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Legal IT

An introduction to legal analytics

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These days, many law firms are using legal analytics. We briefly discussed them in the past in the article on Machine Learning. In this article, we answer the following questions: What are legal analytics? What are the prominent types of legal analytics? What are the benefits, and what are the challenges of legal analytics?

What are legal analytics?

Legal analytics refers to the use of data analysis, statistics, and technology to extract insights from legal information. In essence, it’s applying data science to the legal field. Legal analytics draws from a range of sources to identify patterns and trends. These include court records, case outcomes, judicial decisions, litigation histories, contracts, regulatory filings, and other legal documents. It enables legal professionals to predict litigation outcomes, evaluate judge behaviour, assess risk, and optimize legal operations.

Types of legal analytics

The relevant literature mentions many different types of legal analytics. These are the most prominent ones.

Litigation analytics examines court behaviour and procedural data – including judges, opposing counsel, and motion outcomes – to inform strategic case planning. A key subset, judicial analytics, focuses on individual judges’ historical rulings, such as how often they grant motions, typical damage awards, and case timelines. Law firms use these insights to gauge their chances of success in specific courts or before particular judges. This helps them tailor arguments and manage forum risk.

Contract analytics uses AI to extract structured information from large volumes of agreements. It identifies clauses, deviations, risk exposure, and compliance gaps. It is widely used in due diligence, regulatory compliance, and large-scale commercial transactions. It is often embedded in document management or contract lifecycle management systems. Corporate and transactional analytics are used more specifically in due diligence regarding Mergers and Acquisitions. They assess a target company’s litigation exposure, regulatory history, contract obligations, and legal risk profile.

Predictive analytics are used to estimate the probability of outcomes based on historical data. While predictions are never certain, they can provide useful probability ranges to assist with settlement strategy and risk management. Such tools can, e.g., estimate the likelihood that a motion to dismiss will succeed before a specific judge, or it can project a likely damages range in a given type of commercial dispute.

Another category of legal analytics focuses on operational efficiency. It is used for competitive intelligence and firm management. Attorney and firm analytics can profile opposing counsel or potential hires based on their track record. This includes things like case history, success rates, favoured arguments, and courtroom behaviour, which is useful for lateral hiring decisions. Beyond this, firms also analyse billing patterns, matter duration, profitability by practice area, and client retention trends, reflecting broader developments in legal operations and law firm management software.

Regulatory and compliance analytics monitor regulatory activity, enforcement actions, and agency decisions. It helps organizations understand compliance risk and anticipate how regulators might act in a given area.

Intellectual property analytics track patent filings, litigation trends, licensing activity, and the behaviour of patent assertion entities (also known as “patent trolls”). These analytics are widely used in tech and pharma industries.

Apart from these, there are many other types, like docket and case management analytics, legal spend and operations analytics (often as part of law firm analytics), descriptive, diagnostic, and prescriptive legal analytics, etc.

What are the benefits?

The articles mentioned below list several benefits. Here are the most common ones.

Better decision-making: Perhaps the most fundamental benefit of legal analytics is replacing gut instinct with evidence. Legal analytics help lawyers move beyond intuition and anecdote to assess procedural risks more accurately. This allows lawyers to better evaluate whether to file, settle, move for summary judgment, or adjust procedural tactics. In other words, legal analytics allows for more informed choices about litigation strategy, settlement timing, forum selection, and case valuation.

Improved outcomes: By analysing thousands of similar cases, legal analytics helps lawyers and clients form realistic expectations about how a case is likely to resolve. This reduces surprises, manages client expectations, and helps litigation funders and insurers price risk more accurately.

Improved risk management: Clients expect data-driven risk assessments, and legal analytics delivers exactly that. It can work proactively, flagging risks before they escalate: unusual contract clauses, emerging enforcement trends, or jurisdiction-specific litigation exposure. Even a rough probability range helps clients make smarter business decisions.

Faster due diligence: Contract analytics can process large volumes of agreements rapidly. They can identify non-standard clauses, risk exposure, and compliance gaps. In transactions, this accelerates due diligence timelines significantly. The result is faster deal timelines, lower diligence costs, and a reduced likelihood of overlooked liabilities.

Competitive intelligence and differentiation: Legal analytics also provides a meaningful competitive edge. It allows lawyers to understand how opposing counsel argues, how a judge tends to rule, or how a particular court handles certain claim types. This gives legal teams strategic intelligence that was historically only available to lawyers with decades of local experience. This is particularly attractive to sophisticated corporate clients already accustomed to data analytics in finance, operations, and strategy. In some markets, the ability to provide data-backed insight is becoming a baseline expectation rather than a distinguishing luxury.

