Key takeaways
- Start with a real workflow, not a broad AI ambition.
- Score each opportunity by value, feasibility, risk, data readiness, and adoption effort.
- The best first use case is specific enough to pilot and important enough to matter.
Why AI initiatives often start too broadly
Many organizations begin with the question, "Where can we use AI?" That question creates long lists, unfocused pilots, and tool demos that do not change how work happens.
A better starting point is a business workflow with a named owner, repeated activity, visible friction, and measurable cost. The workflow might be weekly reporting, document review, customer feedback analysis, lead qualification, audit preparation, or knowledge search.
What makes a use case worth moving first
A strong first use case sits at the intersection of value and feasibility. It saves time, improves quality, reduces risk, or unlocks a decision that is currently slow or inconsistent.
It also has enough data access, process clarity, and management support to be implemented. If the team cannot describe the inputs, outputs, exceptions, and decision owner, the use case is not ready for automation. It may still be ready for discovery.
How Kairo scores use cases
Kairo uses a practical scoring model: value, feasibility, data readiness, risk, adoption effort, and operating ownership. The goal is not to rank ideas academically. The goal is to identify what can become a working business workflow.
A high-value, low-readiness use case may need a data foundations sprint first. A lower-risk use case with clean inputs may be ideal for an assisted pilot. A high-volume, stable workflow may justify a more governed automation path.
What a discovery sprint should produce
A useful discovery sprint should produce a prioritized roadmap, a first-use-case brief, success metrics, data requirements, risk boundaries, and a recommendation for the right build path.
The output should be concrete enough for a business sponsor to decide whether to prototype, pause, or prepare the workflow further.
Next step
AI work should start with a workflow that matters, not a generic technology experiment. The first use case needs measurable value, reachable data, a clear owner, and a practical path into daily work.