If you want to play an instrument, you have to practice. If you want to get fit, you have to build a program. And if you want AI to actually work in your organization, you have to prepare for it first.
This was the consensus among legal operations leaders at CLOC 2026: successful AI initiatives require real preparation. That's the nature of change management, and there's no shortcut around it.
Stacy Lettie, VP of Global Legal Operations and Transformation at Organon, was very clear: "There is this urgency with AI. Everybody feels like they need to go out there and get a tool because that's going to make them feel better. Because they're going to feel like, well, we're doing something. We're out there. We're doing AI. We implemented this. We can go back and tell our CEO we are doing AI. But the data is showing adoption isn't as great as it needs to be."
That's the heart of the challenge. You can't announce that you're "doing AI" and expect people to follow. You have to lay the groundwork first.
Here are five things to do before you launch an AI initiative:
1. Define the problem before you buy a tool. You shouldn't be shopping for tools before you know what problems they need to solve. Alexander Shusterman, Staff Technical Program Manager, CLO AI, and Carolyn Wakulchik, Manager, CLO Operations— both from Uber— offered this: "Scope the risk area. What do you care about the most? If you ask everybody, they're going to tell you they want the sun, moon, and stars in the solution, and that is a surefire way to make sure that it doesn't work correctly. Better to start small and focus."
2. Get your data in order. AI is only as good as the data that fuels it. Stacy Lettie described the problem: "You have not gotten your data together. You have not cleaned your data. You have not even put all of your documents in one SharePoint folder, for gosh sakes. And you all of a sudden go, 'Oh wait, I can't do this, and now I've got to stop, and it's going to take me eighteen months to clean my data.'" Don't let this happen to you. Before anything else, make sure the data your AI will use is organized and accessible.
3. Build a governance framework. Guardrails aren't optional. They’re what make AI adoptable for the rest of the organization, and keep your company and your customers safe. Think through policies for accuracy, data protection, and appropriate use. Governance has to be implemented cross-functionally, so involve the right stakeholders early, and make it easy for team members to follow the policies you put in place.
4. Plan for change management. AI initiatives stall when no one owns adoption. Lawyers are risk-averse by training, and they won't use tools they don't understand or trust. Alexander Shusterman from Uber was very frank: "Just like any other legal operations project, the hardest part here is not the tech and it's not the prompting. It is actually the change management piece."
5. Agree on how you'll measure success. Before the initiative launches, define what success looks like, and get alignment from the executive team on those metrics. This discipline helps you scope the problem well and makes it much easier to demonstrate value in the future.
Successful AI initiatives don't happen by accident; they're built on preparation. These steps have been field-tested by legal operations leaders who've done the work. If you’re looking for a framework to start planning your AI projects, this is a good place to start.
Strava, the leading platform for movement, connects more than 195 million athletes across 185 countries. It helps people build new habits, hit new personal bests, and make progress together. More than a few of us on the Ivo team are among them, so we are thrilled that Strava will be joining Ivo as a customer.
Like many high-growth tech companies, Strava's legal team works with a high volume of contracts and a lean headcount. These companies must review more contracts with lean resources, and can’t sacrifice accuracy or quality of analysis. That is exactly the problem that Ivo solves.
We’re excited to be working with Strava’s legal team on the journey ahead.
Welcome, Strava!
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The big theme that came up at CLOC’s CGI 2026 in Chicago this year, in session after session and conversation after conversation: how people behave. Yes, there were conversations about AI and technology, but in numerous sessions, speakers and attendees brought up how important the human element is in successful enterprise AI adoption. It’s not enough to understand the technology. You need to understand how people behave, too.
Here are the three most important—and human-centric—takeaways from CLOC this year.
