AI in legal practice: what can a law firm use it for today?

AI can already perform useful work in legal practice, particularly where lawyers need to search, compare, structure or summarise large volumes of text. Practical applications include contract analysis, case-law research, file summarisation, due diligence and precedent research. The strongest use case today is not replacing lawyers, but accelerating the research and document work that precedes professional legal judgement.

The main limitation is equally important: a language model can produce information that sounds authoritative while being factually wrong. In legal work, that is a serious risk — a fabricated judgment, incorrect citation or misrepresented statutory provision can undermine an otherwise plausible analysis. Legal AI therefore needs more than fluent output: important claims should be traceable to genuine sources that a lawyer can independently verify.

Where AI is already useful in legal practice

Modern language models are particularly capable at processing text. Legal work is consequently a natural field for their application, because much of the profession involves reading, searching, comparing, interpreting and producing documents. That does not make every legal task suitable for automation — the useful distinction is between assisting with information processing and making independent legal decisions. The first category already contains several practical applications.

Contract analysis

AI can identify and compare provisions across contracts: clauses concerning liability, termination, intellectual property, confidentiality, governing law and other recurring subjects. Across a collection of similar agreements, it can also flag differences — which agreements contain an unusual liability cap, or which contracts lack a particular termination provision. This makes AI useful as an initial analysis layer. It does not mean every deviation identified by the system constitutes a legal problem — determining whether a clause is acceptable requires legal and commercial context.

Case-law research

Traditional legal research often relies on keywords, citations, courts, dates and areas of law. AI-assisted retrieval can add semantic search, allowing lawyers to find decisions that address a concept even when they don't contain the exact terminology used in the query. There is an important difference between a general-purpose language model and a system connected to a controlled case-law database — a model answering purely from information encoded during training should not be treated as an authoritative legal search engine. A legal AI system becomes substantially more useful when it retrieves actual decisions first and generates its answer from those sources. That principle — retrieve from a controlled source first, then answer — is exactly what powers our Dutch case-law RAG system.

Summarising and searching case files

Legal matters can contain hundreds or thousands of pages of correspondence, pleadings, agreements, meeting notes, statements, reports and supporting documents. AI can help impose structure on this material: constructing a chronology of events, identifying people and organisations, locating documents relating to a particular issue, highlighting potentially contradictory passages, summarising lengthy documents, and retrieving passages relevant to a specific legal question. The combination of retrieval and summarisation is particularly useful — lawyers can identify where relevant evidence is located without repeatedly reading every document from beginning to end. The underlying source still matters: a summary is a secondary representation of a document, and before relying on it for a pleading, opinion or important decision, the original passage should be checked.

Due diligence

Due diligence involves a similar information-processing problem: large collections of corporate documents and agreements may need to be reviewed within a limited period. AI can classify documents, extract clauses and create lists of potential issues — change of control provisions, guarantees, contract durations, liability limitations and termination rights. This can make AI valuable for first-pass review and issue spotting. The system should not, however, independently conclude that a transaction is legally safe — the significance of an individual provision can depend on other contracts, factual circumstances, transaction structure and applicable law.

Precedent research and internal knowledge

Law firms and legal departments frequently possess substantial internal knowledge collections: previous opinions, pleadings, model agreements, memoranda, templates and research. AI can make these collections easier to search — a lawyer could look for previous matters involving a similar legal issue, or retrieve clauses from approved templates. This is fundamentally different from asking a public chatbot to produce an answer from general knowledge: the system can instead retrieve information from a defined collection controlled by the organisation.

Hallucination is a core risk in legal AI

Generative AI is designed to generate probable sequences of language. It is not inherently a database that returns only verified facts. As a result, a language model can produce an answer that is grammatically polished, legally plausible and confidently expressed while still containing false information — this is generally described as a hallucination.

In legal work, hallucination may involve inventing a judgment, providing an incorrect case identifier or citation, misrepresenting a genuine decision, misstating a statutory provision, generating a quotation that doesn't appear in the source, or combining principles from unrelated cases. The distinctive problem is not simply that AI can make mistakes — humans make mistakes too. The problem is that the form of the answer may provide little indication of its reliability: a fabricated citation can look just as convincing as an accurate one. Legal AI therefore needs a verification mechanism rather than relying on linguistic confidence.

Source verification should be part of the system

A practical legal AI system should make it possible to determine where its claims come from. If an answer says that a court adopted a particular legal principle, the lawyer should be able to open that decision — ideally with a reference to the relevant passage. The same principle applies to legislation, contracts and internal memoranda. This changes the trust model from "the AI says this is the law" to "the AI found these sources and generated this analysis from them" — a fundamentally different reliability model.

