AI that earns its place
Not every problem needs a model. How we help teams pick the AI use cases that actually move the numbers.
The fastest way to waste an AI budget is to start with the technology instead of the problem. Every model we ship has to earn its place with a measurable outcome.
Does this use case earn a model?
Earns it
- Expensive, repetitive and judgement-shaped
- A recorded history of past decisions to learn from
- A measurable baseline that exists today
- Volume high enough to repay the build
Doesn't
- A rule could express it
- Nobody has measured the current process
- It happens a handful of times a month
- The interesting part is the demo
Start with the expensive, repetitive and judgment-light
The best first AI use cases share a shape: they're costly at scale, highly repetitive, and don't require deep human judgment for the common case. Support triage, document extraction and forecasting all fit.
Have a baseline before you have a model
- 1
Measure today
How long it takes, how often it is wrong, what it costs.
- 2
Agree the target
A number, written down, before anyone builds.
- 3
Find where the time goes
Teams are routinely wrong about which step is the bottleneck.
- 4
Only then build
Now an improvement is provable rather than asserted.
Have a baseline before you have a model
If you can't state today's cost, time or error rate, you can't prove the AI helped. We insist on a baseline so success is a number, not a vibe.