Legal AI in 2025: The Future of Contract Review and Document Search

Legal AI is driving a significant revolution in law firms. In 2024, legal tech funding hit a record $2 billion (and almost $1 billion in 2025 already) as advances like ChatGPT made attorneys realize AI’s potential. Clients now demand tech-savvy firms, so AI tools are no longer optional.

In this guide, we’ll explain how AI accelerates contract review, the technology behind it, and provide real-world results (e.g., a case study with 60% faster reviews). We’ll also cover best practices for secure deployment, methods for measuring success, and what’s on the horizon.

Investment in legal tech has skyrocketed. New Crunchbase data show that $2.0 billion was raised in 2024, and nearly $1.0 billion has been raised in 2025 so far. This boom is driven by breakthroughs like large language models; ChatGPT’s viral success has led many lawyers to believe that AI can transform their work.

At the same time, clients are asking firms how they use AI, pushing even conservative law offices to adopt new tools. In short, a convergence of VC interest, AI innovation, and client pressure is fueling the legal AI boom.

AI is automating tedious legal tasks. For example, modern AI systems can quickly scan thousands of contracts, identify key clauses, and flag risks with high accuracy. This is revolutionizing contract review. In practice, what used to take attorneys hours can now often be done in minutes.

Indeed, some AI tools let teams review contracts up to 70% faster than before. Law firms are utilising AI for drafting, due diligence, e-discovery, and more, shifting lawyers’ focus from searching documents to higher-value analysis.

In this guide, we unpack all the essentials. You’ll see how AI-powered search and summarization tackle the biggest bottlenecks in law firms (for example, one case study saw a 60% reduction in review time). We’ll explain the technology under the hood (indexing, embeddings, vector search, LLMs, etc.), as well as how to transition from proof of concept to full deployment.

We also cover best practices (security, integration, and training), key success metrics (time saved and accuracy), and future trends (cross-document reasoning and predictive outcomes). By the end, you’ll know why legal AI is a must-have and how to make it pay off.

Traditional contract review is slow and laborious. Studies show that a lawyer spends, on average, 92 minutes manually reviewing one contract, and searching for specific terms can easily take an additional hour or two. With thousands of contracts in play, this adds up to massive delays (often a 3–4 week lag in closing deals). The sheer volume of documents and the nuance of legal language make manual review a significant drain on time and resources.

AI search slashes that effort. Automated systems can find clauses and answers in seconds, enabling review processes that are approximately 70% faster than manual ones. That translates directly into money. At a conservative rate of $122 per hour for in-house counsel, saving even a few hours per lawyer per week quickly pays off.

For example, cutting review time by just 10 hours a year saves over $1,200 per lawyer. Multiply that by a team of attorneys and paralegals, and the ROI becomes clear: reduced billable hours spent on mundane tasks and more time for high-value activities.

Any team buried in text benefits. Corporate and litigation lawyers, paralegals, compliance departments, M&A and contracts teams, as well as procurement and HR departments (which often deal with NDAs and contracts), see significant benefits. For instance, litigators can find precedents or clauses more quickly, and corporate teams can expedite deal diligence.

In-house legal ops also benefit by avoiding external spending. Overall, the law department, corporate counsel, and legal support staff collectively save time, resulting in faster turnarounds and more effective utilization of attorney expertise.

Modern AI search turns text into math. First, documents are chunked and run through a language model to create embeddings – numeric vectors capturing the meaning of each sentence or paragraph. These vectors are stored in a special vector database.

Instead of a keyword index, the system uses algorithms (like HNSW or FAISS) to organize these vectors for fast nearest-neighbor search. When you type a query, the same model converts it into a vector, and the database quickly retrieves documents with similar vectors. The result: search based on semantics, not just exact word matches.

Traditional search looks for exact words; transformers look for meaning. Transformer-based retrieval embeds both the query and documents into the same semantic space. This means that a query for “breach of contract” can match documents discussing “failure to perform obligations,” even if the exact keywords differ.

In contrast, keyword searches often miss synonyms, paraphrases, and contextual information. As one expert notes, modern vector search “is naturally much better at assessing semantic similarity.” In practice, this translates into finding relevant clauses and case law that a keyword engine would overlook.

To make an LLM truly tailored, firms fine-tune it on their data. This involves gathering a high-quality dataset of your contracts (along with annotations or questions that matter to you) and training the model further so that it learns your firm’s specific language and requirements. Fine-tuning “swaps in a customized target”. For example, teaching the model to format answers according to your firm’s memo style. However, the data must be clean and well-labeled.