Takeaway 1: You are not running behind in AI. No one is. Numerous speakers, from companies large and small, noted that the key to creating successful AI projects was to start with a pilot, tightly define its scope, get buy-in from the relevant stakeholders, and document everything. These specific processes are critical to make sure the initiative actually works. Alexander Shusterman, Staff Technical Program Manager, CLO AI, and Carolyn Wakulchik
Manager, CLO Operations, both at Uber, said in their session, "Scope the risk area. What do you care about the most? If you ask everybody, they're going to tell you they want the sun, moon, and stars in the solution, and that is a surefire way to make sure that it doesn't work correctly. Better to start small, focus, and then scale and increase over time. The goal is to see if it works, and if not, you move on to the next thing, and that's okay."
Takeaway 2: Because of their knowledge of people and systems, legal ops is now considered the center of tech innovation in the entire business. Many speakers talked about legal being considered a support function and a cost center in the past, but thanks to the explosion of AI-powered legal technology and knowledge of how to create systems that work, legal ops is now actually at the forefront of innovation. Alex Gao, Senior Director of Legal Operations at Hilton, noted in her session that her GC told her that all of the legal team’s objectives were about supporting other business functions, except for the technology implementation led by the legal operations team. This led to a business-wide shift in how the legal team was perceived: “We truly shape the perception and are the driver in legal innovation and transformation.”
Takeaway 3: The value center in legal departments has shifted. When AI can do the day-to-day execution work of document drafting and review, the value lawyers provide is less in creating items and more in providing good counsel. Mary O’Carroll, the CEO of LECG, put it very plainly in her session: “We're paying more for judgment. We're paying less for execution." That means legal’s relationship with the business is changing to an enablement role, where legal collects insights from the business and uses them to proactively measure and manage risk.
So many people at CLOC told me—and said on stage—that technology’s capabilities were growing faster than humans’ ability to process it. It’s therefore not surprising that AI adoption is so profoundly influenced by how humans manage change. More than one speaker commented that technology isn’t a virtue in itself; it’s what you do with it that matters. What’s clear is that legal ops departments are doing the hard work of turning AI's potential into repeatable practice, and that this capability will only develop in the coming months.
Every contract review tool on the market reviews your contract against a static playbook. To do so, every clause is evaluated individually to see whether it aligns with a corresponding playbook position. Playbooks are formulaic and unchanging, and lack nuance and discretion as a result.
Today we’re releasing the next generation of Ivo Review. It turns the unnatural style of playbook agreement review on its head; it no longer discretely decides whether a clause matches a particular standard in a vacuum, but instead, applies your playbook across clauses in a logical, coordinated way that mirrors how an experienced attorney actually negotiates. Now Ivo Review can understand not only what your positions are, but how they work together, and discern when a position versus a fallback position makes sense within the context of the agreement.
That context matters so much in a negotiation. A liability cap that's standard in a low-risk, short-term vendor agreement might be completely unacceptable in a strategic multi-year partnership. With Review 2.0, you give the context to Ivo, and it will use that to inform its suggestions. It even identifies and surfaces specific language you’ve previously approved or accepted in similar deals, and provides market-based suggestions.
We wanted to know whether all of these improvements to Ivo Review made a difference in performance, so we ran an independent benchmarking study to assess the performance. We gave 19 commercial agreements to an experienced attorney at an Am Law 25 firm, Ivo Review 2.0, and a leading AI model to review, and asked three independent senior lawyers to judge their performance.
Review 2.0 significantly outperformed off-the-shelf AI and performed on par with the human lawyer, We also found that Review 2.0 did better than the others in surgical redlining and judgment. And finally, Review 2.0 vastly exceeded Review 1.0's capabilities, performing 41% better in overall accuracy.
For the first time, an AI can exercise legal judgment. This judgment comes from contract benchmarks, your playbooks, and the context of the agreement. To do this, we built a direct connection between every clause you review and your actual negotiation history. Every time your team runs a review, Ivo allows users to select applicable agreements in your repository to benchmark against. And within that set of agreements, Ivo automatically narrows it further to similar agreements based on factors like contract type, governing law, your role as a party, and the counterparty's industry. This means that for every clause you review, you can see exactly how it compares to what your team has historically agreed to. You know whether a position is standard for your organization or if it deviates and, if so, by how much. This data is grounded in your own executed agreements and your negotiation outcomes.