Cost efficiency: Legal analytics helps in-house legal departments benchmark outside counsel fees, identify billing inefficiencies, and forecast matter costs more reliably. This makes legal budgets more predictable and easier to justify to finance teams.

Supporting legal innovation: More broadly, legal analytics is driving a cultural shift in the legal profession toward greater transparency, accountability, and data literacy. It is pushing law firms and legal departments to operate more like modern businesses.

What are the challenges?

While the benefits are promising, legal analytics still faces many challenges.

Data quality and completeness: Legal analytics is only as reliable as the data it draws on, and that data is frequently incomplete. Court records vary enormously in how they are formatted and maintained across jurisdictions, many decisions are never published, and older records may not be digitised at all. Settlement data – often crucial for realistic outcome assessment – is typically confidential and therefore absent from public datasets. Available court data can differ significantly across jurisdictions and countries. Gaps in the data inevitably produce blind spots in the analysis.

Privacy and confidentiality concerns: Legal data is highly sensitive, and its use for analytics purposes raises serious privacy and confidentiality concerns. Feeding client communications, billing records, or contract repositories into third-party or cloud-based platforms creates cybersecurity exposure and risks touching on attorney-client privilege. Clients may demand assurances regarding data storage, cross-border transfers, and compliance with applicable privacy regimes. Robust data governance therefore becomes an essential consideration for any firm adopting analytics tools.

Interpretability and explainability: Legal analytics demands a level of statistical and technical literacy that is not traditionally part of legal education. Data does not interpret itself. Analytics can misread correlation as causation, misunderstand confidence intervals, or ignore sample size limitations. All of these can lead to flawed strategic decisions. This challenge is compounded by the fact that many predictive models function as black boxes: they produce outputs without clearly explaining why. In a legal context, reasoning and justification are fundamental. This lack of transparency is therefore a significant problem: lawyers and judges are trained to evaluate arguments, not algorithmic scores.

Algorithmic and systemic bias: A significant concern is that historical legal data frequently reflects systemic bias. Past outcomes may embody disparate treatment, whether in sentencing, enforcement patterns, or procedural rulings. And then analytics models trained on that data will replicate, and potentially amplify, those disparities. This raises serious ethical and jurisprudential concerns, particularly in criminal justice and regulatory contexts.

Regulatory and ethical uncertainty: The use of predictive tools in legal decision-making raises other unresolved ethical questions as well. Bar associations and courts are still grappling with how to regulate the use of AI and analytics in legal practice. There is limited clear guidance in most jurisdictions.

Cost and accessibility: Advanced legal analytics tools are expensive, and their costs extend beyond licensing fees to include integration, training, and internal process redesign. In practice, this means they are primarily accessible to large law firms and well-resourced corporations. This risks widening the gap between well-funded and under-resourced parties, which is the opposite of the access-to-justice potential that analytics theoretically offers. Adoption is further complicated by cultural resistance: senior practitioners may be reluctant to embrace data-driven tools if they perceive them as undermining professional judgment. And without firm-wide buy-in, analytics is likely to remain underutilised.

Legal systems vary dramatically in their transparency, digitisation, procedural structure, and publication practices. This jurisdictional fragmentation is a significant obstacle for legal analytics. Analytics tools that function well in one context may be far less effective in another. This is particularly the case when applied across civil and common law jurisdictions. This limits scalability and complicates cross-border application considerably.

Conclusion

Legal analytics have much to offer, but at present still face many challenges, as well.

 

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Legal IT

The legal technology renaissance

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In this article, we discuss the legal technology renaissance that is occurring. We look at the recent legal technology boom, at some examples, and at the benefits. We observe what is driving this renaissance and what obstacles it has to overcome. We look at the consequences for the legal market, and at how to make it work for you.

The Recent Legal Technology Boom

In recent years, we have experienced a veritable legal technology renaissance, or legal tech boom, as some call it. A multitude of factors contributed to this. We have more computing power than ever before, with cloud computers doing the heavy lifting. We have made significant breakthroughs in artificial intelligence. The legal market has been changing dramatically and the legal technology market has followed suit. Finally, the pandemic too has been a catalyst for change. Law firms were forced to reorganize the way they work and invest in technology to be able to do so. In the process, many law firms took this as an opportunity to also invest in technology to improve their service delivery model. As of June and July 2021, we witnessed the first legal technology IPOs.

