I hear this from teams every time we're scoping a baharu deployment. My answer is always the same: start small.
After years of building and deploying AI, IoT, and neural systems in production at AINNA, I've learned that AI delivers the most value when it's used as a builder, not a worker for repetitive tasks.
For example, instead of asking an LLM to process 1,000 pages of bank statements every month, ask it to build a deterministic extraction pipeline that can process those statements automatically. Once the pipeline is built, deployed, and tested, it runs those 1,000 pages - or ten million - without repeatedly hitting the model for the same operation.
Using this approach, our AI consumption dropped from 32 billion tokens in the first month to around 3 billion tokens the following month.
Don't use AI to repeatedly do the work. Use AI to build the system that does the work.
Mula small. Bina once. Skala forever.
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Worth reading for my answer alone.
The direction on use AI to build is exactly right.
This is where don't use AI finally makes sense.
The framing on ask it to build is better than expected.
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