If the training set contains errors or bias (e.g., outdated clauses marked as correct), the model can learn from those mistakes. With careful preparation and fine-tuning, accuracy on specialized tasks (such as clause extraction and internal terminology) can be significantly improved for your firm.

Deployment typically begins with a pilot, where a use case is chosen (e.g., searching NDA clauses), a limited set of documents is indexed, and a small team tests the AI search. Once the pilot proves the concept and ROI, expand incrementally. In practice, many firms run AI tools as pilot programs first, and about 70–90% of enterprise AI projects start small.

According to experts, law firms often take 12-18 months from the initial trial to committing to a full rollout. Key steps include validating the model’s accuracy, integrating it with your document systems, addressing security, and gathering user feedback at each stage. A structured roadmap ensures the AI tool goes from a promising POC to an integral part of workflows.

AI-powered summarizers use natural language processing and large language models (LLMs) to condense text quickly. These systems automatically analyze documents and distill key points into concise overviews. For example, generative AI can scan contracts or cases, extract key clauses (such as dates, parties, and arguments), and generate a summary in seconds. In practice, AI summarizers work by:

Because these tools are designed for speed, they can generate summaries almost instantly. For example, modern AI models can flag critical clauses or facts and summarise case law “with surprising precision.” However, the output still requires review: general AI may overlook nuanced legal reasoning or make minor errors, so many teams use purpose-built legal AI that adds citation formatting and domain checks.

Overall, AI can produce instant summaries. Still, firms ensure accuracy by pairing generic LLMs with legal-domain models and by having attorneys review the results.

In a recent deployment, our legal team saw dramatic improvements. Before AI, reviewing a large batch of contracts required dozens of staff hours; afterwards, the total review time decreased by over 60. In concrete terms, tasks that took hours now take only minutes.

This mirrors what others report: for example, Luminance’s team cut review time by ~60% with AI. At the same time, the volume of contracts handled stayed the same (or even grew), meaning throughput skyrocketed while effort plummeted.

The implementation spanned a few months. The main phases were data preparation (converting legacy contracts into machine-readable form), integration with our document repository, and user training. Because we used a hosted AI service, we avoided the need for heavy hardware setup, the core work was focused on software integration.

Our rollout was phased: we started we started with a small pilot and then gradually indexed more documents. User acceptance testing overlapped during this period. The investment of time upfront paid off when the tool became an integral part of daily work.

A few lessons stood out. First, even teams outside of legal were pulled in once they saw the benefits: marketing and operations began generating and reviewing NDAs through the system, freeing lawyers to focus on high-stakes tasks. This taught us to involve a broader user base. Second, thorough training was crucial.

Like many, we found that people trust AI only after seeing it work reliably. We held workshops (and even informal drop-in sessions) to show prompt tips and answer questions, which boosted confidence. Finally, we realized a quick ROI: with 60% less review time, we could allocate more resources to strategic client work or new cases. In summary, the biggest surprise was how smoothly AI could be adopted when the value was clear. That lesson is consistent across industries.

Before adopting AI, set specific, measurable objectives for each use case. For example, automate first-draft contract reviews, speed up legal research, or flag issues in e-discovery. Establish “SMART” goals (specific, measurable, achievable, relevant, and time-bound) that align with the firm’s needs and objectives. In practice:

AI quality depends on good data and strong security. Make sure documents and data are well-organized before integration. Equally, protect client information:

Successful AI adoption requires effective change management, which involves training people and fostering adaptability. Key steps are:

After deployment, continuously monitor AI performance and make adjustments as needed. Best practices include:

By defining clear objectives, securing data, training teams, and continually refining their approach, legal organizations can effectively integrate AI and mitigate risks.

Start with efficiency and quality. Standard metrics include time saved (e.g., average review time per contract) and cost savings since nearly half of legal teams track these in AI projects. You should also measure output quality: for example, error rates in summaries or accuracy of clause identification.

According to surveys, UK legal teams often prioritize work quality (52% track it) alongside efficiency. Other useful KPIs are user satisfaction (surveys or NPS) and volume metrics (number of documents processed by AI). Tracking a mix of time, cost (bottom-line impact), and accuracy (risk management) will provide a balanced view of success.