This boom is expected to continue in the next few years. In fact, some say this renaissance is only starting, as the demand for legal expertise is exploding. Gartner, e.g., made the following predictions:

  1. By 2025, legal departments will increase their spend on legal technology threefold.
  2. By 2024, legal departments will replace 20% of generalist lawyers with non-lawyer staff.
  3. By 2024, legal departments will have automated 50% of legal work related to major corporate transactions.
  4. By 2025, corporate legal departments will capture only 30% of the potential benefit of their contract life cycle management investments.
  5. By 2025, at least 25% of spending on corporate legal applications will go to non-specialist technology providers.

Big law firms and legal departments have taken the lead in this legal technology renaissance. By now, mid-size law firms and small law firms are catching up and starting to reap the benefits as well.

Some examples

Let us have a look at some examples where legal technology has changed the ways law firms and legal departments operate. A first area has to do with streamlining the administrative operations of the law firm or legal department. Examples here include document automation, E-Billing, and E-Filing. A second area has to do with streamlining casework, where progress has been made with eDiscovery software and with case management software. In both areas, far more aspects of the overall process have been automated than ever before. A third area has to do with collaboration and exchange of ideas. We are seeing a steady rise in online collaboration tools, in the use of AI-enabled chatbots and virtual legal assistants, in online education, and in video conferencing, where the pandemic resulted in a sharp increase in the available tools. Finally, major progress has also been made in the availability and usage of different kinds of analytics. These provide us with new insights in how law firms and legal departments can be run more efficiently. They also offer new insights into patterns when conducting legal research. Predictive analytics, e.g., allow to predict the chances of success in specific cases.

Benefits

The benefits of this legal technology renaissance are threefold. A first benefit is greater efficiency and better service delivery. Automation reduces errors, speeds up and improves the quality of legal service delivery. It also allows for greater scalability. A second benefit is the greater insight we gain. These are the result of Machine Learning and analytics, but also of analyzing our workflows for automation so they can be optimized. Finally, the boom in legal technology is helping to bridge the Access to Justice gap.

What drives this legal technology renaissance?

There are three key concepts that are central to this legal technology renaissance. First, it Is about automation. The technological progress that has been made allows to automate far more of the legal service delivery process. The mantra has become to automate where possible to increase productivity and efficiency. If law firms want to remain competitive, automation is inevitable.

A second aspect of this boom has to do with Legal Digital Transformation. The Global Tech Council describes it as the digitizing all areas of legal expertise, including service delivery, workflow, procedures, team communication, and client interaction in the legal sector. The Internet has changed the way we live, where we spend part of our lives online, in a digital world. With some delay, the legal sector is becoming part of that digital world, too.

Finally, the legal technology renaissance is about a new legal services delivery model that is more efficiency-driven, more client-centred, and provides all stakeholders with more insight.

Issues / Obstacles

Not everybody is reaping the benefits yet of these technological breakthroughs. Lawyers are traditionally rather conservative when it comes to their adoption of new technologies. Richard Tromans points to two main issues that are obstacles to greater adoption.

A first issue is “the belief that any of the above applications that relate to automation and improved workflows are somehow an answer in and of themselves, rather than part of a much larger integrated approach to legal services delivery.”

The other challenge stems from the fact that these technologies change the way law firms operate. It isn’t as simple as plug and play. The technologies may not meet over-elevated expectations. And the implementation of new technologies needs to be part of a bigger strategy around service delivery. In essence, these changes need an engagement from not only the IT team, but from the lawyers as well, who will need a hybrid mix of skills. Tromans warns that this can lead to disillusionment and people backing away.

Consequences on the legal market

This legal technology boom is changing the legal market. We already pointed out that it changes the way law firms and legal departments operate. As mentioned above, this technology boom is introducing new legal services delivery models that focus on being more client-centred, on increased efficiency, and increased insight.

As second consequence is the introduction of new players on the legal market. There are plenty of alternative legal service providers. Some of these offer services to legal consumers. These include, e.g., legal chatbots like DoNotPay or DivorceBot. Most of them, however, offer specialized services for law firms and legal departments. These include services like eDiscovery, document automation and review, legal research assistance, analytics, etc.

A third change has to do with the hybrid skill set that is needed in this changed service delivery model. More and more bar associations are opening up to changes in the corporate structure of firms offering legal services. Law firms are allowed to have shareholders, co-owners, and directors that are not lawyers. At the same time, corporate entities are being allowed to offer certain legal services. Some bars are even considering giving accreditation to some alternative legal service providers.

How to make the legal technology renaissance work for you

Making the legal technology renaissance work for you is not a guaranteed immediate success story. Here are some considerations that may be useful.

There are four key elements to planning your digital transformation process. The first two are the selection of 1) the best legal platform, 2) and the best IT infrastructure for that platform. This includes deciding whether to host on-site or in the cloud or opting for a hybrid solution. 3) Understand that optimizing workflows involves legal engineering. And 4) If you are going to use AI-powered solutions, you will need Machine Learning support.

