Post

Recently I've been having conversations with IT (mostly networking) teams about incorporating AI solutions, and the first questions I have are 🧵:

1) What problem are you actually trying to solve with AI (that AI would solve better than a simpler/cheaper solution)? 2) What's the nature of the available data? Is it stored in disparate locations? Has it all been pre-processed for analysis? How and how often is the data updated?

The answer to the first question is critical. For some, AI is an initiative handed down by leadership with no clear direction or no clear problem to solve. Also, sometimes the direction and problem are clear, but it can be solved more easily and for far less cost than a sophisticated AI solution.

The answers to the second question are just as important. The data we use in NetOps is both structured (metrics, flows, etc), and unstructured (tickets, email, TAC call transcripts, certain types of logs, etc). The workflow to deal with this in a unified manner is more complex than many realize.

Much of the data we deal with in NetOps is real-time telemetry requiring significant effort to clean, process, store, and analyze quickly enough to be useful for a production environment.

Successful AI solutions are by nature iterative and dependent on the underlying data in that they require constant tweaking. I would argue that any good software solution is iterative to one extent or another, but certainly I agree that an AI solution is much more so.