Hey there,
Here is a number that should make you uncomfortable. Fewer than 25% of AI initiatives deliver sustained, measurable value at scale. Not because the models are bad. Not because the infrastructure fails. Because the teams measuring them are looking at the wrong things.
The most dangerous metric in AI product development is accuracy. Not because it is irrelevant — it is necessary. But it is not sufficient, and treating it as the primary signal of product health is how competent teams build expensive products that quietly fail.
Every AI product that achieves durable success operates in three dimensions simultaneously. Technical Performance — what the model actually does in production, not in your test environment. Business Outcomes — whether value created is measurable, attributable, and economically sustainable. User Experience — whether users trust the output enough to change their behaviour.
Miss any one of these and the product fails — regardless of how strong the other two are. This is the AI Metrics Triangle, and it is the framework behind the playbook I am releasing today.
The playbook covers all three dimensions in full, including specific thresholds, a 15-minute diagnostic audit, the five failure patterns I see in almost every struggling AI product, a stage-by-stage KPI evolution from POC to scale, and a weekly operating ritual that takes 30 minutes and prevents slow-motion disasters before they surface in your board presentation.
☝🏼Download this Playbook: Thirty+ pages. No filler. Built for product leaders who are done getting surprised by metrics they should have seen coming.
Let’s Build What Matters!
Ravi Bheesetty
