Linda Evans is Senior Director, Commercial Legal & Operations at Hootsuite, and has run procurement, supported a high-volume sales organization, and sat through more vendor demos than she can count. She’s also spent the last year getting her legal team to actually use AI and has valuable insights about the experience.
Like many legal leaders at fast-moving tech companies, Linda faced an impossible math problem. The volume of work was rising, and headcounts weren’t increasing. Legal teams are being asked to absorb more work with the same amount of resources. The only answer is to innovate.
“It's part of our DNA to use software where we can. If you don't use the tools that are readily available, you won't succeed," Linda says.
Many legal leaders find themselves in a situation where they have a directive and a budget to find AI tools to streamline the workload, but they face a market full of vendors making similar promises. There’s no established roadmap of how to buy or implement an AI tool yet. So the reality is that you have to figure it out as you go, just like everyone else. And that was the experience at Hootsuite.
Linda came to vendor evaluations with a clear point of view: she knew her problem, she'd done her research, and she didn't have time for a fluffy pitch.
"You've got my time for a reason," she says. "I've done enough background research to get me in the room. Use your time wisely. Show me what you can do."
Her specific need was narrow and well-defined: faster contract review for a small legal team supporting a high-volume sales organization. She didn’t need a company-wide platform, and she didn’t want to roll out a repository that would require change management across the sales org. She wanted one thing done well for lawyers who needed to move faster.
That clarity made the evaluation process easier. She thinks having it is critical for anyone in the market for an AI tool; the best way to get what you want is to know what problem you're solving before you get to the demo. Many vendors will try to sell you more than you want. The right vendor will listen to what you actually need.
Issues that Linda had to consider, as she went through the vendor selection process, were how to mitigate the risk of non-legal professionals using AI tools for legal purposes, as well as how to ensure that the AI tools are actually trustworthy.
General-purpose AI tools are accessible to everyone in a company, and not everyone using them to review a contract knows what they don't know. Anyone could run a contract through a general AI tool and miss something a lawyer would catch. This could create risk exposure that the legal team wouldn’t know about until it's too late.
The answer, Linda says, isn't to ban the tools. It's to build the structure around them.
"That's down to having governance policies, having frameworks, educating a team," she says. "And it has to be from the top down."
Linda’s point of view is that creating these guardrails is a responsibility for leadership, not a legal ops task. It requires clear internal communication about which tools are sanctioned for which purposes, what the boundaries are, and why they exist. The legal team can write the policy, but someone at the executive level has to support it.
When you roll out an AI tool, there’s a secret no one talks about. Things will often go slower before they go faster. And that’s expected. It’s even okay.
Playbooks have to be built, lawyers have to learn to prompt, and the tool has to be trained on how your organization works. In the meantime, work still comes in, and timelines don't shift. It’s a significant ask for a lawyer who is already under pressure to do something the harder way, on purpose, even just temporarily. That’s the situation Linda found herself in.
"In the beginning, the lawyers told me, it's taking us longer," Linda recalls. "And I said, I understand, because you're training it."
If she had the chance to do it again, she’d make a deliberate space in the implementation schedule for that period, and she’d plan it with intention. There might be a structured onboarding roadmap rather than an open-ended expectation that people would learn the tool themselves. There would be dedicated weekly training sessions with the team. There should be a realistic conversation upfront with her team about what the first few months would actually look like.
"Nothing is plug and play," she says. "Nothing is out of the box and immediately you've got an ROI. There is a real time involved in this. And you have to put the time in yourself upfront."
The teams that go through this bumpy introduction period are the ones who actually get value out of the tool. The ones who don't plan for it often wonder why adoption is low, and face renewal conversations they're not ready to have.
The single most important factor in whether a legal team actually adopts an AI tool is whether their leader is willing to use it.
"How can anyone buy into doing something differently if you're not doing it yourself?" Linda points out. "Change comes from the top. You have to walk the walk."
Linda notes that she doesn't ask her team to do things she won't do herself; she models the behavior she wants to see. Not only does she use AI, she shares results in the team Slack channel so everyone can see what she’s doing. To make AI adoption actually stick, the leaders have to be visibly, consistently in the tool alongside everyone else.
The roadmap for adopting AI is still nebulous. But Linda’s experience shows that the teams that are using AI well, with the adoption to show for it, are the ones that treated it as a way to think about changing behavior and ways of working, not as a pure technology problem. People use technology, and understanding how they work and what they need will make its adoption easier and the investment more valuable.
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When Ivo was founded in Auckland, the ambition was clear: take it to the world and win.
