We know that reviewing and managing contracts with AI has made a meaningful difference for legal professionals. And as more and more companies adopt AI to review and extract intelligence from contracts, we’re flowing more and more data through frontier AI models. Large enterprises manage, on average, 350 contracts a week. That’s a lot of information to query and can cost a great deal both in money and time.
As AI has grown and matured, however, new, smaller, and more focused models have emerged that can handle different types of queries. So my research right now centers on being able to dynamically decide which types of queries should go to which types of models. It’s like using a scalpel vs a bazooka. Some use cases are best suited for small, focused models, and others are best suited for generalist models.
You want to use smaller, simpler models for simpler questions, and reserve the big frontier model for when it's actually needed. That way you preserve quality while gaining speed and cost efficiency. Smaller models are exponentially faster. The idea I'm thinking a lot about is: what is a simple question, and what is not a simple question? Once the tool can figure it out, it can route accordingly.
The models that people think of as "AI" like ChatGPT or Claude were designed from the start to be generalists. They were trained on essentially the entire web, and kept growing in size until researchers said, okay, these are too big, let's try to shrink them while preserving capability.
Then a different line of research emerged: what if we made models much smaller and trained them for specific tasks? It turns out that for narrow, well-defined tasks, smaller models can actually beat frontier models, especially after fine-tuning. Recent examples include Chroma’s Context-1 model for information retrieval, while being 10x faster and 25x cheaper; Cursor’s Compose-2, a fine-tuned version of Kimi K2.5 that provides frontier level coding capabilities at a fraction of cost, and Intercom’s Fin Apex, a model fine-tuned for customer service that beats frontier models and provide 65% less hallucination and better resolution rate.
Using frontier models for everything is fine until it isn't. It's expensive, it's slow, and it's entirely outside our control. These models might live on Anthropic's infrastructure, or Azure, or whatever cloud platform. If there's an outage, we're dependent on someone else to fix it. Any centralized service has a risk of disruption due to geopolitical instability, or environmental issues, or any number of factors.
The nice thing about smaller models is that you have much more control over them. You can host them yourself. You could run them locally, or on servers in whatever region your clients require, perhaps to meet UK data residency requirements, for example. There's engineering work involved in hosting and scaling that infrastructure, but you gain control of the model, you get independence from pricing changes, the work is much less resource-intensive, and is a fraction of the cost.
One of the things I find most fascinating about the AI legal space is that the biggest blocker to my research is the lack of legal benchmarks. Legal has almost no benchmarks. There are a few contract-specific ones out there — limited, mostly English-only, not perfect. But the deeper issue is that legal work has never been systematically evaluated. Medicine has outcomes. Code either works or it doesn't. In the legal industry, if a contract term is wrong, sometimes nobody finds out until something goes to litigation.
As we start using AI for more aspects of legal work, we need solid, curated benchmarks so that when we release or update a new model we can compare them: here are the questions, here are the answers we got yesterday, here's what we got today. What changed? Is it better or worse? We're trying to apply quantification to a field that's never had it before. There are really only two options: either trust the frontier model completely, which is risky, because they can make mistakes on surprisingly simple things, or get humans to actually label a set of contracts and build a reference benchmark from that.
A number of larger companies are already moving toward dynamic model selection, but my prediction is that legal AI vendors are going to be thinking about how to do this in the future. I believe that thinking through what use cases need a scalpel vs a bazooka will have a strong influence on how legal AI products are built.
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The single biggest problem that I’m seeing with my customers right now sounds like a simple one. Nobody can find any of their contracts. Most legal teams can tell you where their contracts are stored. Very few can tell you what's actually in them, and even fewer can act on that information in real time. And that creates a lot of unanticipated problems.
The research confirms what my customers are saying. On average, organizations keep their contracts in 24 different systems. This means that there is an incredibly high reliance on institutional knowledge within legal teams to find anything. It also creates enormous onboarding problem which can have far wider business impact. How do legal departments train someone to find answers to questions about their agreements when they themselves don’t know where to look?
