Enterprise legal teams have been promised the world by CLM vendors. Who wouldn’t want a single system to manage every contract, automate every workflow, surface every risk? The promises around CLM capabilities are compelling. However, the reality, more often than not, is a failed implementation, frustrated stakeholders, and a tool that nobody uses.
In fact, Gartner estimates that nearly half of all CLM implementations fail. For enterprises investing hundreds of thousands of dollars, that’s a huge gamble.
So what keeps going wrong? Pramata has been around for over 20+ years, and in our conversations, we’ve heard it all. Flexibility issues, low adoption rates, missing legacy contracts, accuracy you can’t trust; and it shouldn’t be the standard. Continue reading to find out why most CLMs can’t live up to their claims and what our best practices are for making your next implementation a success.
1. Inflexibility is the Achilles’ heel of CLM implementations
CLM technology isn’t usually bad. The issue is that these systems tend to be rigid, which makes integration complicated. In reality, you need to be able to upload your contracts and configure workflows in a system that fits around your specific needs. And most CLMs just can’t. Most are built around a standardized configuration, which means they expect your organization to bend to fit the software, not the other way around. The problem is, no two companies structure their documents the same.
The way your company drafts and names contracts and routes for approval is unique. Getting a CLM to actually mirror those nuances usually means costly added customizations, longer deployment timelines, and a lot of manual upfront work just to get the system to a usable state. That work can stall CLM launches for months, and even sometimes years.
And even when you get there, maintaining it becomes its own problem.
2. Adoption dies when CLMs aren’t designed for the whole enterprise
The ability for a tool to integrate into the interfaces Sales, Finance, and any other team is using is imperative to ensure its full value is realized. But most implementations are just configured for Legal.
For example, a CLM that makes legal more efficient on paper but forces everyone else to learn a new system is a CLM that simply won’t get used. It’s not due to an unwillingness to learn, but because it’s adding one more thing to the tool sheet. Adoption needs to be a seamless process across the board, and the different departments affected need to see that a new tool won’t make their life harder.
Sales teams work in CRM, Finance lives in their ERP, so asking cross-functional stakeholders to log into another tool just to touch a contract is a nonstarter. People don’t want to log into multiple systems, they want access to everything in one place. This is by far one of the most consistent reasons implementations fail.
The buy-in problem tends to happen downstream of the design problem: when a CLM is scoped as a legal tool, it delivers legal-only value. That’s a hard sell when you’re asking the broader organization to change how they work.
3. Accuracy levels aren’t high enough
In modern day CLM implementations, accuracy is a major concern. A CLM that doesn’t deliver answers grounded in trusted data creates new problems, rather than solving the ones already on your list. Executive level insights and trustworthy AI are only possible with a structured, reliable repository. And many CLMs aren’t yet equipped to deliver that level of contract intelligence.
A 99% level of accuracy is imperative to encourage adoption, build credibility, and protect your enterprise’s assets. The regulatory and financial implications of AI errors in the context of enterprise contracts are real, and companies are rightfully more skeptical than they were a few years ago.
That skepticism is driving a shift in how the industry talks about AI. The early narrative we all remember is that “AI will replace lawyers, automate everything!” has given way to something more honest: AI can get you far, but you still need a human in the loop to verify the output. Vendors who are being transparent about accuracy scores and showing exactly where the AI is confident versus uncertain are the ones worth paying attention to. General benchmark claims don’t tell you much. A dashboard that shows actual AI output, clause by clause, is a different thing entirely.
4 best practices of successful CLM implementations
The organizations that get the most out of their CLM have a few things in common. And it all comes back to a solid base of contract intelligence.
Start with a clean data foundation. Before any AI can reliably answer questions about your contracts, those contracts have to be properly cleansed, organized, and structured. That foundation is what makes everything else possible. Without it, there is no search, reporting, negotiation reference, or agentic workflows. This is the foundation for success, and a step you can’t afford to skip.
Scope beyond legal. The use cases that build real organizational buy-in are the ones that cross departmental lines: revenue leakage analysis, M&A diligence, data rights assessments, renewal risk. Not one of these is a legal-only problem; they affect the multiple departments and live in contracts. When the right stakeholders see the value, the implementation has a much better chance of sticking.
Ask harder questions during CLM evaluation. If you’ve been burned by a CLM before, the instinct is to evaluate more vendors more carefully. That’s right, but the key is what you’re evaluating for. Don’t take claims at face value and be sure to dig a little deeper. And don’t worry, we have the questions you need to ask below.
Bring in additional tools when needed. While it is possible to get everything you need from a single CLM, this isn’t the norm. Many companies trust a CLM tool to help with key functions, but layer in a contract intelligence tool to round out its offerings and drive more value. When implementations stall due to a lack of data, contract intelligence software unlocks the data needed to power processes and cross-departmental adoption.
What to look for in a CLM implementation: Questions to ask
If you’re evaluating a new CLM or trying to diagnose why your current implementation is underperforming, ask these questions before you sign another contract or commission another implementation project.
Does it adapt to how your organization actually works?
Your enterprise shouldn’t be forced into a box. Instead, a CLM should mirror your existing contract workflows, naming conventions, approval chains, and document structures. If getting the system to reflect your business requires months of custom development, that’s a red flag.
Can non-legal users access it in their own systems?
If your Sales team has to log into the CLM to check a contract term, they probably won’t. And, if Finance has to submit a ticket to Legal to confirm a billing rate, the CLM isn’t doing its job. Contract data needs to be available in the systems people already use, such as CRM, ERP, Slack, email, rather than requiring them to add another login to their day.
Is the underlying contract data actually clean and organized and complete?
This is the question most organizations fail to ask, and having clean contract data is a competitive advantage for modern day enterprises. That’s because a CLM with great AI capabilities is only as good as the data underneath it. Before evaluating features, ask how the vendor handles legacy contract migration, OCR quality, duplicate removal, and document hierarchy, especially when it comes to inherited contracts from M&A or prior systems. That infrastructure is what separates a searchable repository from a true contract intelligence foundation.
Can it tell you what terms are currently in effect at a glance?
Many CLMs store contracts but can’t answer the question: “What are our current terms with this vendor?” That answer requires deep understanding of the full document family, and knowing which terms govern given the order of precedence.
How does it handle AI accuracy and how transparent is it?
Never accept general accuracy benchmarks at face value. Instead, ask vendors to demonstrate accuracy on your own contracts, not curated demo data. Look for systems that show you where the AI is confident and where it’s flagging uncertainty for human review. A dashboard that surfaces clause-by-clause AI output is fundamentally different from a vendor who simply claims their system is “95% accurate.”
Will the vendor go deep on your specific business problems?
Complex enterprise use cases such as revenue leakage analysis, data rights assessments, and M&A portfolio normalization, require more than an out-of-the-box configuration. Look for a vendor who is willing to understand your specific obligations, industry nuances, and reporting needs, and who can demonstrate they’ve solved similar problems before. The depth of the partnership matters as much as the depth of the product.
The missing piece: Contract intelligence infrastructure
The checklist above describes what good looks like. Having the right foundation underneath your CLM creates an implementation that will drive value and adoption.
That’s the problem Pramata was built to solve. Rather than replacing your CLM, Pramata provides the contract intelligence infrastructure that makes your existing investment actually work. That looks like contracts organized into proper families and hierarchies, terms extracted at 99%+ accuracy with human-in-the-loop validation, and contract data surfaced in the systems your teams already use.
Schedule a demo to learn more.