Vibe Coding vs Power BI

Why Instant Dashboards Fall Apart Without Real Data Foundations

Vibe coding is everywhere right now. It’s the idea that you can describe what you want to an AI tool and watch it write the code for you on the spot. Ask for a sales dashboard, get clean visualizations in seconds. The charts look production-ready. The code runs without any obvious errors.

It’s impressive. It’s fast. And for a lot of people, it’s been a very expensive lesson in what happens when you skip the tedious stuff.

The Promise Sounds Perfect

Here’s the pitch: describe what you want, AI writes the code, and you get instant insights. No waiting on IT. No expensive new hires. Just you, ChatGPT, and a dashboard that looks like it means business.

And it works. Sort of.

The demo always looks great. You ask for a sales dashboard, you get one. You want to see regional performance, boom, there it is. The code runs. The visuals render. Everything feels like progress.

Until you try to use it for something that matters.

Where It Falls Apart

The problem isn’t the code. AI can write syntactically valid DAX, Python, or SQL. The problem is what the code is asking.

Vibe coding builds dashboards fast because it makes assumptions about your data. It assumes your tables are clean. It assumes your definitions are consistent. It assumes “revenue” means the same thing in every system.

Unfortunately, most data doesn’t work that way.

Revenue in your CRM is different from revenue in your ERP. Customer counts change depending on who’s asking. Regional breakdowns don’t match because someone moved territories last quarter and nobody updated the mapping table.

AI has no way to infer any of this. It can’t. It’s writing code based on what you described, not what your business needs.

So you get a dashboard that answers the question incorrectly but, in a fancy, visually pleasing way.

The Semantic Model Problem

This is where Power BI pulls ahead. Not because it’s smarter than AI, but because it forces you to confront the thing vibe coding lets you skip: defining what your data means and cleaning up the messy bits.

A semantic model is your business logic layer. It’s where you decide, for example, that “revenue” means closed deals minus refunds. It’s where you map regions correctly. It’s where you build relationships between tables so every dashboard pulls from the same truth.

It’s not sexy work. It’s the plumbing nobody wants to talk about at budget meetings.

But it’s the difference between a dashboard that looks good and a dashboard people trust.

When you build on a semantic model, every report starts from the same foundation. Your finance team and your ops team see the same numbers.

Vibe coding often encourages skipping or fails to enforce semantic modeling. It builds the house first and hopes the foundation shows up later.

AI Can’t Fix Bad Definitions

Here’s what vibe coding is great at: writing code that does what you asked.

Here’s what it can’t do: figure out what you should have asked.

If your data has three different customer IDs across five systems, AI will happily write queries using whichever one you mentioned. It won’t tell you that two of those IDs are deprecated. It won’t know that finance uses a different ID than sales. It’ll just build the dashboard.

And when the numbers don’t match the board deck, you’ll spend three days debugging something that looked perfect in the demo.

This isn’t a knock on AI. It’s doing exactly what it’s designed to do. The problem is expecting it to replace the strategic work of defining how your business measures itself.

The Right Order of Operations

The best use of AI isn’t replacing Power BI. It’s speeding up what Power BI already does well.

Build your semantic model first. Answer the hard questions about what your metrics mean and how your data connects. Get your definitions right.

Then let AI help you build dashboards on top of that foundation.

Suddenly vibe coding isn’t a shortcut that breaks. It’s a power tool that makes you faster at the parts that don’t require judgment.

You can spin up new views in minutes. You can test different visualizations without writing DAX by hand. You can prototype ideas that would’ve taken your team days.

But only because you did the boring work first.

Small Moves, Real Foundations

We’re not anti-AI. We use it, like everybody else. But we use it after we’ve built the foundation that makes it useful.

If you’re tired of dashboards that look great in demos but fall apart under real use, start with the semantic model. Define your metrics. Map your relationships. Answer the questions that vibe coding can’t.

Then let AI make you faster at everything that comes after.

It’s not the flashy work. But it’s the work that lets you move fast without breaking things.

We help teams build these foundations in weeks, not months. Semantic models that make sense. Definitions everyone agrees on. Dashboards people trust.

When you’re ready to start, we’re ready to help. We’ll help you get the foundation right so AI can do something useful instead of just something convincing.