How to choose AI opportunities that the business can actually deliver
A practical way to compare AI ideas by value, feasibility, data readiness, and operating risk before a pilot begins.
Insights
Notes from delivery work: how to choose AI opportunities, ground generative systems, and keep models inside real software.
Why generative AI needs grounding, review, and a defined job
Unconstrained chat is easy to demo and hard to operate. Useful LLM systems start with sources, boundaries, and evaluation.
A machine learning model is not a product until it has an operating home
Accuracy in a notebook is only the first test. Production models need owners, monitoring, and a place in a real decision.
Automate the stable work before you add intelligence to the exceptions
AI is most useful after a process is understood. Stabilize the routine path, then apply models where interpretation is required.
AI programs inherit the quality of the data foundation underneath them
Conflicting definitions and late pipelines show up later as model issues. Treat data products as part of the AI plan.
Enterprise software is still the place where AI has to earn trust
Models become useful when they live inside applications people already rely on, with permissions, audit trails, and support.