switch, 25 Met. Corp. Counsel 31 (Feb. 29, 2017).

Rees W. Morrison, With Data Analytics, It's Not Always ‘Follow the Money!’, LegalTech News (March 2017).                           

Rees W. Morrison, Fairness Calculations: Letting the Gini out of the Lamp, LegalTech News (Sept. 28, 2017).

Rees W. Morrison, The Power of LDA Algorithms and How They Help Text Mine Your Documents, LegalTech News (June 8, 2017). [Text mining]

Rees W. Morrison, ANOVA Apart: How to Tell If Your Firm Averages are Actually Significant, LegalTech News (Aug. 10, 2017). [ANOVA)

 Legal Operations Director with Nextlaw In-House Solutions and previously served as Prosperoware’s Lawyer in Residence. He has extensive experience in the strategic development, transformation, and direction of operations and technology in a variety of public and private organizations. He previously served as Chief Strategy Officer for a global consultancy and in senior executive roles for a top UK law firm, two AmLaw 200 law firms, and the largest municipal law office in the US. Ben has consulted on projects for multinational organizations including The Walt Disney Company and Chevron and previously practiced law in Chicago where, after clerking for the Federal District Court, he served as legal counsel for the Illinois Department of Professional Regulation. He is a regular speaker on such topics as Data Privacy and Security, Information Governance and Emerging Technologies, and Transformational Trends in Professional Services. He holds a BA in Economics from the University of Michigan and a JD from the University of Wisconsin.

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In the “good old days” of law practice, pricing was a simple concept. It was a sellers’ market, and law firms could name their price. Estimates were used for guidance at best. In fact, a colleague of mine, a former law firm managing partner, regales audiences with tales of how when he first entered practice, his mentor taught him a simple method for pricing work. It was less than exact: At the end of the matter, hold the file of paperwork in one hand and set your price based on the general weight of it (“That feels like it’s about $45,000’ worth of work”). Somehow, that used to be acceptable. Even for those firms that didn’t subscribe to the “weight = cost” formula, a simple ballpark figure quoted along with a minimum retainer accompanied by an hourly rate was commonplace. Firms even passed on a bevy of expenses on top of that formula, charging for faxes, photocopies, long-distance, and other creative concerns. In 2008, that all changed and has continued to evolve.

Firms today need to embrace the best practice of evaluating pricing before a matter is opened, as it allows the firm to have the appropriate enterprise controls. There are two elements to proper pricing of a matter: (1) determining scope (or budgeting) and (2) what pricing or fee type should be used with the matter. Firms are becoming better at the latter but are quite unskilled in the former. Both must be razor sharp and working in concert to form the bedrock of profitability in the new market for legal services.

Legal services has become a buyer’s market, which is why we must reshape profitability. Corporate clients expect the same quality of work but now delivered within price guidelines they set at the outset of a matter. When dealing with outside law firms, there’s no more “guesstimation” in legal billings, and there’s little room to negotiate. A firm’s value is found in the delivery of quality matters at the price and level of expertise they expect. The insertion of price and client-set budgets has upended the law firm business model.

The challenge for firms adjusting to this tectonic shift is that historically, there was little to no understanding of the true cost of delivery for their services, complicating the transition to delivering work — profitably — to budget. Firms must reengineer themselves, and quickly. Clients are aggressively interested in managing rates, are refusing to pay for firms’ inefficiencies, and simply need predictability so they can manage their own intensely scrutinized budgets. It doesn’t get much more essential, economically speaking, than understanding cost and creating the right processes around that cost to ensure the profitability of your business — and that’s what firms must do. 

Rates are a Profit Driver

Although this is obvious, the point still needs to be made: Billing rates remain the core pricing structure used in the legal industry, and the level of a firm’s rates is key in determining its level of profitability. As a rule of thumb, a one-point increase in rates leads to a two-point increase in profit. The reverse applies as well. In our experience, firms typically have a range of 1 – 3 percent increase in profit for each point of rate change. The range exists because it depends on the specific timekeepers involved, and the relationship between the billing rates and the timekeepers’ cost rates.

Discounts happen when clients tell us our prices are too high for particular pieces of work. Specifically, write-downs are reductions from a bill before it goes to the client, and write-offs are reductions requested by the client after they have seen the bill. Write-downs signal that the firm or a partner has decided that a particular effort had no value. Examples of write-downs include:

•        The work is out of scope, and the partner knows the client will not pay for it.

•        The associate was inefficient in their work.

•        The associate did not understand the assignment and did the wrong thing.

Write-downs become important as firms look for ways to lower the cost of delivering a service. By identifying recurring types of write-downs and eliminating the effort before the work is performed, a firm can lower the cost of the service.