It’s having a profound impact on how legal teams are doing their work. We hear from attorneys that “we don't know what we've agreed to.” There are so many obligations, auto-renewals, and pricing issues buried across hundreds of contracts that teams have no visibility into. Those obligations affect the entire business but because no one has any visibility into the agreements, decisions are made without keeping these obligations in mind. “Legal is always the last to know,” one attorney told us.
This isn’t just a people problem, it’s a financial one too. According to Gartner, lawyers spend 25–40% of their time on the administrative burden of unmanaged contracts. It’s not just reviewing them; it’s finding them and figuring out what’s actually inside them. Businesses lose an average of 9% of annual value through poor contract management, due to cost overruns, invoicing errors, delayed delivery, and avoidable disputes.
Ultimately, it forces in-house lawyers to still behave like private practice lawyers, where the company is their only client. They have to operate in a 24/7 always-on mode where they don't get to do anything but react to their client. But they're actually in the business. They could have a bigger impact. They’re full time employees and they have access to all the documents, so why can't they go and proactively solve problems?
Most executives know this is a problem. They’re absolutely aware that this is a slow drain on their business. I believe that’s why the first piece of technology many legal teams turn to is a CLM. If they’re having trouble managing contracts, then it makes sense to choose software that’s designed to take care of that job. But when I hear from people who actually have experience implementing them, it just sounds incredibly painful. They have to do so much work to get a CLM maintained properly that it just becomes something they're not really interested in.
I think it’s important to shift legal teams from a bottleneck to a strategic asset. No one really wants to spend ages doing admin on documents. But when I was trained to be a lawyer, one of my consistent problems was making sure the name of the document was right and it was in the right folder. I’d be working with documents which are titled v.1, v.2, "FINAL VERSION ALL CAPS," and you're wondering, which one's the right version?
It’s a core principle of how Ivo is built; we treat contracts as a specific type of document and understand how they relate to each other. But most contract management systems treat documents like they're just files that are separate and distinct from each other, not understanding their relationships. So showing legal teams Ivo Intelligence is like going from a system of record, like putting books on a shelf, to seeing a complete 3D map. It's going from a 2D world to a 3D world. So when I show them relationship mapping, this is something they're wowed by. They breathe a sigh of relief. They say, “that would solve me spending ages organizing folders in a drive.” That’s the kind of admin work that nobody really wants to do.
I try to conceptualize this like building blocks. I think that starts to get the wheels turning. The first step is that they have systems which have files and then subfiles and then subfiles, and they have to be really pedantic about naming conventions and knowing which one's the correct version. And now they can do this instantaneously. And the next piece is, what do you then do? Now you've got the documents in an organized way, how do you extract the data in a way where it’s useful to you and you can see everything you need?
I recently worked with a customer that has quite specific SLAs about their platform’s uptime, performance and support. And just tracking that is something so simple for Intelligence, but before they had the product, it was almost impossible. They discovered that they have lots of unique clauses in their contracts that they weren’t really aware of before. But they’ve gone from a system where they didn't know, to now they know. And now they can do something about the situation.
The technology exists where legal teams should be able to be proactive, not reactive, about renewals, obligations, and risks, and they should be able to answer questions from the business in minutes, not days. And being able to show people that technology, and help them move their way of working to a more strategic, proactive role in the business, that’s a really satisfying part of my job.
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Want to help companies re-imagine how they work with contracts? Take a look at our open Account Executive Roles.
I recently saw that Ivo is one of the best places to work in the world if you’re an account executive. According to RepVue, we’re in the top 5% of sales organizations globally and the #3 sales organizations worldwide for inbound lead flow.
I’m not surprised. I’ve been working as an account executive at Ivo for the past year, and I can honestly say it’s one of the best places I’ve ever worked as a seller. I’ve been fortunate to work some incredible deals during my time here, and thinking about what’s made my time successful, it’s due to three factors.
Product-market fit. There is a need for our product. What could in-house legal teams accomplish if they weren’t stuck, day after day, in the tedium of manual tasks? We’ve seen time and again from our customers that when you combine the ingenuity of lawyers plus the information processing ability of AI, fantastic outcomes happen. Lawyers, revenue operations leaders, finance departments, procurement teams all want Ivo’s platform; by eliminating the bottleneck for legal, we unlock business insights for other departments.