When choosing your best legal platform, consider that the 2021 ABA Legal Tech Survey support observed that as a rule, most solutions work out-of-the-box, and that no customization is required. Experience has also shown that directly using the solution out-of-the-box allows to reap more benefits and faster.

Experience also demonstrated that an incremental implementation strategy tends to be more successful than a once-off big-bang transformation. Such a staged approach leads to success faster and more consistently.

Digital transformation projects tend to be more successful if the firm has some product champions, i.e., users who commit to familiarizing themselves with the solutions first. They can then assist others, show them how to reap the benefits of the new technologies, and convince others to start using them, too.

While implementing a digital transformation process, focus on business outcomes rather than on features. And set realistic ROI benchmarks.

Conclusion

The legal technology boom is disrupting the legal market for the better. As implementing these new technologies changes the way we work, some growing pains are to be expected. A balanced and staged implementation approach offers the biggest chances of success. To remain competitive in a changing market, law firms and legal departments have no choice but to adapt. Some fear that all these changes will make law firms obsolete. The experts don’t agree. Tromans points out that, while technology is very important in moving today’s legal sectors forward, there will undoubtedly always be a need for a human presence and personal connection with clients.

 

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Legal IT

Machine Learning Applications for Lawyers

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The first legal applications of Artificial Intelligence already appeared several decades ago, but they never really took off. That has changed over the last few years. A lot of the recent progress is thanks to advancements in Machine Learning (ML), Deep Learning (DL), and Legal Analytics (LA). As many lawyers are not familiar with these terms, we will first explain the concepts in this article. Then we will focus on some applications, and finish with some general considerations.

Let us start with the three terms Artificial Intelligence, Machine Learning and Deep Learning, and how they relate to each other. The first thing to know is that Artificial Intelligence is the broadest term. Machine Learning is a subset of Artificial Intelligence, and Deep Learning in turn is a subset of Machine Learning.

The Techopedia defines Artificial intelligence (AI) as “an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning, Problem solving.” Examples of legal AI applications that are not based on machine learning include, e.g., expert systems, decision tables, certain types of process automation (that focus on repetitive tasks), as well as simple legal chatbots that also focus on one or more specific tasks, etc.

Machine Learning (ML) is one branch of AI. It based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It is a method of data analysis that automates analytical model building. To this end, it uses statistical techniques that give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) from the data, without being explicitly programmed.

In an article on TechRepublic, Hope Reese explains that Deep Learning (DL) “uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive, and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives.”

The process of learning in both Machine Learning and Deep Learning can be supervised, semi-supervised or unsupervised.

When applied to legal data, Machine Learning is often referred to as Legal Analytics. It “is the application of data analysis methods and technologies within the field of law to improve efficiency, gain insight and realize greater value from available data.” (TechTarget)

Let us have a look at some of the applications of machine learning in the legal field. The applications that are available are not just for lawyers, but also, e.g., for courts and law enforcement.

In a previous article, we already mentioned Legal Research, eDiscovery and Triage Services. Legal databases are increasingly using AI to present you with the relevant laws, statutes, case law, etc. There are eDiscovery services for lawyers as well as for law enforcement that focus on finding relevant digital evidence. Both typically use triage services to rank the results in order of relevance.

Legal Analytics are also being used for due diligence (where the system creates and uses intelligent checklists),  and for document review, including contract review. In some cases, the system can even go a step further and assist with the writing of documents and contracts (Intelligent Document Assembly). Some more advanced examples of process automation, e.g. for divorce cases where the whole procedure is largely automated, also rely on ML algorithms.

One of the fields where legal analytics has been making headlines is predictive analysis: using statistical models, the system makes predictions. Predictive analysis is not just used by lawyers, but in the broader legal field: there also are for applications, e.g., for courts and for law enforcement. There are systems, e.g., for:

  • Crime prediction and prevention that predict future crime spots.
  • Pretrial Release and Parole, Crime Recidivism Prediction
  • Judicial analytics and litigation analytics predict the chances of success or what the anticipated outcome is in certain cases. These systems can e.g.  be as specific as to take previous rulings by the presiding judge into account.

ML is also successfully being used in crime detection. There are AI systems that monitor what cameras are registering, or that use a network of microphones to detect shots being fired. In the news recently was a story how facial recognition software was used to scan people attending a concert, which led to several arrests being made.

These are just some examples. An article that was recently published in Tech Emergence (“AI in Law and Legal Practice – A Comprehensive View of 35 Current Applications”) gives an overview of 35 applications.

