5 Things to Consider Before DIYing Your CLM

I’m a huge fan of DIY shows: At any given moment, it’s likely that HGTV is on in the background at my house.

There’s nothing more empowering than fixing a leaky toilet, wall-papering a powder room or building a complicated IKEA closet system on your own. My lawyer brain loves learning how things work and building confidence in an area I thought was beyond my capability.

However, I also recognize when DIY stops making sense. I’d never attempt to wire a house after watching a few YouTube videos. Or build a custom HVAC system only to figure out maintenance later. I recognize that certain systems are so interconnected and complicated that the stakes are just too high.

I think we’re reaching a similar moment with AI and enterprise software. At a recent legal operations conference, I was impressed with the universe of DIY building happening within legal departments with tools like Claude. Legal ops teams are getting their hands dirty to understand what AI can do, develop AI fluency and re-imagining how work gets done. That’s the beauty of widely available consumer AI tools.

But just like homeowners contemplating a bathroom reno, legal teams need to have a clear sense of where to draw the line when it comes to buy vs. DIY in the legal tech space. There’s an important distinction between building task-specific point solutions for your team and standing up a system the entire enterprise can depend on. Contract intelligence falls squarely into the second category.

Contracts are enterprise assets that deserve enterprise-grade infrastructure. They govern revenue, spend, risk, obligations, supplier relationships, customer commitments and more. At the legal operations conference, here were five points that consistently came up in the buy-vs.-build discussion:

1. Building might appear easy and cheap. Operating is another story. At this legal operations forum, the prevailing sentiment was that building on widely available AI applications was cheaper than buying. Sure, it’s easy to calculate the time and resources required to create v.1, but what’s harder to predict is everything that comes afterward: maintenance, model changes, integrations, security updates, bug fixes, new business requirements, user support, testing, governance and continuous improvement. Not to mention the ongoing and potentially significant cost of token consumption when teams scale out their solutions. Before going down the DIY path, understand the true costs – talent, time, and tokens – of owning and operating a DIY system at scale.
2. Contracting is a system, not a single use case. Reviewing an NDA or extracting a clause can be a great DIY AI project, but enterprise contracting is much broader. It spans intake, authoring, negotiation, approvals, execution, obligations management, analytics, integrations, permissions and workflows across legal, procurement, sales, finance and IT. Solving one task for one stakeholder team is not the same as seamlessly managing the lifecycle for thousands of contracts.
3. Enterprise systems need governance, security, and reliability by design.
   Contracts contain some of an organization’s most sensitive, strategic information. Enterprise contracting requires sophisticated permissions, auditability, security, compliance, data governance, integrations, and operational resilience. These aren’t features you easily bolt on after a successful DIY experiment. They need to be part of the architecture from the beginning.
4. Dedicated expertise matters. When you buy a mature CLM platform, you’re not just buying software. Icertis and other mature CLM providers have decades of accumulated expertise: Product managers, engineers, security teams, AI specialists, implementation professionals, support teams, contracting experts, and legions more who have helped hundreds of enterprises successfully deploy contract intelligence. When customers buy Icertis, they unlock this network. With DIY solutions, the person who built the first version may move roles, leave the company, or simply have other priorities. If that’s the case and something breaks, who is accountable and will partner with you to find a solution?
5. Your internal resources should focus where they create differentiated value.
   The question isn’t simply, “Can we build this?” With AI, increasingly the answer will be yes (at least a passable v.1). The more important question is: “Is this something we should own, maintain, support, secure, and continuously improve ourselves?” If contracting infrastructure isn’t your company’s core competency, there may be far more valuable places to invest scarce legal operations and technical talent and resources.

Bottom line: Just because you can, doesn’t mean you should.

I am a proponent of generative AI and the opportunities to experiment. I’m excited as legal teams build prototypes, test ideas, create agents, and explore new ways of working. More so, I’m excited for the dividends that this experimentation pays, like AI literacy and confidence.

Despite these benefits, the question isn’t whether you can DIY. It’s knowing what you should DIY. If a workflow is mission-critical, spans the enterprise, contains highly sensitive information, and manages assets as consequential as contracts, it deserves an enterprise solution—and a dedicated team whose full-time job is making sure it works.