The AI Analytics Dilemma

About this webinar

Your organization is already using AI for analytics. They're just doing it in ChatGPT, pasting in data extracts, and taking whatever comes back as truth.

Meanwhile, you're stuck. Leadership wants "AI-powered insights" yesterday. You know the status quo is untenable—data leakage, hallucinated metrics, zero auditability, decisions based on walls of text no one can verify. But the obvious solutions don't work either: locking down AI entirely makes you look like you're holding the business back, while giving analysts controlled access just makes them the human bottleneck.

Everyone's feeling the pressure—exploit AI or get left behind, use AI or lose your job. But no one has a framework for actually navigating this that doesn't force you to choose between governance and progress.

Here's the thing: the problem isn't AI itself. It's that chatbot-style AI treats analysis like a one-shot magic trick. Ask a question, get an answer, trust it or don't. There's no "show your work," no way to interrogate the process, no path for domain experts to validate and build on what it produces. It's a black box that undermines everything data teams have built around trust and auditability.

In this webinar, we'll look at how to roll out AI in your BI responsibly—and what "responsible" actually means when both the technical and cultural sides matter.

We'll cover:

1. Why all the obvious approaches fail — and the trade-offs you shouldn't have to make between speed, governance, and trust
2. The auditability problem — why one-shot AI answers are fundamentally incompatible with how good analysis actually works
3. AI as a collaborator, not an oracle — how Count's AI agent works alongside you on the canvas so you can examine its process, validate its work, and take it further (and what this teaches us about AI in analytics more broadly)

We'll demonstrate how this looks in practice with Count AI, but the principles apply wherever you're grappling with these questions.

Who should tune in?

• Data leaders and executives feeling pressure to "do something with AI" but worried about the risks. You'll leave with a framework for making decisions that don't sacrifice governance for innovation.
• Data teams trying to figure out where AI fits in their work without becoming gatekeepers or rubber-stampers. You'll leave understanding how AI can augment your work instead of replacing your judgment or creating more work validating nonsense.
• Anyone currently stuck between "ban it" and "let chaos reign." You'll leave knowing there's a better path that doesn't require choosing between progress and responsibility.