Write-offs signal that the client did not see value in a particular effort. This may occur because the firm did not communicate the value of the effort properly, or it may just be that the work should not have been done. Note that, in some cases, write-offs are just good old-fashioned flaky clients who don’t pay their bills (a cost of doing business).

Citi Private Bank Law Firm Group recently reported revenue growth has fallen to 3.6 percent throughout 2017, down from 3.7 percent in 2016. Oddly, this is almost entirely in response to increased billing rates — an increase of 4 percent, to be exact. While the difference is marginal, when you consider that the 4 percent increase is much greater than normal, that demand has dropped by 0.2 percent, and that collection cycles are lengthening — it illustrates that raising rates is not a good long-term strategy.

            Firms need to adjust their pricing and pricing strategies in response to the market. The challenge is for firms to understand and adequately address the many moving parts of pricing in today’s market and reflect the actual cost of their matter delivery. This requires technology to harness data.

Beyond the Billable Hour

So, how do firms now need to price? First, there are two classifications of fee types: hourly and non-hourly. The hourly fee types are those that are still based on per-hour rates. Non-hourly fees have no billable rate and are set using many different criteria. The table below lists the most typical fee types.

The typical mix of firms’ arrangements as seen today:

·       20% Standard Rate

·       60% Unique client arrangement (rate plus outside counsel guidelines)

·       20% Non-standard fee types

As firms absorb how much they need to understand how to price, the value of their data has grown significantly, as has the value of correlating data from various systems and siloes of information within a firm. Since the fundamental question both clients and firms need to resolve is whether it is less costly to go to some form of non-hourly fee or absorb the inevitable discounting, write-downs, and write-offs, a firm’s ability to pull together its own information about the time, effort, staffing power, and ancillary costs (such as eDiscovery) of delivering a matter will inform its pricing.

This is best facilitated by software that reaches across departments and is capable of correlating and compiling data on what was required to prepare similar, previous matters, and the historic resolutions of matters in courts or arbitration, and it provides analyses of part discounts and write-downs. The collected data, properly analyzed, becomes a vital part of informing non-hourly pricing structures that focus on matter profitability. Modern systems can correlate massive volumes of data via warehouses and structured tables or cubes, and then leverage this data to calculate more accurate costs. These can then be modeled alongside appropriate matter staffing and margin to understand both delivery costs and expected matter profitability. These types of systems present a significant improvement over “weighing the file” or “back of the napkin” pricing, and are superior to even complex spreadsheet modeling as relied upon by many firms today that have yet to implement newer technologies.

Matter-Level Pricing and Scoping

The struggle of scope is the most difficult issue facing the industry. If the matter is not scoped correctly, the pricing is going to be wrong and jeopardize the partner’s ability to deliver the matter profitably. The reality is that partners or senior associates who are responsible for delivering matters are in the best position to scope them. 

As the work of pricing teams becomes more sophisticated and accurate, the need to further develop and standardize different pieces of a matter — phases or tasks — becomes important for allowing technology to refine the process. Clear and accurate categorization of the components of a matter to be delivered to a client will inform the pricing of future matters, and assist firms in presenting transparent, easily understood matter pricing to their clients.

Phase/Task Code

One of the messiest current data problems in the industry concerns inadequate phase and task codes. The first point of confusion is the meaning of a task code. A task code should be thought of as a sub-phase. It is not a task. The challenge is that many of the coding standards have not been updated since they were originally created in the 1990s. 

Their original purpose was for e-billing, rather than budget management. As clients began to use them in their budgeting and monitoring, they also began adding new task codes (e.g., first-level review in discovery) or rearranged existing codes. The worst outcome was that some firms used completely unique or one-time codes to act as substitutes for sub-matters, making standardization impossible.

Another core problem with phase and task codes is how firms implemented them in time entry. The result was a lack of accuracy. In any data look-up, you want to ideally limit available choices between four and 10 entries. In many cases, the firm would provide an extensive list of codes for a lawyer to select, and many times, the lawyer made the wrong choice. The result is that these nonstandard, haphazardly entered codes have created a mess, adding to the difficulty of understanding internal data and doing effective client value management.

As new standards have not developed, a best practice approach is to develop a list of phases. If the client uses a specific code set, you need to have the lawyers use the client’s code and otherwise use the firm’s code set. In U.S. litigation, leading firms are using eight to nine phases. For transactions, most firms use the new ABA Mergers & Acquisition Code Set for all transactions. Since most client code sets are more detailed, it is possible in most cases to map the client code back to the firm phases.

Modeling Margin

As firms continue to discover, based on annual declines in overall revenue per lawyer figures, matters can quickly become unprofitable when simple discounting is used — see the chart below.

Raising rates and discounting is not working over the long term. Firms have more recently instituted approval processes to make sure that work being undertaken is profitable. Such processes typically require modeling at the client or even matter level. Often, a highly discounted client rate will also require each individual matter to be modeled to ensure that it can be delivered profitably.

