Bad data doesn’t just cause a few awkward reports. It can cost real money. The question is simple… are you using AI to grow your business, or are you feeding it rubbish and hoping for the best?

Because that’s the bit people miss.

The blocker isn’t the tech. It’s what you’re giving it to work with. And fixing that starts with actually understanding where your data is breaking down.

None of this is new, by the way. Bad data has always been around. The difference now is the stakes. AI just amplifies whatever you feed it. Good or bad.

Think of it like this. AI is the building. Your data is the foundation.
If the foundation is weak, it doesn’t matter how impressive the building looks. It’s coming down.

This isn’t about adding AI as a shiny feature. It’s about whether your business can keep up.

The companies that get their data right will move faster, make better calls, and pull ahead. The ones that don’t… won’t. And this isn’t something you park for six months while you “explore a proof of concept.” By the time that’s done, you’re already behind.

Here’s a simple example.

Your sales report says you sold 1,000 products.
999 at £1. One at £100.

Looks fine, right?

Except it isn’t real. Someone missed a decimal point.

Now imagine building forecasts, pricing strategy, or AI models on top of that. You’re not just slightly wrong. You’re confidently wrong.

That’s where this session comes in.

We’re not just looking at it from a tech angle or a business angle. It’s both. Because that’s the only way this works in the real world. Between us, we’ve seen this play out in places like Bank of America, the NHS, McDonald's, and across UK policing. Different industries, same problem. Data quality quietly shaping big decisions.

What we’ll show you is how to actually see the problem.

Pull your data together. Finance spreadsheets, HR systems, platforms like Dynamics 365. Look at it as one picture, not a bunch of disconnected bits.

That’s where tools like Microsoft Power BI come in. Not as the end goal, but as a lens. A way to surface what’s really going on.

Because once you can see it, things get uncomfortable… but useful.

You start spotting outliers. Numbers that don’t make sense. Gaps. Inconsistencies. The kind of stuff that quietly breaks AI but never gets noticed.

And then the conversation changes.

It’s no longer “should we invest in AI?”
It becomes “are we actually ready for it?”

That’s a much better question.

This isn’t about leaving it with IT or a data team to figure out. It puts that visibility in front of the people making decisions. Clear enough to act on.

If your data is in a good place, great. You move forward with confidence.

If it isn’t, that’s fine too. At least now you know what to fix, and where to start.

Because AI doesn’t fail because the models are bad.

It fails because the data is.