Access to the product team. One thing I have noticed, when selling to enterprise customers, is that they expect the product to be configured to how they work. At Ivo we approach this customer need with deep curiosity and a genuine collaborative spirit - something that has not been my experience prior to joining Ivo. Sellers at Ivo have access to the engineering team, product design team, leadership and that means that end users have access to these individuals. Perhaps it is my former life as an attorney, but I feel that one of my main responsibilities as an Enterprise Account Executive is to advocate for my customers' needs. Ivo has embraced my approach, and has created an exceptional environment of cross departmental collaboration.
Culture. This factor is hard to define but easy to identify. Part of our brand promise is creating a world-class product, and doing that is incompatible with indifference. People here care about our work, they care about our customers, they care about the problem we’re solving, and we care about each other. You can see it in everything they do. And when you have an organization that takes such deep care in its work, that makes the practice of selling a thousand times more meaningful.
When you’re selling an AI product, you’re not relying on a traditional software sales playbook. You’re selling a partnership based on a shared vision and a promise to evolve together as the technology evolves together. That means that our values and our customers’ values align.
Ivo has fully returned to the office; the team is in San Francisco five days a week. I don’t think I would have been able to fully immerse myself in the Ivo culture so quickly, nor would I have had the access to the Ivo team that I need to close deals if I weren’t in the office. It really is true that collaboration is so much easier to achieve face-to-face than over remote tools, and I can often get requests completed in minutes that might take hours if I asked for them in a message. Everything is a tradeoff, but for me, the benefits I experience as a seller by being in the office outweigh the drawbacks.
I’m excited to have a front seat to the future of AI technology working with Ivo. If you feel the same, take a look at our current job openings and see if there’s something that interests you.
Ivo Assistant can now review a contract in the context of your historical agreements housed in Ivo Intelligence. There is so much valuable knowledge in your contract portfolio about historical negotiating positions, or how a company tends to negotiate specific provisions, which is too often locked away in documents or inside lawyers’ heads. Now, Assistant makes that knowledge available to make negotiation easier and contracting more consistent.
This new capability is beneficial for in-house legal teams for a number of reasons:
It turns your past agreements into institutional knowledge. Many legal teams have years of negotiated contracts sitting in a repository, but that institutional knowledge is locked away. Now, all of that historical intelligence is both accessible and actionable. You can actually use every precedent your team has ever set in your next negotiation.
It gives negotiators a defensible baseline. A difficult part of contract negotiation is knowing what the market standard is for your own organization. With this new capability, teams can now point to concrete historical data in their negotiations, e.g. "We've accepted this clause in 12 of our last 15 agreements,” rather than relying on historical memory.
You no longer have to reinvent the wheel with every contract. Lawyers spend a shocking amount of time digging through past agreements to remember how they've handled a particular clause before. This reduces the research time from hours to minutes, and provides reliable, accurate results as well.
It standardizes legal processes. Legal ops leaders know that legal teams need consistent, repeatable processes to scale and provide business value. Being able to benchmark a new contract against the historical norm creates consistency across regions and teams, and helps reinforce standard procedures.
Here are a few prompts that you can use with Ivo Assistant to take advantage of comparing new contracts against historical precedents :
Here are some best practices to keep in mind as you prompt Assistant:
We’re excited about this new capability, as we think it will have numerous benefits for in-house legal teams. Give it a try and let us know what you think.
We recently ran a survey of legal professionals to test something we’d been hearing anecdotally from our customers for a while: evaluating whether AI tools will actually deliver on their promises is genuinely difficult. The results have been confirmed. 3 out of 4 general counsels and legal leaders that we surveyed agree that it is very challenging to assess the performance of legal AI tools, and over half of the survey respondents have been asked to do exactly that.
For legal teams already stretched thin, this is an obstacle to making good technology decisions.
So what makes evaluating AI tools so hard? And what should legal leaders actually be looking for?