While firms may not necessarily share matter costing models with their partners, most finance teams will typically create them. The models determine the cost per hour to perform work and provide a simple mechanism for computing profit margin. The numerous different approaches to calculating cost models require their own separate white paper. Without such cost models, the only other mechanisms for measuring profitability are via combinations of realization/recovery and leverage/gearing. 

For new work, the modeling of a potential matter or client relationship can be based on either:

•     Hours and resources (person or class/level)

•     An amount with a ratio of staffing

•     Leveraging a priori matters as the starting point

•     Unit pricing/procedures

Guidelines for outside counsel add another layer to the modeling process. As clients focus on controlling costs, they may offer stipulations and conditions to firms seeking work, and these must be considered in evaluating the profitability of such a relationship. The reality is that most matters are still priced on an hourly basis, and the budget communicated to the client acts as a de facto fee cap. Factor in the customary practice of giving many clients significant discounts on their hourly rates, and it’s clear that firms have shifted the risk completely onto themselves. 

To survive in a market where previous billing and pricing models are now regarded as gentle suggestions at best, firms must harness their own data — and the technology that helps them to effectively reshape their pricing practices — or struggle to remain profitable in a new economic era.

Richard Tromans is the founder of TromansConsulting, which advises legal businesses on strategy and innovation, including advising on the adoption of AI systems. He also runs the site Artificial Lawyer, which is a site dedicated to new developments in legal AI and automation. He is based in London, U.K.[N.B. This report was first published in a different format and with other content via Thomson Reuters, and has been lightly edited.]

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Although several legal AI companies launched as early as 2010, the technology and how to make use of it has since 2016 become a headline issue for many law firms and large corporations.

This is not because the technology has radically changed between 2010 and now, but rather that the traditionally risk-averse and often conservative legal market
is now finally ready to adopt software solutions making use of natural language processing and machine learning.

This move toward AI adoption in part has been driven by increasing client pressure on law firms to be more efficient and a growing unwillingness to pay for what they regard as process level work. As clients demand fixed fees for large projects, law firms have little choice but to make use of technology to improve efficiency and protect margins. Clients are also increasingly asking law firms to show them proof that they are innovating and embracing the latest wave of legal tech to provide a better service and value proposition.

And, it is probably fair to say, that as more lawyers see rival firms adopting AI systems and clients welcoming such moves, then more firms will seek to embrace the same technology tools. No firm wants to allow a rival to get so far ahead in terms of using new technology that they begin to have too great a competitive advantage.

This report describes the current shape of legal AI and suggests some uses of AI, as well as some that may emerge. It should be seen as an initial starting place for those interested to learn more.

What is Legal AI?

“Legal AI” is the use of AI technologies, such as natural language processing (NLP) and machine learning (ML) in relation to legal tasks.

NLP is the use of a special type of software that is able to read “natural language,” i.e., normal text that we all use. As the law is in large part constructed from the written word, the power to read, at great speed, legal texts using NLP provides a considerable new capability to which lawyers and clients did not previously have access.

NLP, for example, could be used to read a contract and tell you what the key clauses were and if they differed from standard clauses you would normally expect in that type of contract. Or, it could be used to understand a user’s legal query and then search legal data to find not just any document that used certain key words, but rather return information that truly matched the concepts in the
user’s question.

While such sophistication is not infallible or as subtle as an experienced lawyer’s work, it can provide a junior lawyer or paralegal with some very compelling competition.

Machine learning refers to the ability for software to learn and to become more accurate in its outcomes. In the context of legal AI and reading text, this would mean having the ability, often with some human intervention, to improve its level of accuracy.

AI can be used within a law firm or by in-house lawyers. We should not be too proscriptive about how and where certain systems can be used, even if they are used by a certain customer group today.

Because AI applications are in effect “tools,” any lawyer could make use of the systems when and if there is a use case to do so. They are not specific to any one practice. The limits on AI’s use are often more about the imagination of the users than the technology itself. That is to say, NLP can be used in a wide variety of ways and become a useful tool in multiple legal tasks.

In fact, because non-lawyers also need to deal with legal issues such as agreeing to or referring to legal contracts, some legal AI “tools” are also designed to be utilised by non-lawyers. This is already becoming a growing segment of the legal AI market, for example, in relation to contract generation and completion.

In short, legal AI has a potential use wherever there are people who must deal with legal documents or address legal queries, especially where those legal needs are expressed through text, which AI experts refer to as “unstructured data.”

With regard to eDiscovery, some vendors in this space are making use of AI software, but not all. For this reason it isn’t listed as an AI group of its own. However, AI-driven eDiscovery is most similar to contract analysis.

