The Definitive
Legal AI Buyer’s Guide

How to choose a Legal AI solution

Generative AI (Gen AI) is transforming the legal industry, offering tools that can save time, enhance accuracy, and improve client service. But with so many options available, how do you choose the right solution for your organisation? This guide cuts through the noise, providing a clear framework to evaluate Gen AI legal research tools.

You’ll learn:

Key capabilities to prioritise
Privacy & security, answer quality, performance, and ethical AI principles.

What makes a Gen AI model effective
From architecture and training data to fine-tuning and multi-model approaches.

How to ensure reliable outputs
Look for solutions with robust semantic search (semantic search understands the intent and contextual meaning of a search query rather than just matching keywords), citation validation, and minimal hallucination risks (false outputs).

Why ethics matter
Choose a provider with a responsible AI framework to avoid reputational
and legal pitfalls.

Whether you’re on the fence or ready to invest, this guide equips you with the knowledge to select a Gen AI solution that delivers real value— grounded in authoritative content, proven performance, and ethical development. Use this guide as your checklist. These criteria come directly from empirical research and real-world lessons.

The future of legal research is here. Make sure you’re prepared to embrace it.

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Section 1 A Framework for Legal Gen AI Success

It goes without saying that being a lawyer involves all sorts of different tasks. But what those tasks have in common is that they all take up a lot of time and brainpower. Every day, lawyers have to go through a ton of information, often doing the same things over and over again - and this is all before we get to big-picture strategising. This is where AI is shaping up to be a game-changer in the legal industry.

“It’s like having a super-smart assistant that can crunch through data, make predictions and help us make decisions, and generate deliverables that are specific to the audience,” says Belle Jing, Partner in the Corporate group at Marque Lawyers and AI enthusiast. “And it does it much faster than a person could do it on their own.”

A common truth for lawyers (as with professionals in many industries) is that so much of the day-to-day work is not actually adding value to the client experience. In that way, AI stands to seriously revolutionise things.

In 2024, LexisNexis^® surveyed over 560 lawyers and legal professionals throughout Australia and New Zealand to better understand overall awareness and use of Gen AI.

The LexisNexis survey found that half of respondents are already using
Gen AI for a variety of different types of legal tasks.

Lawyers have gone from having very little understanding of Gen AI, to a high level of understanding, with three-quarters of respondents in the LexisNexis survey having at least some understanding of Gen AI tools and how they can be used.

As AI continues to surge into industries across the globe, awareness of its capabilities and nuances is growing quickly. This rapid market adoption has been accompanied by a surge in the number of vendors who are vying for the attention of law firms, so it’s important to separate the Gen AI hype from the reality of how these tools can improve the way your organisation operates and how your lawyers serve their clients.

Key AI Capabilities to Evaluate

It can feel overwhelming to sort through all the technical terminology and innovation breakthroughs related to Gen AI. But for practical purposes, it doesn’t need to be so complicated if you start by focusing on the key functionalities of a Legal AI solution.

It’s also important to remember that AI in the legal industry isn’t only for legal tasks. It can help in other areas, like budgeting, recruitment and employee training, and even marketing activities.

There are six specific criteria you should evaluate when considering an investment in a Legal AI product:

	Privacy & Security
	The Gen AI Model
	Quality of Responses
	Performance
	Evaluating the ROI of Legal AI Technology
	Ethical AI Principles

Section 2 Privacy & Security

The rise of powerful Gen AI models that can create synthetic media, text, code, and other content raises important questions around privacy and security. As these systems leverage large datasets and can mimic existing content and styles, there are risks that need to be addressed for the safety of your organisation and clients.

Some of the core concerns include:

Data Privacy
How training data is obtained and used.
Data Bias
Potential to perpetuate unfair biases in the training data.
Misinformation
Creating fake or misleading content.
Attribution
Properly crediting source materials used by generative models.
Transparency
Lack of detail in how models work.
Regulation
Need for thoughtful rules and governance of the technology.

What are some questions to ask the provider about how their model is trained and how your data is managed?

Will my search history and usage information be stored or used to train the model?
How does your Gen AI solution address the security concerns related to platforms like ChatGPT?
How does your Gen AI solution protect customers’ intellectual property and your own?
What data security measures are in place for any original generated content?
What safeguards exist to prevent exposure of privileged or confidential client information through generated content?
Where is my data stored and will it leave Australia?

Section 3 The Gen AI Model

Not all Gen AI models are created equally.

Public fascination over the potential of these tools was piqued by the release of ChatGPT, but many are unaware of the rapid improvements in the underlying technology since that initial product launch. There is no need to become a software engineer in order to be a savvy buyer of a Legal AI solution, but it does help to understand a little about Large Language Models (LLMs) and how they operate.

