u/46X46

Hi everyone,

I’m currently working on a fairly complex Power BI project at work, and I’m wondering whether problems like this can be solved more effectively with AI instead of just relying on normal research, forums, and a lot of trial and error.

A quick overview of what I’m building:
I’m creating a dashboard with multiple levels of project data. Simplified, there is:
- a top project level
- below that, main orders / sub-projects
- below that, sub-orders

My model now has roughly 10 to 15 tables, including several fact and helper tables for project data, KPIs, revenue, workload/capacity, reference lists, permissions, and also some prepared variants for specific report pages.

What I’ve done so far:
- built and rebuilt a lot of DAX measures
- adjusted Power Query logic
- built KPI cards and bar charts
- implemented actual vs. plan logic with month-end / snapshot logic
- built a row-level security / permissions concept
- connected several slicers and filtering logics across multiple tables

My biggest pain points:

1. Filtering logic across multiple tables
   This is currently my biggest issue.
   Example: if I first select a project and then additionally filter by department, project manager, or commercial/project controller, it often does not behave cleanly. Some tables react, some KPI cards do not, and some charts show 0 or wrong values.
   The core issue is that the filters do not all come from the same table, so I have to rebuild a lot of the logic inside measures using CALCULATE, TREATAS, SUMX, REMOVEFILTERS, etc. That makes the filter context inconsistent, overly restrictive, or contradictory very quickly.

2. Hierarchical logic
   If someone is responsible at the top project level, they should also see everything below it. But if someone is only responsible for a lower-level item, they should not automatically see everything above or around it. Building that hierarchy properly in RLS and measures is much harder than I originally expected.

3. Actual vs. plan vs. month-end logic
   Some values cannot simply be summed from monthly planning columns. They need to switch between actuals and plan depending on the latest month-end snapshot. This becomes especially error-prone for cumulative values and forecasts.

4. Scalability
   I do not just want individual formulas to somehow work. I want a model that remains stable and does not break every time I change a filter, relationship, or permission rule.

Why I’m thinking about AI:
I already use ChatGPT Pro, and even there I notice that this kind of help eventually hits limits. Sometimes it helps a lot, but sometimes it also introduces new errors, contradictory logic, or solutions that fix one thing while breaking something else in a more complex model. That is why I’m wondering whether there are better AI workflows or tools for BI / analytics problems like this.

Important:
I probably am not allowed to install an MCP server or similar tooling on my work laptop, most likely because of data protection / company IT policies. It might be possible on my private PC, but of course only with anonymized or recreated sample data, not with real sensitive company data.

My questions:

1. Does anyone here seriously use AI for Power BI / DAX / Power Query / data modeling?
2. If yes, which AI or setup actually helps?
3. Is a local or private-PC setup worth it for this kind of work?
4. How do you handle data protection when real company data cannot be used?
5. Is AI genuinely useful for complex BI logic like this, or does it end up creating even more chaos?

I’d really appreciate honest experiences, especially from people working with more complex BI models, DAX, filter context, and row-level security.