Legal AI: A Beginner’s Guide

One can divide up the many applications of legal AI into roughly three main branches, though these will be, and to some extent already are, added to by new inventions. That said, an easy way to start is to focus on the following three groups of uses:

1. Contract review: Reading and analysing legal agreements, such as commercial contracts and leases, then extracting useful data from them, and/or checking them against rules/current law. In some cases this also means helping people to finalise contracts.

2. Legal data research: Legal research and litigation prediction systems, covering statute and case law as well as case outcomes, i.e., not specifically looking at contracts, but rather examining the data produced from the practice of law and from laws/regulations.

3. Intelligent interfaces: Interactive, web-based Q&A systems that clients can engage with via text input to gain legal information or that can guide lawyers/non-lawyers in completing basic legal documents and forms.

To some degree there can be some overlap between
these three. They could also be linked together in some applications that will emerge. But as far as the present day is concerned, the main vendors of legal AI appear to branch into these three general groups.

Legal AI

Contract Review

Contract review covers the reading via NLP of legal agreements, such as leases or due diligence documents.

What a user wants to look for, or what certain vendors tailor their systems to do, varies. But the fundamental process is the same in each case. There are many potential uses for such technology; some of the applications that law firms and/or vendors have already identified include:

.         Due diligence 

.         Lease review 

.         Compliance and risk review 

.         Sales/procurement contract review 

.         Employment contract review 

.         Financing/OTC derivative agreement review 

.         And, as noted, some types of eDiscovery.

            Natural language processing is many times faster than human lawyers at reading contracts, while accuracy levels in matters such as due diligence is generally higher than that achieved by human lawyers.

Legal AI: A Beginner’s Guide

Structure of Contract Analysis Market

Although there are hybrids in the AI contract review market, it can still be said to have two main product varieties:

Volume Contract Review

These are systems that are focused on analysing large numbers of documents. The objective is usually to seek out specific legal issues in contracts and leases. Sometimes this is to give an overall picture to the client of the legal status derived from the document group; in other cases the aim is to find anomalies (such as in due diligence) or to spot areas that need further legal attention (such as in compliance review).

Contract Assistance

These are systems that tend to be focused on smaller numbers of contracts, sometimes even single contracts. Some vendors aim such systems at non-lawyers who wish to understand what a contract contains (for example, a procurement executive who wants to know what is in the 50-page procurement contract on his desk). Some of the systems are also focused on the pre-signing phase and help the client to spot clauses the other party has included in a contract that they may need to reexamine, or where they may need to add in certain legal clauses to meet standard internal practices/rules for that type of contract. 

A Note on eDiscovery

People often ask whether AI contract review is the same as eDiscovery. The simple answer is that although some of the latest litigation eDiscovery platforms do seek to make use of NLP and machine learning to analyse documents, it is perhaps better to see such uses of AI techniques as operating with a parallel, but often quite different use case to contract review for due diligence or lease review, for example.

In fact, as most readers will appreciate, eDiscovery
is already a vast legal technology industry in its own right, with more than 200 vendors providing a wide range of technologies and methodologies.

Legal Data Research


AI systems can also be used in a broader support role beyond contract review.

These uses can be roughly divided into:

•        Knowledge systems: e.g., legal research along practice lines; and 


•        Predictive systems: e.g., case outcome prediction based on specific matters and/or litigation trends based on court outcomes. 


Knowledge Systems

An AI-driven knowledge system is a piece of software that taps data held or linked to a law firm or in-house team. Data could be expert opinions on legal matters by the partners of the firm; statements of fact about laws and regulation; relevant cases and commentary by judges; and any associated case notes or updates that the firm has created itself or is linked to.

In short, the system can do an “intelligent deep dive” into the material available, working in natural language (i.e., normal English, often in sentence form) to provide the answers you require.

What makes these research systems better than simply an enterprise search or a database trawl is that the system
is not only learning from the questions a lawyer is asking, but also seeking to infer the best responses from the data. It
is not just a key word search that brings back hundreds of documents; instead, the NLP seeks to isolate what the lawyer actually needs.

Such research alone clearly does not remove the need for detailed analysis by senior lawyers of the research that has been delivered. However, it may significantly speed up basic legal research conducted by junior lawyers who are working as part of a larger team. It may also be made more valuable when and if it links to other AI systems and document automation processes; for example, where a document may take note of certain key, though “vanilla” legal points that the firm wishes to add for the client’s benefit.

It may also reduce the need for lawyers working in PSL (professional support lawyer) roles, or at least those handling relatively straightforward research matters.

Predictive Systems

Predictive systems are a variation on the above knowledge systems and could arguably be called a subset of them, though they could also operate on a standalone basis. They are seen as primarily of use in pre-litigation planning.

AI-driven software can examine huge numbers of cases and all the publicly available court documents and rulings made by judges in the past up to the present day that are relevant to a case along, with many other types of useful public data.