Most respondents to the survey noted that their frustrations with evaluating AI vendors fell into three major areas:
Vendors promise too much: One respondent told us, “Many companies overstate the AI capabilities. The ideas are there and they may be starting down the road to development, but the reality is not there." In addition, we heard that many vendors show nice demos, but haven’t really dug into the actual use cases legal teams need. “With most companies, you really need a proof of concept to attempt to actually evaluate their product,” one respondent said. “Their usefulness in a demo or on a website just doesn't show how they would work for your use case.”
Every vendor sounds the same: If every vendor says they can do the same thing, how can anyone differentiate one from another? One survey respondent said, “The accuracy of AI is hard to define. Results vary dramatically based on prompt quality, document structure, data cleanliness, and user expertise. And, after a while, I get AI vendor merge where they all seem to offer the same software functions."
Verifying accuracy is difficult: Over a fifth of respondents mentioned this. Lawyers, rightly, are very worried about accuracy and hallucinations, and don’t want to do the manual work of checking and cross-checking AI. We heard from one senior counsel “Sometimes these products do not include the right information when trying to really narrow down a specific law or case. Sometimes I've found fake cases."
We know that attorneys are under pressure. In-house legal teams are leaner than ever and contract volumes are growing. Our customers tell us that increasingly, their leadership expects AI to be part of the solution. But that means that the stakes of adopting the wrong tool are very high.
The data reflects this tension. According to the ABA's 2024 Legal Technology Survey, 74.7% of attorneys identified accuracy as their top concern with AI implementation. And a Paragon Legal study found that over a third of legal professionals have relied on AI-generated outputs they don't fully trust.
Choosing the wrong AI legal tool isn’t just a waste of budget. In the worst-case scenario, it could introduce real legal risk. And when something goes wrong, and the person who championed the tool also has to explain the errors, the stakes become personal, not just professional.
No wonder lawyers are reacting strongly to a crowded AI legal tech market full of vendors making claims that may or may not be relevant to real-life use cases. The cost of failure is very high.
In a market where every vendor claims to have “AI,” here are the true differentiators:
The most prominent tools in this space are redefining what’s possible by collapsing implementation timelines, surfacing patterns across entire contract bases, and keeping playbooks automated and evergreen.
Quora’s approach to solving this problem was both comprehensive and well-suited to their particular needs. They identified seven criteria that were important to them as they considered how an AI tool would fit into their workflow: everything from the UI, to AI features, to customer support capabilities, to security.
Adrie Christensen, Legal Operations Lead at Quora, noted that the process involved defining clear success criteria with her general counsel, which they organized into a detailed scorecard for consistent vendor evaluation.
This evaluation framework gave them a good baseline to agree on what was important to them as a business. This gave them clarity and specificity about adopting technology to serve their needs and integrate into their existing ways of working.
For a starting point to develop your own framework, take a look at our whitepaper, The State of Legal AI: How to Futureproof your Tech Stack. It contains a simple decision-making infrastructure for you to use and customize when choosing AI solutions, as well as a checklist of what capabilities you should expect from your AI tool.
You might be familiar with the Yin-Yang symbol from Taoist philosophy. The black sections are yin - mysterious, chaotic, unknowable. The white sections are yang - obvious, ordered, legible. Yin and yang are fundamental properties of the universe. Just as the Heisenberg Uncertainty Principle balances any attempt to measure a particle's position with chaos in the particle's velocity, the universe conspires to balance any yang - legibility - with yin - mysterious chaos.
To non-programmers, software engineering is yin by nature. Bring any normal person into the engineering department and watch them marvel at all the black terminals and arcane symbols. Of course, this external yin is balanced out by internal yang - all the programmers know what's going on. We can, after all, read each others' code.
Yin-on-the-outside and yang-on-the-inside is the natural balance of a software engineering team. For the programmers, working inside the yang is generally a nice experience. Information flows smoothly, colleagues are helpful, and accomplishments are naturally rewarded with respect and admiration. A well-functioning software engineering team is a wonderful, collaborative environment - on the inside.
For the rest of the company, however, the yin on the outside is difficult and annoying. How is the company supposed to communicate its roadmap, when features just randomly emerge out of a mysterious black box?