We also changed the playbook itself. In Review 1.0, every position had to be broken into formulaic checklist items. The AI had no discretion on what to surface or when. If a position was in the playbook, it flagged it, whether or not the context made it relevant.
Review 2.0 reads your playbook the way an experienced attorney would. It understands not just what your positions are, but when to apply them, which fallbacks to recommend, and when the deal context means a position simply doesn't apply. Teams managing playbooks across multiple jurisdictions can now work from a single global playbook. Review 2.0 identifies the applicable jurisdiction and applies the right position automatically. You can also generate playbook positions directly from your existing templates and historical agreements in minutes.
Finally, Review 2.0 has a capability that no playbook can capture. It understands the context of the deal itself. For example, a liability cap that's standard in a low-risk short-term vendor agreement might be completely unacceptable in a strategic multi-year partnership. Your playbook can't know which situation you’re in, and, until now, neither could your AI.
With Review 2.0, you define what the deal is before review begins, and that context shapes everything. What to push on and what to accept. The AI isn't just checking your contract against a list of positions. It's applying judgment informed by what this deal actually means to your business.
Review 2.0 assigns a separate AI agent to evaluate each topic in your agreement. A superior agent sits above them, reconciling conflicts and consolidating their findings. This allows Ivo to reason across your entire agreement holistically, rather than processing it item by item.
What you get isn't a wall of overlapping checklist items that still require a lawyer to sort through. You get a clean set of consolidated recommendations, organized by topic, with the reasoning behind each one.
Every contract your team signs is a decision about risk. And the quality of those decisions depends on the quality of the context behind them. Until now, that context has been fragmented, either scattered across static playbooks, buried in old agreements living in someone's hard drive or even a filing cabinet, or living in people's heads.
Review 2.0 changes that. Every review now carries the wisdom of everything your team has ever negotiated, surfaced automatically and applied intelligently at the moment it matters most. It's your playbook, shaped by your precedents, sharpened by deal context, and backed by the kind of judgment your best lawyer brings to every contract.
Book a demo today and see Review 2.0 in action.
I’ve been working in customer success for over a decade, and one thing I’ve learned is that, for my customers, I am the product. Not the software. Not the logo. Me. I am the face of the product and the company I work for. So the only way to do this job well is to believe in the product, to believe in the vision, and believe in the team behind it. And that’s why working at Ivo is so exciting to me.
I had a job once where the product was pretty lackluster and could be easily copied by its competitors. That’s where I learned why it’s so important to believe in the product and the company you’re working for. The only way you can do this job well is if you actually believe in what you’re representing. And at Ivo, that works really well, because I know that people love the product and what it can do for them. So if I can help create a great experience for my customers, they'll learn to love working with me, and love working with Ivo. But if the product is bad and the overall experience with Ivo is lackluster, that's my reputation on the line, too. So it’s really important to provide that level of service consistently.
Fortunately, the rest of the company makes my job easy. It’s an incredibly collaborative place. If I need to go to the engineering team because there's a bug, I need to go to sales because I'm looking for an expansion opportunity, or the legal engineers for a technical question, everyone plays a role and pitches in to help out. I have to make sure I have what I need from everyone else before I communicate it to the customer, and because we work together so well, it’s simple.
There are ways I can tell that a company is going to be the kind of organization I want to represent. When I spoke with Min-Kyu, our co-founder, I saw the vision in introducing AI into the contract review workflow. It seemed like such a no-brainer. It's a manual, tedious process. But with AI, you can utilize that technology to do the same thing and then do the fine-tuning yourself, actually creating the strategic component of the work. And then, hearing Min-Kyu’s passion and his vision, I realized that he really cares about what he's doing. During the interview process, I was invited to a lunch celebrating the Series A with the team, which at the time, was close to 15 people. I immediately felt at home and noticed nothing but good vibes. You could tell that everyone there really enjoyed working together and they were taking the time to actually celebrate a huge milestone. So when I got the call from Min-Kyu with an offer to join, it was sort of a no-brainer. There was no question about it.