Predictive Analysis

The main aim is to reduce the volume of manual research and provide lawyers and clients with actionable insight into previous cases, the actions of lawyers on similar matters, and — where possible — to gather evidence on the terms of likely success of a matter compared to previous similar matters, and/or give some indication of the damages that could be awarded by such a matter and/or other fee/value data.

Intelligent Interfaces

The third main branch of legal AI is the development of intelligent interfaces that can guide lawyers or clients to specific legal information, or to “triage” their legal needs. The aim of the technology here is not so much to conduct primary research or analysis, as the above applications do, but to help guide a user through to the right outcome.

Expert Systems

AI-enabled systems can help clients and lawyers to conduct rapid and routine legal tasks that require some “expert” information to complete.

In some cases they may be using NLP to understand queries a lawyer or client has typed into a dialogue box. Machine learning may also be used to help the system better provide the right answer that is tailored to the user’s needs.

That said, some expert system are not using AI technology, but rather conditional logic and/or word tagging to understand queries and respond to them. The reality is there is a grey area here that is still being explored by vendors. But even those not making use of AI systems look likely to move in that direction eventually.

These applications are often used when a person is guided through an “intelligent checklist” that allows them to gain the right knowledge, or in some cases to complete very simple legal documents, such as NDAs.

The software usually uses drop-down menus and check-boxes to move the user through a series of steps so that they can either be given the correct data they need, for example in response to a specific legal question, or be used to fill in the missing elements of a standard document.

They may be outward-facing for clients to use, or inward-facing, allowing lawyers to interrogate the expert system for their own specific needs and/or help them to complete a legal document.

Expert systems, whether outward-facing or inward-facing, are carefully designed to provide informational support to
a specific need, such as how to respond to a certain type of employment dispute, or to help add in data to a certain type of legal form.

Triage Services

The potential exists to use an outward-facing AI system to act as a triage service. At present, most law firms do not use these types of systems, though banks and other financial service companies are developing automated customer relations systems. A hypothetical example for a law firm is set out below:

Such triage/customer-directing interfaces already exist in a very basic way at some law firms — usually those that deal with the public and ask clients to write a short email to describe the matter. However, these could be far more effective and not just serve individual clients.

Rather than asking the client to do all the work, an AI system could be used to help guide clients to the right outcome
in terms of an advisory path and understand their queries using NLP. It could also make use of machine learning to steadily improve its responses to certain types of client query over time. The AI could also immediately link the information provided via the triage system to the firm’s own research into the types of case worth pursuing, as well as link to the firm’s CRM system.

Legal Bots

Many lawyers will be aware of “bots” or “chat bots,” though perhaps without considering how they could be used in
the legal space. Apple’s Siri is probably the best known “bot” — what one might call an AI-driven personal assistant. Essentially, this is an interactive interface that operates in natural language, whether written or spoken, with the latter clearly being far more complex.

At present there are “access to justice” legal bots that operate using written text, which help to give preliminary advice on matters such as criminal law to members of the public. Another example is a bot that guides members of the public through the process of completing a challenge to a parking ne.

However, these systems are, as yet, relatively narrow. That said, the market for legal bots is continually evolving, and there are already new bots surfacing that are capable of a far broader range of legal topics.  

Legal AI conclusion

This is a relatively short and succinct overview of legal AI, which is a market that is rapidly evolving.

Nearly every week a new legal tech start-up launches an application that makes use of NLP and machine learning techniques, and so the picture inevitably is more complex than the simplified version set out here. But, we all have to start somewhere, and getting to know some of the key strands of legal AI is probably a good way to begin to structure one’s thoughts.

We have now moved past what was a period of speculation, and into a period of factual and real-world uses of legal AI systems.

The shape of the legal AI market will no doubt also be quite different by 2018. More AI companies will emerge that may bring together several of the strands set out above. Others may perhaps merge together, or invent entirely new ways of using NLP and machine learning in the legal sector. It is truly a dynamic area and therefore all the more necessary to stay up to date with.

One thing is certain: We have now moved past what was a period of speculation and into a period of factual and real-world uses of legal AI systems. The number of vendors will increase; the number of law firms, in-house teams and non-lawyers using these AI systems will grow. Eventually legal AI will become a key element of the legal sector that many thousands of people rely upon and use every day, just as many other technologies have done so before.

I am responsible for leading Morae’s law department strategy and technology consulting including the spend management business. I bring over 25 years of experience providing impactful strategy and technology consulting to law departments of global companies, law firms and government agencies. I am an accomplished professional offering a unique combination of leadership, sales, team building, solution design, process improvement, and legal and technology experience. I have extensive experience interacting with Fortune 100 General Counsels and other Legal Leaders on procurement strategies, legal service delivery models, operational improvement, cost reduction, organizational design, change management and technology innovation.

