Enterprise AI
Legaltech
Also known as: legal tech, legal technology, AI for legal
Legaltech is the category of software used to run legal work, covering contract management, e-discovery, case and matter management, research and document automation. AI has extended it into drafting, clause review and summarising large document sets. Buyers judge it on accuracy, confidentiality and whether output can be verified by a qualified lawyer.
What it is
Legaltech spans tools for law firms and for in house legal teams, from practice management and billing through to contract lifecycle management, legal research platforms and e-discovery. The AI layer typically adds retrieval over case law or a contract repository, clause extraction, risk flagging and first draft generation. Adoption is usually assistive, with a lawyer accountable for the final advice or document.
Why it matters
Legal review is often the slowest step in sales, procurement and compliance, so improvements there show up in commercial cycle times rather than just in legal costs. For vendors in the space, discovery is difficult because buying committees include lawyers, security reviewers and IT, each searching for different evidence. Professional duties around confidentiality, privilege and competence mean that trust content, accuracy claims and data handling detail carry more weight than feature lists.
How it works
Teams deploy it by connecting the tool to a document repository or matter system, defining playbooks for acceptable clause positions, and setting escalation rules for anything outside them. AI features are tested against a sample of past matters to measure accuracy before rollout, and outputs are usually presented with citations or source passages so a reviewer can check them. Practitioners commonly start with high volume, low risk work such as non disclosure agreements before extending to complex negotiations.
When it applies
It applies wherever legal work is repeatable and document heavy, and wherever contract turnaround, disclosure volumes or research time create a measurable bottleneck.
Examples
- An in house team routes inbound non disclosure agreements through a review tool that flags deviations from an approved playbook and drafts fallback wording.
- A litigation team uses e-discovery with AI assisted review to prioritise relevant documents in a large disclosure set before lawyer review.
- A firm uses a legal research assistant that answers with linked citations, which associates then verify before it goes into a client note.
How it is measured
- Average contract turnaround time before and after deployment
- Share of standard agreements handled without escalation to external counsel
- Accuracy of clause extraction or review flags measured against a lawyer reviewed benchmark
- Hours of fee earner or in house time released per matter type
Related terms in Enterprise AI
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- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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