The yin is particularly problematic when a non-programmer is granted administrative power over the team. How does the administrator decide who gets what work? Who to reward with raises and promotions or who to performance manage? These are all important questions that must be answered correctly for the engineering org to function properly, but they're very hard to answer from outside the yang.
Unable to penetrate the mysterious yin, the non-technical administrator responds by shining the flashlight of yang over the org. The flashlight usually takes the form of metrics - individual/team KPIs, semi-annual performance reviews, etc.
Unfortunately, Goodhart's law rears its ugly head:
When a measure becomes a target, it ceases to be a good measure.
Once individual performance becomes spreadsheetified, individuals re-orient their behavior to optimize their metrics. All job functions are vulnerable to this phenomenon, but software engineering is particularly vulnerable due to the enormous amounts of yin inherent in the system. The flashlight of yang is unable to eradicate the mysterious yin, merely to redistribute it - down to the atomic, i.e. individual contributor level.
An individual contributor whose compensation is tied to, say, shipping a specific feature, will prioritize shipping that feature over helping out their mates. Intentionally convoluted code will start infesting the codebase, so its maintainers can never be fired. Estimates balloon out of control, fiefdoms are carved out, and helpful information is jealously guarded.
The perfectly yang metrics in the administrator's spreadsheet come at a cost - the internal yang of the engineering team.
You can't have a double-yang situation. Law of the universe. Very sorry, take it up with Heisenberg.
At Ivo Engineering, we believe in working with the universe. We don't want to mess up the yang. As such, we embrace the following principles:
ICs are the bedrock of an engineering org. How do we ensure that our "holistic evaluations" of them are fair and understood? The following is a (non-exhaustive) list of qualities that managers will keep in mind when evaluating engineering ICs' performance, which will be done on a continual basis.
We believe that contracts are fundamental to business. Every professional relationship begins, is maintained, and ends with a contract. They are the cornerstones of human commercial activity. Without them, business simply wouldn’t exist.
Ivo paved the way for the category of AI-powered contract review. We recognized that manual contract review is a tedious, thankless task, and one that creates bottlenecks throughout the business. We’re proud to have solved this challenge for hundreds of businesses around the world, including enterprises like IBM, Uber, Atlassian, Shopify, and Canva.
But as we like to say, paper creates possibilities. Contracts aren’t just meant to be executed and forgotten. There’s a wealth of strategic data buried inside your contract portfolio, and Ivo’s AI-powered platform is perfectly suited to getting it out so businesses can make better, smarter decisions.
The core mission of our company is to turn contracts — and the people who work with them — into strategic assets for the business, enabling contracting teams to discover risks and opportunities for competitive advantage.
We believe that contract intelligence is as critical a business transformation as the advent of the personal computer. The data housed within contracts is a foundational source of business data; AI’s ability to enable organizations to extract and analyze that information will change the way they operate forever, and will be a critical difference between businesses that succeed and those that don’t.
Our new look and feel as a brand highlights the new importance of contracts and contract work in the enterprise, and evokes how powerful an engine of discovery and opportunity the contract really is. Contract work shouldn’t be monotonous. Instead, it should feel vital.
Early legal AI focused on automation; the goal was to move faster, reduce manual effort, and get through the backlog.
That work still matters. We often hear from our customers that they’re being asked to review more contracts in less time with fewer resources. Since we can save 75% of the time it would take to manually review contracts, it’s our pleasure to help them.
But this work is no longer enough.
Today’s in-house contracting teams are being asked to do more than review contracts. They’re expected to spot risks earlier, provide strategic data to the rest of the business, and offer good legal judgement, not just redlines. That requires intelligence, not just efficiency.
Ivo is built for this new reality. Our AI-powered contract intelligence platform doesn’t just help teams review contracts faster. It helps them understand the risk profile of their contracts, the relationships between contracts, and how contracts have changed over time, as well as where opportunities exist — just like talking to a colleague. Moreover, it helps unlock the intelligence that’s already embedded in these contracts, without having to manually examine each one to do so.