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There is a vast array of legal technology available today, ranging from core products like matter management/e-billing systems to highly sophisticated analytics reporting tools and collaborative portals that allow for the communication of information inside and outside an organization. Within the past few years legal technology has continued to evolve, with more options for platforms such as enterprise legal management (ELM) software, more systems being based in the cloud, and the development of specialized rapid deployment tools that can address specific “fill the gap” needs. For each technology tool there is also a variety of vendors, and every vendor’s product has its strengths and weaknesses. With all of these options, identifying the right technology and justifying its expense can be a challenge. A systematic approach to technology planning — understanding the available options, identifying what is actually needed, and evaluating the proposed system’s cost effectiveness and return on investment (ROI) — will help avoid expensive mistakes and lead to the selection of the right technology to yield the most effective long-term benefits.

Technology Benefits

The right technology offers a number of benefits. First and foremost, it helps law departments and law firms meet their clients’ evolving needs and expectations. Today’s clients expect their in-house counsel to have business acumen in addition to legal proficiency, and to provide legal services in an efficient, cost-effective manner. In turn, law departments impose similar expectations on their law firms, with a heightened emphasis on value, efficiency, and cost effectiveness of the services provided, in addition to the actual results. Technology can help law departments and law firms operate more efficiently and cost effectively. Beyond basic functions such as tracking and organizing work and cost, the emphasis of the newest technology is on enabling the efficient execution of work by legal professionals. Technology can improve communication, both internally and between inside and outside counsel. It can assist in benchmarking and provide information for advocacy regarding achieved results. Through the opportunities it offers for consistency and timeliness, it can reduce risk. Finally, it allows counsel to be more proactive, facilitating the collection of information to make better-informed decisions, allowing counsel to answer business questions on a timely basis and to communicate insights based on the most current information.

Technology Options

Organizations’ technology maturity varies widely, from having no technology or using it on an ad hoc basis, to some processes and tools that facilitate repeatable functions, all the way to fully mature programs in which technology facilitates the improvement of processes through qualitative feedback. Where an organization falls on the technology maturity spectrum may depend on its size, the nature of its business, or a variety of other factors.

Law Departments

As a best practice, law departments should begin with the core systems that facilitate the department’s ability to track and provide legal services, and are the foundations for other, more sophisticated technologies. Below is an illustration of a mature law department’s technology infrastructure, based on a “matter-centric” design in which all systems are linked by a common matter identifier (for example, a “matter number.”)

Core Systems

Law department core technologies are generally thought to be matter management/e-billing systems, document management systems, or intellectual property (IP) management systems if the department has an IP portfolio.

Systems Supporting Legal Services and Company Functions

Ideally, a department’s core technologies can integrate with other technology used by the department such as management reporting systems; systems supporting specific legal services (for example, e-discovery and legal holds, the latter being a process in which information is preserved in anticipation of litigation) or company functions in which the law department is a stakeholder (records and information management, contract management, etc.); and systems that support operations, including enterprise-wide technologies.

Legal Holds and E-Discovery Systems

Organizations must choose which, if any, e-discovery functions to perform in-house versus those they wish to outsource. The most commonly in-sourced e-discovery functions include legal holds and collection. Most organizations outsource the traditionally commoditized e-discovery functions of processing, hosting, review/managed review, and production.

Legal Hold Systems

Effective management of legal holds increasingly requires the use of technology. The most widely recognized benefit of legal hold systems is that they automate the tracking of custodian acknowledgements/responses to legal holds and the necessary follow-up procedures (e.g., automated resends, manager escalation, and periodic reminders). Their reporting capabilities help demonstrate defensibility of the organization’s legal hold process. Legal hold systems commonly integrate with matter management systems to readily share important matter information, avoiding the need to re-key the same information multiple times or manage it in disparate systems. Legal hold systems can also integrate with HR systems, IT inventories, and RIM (records and information management) systems, aiding in custodian and data source identification, and thereby improving scoping efforts. All organizations must have a sound legal hold process, and those with a moderate amount of litigation should consider investing in legal hold systems.

Collections Technology

Organizations are increasingly building dedicated in-house teams equipped with a toolbox of collection technologies ranging from IT backup software to highly specialized stand-alone and network tools. Organizations should exercise caution when in-sourcing collection, however, because it can be complex, and the process must be forensically sound and legally defensible. In many instances, companies continue to look to external assistance for highly contentious matters or when expert-level forensics, analysis, and/or testimony may be needed.

Records and Information Management Systems

RIM systems assist in the indexing, storage, retrieval, and disposition of records. Some track and control documents, folders, and/or boxes from creation to final disposition, and can automate records retention schedules. RIM systems can be integrated with enterprise content management (ECM) systems and litigation hold systems.

