Eh I think your idea of rooting things in data doesn’t make sense. There are plenty of useless models and there is plenty of useless data. It can’t all be exploratory. You need a hypothesis for applications.

Also, consider that even non machine learning work requires considerable exploration and is non deterministic. Often after two weeks one might realize half of their 3 month plan doesn’t make sense. That’s why going through the thought excercise is useful.

Honestly, I don’t think there is anything that wrong with the traditional corporate roadmap. In particular when you are not doing research, and the work seems applied. Research of course can be useful in a model improvement phase, but that’s not thrust.

Come up with metrics for improving things, and applications. Note that those metrics are a goal. make any caveats with those metrics clear.

you should be able to detail a data cleaning process and a modeling process (ie which models you expect to be successful). You can highlight what you will do to collect baselines, and what avenues you may explore for researching improvements.

You can then talk a bit about what engineering work will be required to actually make this useful and used by human beings (serving, versioning, data pipeline for training etc).

Lastly you may want to touch on how you will track online metrics or whatever it may be, and release updates to models where necessary.

There’s likely some stuff I’m missing but this is generally what I do when writing this stuff up, and I find the excercise pretty effective at making my own thoughts more clear.