Why AI Will Break Your BI Before It Delivers Value

Here’s the uncomfortable truth: a lot of BI only works because people know when not to trust it.

Numbers get sanity-checked. Definitions get explained in Slack. When two reports disagree, someone shrugs and decides which one is “closer.”

That approach collapses the moment analytics gets reused by software. There’s no one there to explain intent or smooth over inconsistencies. Whatever logic exists is what runs.

AI doesn’t create that problem. It just stops hiding it.

How BI Logic Ended Up Spread Everywhere

BI didn’t break overnight.

Over time, logic was added wherever it was easiest to ship answers: calculated fields in dashboards, SQL queries written for a single report, spreadsheets used to reconcile differences when numbers didn’t line up.

As long as analysts were in the loop, this was manageable. People knew which numbers to trust more than others. Context lived in conversations and documentation, not in the system itself.

Today, many teams are careful about making changes to BI at all. They know the same metric can return different results depending on where it’s used. They know a small change can have unexpected side effects. And they’re still spending a lot of time and money just keeping things running.

That setup doesn’t work when analytics needs to be reused by software.

If You Can’t Explain the Number, AI Can’t Use It

This is the part that becomes obvious once automation enters the picture.

AI systems don’t interpret intent or history. They work with definitions. When those definitions aren’t consistent or live deep inside dashboards, automated use cases become unreliable very quickly.

That’s when teams start talking about hallucinations.

In most cases, the system is behaving as designed: executing logic that was never centralized, never reviewed as a whole, and never intended to be reused outside a single report.

Traditional BI assumed human judgment. Automated systems don’t have that safety net.

Why Many BI Migrations Disappoint

At some point, teams decide they need to move their BI to a platform that can support what comes next.

The problem is rarely the decision to migrate. It’s the way migration is approached.

Too often, the focus is on recreating dashboards first and dealing with the logic later. That usually means carrying existing problems into a new tool, then spending months trying to untangle them after the fact.

Progress slows. Teams run two systems longer than planned. Confidence drops. The move ends up feeling like a lot of effort without much improvement.

That’s not because migration is a bad idea. It’s because the hard part was deferred.

Fix the Logic as You Migrate the BI

Dashboards need to move. So do models, metrics, and the logic behind them.

The difference is whether that logic gets carried over as-is, or whether it gets cleaned up along the way.

A more practical approach is to treat migration as a chance to review and fix what already exists. Existing BI assets contain years of business logic, even if it’s inconsistent or duplicated. That logic can be pulled out of legacy tools, converted, and standardized rather than left embedded in dashboards.

In practice, that means:

• extracting logic from existing BI tools
• automatically converting and cleaning it
• establishing a governed semantic layer as the system of record
• rolling changes out in phases, without taking dashboards offline

In practice, AI-assisted tooling can now automate much of this work, often covering around 80% of the effort and making this kind of migration feasible without putting delivery on hold.

This is the approach behind GoodData’s AI-driven BI migration. Everything moves, but the foundation improves instead of staying the same.

What Changes Once Logic Is Centralized

When BI logic lives in one place, teams work differently.

Metrics behave the same way everywhere they’re used. Changes are easier to review. Fixes don’t require hunting through dozens of dashboards. Teams spend less time reconciling numbers and more time improving the model itself.

This also makes analytics usable outside of dashboards — in applications, APIs, agents, and automated workflows without introducing new risk each time something changes.

The Risk of Carrying Old Assumptions Forward

AI isn’t replacing BI. But it is changing how BI gets used.

Organizations that get value from AI won’t be the ones that avoided migration. They’ll be the ones that deliberately modernized their BI and made it reliable for software, not just humans.

You don’t need a perfect system. But you do need one you can explain and trust before you automate decisions on top of it.