Contract Management Systems

Today’s contract management systems span the entire contract lifecycle. Many contract lifecycle management (CLM) systems include functionalities that track approvals and other steps in the process, send email reminders to the parties involved, and have built-in redundancies that allow for escalation or sidestepping in certain circumstances, facilitating better management of the approval process. These systems now often provide for electronic signature. New tools are available, some embedded in traditional CLM platforms and others that are add-ons, which can automate aspects of contract generation and greatly expedite time to market, ensuring compliance for internal or external requirements. Some of the newest tools are custom-designed and incorporate logic components so that they can be used on more sophisticated contracts that may previously not have lent themselves to more basic template-based technology. These new tools are simple to use, powerfully dynamic, and can be client-facing, allowing self-service. The technology currently available helps speed time to market and manage contract obligations, both of which enhance the organization’s revenue capture.

            Law Firms

Law firms’ core technology includes the systems that allow firms to conduct their business on a day-to-day basis. This core technology includes time and billing systems; financial management systems; financial analytics/business systems, and document management systems. Most firms have cost recovery systems (connected with copiers, phone calls, etc.), although those may be becoming antiquated as more clients disallow those expenses. File management systems were essential in the past, but they are now evolving to more sophisticated records management systems. HR systems are also useful. Depending on the size and market position of the firm, customer relationship management (CRM) systems may be important.

Beyond these core systems, many law firms find case management, project management, and budgeting technology useful, as corporate clients in particular become more insistent that their outside counsel have these capabilities. Workflow management technology serves a similar function. Knowledge management systems are becoming common as well in order to keep better track of the firm’s work product and research.

Communications within and outside of law firms are evolving, and firms’ technology has evolved as well, including email, cell phone communications, shared platforms such as Microsoft SharePoint, video conferencing, messaging, and more. These technology developments create new challenges with respect to maintaining privilege and confidentiality along with data privacy and security.

Finally, depending on a law firm’s philosophy about e-discovery (meaning whether it wants to provide direct e-discovery services to its clients), it may have various types of e-discovery and litigation support technology. Almost all firms that serve corporations have a document review tool of some type.

Assessing the Organization’s Technology Needs

Before diving into new technology, it is important to assess the organization’s needs; the law department’s or law firm’s business objectives and process requirements should drive technology solutions, rather than the technology itself. Acquiring technology before assessing the real need can result in disappointment and failure to gain the anticipated return on investment. There are a number of triggers that may indicate the potential need to acquire or refresh technology, ranging from lack of the basics to gaps in the performance of existing technology, such as the need for better financial reporting; better project management tracking; better risk or compliance management; or consistent use of existing technology. There could be a specific client request or need to be addressed, such as specific compliance areas. When the time comes to make that assessment, the following questions can help:

What is the business need for the technology?

Business needs should be the lens through which to examine technology. For purposes of the assessment, business needs should include the strategic objectives the law department or law firm wants to achieve. Potential objectives might include exceptional client satisfaction, improved cost management, enhanced revenue, improved teamwork, or increased productivity, to provide some examples. For these objectives, what are the essential functions and what are the essential processes for these functions?

It is important to take into consideration future expectations such as the law department’s and organization’s current size, expected growth, anticipated spending, and other factors.

What technologies are currently in place, and what is the age and current usage of existing technologies?

Does the department or firm have the core technologies in place? Are they performing all the needed functions, or should they be updated? At a minimum, most law departments and law firms should have the core technologies identified above.

If the technology is seriously dated, it may lack new, cutting-edge functionality that could significantly improve productivity, such as Outlook integration or portals for collaboration between law departments and their outside counsel. If people are not using a certain technology, it is an indication that it may not be doing what it is supposed to do or is considered an administrative burden, and the return on investment is not being maximized or has been diluted over time.

What are the perceived opportunities for improvement?

Are there existing problems that technology could alleviate or opportunities it could facilitate? For example, could a law department increase collaboration and communication, and eliminate silos by putting in a new matter management platform, or begin to do a better job managing compliance risks by installing an enterprise governance, risk management, and compliance (GRC) system? Does a law firm’s knowledge management system do the best job of preserving existing knowledge and making it more universally available?

What are the relevant best practices, and what are the trends in the legal technology industry?

A clear understanding of the functions that need to be accomplished and the processes that can potentially be improved with technology mapped against the department’s current technology maturity level provides the starting point for prioritizing needs and developing a technology strategy. From there, in order to identify potential technology solutions, look to best practices for entities that share the same size, scope, and risk profile as the organization in question, and examine current trends. Peer organizations can be excellent sources of information about technology choices.

