Learning From Clients To Improve Decision-Making In The Legal Industry

Ark Group

The following article was authored by Gill Eapen and Aileen Leventon* for Ark Group’s Business Intelligence and Analytics for Law Firms: Insights for a shifting business ecosystem publication.

Practicing lawyers and those who manage law firms and law departments make decisions and judgments with incomplete information. When clients seek legal advice to guide their business decisions, lawyers frequently respond with: “It depends.” Those who manage law firms seek to grow profits by increasing revenue and controlling costs. Those who manage legal operations are charged with implementing tactics to add value in serving the business while also optimizing legal spending, in pursuit of adding to the company’s bottom line. The management of a legal organization requires a great many trade-offs in allocating resources, assessing risks and making choices to formulate high impact strategy. Both the practice of law and the stewardship of an organization require assumptions based on incomplete and dynamic information. Both entail the use of appropriate heuristics, data and analytics.

Faced with many choices and much uncertainty, is there a way to improve decision-making? When a lawyer states that “It depends,” how might we go beyond intuition, personal experience and cognitive biases to use the right kind of information to foster better outcomes? In law practice there are many possibilities: the lawyer’s experience; empirical information; the client’s risk profile and tolerance for complexity; and the lawyer’s judgment.

Management questions about allocating resources, staffing, lateral hiring, insourcing and outsourcing, business development, and pricing involve similar challenges. Should we benchmark against others, predict the future, or use statistical models from which to draw inferences? Have we framed a valid hypothesis to ask the right questions in using statistical models?

Experience from outside the legal industry

Many believe that the legal industry is different. Lawyers generally and law firm management in particular are excellent at spotting issues, identifying risk, and marshalling the power of precedent. The case for change is evolving slowly, but the legal industry tends to see the solution as a technology choice, when in fact it is about making better business decisions. The legal industry appears to be lagging compared to other industries in the use of rigorous methods from the world of empirical analysis, statistical modelling, econometrics and data-driven decision-making. These are not technology solutions; they are a different way of solving problems in uncertain and evolving situations.

Experience in other industries provides guidance for the legal profession. Artificial intelligence and machine learning methodologies have transformed decision-making practices and improved shareholder value for companies in industries ranging from consulting, healthcare, food manufacturing, biosciences, automotive and, agriculture, to name a few. Combining the latest ideas in machine and deep learning, it is possible to develop predictive models that assist in answering questions that impact legal and business risk and value.

This chapter provides examples from other industries and then suggests ways in which the same methodology may be applied in the legal industry. We start with two basic case studies.

  • Industry: Professional services consulting
    • Challenge:

Pricing of engagements is inconsistent, often driven by the fear of the managing director failing to be retained by the client, and profitability of engagements fail to meet economic targets for profitability required by the firm.

    • Question:

What’s the optimum price for an engagement based on all available characteristics, including but not limited to project and client attributes, availability of internal personnel and skills, and strategic aspects of the relationship with the client?

    • Solution:

Development of a robust pricing model that provides standards to managing directors to improve the profitability of engagements.

    • Impact:

A more disciplined approach to pricing based on predicted optimum price providing decision-guidance for managing directors to take into account any qualitative considerations, with improved profitability of engagements.

  • Industry: Healthcare
    • Challenge:

Develop a strategy for managing disease risk for patients in such modalities as hypertension, diabetes and COPD. In the current regime of increasing value and quality-based payments, providers are under increasing pressure to enhance patient outcomes while simultaneously optimizing the economics of their practices. It is possible that the industry will slowly migrate to full capitation and providers will be forced to use technology to fundamentally transform their practices.

    • Question:

What is the probability that patient will develop certain conditions in the future and, if this is known systematically, how can providers proactively engage with such patients to prevent diseases or to improve outcomes?

    • Solution:

Artificial intelligence-based agents constantly operating on electronic medical record data can provide decision guidance to physicians to manage the patient populations. Such agents can also get better over time based on newly arriving data and past experiences.

    • Impact:

Physician practices can move into personalized care to manage their patients to improve outcomes patient by patient, enhance overall population health as well as optimize the economics of their practices.

Taking these case studies further, consider more complex business environments.

Food manufacturing

How do companies make decisions that impact a company’s compliance with government regulations, resource allocation, corporate reputation, as well as the cost of a product to consumers? Assessments about legal and business risk factor significantly in the management decisions in the food manufacturing industry. The quality of the manufacturing process is critical, and getting it right involves many choices and trade-offs. How many quality assurance professionals are required per number of units produced? Are there factors that impact quality (or defects in manufacturing) based on the season, the work shift, the demographics of personnel? Which regulations pose the biggest risks if there are compliance failures? Can those risks be quantified? Some of these questions have been considered in the application of lean manufacturing practices, but the decision-making process has been improved through a systematic approach based on a predictive economic analysis. Shareholder value is enhanced when a company makes effective trade-offs between productivity and risk to enhance the manufacturing workflow.