Set up a review process. Many firms use a combination of automated checks and human audits. You might run a small dataset of contracts through the AI and compare the output to expert-written summaries, using metrics such as ROUGE to obtain an initial score. But the gold standard is still a lawyer’s review: randomly sample AI-generated summaries and have attorneys rate them for correctness and completeness.

Over time, track the percentage of AI summaries that meet or exceed a quality threshold. For mission-critical docs, you may require 100% human validation. Either way, continue to measure and tweak prompts or fine-tune them if accuracy dips.

Usage is a key sign of success. Track metrics such as the number of active users, frequency of searches or summaries performed, and the percentage of documents accessed via the AI tool. User engagement (daily/weekly active users) is as critical as the raw outputs. If only a few attorneys use the system, you won’t get full ROI.

Compare the number of contracts that go through the AI workflow versus those that are processed manually. Surveys or feedback scores can also indicate whether teams feel the tool saves them time. Ideally, you want adoption to climb steadily after launch, which shows the tool has found its place in day-to-day work.

Measure savings versus costs. A simple way is: Hours saved × billable rate AI costs. For example, if the system saves your team 100 hours a month and your blended legal rate is $122 per hour, that’s $12,200 per month in value. Subtract your AI subscription or hosting fee to get net ROI. Put these numbers in a dashboard over time.

You can refine it by showing metrics like “hours per contract before/after” or “average review cost” to make it concrete. Many finance teams also like to see cumulative ROI (say, year-over-year). By tying the AI gains to real dollars (or time), you demonstrate its impact. For instance, our case study firm used a dashboard showing “60% faster reviews = $X saved per year” to justify expansion.

Legal AI is evolving from simple tasks to agentic systems that can plan and execute multi-step workflows. Instead of just summarising text, agentic AI can consume information, strategise, and complete tasks automatically. For example:

Beyond summaries, AI will deliver more interactive and predictive capabilities:

AI’s role in litigation and public law is expanding:

Even as AI becomes more capable, human lawyers remain essential. The future lies in collaboration:

In summary, after 2026, legal AI will evolve far beyond static summaries to intelligent assistants and agents that can execute complex legal processes, enabling faster and more accurate work. Firms that stay ahead of these trends, investing in the right tools, skills, and governance, will gain a strategic edge in the evolving legal landscape.

Begin by assessing your needs and data. Identify which document repositories (file shares, DMS, etc.) you’ll index. Choose a solution (some vendors offer turnkey setups specifically for law firms). Start with a pilot: select a representative document set and a small user group. Collaborate with IT to securely connect the AI engine to your data.

Configure access controls (so only authorised users see the results). Train a few attorneys on how to pose queries. Measure results (see if relevant documents are found quickly). Finally, roll out the setup in phases, allowing feedback to refine it. In short: pilot → refine → expand, ensuring security and user buy-in at each step.

There isn’t one “best” tool; it depends on your needs. Many firms begin with general LLMs (such as GPT-4 or Claude) via an interface or API, which are ideal for free-form summarization. There are also specialized legal AI platforms, such as Casetext CoCounsel and HarveyAI, which are explicitly built for the law. At the same time, Luminance’s LuminanceAI and Eigen Technologies focus on contracts.

Popular DMS vendors (e.g., iManage) are adding AI features too. When choosing, consider whether you need an on-premises or cloud-based model, the model’s legal training, and features such as source citation. The right tool handles your firm’s specific content and integrates with your workflow.

Not entirely-not yet. AI summaries and draft briefs are invaluable for first drafts and research. Still, human lawyers must review and refine them. Generative AI can quickly outline issues or rephrase content, but it can’t fully grasp nuance or strategy. As Thomson Reuters experts note, “AI must be overseen by (human) lawyers to ensure accuracy”.

In practice, utilize AI to perform the initial heavy lifting, then have a lawyer refine it for legal precision, tone, and strategy. This hybrid approach yields big-time savings while maintaining the quality clients expect.

They should follow the strictest standards. At a minimum, look for SOC 2 and ISO 27001 compliance. These certifications indicate that the provider has audited controls for data access, encryption, incident response, and other key areas. Additionally, ensure compliance with relevant privacy laws, such as the GDPR (for EU data) and the CCPA (California), as well as any applicable industry-specific regulations (e.g., HIPAA for healthcare-related data).

In practice, vet your AI vendor just as you would any other legal software: ask for their certifications, data handling policies, and audit reports. No deployment should start until these fundamentals are confirmed.