One commonly used technique is Retrieval-Augmented Generation (RAG): a system retrieves relevant information from a defined document collection before the language model generates an answer, and the retrieved material is supplied to the model as context. For a law firm, that collection might contain case law, legislation, approved templates, internal memoranda and client documents that the particular user is authorised to access. RAG does not eliminate hallucinations — a model can still misunderstand a retrieved passage — but it makes the process considerably more auditable and reduces reliance on information implicitly stored in the model during training.

Source-backed AI still requires scrutiny: a model may identify a genuine judgment but draw a broader conclusion than the decision supports, presenting a statement from a fact-specific dispute as a generally applicable rule. Source verification therefore has two levels. First: does the source actually exist, and does it contain the information attributed to it? Second: does the source genuinely support the legal conclusion being drawn? Technology can assist considerably with the first question; the second frequently requires legal expertise.

Professional secrecy and client confidentiality

Reliability is only one side of legal AI — the other major issue is data handling. Client files can contain personal data, trade secrets, litigation strategy, financial information and other confidential material, and lawyers may also be subject to specific professional secrecy obligations. Submitting such information to an external AI service creates a data flow to another party and potentially to infrastructure outside the organisation.

This does not mean every US-based cloud AI service automatically violates European data protection law or professional confidentiality requirements — that would be too broad a conclusion. The actual assessment depends on factors including contractual arrangements, processing roles, retention policies, security controls, international data transfers and the nature of the information being processed. Legal organisations should therefore make this an architectural and governance decision, rather than leaving it to individual employees to decide which public chatbot receives client documents.

When on-premises or EU hosting becomes relevant

One option is to run AI within the organisation's own infrastructure: on-premises AI. The model, document store and related systems then remain under the organisation's direct technical control. Another option is to use European infrastructure or an explicitly selected EU data region. Both approaches can improve control over data sovereignty, but "hosted in the EU" should not be treated as synonymous with compliance — the identity and jurisdiction of the provider, subprocessors, logging practices, administrative access, backups, support arrangements and contractual terms can all matter.

The more useful question is therefore not simply "is the server located in Europe?" but "who can access the information, under what circumstances, where is it processed and stored, how long is it retained, and which legal arrangements govern that processing?" For some applications, an enterprise cloud AI service with appropriate contractual and technical safeguards may be suitable. For particularly sensitive matters, private or on-premises infrastructure may offer stronger control — the appropriate architecture depends on the risk profile.

Access control remains essential

An internal AI platform creates another potential problem. Centralising a firm's documents in an AI knowledge system can make information significantly easier to discover — useful only if existing access restrictions continue to apply. An employee who is not authorised to access a particular client file should not be able to retrieve its contents through an AI assistant. Legal RAG systems therefore need to respect document-level or matter-level permissions: AI should not accidentally dismantle information barriers that existed before the system was introduced.

What does not work reliably yet?

The most problematic assumption is that current AI can function as an autonomous digital lawyer. Modern models can generate legal reasoning, but producing plausible reasoning is different from carrying professional responsibility for legal advice. Fully autonomous legal advice should therefore not be considered the normal professional use case for current generative AI — particular caution is required where a system would independently determine disputed facts, evaluate conflicting evidence, choose litigation strategy, interpret novel or ambiguous case law, balance commercial interests against legal risk, or send final legal advice to clients without professional review. AI can assist with all of these activities, but the final assessment should remain subject to human judgement.

Human-in-the-loop is more than a temporary safeguard

Human review is sometimes described as a temporary measure that will disappear as models improve. That interpretation overlooks the nature of legal practice. A lawyer does more than retrieve legal information: professional legal advice involves interpretation, responsibility, knowledge of the client, strategic judgement, proportionality and choices between multiple legally defensible options. A human-in-the-loop model is therefore not merely a technical workaround for imperfect AI — it reflects the professional nature of legal work. For the foreseeable future, AI is better understood as a powerful research and document assistant: it searches, compares, structures, summarises and prepares drafts, while a qualified professional verifies the evidence and determines the final legal conclusion.

Conclusion

AI can already perform useful work in legal practice, particularly in contract analysis, case-law and precedent research, file analysis and due diligence. Its strongest applications involve processing large volumes of legal text more efficiently. The limits are reliability and responsibility: a convincing AI answer is not necessarily a correct legal answer. Source verification, citation checking, access controls and professional review therefore need to be built into the workflow. Legal organisations must also make deliberate decisions about where confidential data is processed — on-premises and EU-hosted systems can provide meaningful advantages in control and data sovereignty, although neither architecture creates compliance automatically. For now, AI is most useful as a tool used by lawyers — not as a substitute for the lawyer.