Yes, imagine data analysis scenarios like Excel users or Jupyter notebooks, or operational investigations like user 360's and security incidents. Just now defaulting to natural language and connected to your data silos and a variety of analytics tools & libraries.
We try to make the generated code and backing data explainable. Users are figuring out the scenario by having the AI go ahead for them, and automating much of the debug loop in typical coding and investigations, so folks can focus more on the analysis, less on syntax, schemas, libraries, and be more ambitious on each step.
Importantly, it is still kind of like making a much more accessible Jupyter notebook or editable excel/doc, vs a linear chat session. Instead of generating the whole notebook and it being buggy and you starting over (~= Devin, or notebook.io's ChatGPT plugin), you drive it forward only 1-3 cells at a time, and as it is an interactive document so you can edit those, go to the next, or non-destructively edit earlier ones. In contrast, ChatGPT's data assistant deletes cells below the current edit, which would stink in a normal data env.
There are other differences, but from a perspective of using genAI well, we budget 3-60s for genAI assisting in 1-3 steps, aiming for 10-100x productivity wins and a lot more peace of mind during it. Taking 1-3 steps forward may mean the AI takes 3-10 internally due to backtracking / CoT / etc
We could let the system take 100 turns, and have interesting experiments there such as around security investigations, but the use cases become more niche due to cascading errors => reliability.