When we design our products, we think extremely carefully about how they are built and spend much more time than most vendors crafting the models, the reasoning, and the user experience. As our Head of Engineering, Didier Smith, puts it, “It's easy to build demo-ready contracting software, but the road to good contracting software involves hammering down a very long tail. Our willingness to spend months and years hammering down the tail is our biggest competitive advantage.”
Not only that, but we want to recognize the extremely powerful problem-solving skills lawyers and contracting teams bring to the business. “Lawyers are creative,” a general counsel told us. “They are strategic. We know that we have the ability to solve big challenges and find valuable opportunities. And with Ivo, we now can.”
When we thought about the next evolution of Ivo’s brand, we centered around adjectives like crafted, intelligent, and warm. We thought about our customers, who are solving the biggest strategic questions for their organizations. And we thought about the unique approach we take to building our product, which works to understand contracts like a lawyer would, not like a machine.
Our new brand mirrors the craft of producing our products themselves. Our platform is exciting. It solves contracting teams’ problems in ways they’ve never imagined. We hear from our customers on a daily basis that Intelligence and Review are “awesome” and “mind-blowing.”
Ivo unlocks the strategic and creative abilities of contracting teams. To reflect this important part of our purpose, we have partnered with a digital artist whose own work showcases creativity and technology together. Myriam Wares is a French-Canadian artist based in Montreal, who has worked with clients in editorial, advertising, and package design. We want to underscore the message that AI-powered contracting isn’t always about doing more things faster. Instead, we believe AI has the ability to help our customers do the right things well.
We’ve heard from our customers that sound legal judgement and AI together are driving teams forward. The partnership between human expertise and AI’s access to facts is what’s creating powerful outcomes for businesses of every size.
As AI continues to evolve, we’ll be continually evolving our platform. We'll be guided by our customers’ needs, grounded in our craft and focused on turning contracts into business insights.
The future of contracting isn’t just faster. It’s smarter and more strategic. That’s what we’re building at Ivo and why we’re excited about what’s to come next.
Want to see what Ivo can do for you? Schedule a demo.
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It wasn't long ago that I was a corporate lawyer in New Zealand with big dreams of changing the way people worked with contracts. I spent my days reviewing agreements line by line and digging through iManage folders for precedents I knew existed somewhere. I left and taught myself to code because I believed there had to be a better way.
Starting Ivo was an early bet that AI would transform how in-house legal teams work; not by replacing lawyers, but by giving them superpowers. At the time, "AI for legal" meant keyword search and basic templates. We had to convince skeptical GCs that AI could actually understand contract language.
We’re happy to share that we've raised $55M in Series B funding led by Blackbird. We're deeply grateful to our earliest believers and customers, as well as to the incredible team of builders who've joined us to turn Ivo into what it is today.
The legal teams succeeding with Ivo include the largest, most sophisticated, and most demanding in the world. Our customers span Fortune 500 enterprises to the fastest-growing technology companies. These are organizations that review thousands of contracts per year and need AI that actually works.
We've grown our ARR 6x over the last year because we've stayed relentlessly focused on helping in-house legal teams move faster without sacrificing quality. When we go head-to-head with competitors in trials, we win 85% of the time. Lawyers are skeptical and discerning; no amount of marketing dollars will trick them into working with you. The only way to earn their trust is by delivering a product that works.
Ivo's contract review tool lives inside Microsoft Word, right where lawyers already work, surfacing risks, suggesting redlines, and accelerating negotiations. Our contract repository transforms executed agreements from static files into searchable intelligence, helping teams answer questions about their documents, understand the web of relationships across their agreements, and visualize deviations from their standard positions.
We will use our new funding to deepen our platform, increase our enterprise capabilities, and expand internationally.
Ivo is designed for the unique needs of in-house legal teams, not law firms. We're doubling down on investments to build at the frontier of what's possible across the entire contract lifecycle:
We're also investing in professional services to ensure our largest customers are incredibly successful and we’re opening offices in London and New York.
What excites us most about AI is its potential to amplify human judgment. Legal work is nuanced and contextual. It’s fundamentally about helping businesses move forward, manage risk, and build trust through agreements. The best outcomes happen when experienced lawyers have tools that match the speed and complexity of modern business.