Since then, the job’s just been more and more rewarding. I get to sit in the passenger seat while the customers are driving and observe and tell them maybe you could have done this a little differently and gotten a better result. And then watching the looks on their faces when they're like, oh my God, I can do that? That, to me, is rewarding in and of itself. And getting to do it alongside my team: all of us have that drive.
Recently I had a customer at an education company, and they are federally mandated to provide responses to specific situations. It’s a very laborious, manual process. So I suggested to my customer, “why don't we try to use Ivo’s Assistant to do that?” It worked. And I realized that Ivo can do a lot more than I thought. It’s really just limited by your creativity. Every day I'm running into stuff like that. Seeing that happen more regularly only gives me an added conviction that we're on the right track and I joined the right company at the right time.
The best part of my job is the creativity and the sense of ownership. At larger companies, they give you a playbook, a handbook — this is how you're a customer success manager here, follow all these rules, you'll do a good job. But at Ivo, we're creating the rules, and that's what's exciting for me. No one's done this before. We haven't scaled like this. So it's all new and exciting, and we get to dictate how that goes. I get to solve challenges together with my partners, and I think that's pretty rewarding.
So if working in a fast-paced, challenging environment excites you, and you enjoy being amongst other people who are equally driven to provide a world-class customer experience, I think Ivo is a great place to be.
Does being on the customer success team at Ivo sound like an interesting opportunity? Check out our open roles if so!
In-house legal teams spend hundreds of hours a year reviewing contracts; for enterprise teams, that could balloon to thousands of hours of manual work. Savvy legal teams realize that generative AI could create real efficiencies for their teams by automating slow, laborious contract review processes, and as a result in-house legal teams’ use of AI has doubled in the past year. The AI-powered contract analysis tools market is now worth $4.3 billion and is projected to reach $12.06 billion by 2030.
The category of AI-powered contract analysis is now so crowded It’s become difficult for many legal professionals to compare the performance of one tool with another. But they need to be able to figure out the right tool for their needs. There are three critical questions lawyers want answered:
Ivo conducted a research project to compare its purpose-built legal AI contract review solution against general AI review and human attorney review. We asked three attorneys with recent experience either working with an Am Law 100 firm or serving as in-house counsel at technology companies to score redlines conducted by Ivo, Claude for Word, and a practising Special Counsel at an Am Law 25 firm. The redlines were created on 19 real, anonymized contracts that spanned a number of common enterprise agreements like NDAs, MSAs, and DPAs. These agreements were reviewed independently by the judging team; all identifying information was removed from each output and scored blind by the three judges.
Ivo’s performance was nearly indistinguishable from an accomplished human lawyer and significantly outperformed Claude on the five judging criteria—Issue Spotting, Surgical Redlining, Formatting Retention, Comments, and Judgment.
Human: 4.56
Ivo: 4.52
Claude: 3.50
Ivo outperformed Claude on every judging criteria; the largest score difference was in Surgical Redlining and Judgment. In addition, Ivo also excelled at redlining more complex agreements, either containing multiple parties or with complicated commercial transactions.
The test also determined that Ivo’s output is broadly comparable to a senior practicing attorney at a highly regarded law firm. The human attorney and Ivo had very similar scores, suggesting Ivo’s output could be compared to that of a high-performing senior lawyer. However, the attorney completed their redlining tasks in 10 hours, whereas Ivo took, on average, 2 minutes and 45 seconds to review a contract.
For real-world contract review specialist legal AI tools outperform general AI tools by a significant margin. Ivo’s team has spent a great deal of time perfecting its surgical redlining abilities, meaning it redlines documents with precision and accuracy, exactly as a lawyer would. In addition, Ivo scored highest—even higher than a human attorney—in Judgment, the category that assessed whether the outcome selected was the right one for the client.