A note about the cloud

A note about the cloud One question that has generated some attention and debate in the market is whether to use cloud-based technology. The pendulum is swinging toward cloud-based technology, primarily because of lower supporting IT costs. Many law departments and law firms that were initially hesitant about the cloud have now determined that the benefits of cloud-based solutions may outweigh the shortfalls, although there are still some organizations that are reluctant to go in that direction for security or other reasons. While a detailed analysis is beyond the scope of this chapter, in general, keeping technology in-house offers more control and more security, but it also costs more to support and utilizes storage capacity. The cloud is typically lower cost, has virtually unlimited storage capacity, allows for smoother upgrades, requires less IT involvement, and allows external access, but also has more associated risk. Some of the potential risk considerations can include data security and privacy; access control; back-up and archiving policies; records management and e-discovery issues; difficulties with integration and data usage; and an exit strategy for terminating the relationship and transitioning the data.

With the answers to these questions, examine the current technology infrastructure through the lens of the identified strategic objectives. Develop a prioritized plan for technology improvement, taking into account the current infrastructure, strategic objectives, future requirements, perceived needs, and best practices.

Technology Return on Investment

Especially with today’s shrinking budgets, it is important to be able to financially justify any technology investment. Many find it helpful to use return on investment (ROI) models when making the case for technology purchases. Being able to articulate the ROI will help answer the question of why (and whether) new or upgraded technology is needed.

Most vendors will provide ROI information that can serve as a starting point. Since vendor ROI models tend to be fairly generic, it may be a good idea to expand on the vendor-provided information or use it as a benchmark against which to make organization-specific calculations.

The following are a few practical tips to consider when developing the ROI model:

·       Extend the analysis over several years. The expenses are likely to be heaviest on the front end of the investment, whereas the projected gains are likely to increase over the course of several years as use of the system matures. 

·       Include costs for system selection; licensing and hosting costs; basic implementation costs; costs for integrating with other in-house systems; project initiation costs; and the cost for any advanced features or customization of the system.

·       Carefully consider the assumptions built into the cost calculations. Make sure the model takes into account variables such as growth or other factors that will add to the cost. For example, many providers bill for software licenses based on the number of users, so if the law department or law firm is expected to grow in the next few years, licensing costs may increase.

·       Take into account both hard and soft savings when looking at the projected gain. For instance, when law departments implement e-billing systems, they typically see savings through better invoice validation and control over timekeepers, rates, and fees. E-billing/matter management systems also can trigger indirect savings by providing data for more effective rate negotiation or for development of convergence programs, or by facilitating improved management of internal and external human resources.

·       Do not forget to include savings related to the improved efficiency and cost avoidance from using technology instead of people for various tasks.

System Implementation

Beyond assessing business process requirements and the functional and technical needs they drive, a comprehensive selection process should also consider vendors’ implementation capabilities and the support needed to oversee the successful rollout and adoption of new technology. Implementation support should extend beyond standard system configuration and delivery to include process design, user acceptance testing, process training, change management, and post-production support. Third-party implementation partners can often provide considerable value and increase ROI potential.

Conclusion

Technology selection and implementation is most successful when it is part of a well-planned strategy based on a careful assessment of actual needs and priorities. Many law departments and law firms find it helpful to seek outside assistance for some or all stages of this process. For example, a consultant with specific knowledge of the legal technology industry can conduct a technology assessment to evaluate the organization’s technology maturity and opportunities for improvement, and can work with management to prioritize needs and develop a strategy and plan. A consultant may also be helpful in the vendor selection process, as reputable consultants work with a variety of vendors and are familiar with their pros and cons, the degree to which their products can be customized, and other factors, and can offer advice regarding how their specific products will most closely meet the organization’s needs. If there are multiple potential options, a consultant can assist with the process of requesting and evaluating proposals, and can also help develop an ROI model to support the technology acquisition. Finally, a consultant can assist with the full implementation process, overseeing everything from system configuration and delivery, to data migration, to process development, to training and change management, and much more

Ron Friedmann is the Senior Director Analyst at Gartner. He assists law firms by improving their practice and their firm’s business efficiency. Friedmann has extensive experience in legal project management, knowledge management, legal technology, outsourcing, process design, eDiscovery, consulting, and marketing. Prior positions include Integreon (SVP); Mintz Levin (CIO); Wilmer Cutler (head of practice support); and Bain & Company (consultant). He is a fellow and former trustee of the College of Law Practice Management and on the Board of Governors of the Organization of Legal Professionals. He publishes, speaks, blogs, and Tweets regularly. Education: J.D., New York University; B.A., Oberlin College.

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Carolyn Southerland has more than 20 years of experience as a commercial litigator in one of Houston’s largest law firms. She handled complex matters involving contract disputes, patent infringement, professional malpractice, and energy-related matters. She also has extensive experience in representing clients in matters before a variety of regulatory agencies. In 2007, she left the practice of law to enter the world of consulting on electronic discovery issues with Huron Legal, where she served as a managing director until 2015. She also served as managing director at Morae Legal. She is a graduate of the University of Texas and the University of Houston Law Center. She is a frequent speaker and author on various issues involving electronic discovery.

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