The AI Apps Playbook

Every company has the same problem. They have BI that provides insights, but it can't do the the work.

That's because BI wasn’t designed to run workflows like approvals, assignments, and updates. So teams pull everything out of the tool and into spreadsheets to take action.

But spreadsheets weren’t designed to run at scale.‍The result is that teams bounce between dashboards for insight and spreadsheets for action. And the “last mile” becomes manual, fragile, and impossible to govern.

The moment you need to do real work like update values, approve changes, assign owners, trigger follow-up, the need for traditional BI stops. And while orphaned spreadsheets "workflows" pick up the slack, they also introduce weird versioning (you know: v2_final_final), access, and audit issues.

AI Apps solve that gap by combining live warehouse data with input, workflows, and automation within a single, governed experience. This means teams can move from decision to execution without leaving the system.‍‍

What’s inside:

• The structural reason the insight-to-action gap exists
• Common AI App use cases: demand planning, pipeline forecasting, finance automation
• A practical build guide for AI Apps in Sigma Computing (no custom front-end required)

📥 Download the guide.

‍