Consider an even more complex challenge faced by the biosciences industry, which invests hundreds of billions a year on research and development programs that aim to discover and develop new therapies to prevent, diagnose, cure, or alleviate diseases affecting humans and animals. These expenses are spread over a long time, often decades, from idea generation stage to marketed product. With only one out of over 100 ideas turning into a commercially viable product, the risks are enormous. Traditional financial analytics methods and qualitative methods are insufficient. A focus on building systems that collect large amounts of data to tackle uncertainty is also misguided and has led to significant data intensity, which has not necessarily increased the quality of decisions. The ability to make these decisions more effectively – how to best allocate money, people and time – is a driver of competitive advantage.

To meet the objective, R&D programs target to develop a number of different “candidates” that may fit a general profile. Each candidate has expectations in terms of the end products, delivery mechanisms, pricing, and other factors. Each candidate also has a project plan with various investment options and schedules. Adding further to the complexity, the development plan for each candidate may also include many internal departments and partners outside the company who may provide resources and services specific to the program.

The complexity of these development plans creates significant uncertainty about outcomes — but along with such uncertainty comes considerable opportunity to build managerial flexibility into the design and execution of such programs. Using the Decisions Options® methodology, author Gill Eapen has achieved significant improvements in maximizing the value from limited resources as well as making decision-processes nearly real-time in some of the largest life sciences companies.

Translation to the legal industry

Powerful tools that have had impact on shareholder value in consulting, health care, food manufacturing, biosciences are available to the legal industry as well. We consider below some “use cases” in the management of a law firm and law department, and the substantive practice of law as well.

Pricing matters and portfolios of matters

The experience in the professional services consulting industry is directly relevant to the legal industry. The sources of the data may be different, but both have fragmented incomplete systems as well as qualitative data sources that are capable of analysis. Although the model starts with historical information, once put into service the model updates continuously. By building in to the system the opportunity to learn from ongoing experience, the model adjusts and offers insights to provide real-time guidance.

Case strategy

The example from healthcare provides guidance in developing a case strategy. Most cases settle or there is a payout after trial and appeal. If we treat this as analogous to the outcome in the healthcare example, we have the parallel challenge of assessing whether there is an optimal amount of legal work and associated fees and expenses. Is there a certain point at which further work on a matter will not produce a better outcome? Are there certain cases that should be handled differently because of their attributes?

Artificial intelligence-based agents are able to operate on data from both a client’s docket and reported cases (whether they are court documents or announcements in the media) to provide guidance to lawyers about pursuing or settling a case. These AI agents improve over time based on newly-arriving data and past experiences. A client could look at all its cases of a certain type (all products cases; all employment cases) to make decisions about when to settle a case. In the case of contracts with suppliers, the analysis could expand to assess whether there are attributes that indicate potential disputes (e.g. medical conditions) and whether business people involved in the arrangement might be counselled to conduct themselves in a manner that mitigates risk.


Just as the results of R&D efforts create the value in the biosciences industry, lawyers are the most valuable asset for a law firm and a law department. Is there a way to predict success beyond the current methods?

There are many choices to be considered in making decisions about who should be hired, at what level (partner or associate), and when. Once a hire is made, what kind of integration efforts does the firm make to support laterals? Are there programs to foster skills development in critical substantive, business generation and other core areas? What impact does such factors as office location, virtual work and demographics have on success? These and other questions are asked every day; but they may all be the wrong questions because they only reflect the biases of the firm’s managers.

Decision Options is an approach that analyzes data from a wide range of systems, such as personnel files, time and billing systems, dockets, new matter intake records, and so many more. From this, it is possible to develop a predictive model that looks for patterns in the data of the organization (i.e., law firm, law department, practice group, office location). After patterns emerge it is possible to formulate hypotheses to test, and ultimately develop a decision-making methodology. The data provides insight that is continuously updated as it is used to gain an unbiased look into the firm; the model self-adjusts over time. This is a machine learning model, which and can add value to the law firm by getting a better return on its investment in its principal asset – its lawyers.


What is the best mix of lawyers and other professionals for staffing a matter? Does the type of matter, location, client, existence of a client team or relationship partner, or the amount at stake make a difference? The approach used in the food manufacturing example is relevant because that situation involved trade-offs that affected quality, productivity, risk and economics. The same applies in law practice and matter management

Staffing decisions are often based on who has the right skills, whether they are available and whether they meet the client’s expectations about the profile of a team. Although these qualitative choices are important, do they necessarily create the best outcome in levels of client satisfaction? And if a staffing model is adjusted, what are the trade-offs in quality, risk and profitability and pricing structures?


Clients are harnessing available data to make more effective decisions and improve their financial results. Typically, the data is unstructured, incomplete, and pulled from many different systems within the company. Publicly-available data may also be used to enrich the analysis. The legal industry is well positioned to learn from the experience of its clients.

When there is data – even when there are gaps and the data is “dirty – there is information to be found. Using well-established tools from mathematics and combining it with extraordinary increases in computing power, the profiled companies are better positioned to achieve and maintain a competitive advantage. The legal industry should take advantage of the invaluable opportunities that have enabled improved outcomes and economic results for their clients. The lawyers who take advantage of these techniques will be better positioned to achieve a sustainable competitive advantage and add value to the clients they serve.

*Aileen Leventon along with Karen Dunn Skinner will be hosting Ark Group’s one day workshop Leading Client Service Delivery Teams (September 19th, Chicago) for more information visit www.ark-group.com

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