Ivo is proud to announce a partnership with New Zealand Football as the Official Legal AI Partner of the All Whites, New Zealand's men's national team.
Founded in New Zealand and now headquartered in San Francisco, Ivo has spent the last several years building an AI-powered contract intelligence platform for the world's most dynamic organizations, including Meta, Uber, IBM, Shopify, and Mitsubishi Electric. Ivo has increased ARR sixfold and recently opened offices in London and New York.
"As a New Zealand-founded company, we know what it takes to build something and take it to the world stage," said Min-Kyu Jung, CEO and co-founder of Ivo. "Standing with New Zealand Football as they prepare for one of the most significant moments in the program's history is something we're really proud of."
Ivo branding will appear on the front of the All Whites' training and press kit as the team prepares for international competition, as well as across select New Zealand Football initiatives. New Zealand Football will use Ivo's contract intelligence platform to draft, review, and analyze agreements across all their teams.
For New Zealand Football, the partnership is a natural fit. "Ivo is a real Kiwi success story of a company taking on the world, and that's exactly what we strive to do every time our national teams step onto the field," said Andrew Pragnell, Chief Executive of New Zealand Football. "It's exciting to partner with a New Zealand-founded company experiencing strong global growth while remaining connected to its roots."
We're excited to support the All Whites as they take the field.
It sounds like an old riddle. Q: When is a contract not a contract? A: When it’s a source of enterprise intelligence. The subject came up numerous times at CLOC 2026. As a department that reviews contracts and mitigates risk, the legal team can be seen by finance departments as a support function and a cost center. But once legal teams can speak the same language as finance teams, and can care about the same things that finance cares about—margin, revenue recognition, risk exposure—then that gives legal teams a bigger seat at the table.
Until about two years ago, computers couldn’t read contracts. They could store them, search by keyword, and extract a few fields if a human told them where to look. But they couldn't understand a contract. When LLMs emerged, they offered a new capability to read contracts at scale. The shift that made it possible for an AI to write a coherent essay also made it possible for an AI to read a contract the way a senior associate would and understand the obligations it creates, the relationships it has to other agreements, and the way its terms compare to your standards and your history.
When contracts can be read at scale, they can be analyzed for insights. Emelita Hernandez-Bravo, Head of Legal Operations for the Royal Caribbean Group, noted: "We are the data stewards of this contract data. It's different data that means different things to different people within the organization. There's the risk data—limitation of liability, that's important to the CLO. But then there are obligations tracking, which is important to our CFO."
The intelligence buried in contracts contains valuable insights that the entire business can benefit from. Legal ops leaders at CLOC described specific, high-value data points their teams can now surface to the rest of the business. This data used to live in institutional memory, in a spreadsheet or an old PDF, or nowhere at all.
Alex Gao, senior director of legal operations at Hilton, described one contract intelligence use case—a founder attorney needed to find a clause she'd negotiated years earlier on a specific hotel deal. Previously, that meant asking around, hoping someone remembered. With their CLM's AI search capability, she typed a natural language query and got five results, including the right one first, with citations. "That's the power of AI," Gao said. "Switching that tacit knowledge—institutional knowledge that resided in somebody's mind—to structured, explicit data."
Alyse Wilkinson, Senior Director, Global Legal Operations at WSP, described her goal of mapping the total limitation of liability exposure across her entire contract portfolio. "If you look across your ecosystem of contracts, how many of you would know off the top of your head what your total exposure is in limitations of liability?” she asked. “That would be a huge, powerful data point for our risk professionals, our insurers, our CFO, our CLO." Most organizations wouldn’t know the answer to that question. And not knowing can be costly.
The unspoken theme beneath all of these conversations at CLOC is that AI is changing where lawyers create value for an organization. When AI takes over the execution layer of legal work—manually reading, reviewing, and analyzing documents—then the contribution legal makes to the rest of the business is concentrated in judgment, analysis, and strategic counsel. The role of legal is shifting from gatekeeper to strategic partner, and the teams moving fastest are those who have structured their contract data well enough to support that shift.
When legal can identify strategic challenges like risk exposure and revenue recognition impact, and offer quantifiable solutions to those challenges, it’s not making a case for relevance anymore. It’s delivering data that the rest of the business needs.
The data has always been there, sitting in executed agreements, buried in clauses, scattered across systems. The opportunity now is to organize it, and to hand the rest of the organization answers to questions it didn't know legal could answer.
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.
Connect with us now to learn how we can streamline your contract review process