Different LLMs have different strengths and weaknesses. Here are five core differences between LLMs that are used to power various Legal AI solutions in the market:

Architecture: LLMs have different underlying “neural network architectures” that impact their capabilities. For example, some are better at certain tasks such as translation or summarisation.
Size: LLMs can range from millions to trillions of parameters. Larger models are generally more capable, but smaller models can sometimes be more efficient.
Training Data: The data used to train LLMs affects their knowledge and performance. Models trained on legal data will have different strengths than those trained on general-purpose text.
Fine-Tuning: LLMs can be fine-tuned on niche datasets to improve their domain-specific capabilities.
Public vs. Proprietary: Open-source LLMs allow transparency while proprietary models offer a deeper understanding of the user’s intent to deliver higher-quality responses.

There is another strategy available to Gen AI development teams: A multi-model approach that draws from more than one LLM in the creation of a new tool. By combining the outputs of different models, the overall predictions and performance surpass that of any one model, allowing users to benefit from the unique capabilities of each LLM while balancing out each one’s weaknesses.

What are some questions to ask the provider about their Gen AI model?

Do you use a single model or a multi-model approach for creating your solution?
What is the average time the AI takes to return an answer?
Are there any limitations on the number of prompts you can pose each day?
What is the underlying architecture of the AI model and how does the design impact its capabilities to perform legal-specific tasks?
Was the Gen AI model trained on legal-specific data or open-source data?
Does your AI solution incorporate a retrieval-augmented generation framework to find and link relevant source documents?

Section 4 Agentic AI

Agentic AI is a type of artificial intelligence that can make decisions, learn, and take actions independently. It can adapt to new information, solve problems, and work with humans and has the capability to act autonomously and make decisions based on programming and learned data.

In the context of legal technology, Agentic AI significantly enhances the efficiency and effectiveness of legal processes by automating tasks, analysing vast amounts of data, and providing insights that can inform legal strategies. It can work independently or collaborate with lawyers.

Implications of Agentic AI for the Legal Industry:

Efficiency
Agentic AI can streamline legal research processes, allowing for faster retrieval of relevant case law, statutes, and legal precedents.

Enhanced Accuracy
By leveraging machine learning algorithms, Agentic AI can improve the accuracy of legal research outputs, reducing the risk of human error.

Predictive Analytics
Agentic AI can analyse historical data to predict outcomes of cases, helping legal professionals make informed decisions.

Cost Reduction
Automation of routine tasks can lead to significant cost savings for law firms by reducing the time lawyers spend on research.

What are some questions to ask the provider about Agentic AI?

How does the AI decide when to act independently vs. involving humans?
What methods does the AI use to improve and adapt to legal changes?
What factors influence the AI’s predictions, and how accurate are they?
How is user feedback used to enhance the AI’s performance?
How can users interact with and adjust AI-generated recommendations?
How clear is the AI’s reasoning? Can users access explanations?
How seamlessly can the AI integrate with current systems?
Does the AI offer features to track performance and outcomes?

Section 5 Quality of Responses

When it comes to the application of AI in the legal industry, some commonly performed tasks lend themselves well to AI. Legal research is undoubtedly one of the most prominent because it pervades all areas of law. AI has the potential to change the way lawyers conduct research. Legal research has until now relied on humans to input search terms that hopefully match the language that the courts or the legislatures had used. New AI models can understand human thinking in a way that predicts what outcomes they are after — enabling it to not only sift through large volumes of information but to successfully get to the core of the issue under consideration in a fraction of the time it used to take.

Any legal research solution is only as good as the breadth and depth of the information repository from which it draws its answers to your search queries. It is important to choose a Legal AI solution that is powered by a global database of authoritative legal content, then deploys semantic search technology to understand your question’s intent and pick up on related terms and contextually relevant documents to surface the most comprehensive, verifiable set of results that are responsive to your query.

Any legal research solution is only as good as the breadth and depth of the information repository from which it draws its answers to your search queries."

There are a few important measures you can use to understand the inner workings of a Legal AI solution, helping you to assess the quality of its answers:

Comprehensiveness of Results

LLMs require massive data sets. The provider must be able to draw from a large repository of authoritative and up-to-date legal content that serves as the data for the model to deliver comprehensive results grounded in authoritative content.

Semantic Search Capability

Semantic search can understand the underlying meaning of your search query, reading between the lines of the words you typed to grasp your intent, and then matching your query to related concepts. This is distinct from keyword search, which simply retrieves answers that match the text entered in the search box. Semantic search is a superior model for a Legal AI solution because it increases the precision of your results, delivering answers that are more relevant and saving you the time required to wade through extraneous information.