So, a lot of progress has been made in recent years in the fields of legal analytics /  legal machine learning. Still, there are certain issues and limitations to take into account when it comes to the legal field. A first issue has to do with privacy and confidentiality. Law firms who want to use their client data may need consent by those clients, and will have to anonymize the data. They also have to remain GDPR compliant. A second issue has to do with bias: in a previous article we mentioned how these AI systems inherit our biases. A third issue has to do with transparency: most neural networks present a conclusion without explaining how it came to that conclusion. If used in criminal cases, this constitutes a violation of the rights of defence. In civil cases, too, judges have to explain their decisions, and merely referring to the decision an AI system made is not sufficient. Lastly, there also is a cognitive aspect to the work lawyers do, and at present the cognitive abilities of legal ML systems are (still) extremely limited. They do not, e.g., know how to appreciate or emulate common sense.

 

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Legal IT

AI and Contracts

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Artificial Intelligence (AI) is changing the way law is being practiced. One of the areas where AI, and more specifically Machine Learning (ML) has been making great strides recently is contract review. The progress is not even limited to reviewing contracts: automated contract generation, negotiation, e-signing and management are fast becoming a reality.

Using AI for contracts is the result of an ongoing evolution. Ever since lawyers started using word processors, they have tried to automate the process of creating contracts. Using advanced macros allowed them to turn word processors into act generators that used smart checklists to fill out templates and add or remove certain clauses. But now the available technology is sufficiently advanced to take it all a few steps further.

Some years ago, commercial lawyer Noory Bechor came to the realization that 80 percent of his work was spent reviewing contracts. As a lot of the work involved in reviewing contracts is fairly repetitive in nature, he figured the service could be done much cheaper, faster, and more accurately if it was done by a computer. So, in 2014, he started LawGeex, which probably was the first platform for automatized contract review. Users can upload a contract to LawGeex.com, and, within a reasonably short period of time (an hour on average), they receive a report that states which clauses do not meet common legal standards. The report also warns if any vital clauses could be missing, and where existing clauses might require further attention. All of this is done automatically, by algorithms.

By now, there are other players on the contract review market as well, and the technology is evolving further. At present, AI technology is able to scan contracts and decipher meaning behind the text, as well as identify problem areas that might require human intervention. This technology can scan millions of documents in a fraction of the time it would take humans (think ‘hours’ as opposed to ‘days’ or ‘weeks’). As a result, AI contract review has reached a point where it can already do 80% of the work a lawyer used to do. For the remaining 20%, it can, at present, not reach the level of skill and comprehension of a human attorney. AI contract review, therefore, focuses attorneys’ efforts on higher-level, nonstandard clauses and concerns, and away from more manual contract review obligations.

The progress made in Machine Learning algorithms means the usage of AI is not limited to contract review. Juro is a company that tries to automate the whole contracts workflow. It has developed an integrated workflow system that allows companies to save time on contracts through automated contract generation, negotiation, e-signing and management of contracts. For this, it relies on machine learning algorithms that try to understand the data within contracts and learn from it. This can be done, e.g., by analyzing all the contracts in a company’s ‘vault’ of historical contracts. Based on these contract analytics, Juro can also provide so-called ‘negotiation heatmaps,’ where customers can see at a glance which of their contract terms are being most hotly negotiated. Knowing what other customers have negotiated can help you (based on data) decide what the contract terms should be and what you should agree to in negotiations.

Another interesting evolution is the idea of ‘smart contracts’. Stephen Wolfram, the founder of Wolfram Alpha, believes contracts should be computable, and that a hybrid code/legalese language should be developed. One of the main advantages of such language would be that it would leave less room for ambiguity, especially when it comes to the implications of certain clauses. Computable contract language becomes more valuable to the legal sector, once we start using ‘smart contracts’ that are self-executing. There is also already some interesting work in this area, namely by Legalese.com based in Singapore. If law is going to be made computable then the world needs two things: lawyers who can code and a legal computer language that is an improvement on today’s legalese.

The next step would then be to move from ‘smart’ contracts to ‘intelligent’ contracts. Smart Contracts resemble computer code more than typical legal documents, relying on programing to create, facilitate, or execute contracts, with the contracts and conditions stored on a blockchain, or a distributed, relatively unhackable ledger. Intelligent contracts would not just be smart, but also rely on artificial intelligence (hence ‘intelligent’ contracts). In the words of Kevin Gidney, intelligent contracts would use an AI system that “is taught to continually and consistently recognize and extract key information from contracts, with active learning based on users’ responses, both positive and negative, to the extractions and predictions made”.

 

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