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Academy module

From Smart Insights to Just Ask — Generative Analytics in SAC

From Smart Insights to Just Ask — Generative Analytics in SAC — Analytics Legends section illustration for the SAP Analytics knowledge base (concepts, studies, Academy)

As of 2026-09-25

Edited by , sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

Draws the line, using SAP's own help documentation, between SAC's statistical/ML augmented-analytics features (Smart Insights top-contributor analysis with its 5-dimension/10-member ranking, 200K-node hierarchy fallback and 1M-row ceiling; Smart Discovery's Target/Entity/Measure exploration with its 10-influencer ranking, multi-hierarchy-account exclusion and 1M-cell ceiling) and its genuinely generative-AI features (Just Ask's natural-language query engine, with its four-language limit and Datasphere-access prerequisites; Joule's analytical insights, which reach into SAC's Just Ask engine rather than bringing Joule into SAC, under the Joule Base entitlement; and a set of AI-assisted features — calculations, chart summary, commenting, data actions, Prompt Insights, story code generation). Positions the bridge to M326 (Joule agents) and to a client conversation about SAC's real generative-AI footprint.

What you will learn

  • Classify any SAC 'smart' or 'AI' feature correctly as classical statistics/machine learning or generative AI, and explain why the distinction matters commercially
  • Explain Smart Insights' top-contributor algorithm and its documented limits (5 dimensions, 10 members, 200K-node hierarchy fallback, 1M-row ceiling)
  • Configure a Smart Discovery use case correctly given its Target/Entity/Measure model and its restrictions (multi-hierarchy accounts, 1M-cell ceiling)
  • State Just Ask's supported languages and the two administrative prerequisites for a Datasphere model, and diagnose a rollout failure against them
  • Explain the direction of the Joule/SAC integration and name at least four AI-assisted generative features beyond Just Ask

Module overview

Who this is for. You have delivered SAC stories with Smart Insights and Smart Discovery for years, and a client has just asked whether SAC "has generative AI like Joule now." The honest answer is: some of it, layered on top of features that were never generative AI to begin with, and confusing the two costs you credibility in the room. This module draws the line precisely, using SAP's own help documentation, and covers M335/M336's Smart Predict as the statistical baseline you already know, moving forward into what is genuinely new.

Prerequisites

  • Working experience delivering SAC stories, including Smart Insights or Smart Discovery
  • Module M333 (AI & LLM Fundamentals) or equivalent working knowledge
  • Modules M335 and M336 recommended, so Smart Predict is understood as the statistical baseline this module builds away from

Outcomes

  • Explain to a client, feature by feature, which parts of SAC's 'smart' surface are statistics/ML and which are genuinely generative AI.
  • Configure Smart Insights exclusions and interpret its hierarchy and row-limit behaviour correctly.
  • Scope a Smart Discovery use case against its Target/Entity/Measure model and documented restrictions.
  • Diagnose a Just Ask rollout issue against its language support and Datasphere-access prerequisites, and explain the Joule/SAC integration direction correctly.

Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.

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