Citation Validation & Grounding

Legal industry observers are by now very familiar with the fact that early adoption of Gen AI by law firms was not without its troubles, most notably the risk posed by open web Gen AI tools that infamously “hallucinated” various case citations that didn’t exist in reality. Traditional Gen AI models struggle with legal use cases because the underlying content feeding the models may be dated, lack citation authority and are prone to factual and conceptual hallucinations.

What are some questions to ask the provider when evaluating their Legal AI solution on quality of responses?

What is the size of the primary and secondary legal database that your solution accesses to surface authoritative legal content in response to search queries?
Does your tool require a separate subscription to access those primary and secondary sources or is it all integrated under one product experience?
What options are available for combining keyword and semantic search techniques while conducting research?
Is semantic search available across all content types (e.g., case law, statutes, practical guidance, etc.)?
Do you offer any training for learning how to improve semantic search techniques?
Does the Legal AI output provide in-line citations and links back to the original source material used for creating its answers?
What steps do you take to minimise hallucination risks?

Section 6 Performance

The promise of Legal AI technology is to deliver answers to search queries quickly, saving lawyers valuable time on tedious tasks so they have more time to focus on creative problem-solving and strategic thinking.

Look for a Legal AI solution that excels at the specific legal tasks you need — such as legal research or summarising documents — and then conduct due diligence to evaluate its real-world speed in generating outputs.

As this guide has explained, not all Legal AI tools are created equally, so it’s important to assess the empirical data surrounding performance of the tool when placed in the hands of practising lawyers.

For example, a Legal AI solution should accelerate execution of these daily tasks:

Legal research: A Legal AI tool can rapidly analyse thousands of documents to identify those most relevant to your matter.
Case summarisation: Your selected Legal AI solution should have the capability of summarising a case in a way that provides legal professionals with succinct, relevant overviews that enable users to quickly gain an understanding of the pertinent information. This might include key legal holdings, a list of relevant material facts, controlling law, the rationale of the court and the outcome of the case.
Insightful recommendations: A Legal AI Solution can uncover additional persuasive precedent cases and analysis to help strengthen litigation arguments or transactional deal points that lawyers may have overlooked.
Automating tasks: Legal AI can assist lawyers by automating repetitive tasks such as drafting documents and analysing contracts. It can streamline document management by organising, categorising, and retrieving documents efficiently.

What are some questions to ask the provider when evaluating their Legal AI solution on performance?

What specific use cases has your tool proven to improve lawyer performance?
Do you have any documented results to illustrate time savings in various use cases by practising lawyers in their day-to-day workflow?
How has your solution performed in head-to-head comparisons with competitive solutions in the legal market?

Section 7 Evaluating the ROI of Legal AI Technology

Investing in Legal AI can significantly impact your organisation, and assessing its Return On Investment (ROI) is essential. Focus on measurable outcomes and long-term impact to ensure your investment pays off. The future of legal practice is here—make your AI investment a wise one.

Here’s how to evaluate the ROI of AI legal research products effectively:

Measure Time Savings:

Document Time: Track time spent on tasks before and after implementation.
Time Saving: How many hours are saved on research or drafting?
Financial Value: Calculate the financial value of reclaimed time redirected to higher-value work, such as client strategy.

Assess Productivity Gains:

Output Volume: Can lawyers manage more cases in the same timeframe?
Accuracy & Efficiency: Does the tool reduce errors and rework, enhancing overall quality?

Evaluate Cost Reductions:

Outsourcing Costs: Are you spending less on external research or temporary staff?
Training Costs: Does the tool simplify onboarding and reduce training needs?

Quantify Improved Client Outcomes:

Client Feedback: Are clients more satisfied due to faster results?
Retention & Acquisition: Are you winning more business or retaining clients longer?

Analyse Competitive Advantage:

Market Differentiation: Does the tool help you stand out with innovative solutions?
Reputation Impact: Are you recognised as a tech-savvy leader in legal services?

Factor in Risk Mitigation:

Compliance & Accuracy: Does the tool minimise errors, such as incorrect citations?
Data Security: Is client confidentiality protected, reducing liability risks?

Calculate Long-Term Value:

Scalability: Can the tool accommodate increased workloads without proportional
cost increases?
Innovation Potential: Does the solution provide ongoing updates and improvements?
Behaviour change: Consider the time it may take to adapt to new Legal AI technology.
Training programs: A solid onboarding and training program will help accelerate the rate of adoption.

What are some questions to ask the provider when evaluating their Legal AI solution on ROI?

