> For the complete documentation index, see [llms.txt](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/wave-1/wave-1-vision-and-identification.md).

# Wave 1 – Vision & Identification

### **Getting started: is this process "automatable"?**

The first step to convert any existing legacy process to its M2ML counterpart is to define which processes in a domain are pragmatically automatable. 

TLDR - they are either 1) repeatable or 2) facilitate discovery.

In identifying **1) repeatable cycles**, W1 piggybacks on the powerfully simple “Don’t Repeat Yourself” framework that emerged in software development, envisioning how repeatable tasks translate to automated cycles.

Alternately, we often encounter *siloed libraries of “tribal knowledge”* within organizations across sectors. This represents *the most human component of any operation*: processes that are manned by expensive operators aiming to create breakthroughs in desired output parameters through discovery, decision making and execution. W1 can push the limits of human intuition and identify a **2) discovery process** that outcompetes human ability in said domains.<br>


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