Costs vary widely based on scale and choice of vendor. You’ll have upfront costs (project scoping, integration, training) and ongoing subscription or licensing fees. Some AI search services charge per document or search, while others charge a flat rate per seat or day.

For a mid-sized firm, a basic system might start at around the low five figures annually. In comparison, a fully customized enterprise solution could cost six figures or more. Remember to account for hidden costs, such as additional cloud infrastructure or IT support. To evaluate ROI, compare these costs to the tangible savings (e.g., lawyer hours saved at $ X per hour).

Most modern solutions offer APIs or connectors to popular legal systems, allowing seamless integration with these systems. For example, you can integrate AI search with Matter management systems (such as Clio Manage, MyCase, or iManage) so that contract retrieval occurs within the case file. Many best AI service providers also offer plugins or Zapier connectors for broader tools (SharePoint, Box, Salesforce, etc.).

If you use a proprietary system, you can often call the AI via a REST API. The case management software sends document content or IDs to the AI service and then displays the results back in the UI. Always ensure that these integrations also adhere to security protocols (such as OAuth or encrypted links).

ROI = (Value of time saved) minus (cost of tool). Track how many attorney hours are reduced by the AI. For example, if contract reviews shrink by 60% and you know a lawyer’s loaded rate, you can calculate dollar savings. Use the Juro data as a benchmark: an hour of in-house legal time is roughly $122.

So, if AI saves 50 hours per month across the team, that’s over $6,000 saved each month. Then, subtract the AI system’s monthly cost to see a net gain. Place this in a dashboard that displays actual versus expected savings over time. Concrete metrics, such as “contracts reviewed per month” and “hours per contract,” help validate the ROI calculation.

Yes, though it’s less common. Many AI tools are SaaS/cloud-based, but some vendors offer on-prem or private-cloud options for firms with strict data policies. On-premises deployment provides maximum data control and may facilitate compliance. Still, it comes with higher costs (you must provide the servers and maintenance).

As one analysis notes, on-prem systems require “a dedicated IT team” and frequent upgrades. If security is a top concern, on-prem may be worth the investment. Otherwise, a hybrid approach (keeping documents in-house while using a secure cloud AI service) often provides a good balance.

Plan for regular upkeep. First, you’ll need to update your document index whenever new contracts are added or existing ones are modified. Second, monitor the model’s accuracy: if it drifts, consider retraining or fine-tuning the latest documents. Third, apply software patches and security updates promptly (just as you would with any system).

Many providers handle model updates themselves (releasing a new version periodically), but you must manage dataset refreshes. Also, watch your usage: if query volumes grow, you may need to scale up your resources. Finally, gather user feedback continuously to tweak prompts or settings. Treat the AI system like any mission-critical software: with routine audits, backup plans, and training refreshers.

Training is key to adoption. As noted earlier, nearly all legal teams offer some form of AI training. Still, many admit that it’s not yet entirely effective. To do better, combine theory with hands-on practice. Run interactive workshops where lawyers use the search and summarizer on real contracts. Use example prompts and show how to refine them. Provide cheat sheets of best-practice prompt patterns. Involve your “champion” lawyers as co-trainers.

Additionally, integrate training into the onboarding process for new associates. Because Thomson Reuters points out that lawyer expertise should feed into the AI prompts, your training should emphasize how their legal judgment interacts with the tool. In short, train early, train often, and make it practical.

Recap: AI-driven document search and summarization are transforming legal work. We’ve seen how AI solves the painful manual review process (e.g., slashing review time by ~60%). We’ve explained the technology that makes it possible (embeddings, vector databases, large language models).

We also covered best practices (from data security and integration to training) and how to measure success (time saved, accuracy). In short, firms that adopt legal AI properly can dramatically increase efficiency and reduce risk.

Why Alaris AI? Our platform embodies these principles. We offer a secure, SOC/ISO-compliant AI solution customized for law firms, with both no-code and API integration options. Our pre-trained legal LLM and vector search engine let your team search and summarize contracts instantly, and we can fine-tune the model on your corpus. Clients who switch to Alris AI report faster deal turnaround and significant cost savings.

How Can You Get Started? Ready to see legal AI in action? Contact us to schedule a demo of Alris AI and discover how we can integrate with your existing systems. This shows the math behind AI savings in a law firm. With Alris AI, you’ll join the forefront of legal innovation and leave slow, manual review in the past.