Our goal with this study is to help answer a key question legal teams are asking themselves; and are specialized legal tools worth the investment? For contract review tasks, specialist tools provide better performance and ultimately a better result for the business.
Extracting insights from agreements with AI is an iterative process. You set up a field, run it across a handful (or even a large number) of contracts, and eventually you notice it's pulling the wrong values or missing nuance that your team actually needs.
That doesn’t have to happen anymore. The new “Optimize Prompt” feature on AI fields runs in the background and automatically analyzes how a field's prompt is performing across the contracts in your workspace. It identifies where extractions are going wrong, and generates guidance instructions to improve accuracy without the need to manually edit prompts.
A good-quality prompt is the difference between extracted insights you can trust and ones that you have to double check. But diagnosing how a prompt is going wrong isn’t always easy, especially for large enterprises that are working with thousands of contracts with varied language, structure, and deal terms.
Now, prompts are improved automatically, using the real contract data in your workspace to understand where the prompt is underperforming. The result is a field that gets more accurate without requiring you to become an expert in prompt engineering.
This feature is currently available to all Ivo customers, and can optimize 5 prompts at any one time. It’s a change that will have a meaningful effect on data quality, and is one less thing your team has to manually manage.
We hear anecdotally that trying to figure out which AI contract review tool is the right one to purchase is a very difficult process. There are a lot of companies in the category, they all say the same things, the category is noisy. “We spent upwards of two years evaluating these tools,” one customer told us. “It was a grueling process.”
We wanted to know if the data supported what we’ve been hearing, so we surveyed 50 legal leaders across enterprise and mid-market companies to find out what it’s like to evaluate AI legal tools.
More than half said it’s difficult to determine which tool is the right one. Only one in five said it was easy.
Lawyers are certainly using AI; 87% of general counsels say that their teams use generative AI for all sorts of legal tasks, like research, document drafting, and contract analysis. What has happened in the last several years is that AI’s increasing capabilities have led the software category of AI-powered legal tools has grown exponentially; in fact, faster than buyers’ ability to assess it.
In-house counsel, legal ops leaders, and procurement teams told us they are being asked to make significant purchasing decisions in a category that's crowded, noisy, and in some cases, hard to trust. And the industry isn't making it any easier.
The responses from the 52% of lawyers who found evaluating tools difficult clustered around five distinct themes.
The frustrations they’re feeling are clear; these are all signs of a noisy market making claims that are hard to verify.
The most common complaint, cited by nearly a quarter of respondents, was that it’s difficult to differentiate one legal tool from another.
"There are lots of overlapping claims but vendors are at different stages of maturity, accuracy and security," said one general counsel at a mid-market pharma company. "So you have to pilot so you can compare tools."
Another respondent, a senior counsel, put it more bluntly: "After a while, I get AI vendor merge where they all seem to offer the same software functions."
When every vendor claims to do contract review, redlining, playbook management, and AI-powered analysis, buyers lose the ability to distinguish between them, and that’s not a way to make great business decisions.
Another common concern was the difficulty of assessing these tools’ accuracy.
For legal professionals, this isn’t an academic concern. The stakes are high. Nobody wants to get sued because an AI hallucinated or came up with the wrong answer. So when legal teams evaluate a tool, they want proof it actually works. But proof is hard to get.
"The top two things I'm worried about — hallucination and accurate representation of the law — seem nearly impossible to evaluate without redoing all of the work manually," said one senior counsel. "I'm not sure how to better investigate the accuracy."
Another respondent described numerous variables that make accuracy almost impossible to pin down: "The accuracy of AI is hard to define. Results vary dramatically based on prompt quality, document structure, data cleanliness, user expertise."
Sixteen percent of respondents identified the gap between a vendor demo and real-world performance as a primary frustration. The pattern was consistent: tools look like they work well in controlled conditions but disappoint in actual use..
"When you see a demo, it is showing the product in its best light, but when you use it, it may not be as effective," said one corporate counsel responsible for AI technology procurement at a large manufacturer.
