The dashboard looked incredible during the demo. I remember sitting in that conference room, watching the sales engineer click a button that instantly visualized our renewal risk across three geographies. It was the kind of clarity we had been desperate for. We assumed that once we bought the tool, that clarity would simply become our new reality.
We were wrong. We treated the software as a generator of truth, rather than a container for it.
Six months after deployment, I ran a report on our "Payment Terms" field. I expected to see a breakdown of Net 30 versus Net 60. Instead, I found that 40% of the fields were blank, 30% were marked "See Document," and the rest were a mix of formats that made aggregation impossible. The tool wasn't broken. The process of feeding it was just too heavy for the people we asked to carry it.
The Friction of Data Entry
The friction wasn't technical; it was purely behavioral. We had asked our in-house counsel to become data entry clerks. After spending three hours negotiating a complex indemnity clause, the last thing any lawyer wants to do is spend another ten minutes filling out a metadata card with twenty required fields.
So they didn't. They uploaded the PDF, bypassed the optional fields, and moved on to the next fire they had to put out.
This is the part of the implementation roadmap that never gets a dedicated slide. We underestimated the sheer discipline required to maintain a structured dataset in a high-velocity environment. We assumed that because the value of the data was high for the organization, the incentive to input it would be high for the individual. That was a fundamental miscalculation. The person entering the data rarely feels the pain of its absence; that pain is reserved for the operations team trying to run an audit six months later.
We often hear stakeholders ask, "Why can't the AI just pull all the dates and terms for us automatically?"
While extraction models have improved, they still require human validation to be legally actionable. If you don't budget time for a human to verify the AI's work, you aren't automating the process; you are simply automating the creation of errors that you will have to fix manually when the stakes are higher.
The Cost of Bureaucracy
There is a specific type of fatigue that sets in when a tool demands more from you than it gives back in the immediate moment. We saw this with our sales team. We integrated the CLM with Salesforce, thinking it would streamline their workflow. But because we configured the system to block any contract request that didn't have a corresponding budget code, we inadvertently turned the contract request button into a bureaucratic checkpoint.
The result? They stopped using the request button. They started emailing the legal team directly, attaching Word documents and bypassing the system entirely. Our "Single Source of Truth" became a repository of only the most compliant transactions, while the messy, urgent, and often risky deals happened in the dark, completely outside the governance structure we had spent six figures to build.
If your volume is low but your complexity is high—like a boutique firm doing bespoke M&A deals—a rigid CLM might actually be an impediment. In those cases, the nuance of the deal structure often defies the standardization that these platforms require. You end up fighting the tool to make it accept the reality of your business.
Simplifying for Survival
We eventually had to strip our requirements down to the bone. We went from thirty required fields to five. We accepted that we wouldn't be able to report on everything, but at least we would have 100% compliance on the things that mattered most, like effective dates and termination rights.
It felt like a defeat at the time, scaling back our grand data ambitions. But looking back, it was the only decision that saved the system from total abandonment. A messy, incomplete database that people actually use is infinitely more valuable than a pristine, empty one.
Reflecting on the balance between data ambition and operational reality. For more on structuring your team's approach to these tools, see our notes on Legal Operations Maturity.