I hear this from teams every time we're scoping a 新 deployment. My answer is always the same: start small.
After years of building and deploying AI, IoT, and neural 系统 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 令牌 in the first month to around 3 billion 令牌 the following month.
Don't use AI to repeatedly do the work. Use AI to build the 系统 that does the work.
开始 small. 构建 once. 规模 forever.
#Artificial智能 #AI #商业自动化 #DigitalTransformation #系统工程 #自动化 #中小企业 #创新 #NeuralOps
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同意作者对use AI to build的判断，方向很清楚。
我喜欢文章对3 billion 令牌 3 billion保持务实的态度。
这篇文章对start small的解释很清楚，实际操作的重点也很容易理解。
总结部分让ask it to build的重点更加清楚。
看第二遍才注意到don't use AI的细节。
我会把from 32 billio 32 billion这一段分享给需要了解技术的同事。
收藏了，主要是为了my answer。
如果有更多i've learned that AI delivers的数据和结果会更完整。