Your Agent Didn't Fail. Your CRM Data Did.
Agents in sales and revenue ops mostly break on foundations — ICP, routing, mapping, duplicates — and AI just makes those failures visible faster. The fix is contractual as much as technical: the outcome, the metric, and the data elements required belong in the SOW as a line item, not in a separate technical workstream.
Transcript
Okay, we know a lot of agents are failing. And so I'm going to tell you something that you really need to consider next time you're building an agent.
I see this specifically in the sales operations and revenue operations space, where I'm very familiar. It's not the AI, for the most part — it's usually the bad foundations. Ideal customer profile, routing, mapping, data cleanliness, duplicates. Basic stuff in your CRM that AI only accentuates its visibility, or amplifies — whatever, you know what I'm saying.
So the key thing here is that when you build an AI agent, focus on the business outcome, and that should be scoped. That's the expectation.
So right now folks are splitting up and saying, okay, here's the technical details, we're going to build the agent, we're going to launch the agent, and then whatever. But they didn't figure out that there's a fundamental underlying data problem of duplicate leads — and then the agent is useless in that scenario.
So if you build it, or hire someone to build it, the outcome has to be in the SOW. That has to be the line item. It has to be scoped to that focus. Here's what we need this thing to do, this is the metric we're tracking towards, and this is the data elements we're going to need successfully to get it done. That's part of the package.
And if you split the two, it's never going to work. If you have that tight scope where it's like, don't go beyond the scope — it ain't going to happen.
