What are AI Apps?

1. What are AI Apps?

Welcome to the Sigma course on AI apps! In this course we’ll learn about AI apps, which are purpose-built solutions that connect directly to your data platform to seamlessly integrate live data, workflows, and user inputs.

2. Welcome!

I’m your instructor, Mandy Gray with Aimpoint Digital. Aimpoint Digital is a leading analytics advisory and solution engineering firm,

3. Welcome!

trusted by Sigma as a key strategic training & implementation partner. We help organizations turn data into scalable, user-centric, and insight-driven solutions that deliver measurable business impact.

4. Welcome!

Together with my colleagues and co-developers Klim Kapuka and Jenny Li, we look forward to guiding you through your Sigma journey.

5. Oakmark Bank needs your help once again

To illustrate how AI apps work, we’ll step into the role of a loan officer at Oakmark Bank. We’ll build interactive apps to speed up our workflows for approving or rejecting loans and for nominating loan officers for promotion. To start, let’s talk about what Sigma apps are, and some real-world examples where they are useful.

6. AI Apps in Sigma

Apps are not a single type of element, feature, or document in Sigma. Apps are a combination of elements that create an interactive and customizable tool for executing the work of your business.

7. When does a workbook become an app?

Apps differ from regular Sigma workbooks. Workbooks support reporting, exploration and visualization. User interactions in workbooks are often superficial, limited to organizing and filtering data. Apps, on the other hand, are designed to integrate user input and to trigger action-based workflows. For example, you might receive requests for forecasting, or projecting ‘what if’ scenarios.

8. AI Apps require user input

Apps are a great fit for situations where analysis is highly dependent on the user and their inputs. Users could enter thresholds and estimates in an app, and the app could generate expected outcomes based on their input.

9. AI Apps in this course

While Sigma offers AI tools to build apps, and to integrate AI into the app, we’ll focus on manually constructing an app, to help you understand features you might use, or considerations you might make when building and maintaining an app in Sigma.

10. Demo script

Apps are made up of many elements and can contain tables, pivot tables, input tables, charts and interactive control elements. They may also contain navigational aids like buttons, layout elements like tabbed containers, or action-based workflows. They’re often formatted to align with an organization ‘s branding, through color schemes, fonts and templates. In this course, we’ll build two apps from start to finish. During the on-screen demonstrations, I’ll build an app that helps Oakmark Bank’s loan officers approve or reject customer loan applications. In the hands-on exercises, you’ll also build your own app for reviewing loan officer performance and nominating them for promotion. Before diving into the details, let’s spend a moment exploring the finished versions of the apps we’ll build in this course. In these lessons, you’ll use a sandbox version of Sigma unique to this datacamp course. It's possible that the appearance of the course version of Sigma differs slightly from your organization’s version of Sigma.

11. Let's practice!

Don’t worry - the overall experience will be very similar.

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Introduction to AI Apps in Sigma

BeginnerSkill Level

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Chapter 1 introduces the foundational concepts behind building AI Apps in Sigma, with a focus on how apps integrate user input to drive action-based workflows. A major emphasis is placed on linked input tables, one of the most important building blocks for Sigma apps. You explore how to create linked input tables that display warehouse data alongside user-entered values, enabling direct interaction with underlying datasets. The chapter also covers best practices for governing user input, including controlling which data types can be entered and defining when tables are editable. By the end of this chapter, you’ll have a strong understanding of how to safely and effectively collect user input while maintaining data integrity within your app.

Exercise 1: What are AI Apps?

Current Exercise

Exercise 2: Exploring a finished app for loan approvals Exercise 3: A finished app for loan officer promotions Exercise 4: Enriching existing data with linked input tables Exercise 5: Understanding linked input tables Exercise 6: Creating a linked input table Exercise 7: Capture promotion nominations Exercise 8: Testing linked input table behaviors Exercise 9: Governing linked input table data entry Exercise 10: Managing edits to the loan officers input table

Chapter 3 focuses on refining the user interface and overall experience of your AI apps. You begin by learning about popovers, a versatile UI element used to hide filter menus or display contextual information only when it’s needed. This helps keep apps clean while still providing users with easy access to controls and details. The chapter also explores a range of design and organizational features that enhance both usability and visual appeal. You learn how to incorporate navigational buttons, dividers and tabbed containers to structure content and guide users through your app more effectively. By the end of Chapter 3, you’ll understand how thoughtful layout and design choices can make your AI apps more intuitive, polished, and enjoyable to use.

Exercise 1: Using popovers to simplify an app's interface Exercise 2: Configure a popover to contain page controls Exercise 3: Leveraging buttons for navigation & interaction Exercise 4: Adding a 'Clear filters' button in a popover Exercise 5: Adding UI & Layout elements for organization Exercise 6: Tidying up the loan officer promotions app Exercise 7: Bringing it all together Exercise 8: Wrap-up