What measurable outcomes have other clients achieved?
Can you provide data on time savings, cost reductions, and productivity gains?
How does your pricing align with the value delivered?

Section 8 Ethical AI Principles

Responsible AI, sometimes referred to as ethical or trustworthy AI, is a set of principles used to document and monitor how AI systems should be developed, deployed, and governed to comply with ethics and laws.

The Legal AI solution you select should be developed with a framework that is guided by pre-defined principles, ethics, and rules.

This is an important risk management guardrail to increase confidence that the product used by your lawyers will avoid potential reputational and financial damage in the future.

One of the biggest factors when purchasing legal AI is to make sure the data the AI platform is using is reliable. There have been prominent cases where lawyers have relied on AI for research and cited cases that didn’t exist, and then got into trouble for misleading the court. This is an important reminder that lawyers are ultimately held accountable for the advice that they give and the documents they create. Lawyers need to hold themselves to the same high standard of care that they’ve always been held to and accept AI as a tool to be wielded - not a solution in and of itself.

Responsible AI Principles at RELX

RELX, the parent company of LexisNexis, is a global provider of information-based analytics and decision tools for professional and business customers.

RELX established a core set of Responsible AI Principles that provide high-level guidance for any RELX professionals — including those at LexisNexis — who are working on products that involve the delivery of machine-driven insights to customers. These principles provide a risk-based framework drawing on best practices from within our company and other organisations.

View the Responsible AI Principles at RELX

What are some questions to ask the provider about their ethical AI principles?

Do you have a formal responsible AI framework with defined principles and policies that guide your approach?
How are ethics and compliance with laws and regulations addressed within your responsible AI policies?
What processes do you have in place to monitor AI systems for unintended bias, fairness, and other ethical risks?

Section 9 Regulation of Gen AI for the Legal Profession

LexisNexis acknowledges that each state in Australia may adopt varying positions on the use of Gen AI in their judicial systems in response to the fast-paced developments in technology.

The NSW Chief Justice’s Practice Note SC Gen 23, effective February 3, 2025, governs the use of Generative AI in legal proceedings in NSW. The Practice Note ensures technology does not undermine accuracy, fairness, or professional accountability which has implications for legal practitioners in all jurisdictions.

Key points of the Practice Note include:

Lawyer Obligations: Understanding the risks of Generative AI, including potential fabrication.
Prohibition on Unverified Use: AI-generated materials must be thoroughly fact-checked before use.
Accountability: Lawyers are personally responsible for all submissions, with no delegation of legal judgment to AI.
Transparency: Disclosure of AI use in document drafting may be required in court, especially for inaccuracies.
Guarding Against Misuse: The note highlights risks such as AI hallucinations and advises against unverified AI-generated text in legal documents.

The implications of the New South Wales Practice Note focuses on ensuring compliance with existing laws and ethical standards and highlighting the necessity for legal professionals to navigate the integration of AI thoughtfully and responsibly.

Implications for legal practitioners include:

Professional Responsibility: Lawyers must uphold client confidentiality and the integrity of legal advice while using AI.
Transparency and Accountability: There may be requirements for lawyers to disclose AI usage clearly to clients.
Data Protection: Compliance with data protection laws is critical, especially in handling sensitive client information.
Bias and Fairness: Practitioners must be aware of potential biases in AI algorithms that could impact legal outcomes.
Regulatory Compliance: Adherence to any relevant regulations on AI use in legal practice is essential.
Continuing Legal Education: Ongoing education on AI tools is necessary to ensure lawyers remain competent in their application.
Ethical and Legal Risks: The use of copyrighted material in AI training datasets could result in unintended breaches of intellectual property laws.

Key Take Aways

Gen AI is a new category of technology that has the potential to transform the way that law is practised all over the world. The way your firm or organisation deploys Gen AI is crucial to your ability to serve clients efficiently and effectively.

It is important to select a Legal AI solution that has been trained specifically for the legal profession to ensure that you are adopting a responsible and transparent Gen AI platform. This requires the identification of the key features and functionalities to look for in a Legal AI product and an understanding of the ethical principles that should be followed in its development.

Gen AI can unlock amazing potential, but the tech alone isn’t enough. With Legal AI, what matters most is having the right experience behind it. Make sure the solution you choose is producing outputs that are grounded in authoritative content so you can trust the quality of the responses you receive. Focus on selecting a tool that has proven its speed and performance in the day-to-day workflow of practising lawyers. And choose a Legal AI partner that combines leading-edge Gen AI capabilities with human insights from the legal industry to develop tools in a responsible way.

The future of legal research is AI.