A privacy and cyber liability attorney framed it as a structural problem: "Most companies you really need a proof of concept to actually evaluate, because their usefulness in a demo or on a website just doesn't show how they would work for your use case."
This gap isn't unique to legal AI. It’s tough to take a category where accuracy and workflow fit are non-negotiable, and where the cost of a bad decision is not just measured in time or money, but in serious negative consequences for the company.
Fourteen percent of respondents called out vendor overpromising as an obstacle to good evaluation. Examples cited included products pitched as production-ready that are still in development, RFP responses full of buzzwords that don't map to actual features, or products designed by people who don't understand legal workflows.
"Many overused buzzwords in the RFP responses and some proposed products/solutions were still in the ideation phase," said one senior legal counsel currently running a formal RFP process for their organization.
A lead counsel at an enterprise SaaS company noted: "Many of the features they show are either basic and should be in any type of tracking tool, or are not something attorneys need. You can tell which were designed by attorneys."
Legal professionals’ work is specific and well-defined. That’s why specialized tools that reflect their workflow are so important. AI tools have to suit how attorneys already work, not force lawyers to fit their work into new directions.
There were several key themes that arose from the 22% of legal professionals that found assessing AI tools easy. During the evaluation process, they had clear decision-making criteria, direct access to test the tool on their actual work, and enough familiarity with AI to know what questions to ask.
"I find it pretty easy to evaluate AI vendors because it comes down to whether their product works well or not," said one patent attorney. "Many tools don't seem to understand the actual needs of patent attorneys or understand what our workflow is really like."
The vendors that make evaluation easier are the ones that provide real evidence — in benchmarking data, real-world trials, and customer references — that their products work in conditions that resemble the buyers’ own.
Legal professionals have an aversion to puffery. They’re not fooled by slick demos and marketing fluff. They want to know whether AI tools will do the specific work they’re tasked to do.
The vendors who earn their trust will be the ones who make evaluation easy but by providing transparent, evidence-based information that legal professionals can use to make good decisions.
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This post draws on a survey of 50 in-house legal professionals conducted via the Wynter panel in January 2026. Respondents ranged from associate attorneys to General Counsels across enterprise and mid-market companies.
“I’ve been looking at contract review tools for many years, and Ivo was the first one that actually did what it claimed to do,” Suhayb Ahmed, the Head of Commercial Legal at Canva told us. “That was a real wow moment for me.”
Suhayb has been leading the charge to build an AI-first legal team at Canva. To him, that means redirecting lawyers’ time to do the work they can do better than anyone else: using their strategic judgement to solve problems. "An AI-native legal team means using AI to handle the high-volume, low-impact work that takes up so much of our time, so we can focus on what makes us human: applying strong judgment, making decisions, building relationships."
That strategy has changed how Canva's legal team approaches contract review and redlining entirely. Instead of spending time crafting the exact right language, lawyers focus on the judgment calls that matter most.
"How do we best protect our business?” Suhayb notes. “How do we help move the deal forward while still achieving the needs of the counterparty? We can use Ivo to say, this is what our business needs to achieve, so we can focus our efforts on what actually has the bigger impact pays lots of dividends in allowing us to focus our efforts in the right places."
One of the most compelling things about Suhayb's story is how quickly Ivo's value spread beyond the legal team. For example, when Canva's revenue accounting team needed a quicker way to review executed contracts for key terms, Suhayb showed them what Ivo Intelligence could do.
"They're reviewing every single contract for a bunch of terms to ensure that they're recognizing the revenue in the right way. With Ivo, they can just build a spreadsheet with a prompt for each of the key terms that they're looking for. They've got all the details that they need and they can then apply the revenue accounting principles based on the details that are already surfaced to them."
If you’re just getting started with AI, Suhayb has very direct advice.
"Don't wait for the perfect prompt. Just start experimenting and iterate from there and you'll find these amazing solutions that just make your life a lot easier."
Take a look at how Ivo could create value for your team.
Connect with us now to learn how we can streamline your contract review process
