Analytics Legends The knowledge platform for SAP Analytics
Academy module

SAP AI by Industry — Verified Cases in Manufacturing, Retail and Financial Services

SAP AI by Industry — Verified Cases in Manufacturing, Retail and Financial Services — 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.

An advanced module teaching SAP analytics consultants to read SAP AI case studies like an auditor and build a defensible, three-sector evidence library. Covers five verified manufacturing cases (Bosch Digital, AMD, Smart Press Shop, Schaeffler, Sonalika), four verified retail cases (Schwarz IT, Salling Group, The ALDO Group, LC Waikiki) plus the honest gap on Retail Intelligence's unnamed customers, and the financial-services pillar (Zalando Payments, Accenture, Darussalam Assets, Galicia) with an explicit statement that public, named, primary-sourced core-banking evidence is thinner than in the other two sectors. Teaches a four-question audit method and the ethical line between citing evidence and implying personal delivery experience.

What you will learn

  • Cite at least two named, dated, primary-sourced SAP AI cases per sector (manufacturing, retail, financial services)
  • Distinguish a generative-AI case from a classic-machine-learning or process-automation case within a single vendor case list
  • Apply a four-question audit (named customer, date, AI layer, source domain) to any SAP AI case study before citing it
  • State honestly where public evidence is thinner (financial services core-banking use cases) rather than filling the gap with an unnamed story
  • Use verified cases to establish category feasibility without implying personal delivery experience you do not have

Module overview

Who this is for. Consultants who have built or planned the three portfolio demos (M382) and worked on positioning (M383), and now need industry-specific evidence for a proposal, an interview or a use-case discovery workshop (M377). This module does not teach a new SAP feature. It teaches a discipline: reading a vendor case study the way an auditor reads a financial statement, and building a small, honest library of named, dated SAP AI results in three sectors — manufacturing, retail and financial services — that you can defend if challenged.

Prerequisites

  • Completion of M382 (Your SAP AI Portfolio) and M383 (Positioning Yourself as a Freelance SAP AI Consultant)
  • Comfort reading and cross-checking primary SAP sources (sap.com, news.sap.com, community.sap.com)
  • Basic familiarity with the distinction between generative AI, classic machine learning and process automation (M333)

Outcomes

  • Present a three-sector case file (manufacturing, retail, financial services) with named, dated, sourced results.
  • Audit an unfamiliar SAP AI case study using the four-question method and state its evidence strength.
  • Explain to a financial-services client, honestly, the current gap between HR/finance-process evidence and core-banking evidence.
  • Cite named cases to support a proposal without implying personal delivery experience that is not true.

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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