On paper, building your own health score or AI solution sounds doable. You’ve got data, smart people, and a clear use case. But many teams underestimate just how complex it is to create something that not only predicts risk or growth but actually drives action and delivers results.
It’s not just about modeling data. It’s about continuously maintaining that model, validating it against outcomes, aligning it to your GTM strategy, and ensuring it’s actionable across CS, RevOps, and leadership.
If you’re considering building in-house, here are a few questions worth asking:
Do we have the right data structure - and is it clean and complete?
Can we connect signals to workflows that drive real behavior change?
Who owns the model long-term, and how will we keep it aligned to revenue goals?
Will this help us scale faster, or slow us down?
In this guide, we break down what it really takes to build a successful AI-driven health model internally.
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