The opportunity in front of us is massive, but so is the responsibility. Legal teams are trusting us with some of their most important work, and we have to get it right.
If you're interested in joining us on this journey, we'd love to hear from you.
Every year, there is a flurry of predictions about what will happen with AI and how it will change and shape the landscape of work. And sometimes, those predictions even come true. Here’s a look at the year’s top predictions about AI that actually came true, and what that says about what we can expect in 2026.
The American Bar Association's Legal Industry Report documented a modest shift in legal professionals’ AI use: 31% of legal professionals used generative AI at work in 2025, up from 27% the previous year. While a 4-percentage-point increase might seem incremental, it represents continued momentum in an industry historically resistant to rapid technology adoption.
Other studies have shown that as many as 79% of legal professionals are using AI. This suggests individual adoption of AI is moving much faster than firm or corporate-level AI implementation. This is not a surprise, with governance concerns and risk management making corporate deployments slower than individual use of AI tools.
Stanford HAI's 2025 AI Index Report showed an increase in AI-related regulatory activity. AI-related legislative mentions rose 21.3% across 75 countries. The United States alone introduced 59 of those regulations. This confirmed the prediction that legal teams, when adopting AI, will need to navigate an increasingly complicated world of compliance.
A number of studies showed that AI tools have evolved from simple chatbots to specialized AI agents. Numerous vendors launched more sophisticated AI products capable of document drafting, search, and metadata extraction. This is indicative of a fundamental change in how legal technology delivers value. It is designed to augment and streamline actual legal work, and lawyers have an expectation that it will deliver work held to the same standard as they are.
Do the predictions that came true in 2025 hold a clue for what will come in 2026? We believe that they do. Here are our predictions for the state of legal AI in the coming year:
We spoke with Evan Wong, the CEO of Checkbox.ai, and he predicted that there would be an increasing capability gap between AI-native legal tech solutions and older, legacy legal tech solutions. He likened the transition to the transformation from paper to digital or on-prem to cloud.
He noted:
“We're now shifting from SaaS to AI-first, which is another technology transition that we're going through as an industry and as a society. As we step into 2026, the gap between companies that can move quickly on AI and those who can't will profoundly widen. My prediction is that buyers will no longer tolerate vendors that are not driven by AI. Buyers will want a best-of-breed approach to AI solutions.”
Legal teams know they need to measure the value of their AI investments, but there are a couple of obstacles to quantifying these tools’ effect on the business. Traditionally, ROI for legal teams has been measured in cost avoidance, risk mitigation, and operational efficiency. But trying to calculate the value of what didn’t happen vs what did is incredibly difficult. And even if legal teams had greater responsibility for revenue-generating initiatives, they’re difficult to attribute directly to the legal department.
When legal teams are evaluated using metrics designed for revenue-generating departments, there is a mismatch in expectations. Legal prevents disasters and mitigates risk, which are valuable to the business but don’t always fit within the neat ROI formulas that finance teams expect. As one of our customers recently told us, “I do not want to be viewed as a cost center. I want to be a business partner.”
Like it or not, in the current economic climate, it’s critical to be able to explain the value of AI tools in a way that finance teams understand in order to lessen the resource pressure on legal teams. The most forward-thinking GCs we’ve met face this problem head-on and define the metrics by which they’re measured themselves, in partnership with their finance colleagues, and we expect to see more of this in the coming year.
The increasing amount of AI-related legislation, as well as greater amounts of end-user adoption, is leading to the presence of in-house AI governance frameworks. We expect that an emphasis on AI oversight roles, vendor guidelines, and policy guardrails will become a standard part of AI implementation in 2026.
The 2025 predictions that came to pass reveal the secrets to successful AI adoption in 2026. In-house legal departments will set up governance, operational, and measurement guidelines for successful AI adoption. Companies will continue to be cautious. And buyers will expect more from technology that evolves extremely quickly.
Learn more about successful AI adoption with the following resources.
Connect with us now to learn how we can streamline your contract review process
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