AI Ethics Handbook

Strategic Center Evaluation

Adjacent Publisher Context

No adjacent SAP publications are reported. Related SAP material (such as the SAP Global AI Ethics Policy, which the handbook applies) could not be link-verified for this assessment and is not included in the /25 coverage score.

Narrative Strengths & Gaps

Internal Pressures & Capabilities. This is one of the handbook’s two centres of gravity. Each use case passes through an AI Use Case Impact Assessment at Ideation, which screens for red lines and five high-risk criteria: personal data, automated decision-making, consequences for people, high-risk sectors and agentic AI. High-risk cases go to the AI Ethics Office and, if needed, the AI Ethics Steering Committee. Eight named roles, from the AI Use Case Owner to the AI/LLM Ops Engineer, carry responsibilities across the lifecycle, and each of the five stages has a principle-by-principle checklist that ties ethics to existing security and quality processes. The assessment instruments give each use case a clear picture of its position, but not the organisation’s AI strategy as a whole (Map: Not met). The five-stage lifecycle gives each use case a sequenced, gated route with a loop back for continuous improvement, but not a route for the organisation (Course: Partly met).

Global & Geopolitical Dynamics. The handbook applies one policy worldwide, grounded in UNESCO’s Recommendation and the UN Guiding Principles on Business and Human Rights, and notes that the EU AI Act reaches companies wherever they are headquartered. It asks teams to test multilingual performance and cultural nuance. Sovereignty, export controls and national AI competition fall outside its scope.

Societal & Regulatory Pressures. This is the handbook’s second centre of gravity, and regulation is built into the method. The red lines incorporate the EU AI Act’s prohibited practices, the high-risk criteria mirror the Act’s high-risk sectors, and the Productization checklist requires labelling or watermarking of AI-generated content. Teams settle at Ideation whether SAP acts as data controller or processor and follow the Global DPP Policy at every stage. Societal expectations go beyond compliance: environmental harm is a red line, the energy, water and hardware footprint of AI is considered, and teams are asked whether lower-energy approaches would do the job.

Industry & Competitive Pressures. The handbook frames SAP, its partners and its customers as “a chain of responsibility”. Partner AI apps built on SAP BTP can be certified for responsible AI, resold partner solutions face the same product standards, and third-party model suppliers are reviewed across risk, privacy, security, legal and licensing before onboarding. Customer data is not shared with third-party model providers for training. These are SAP’s own ecosystem processes; competitive positioning and market strategy fall outside its scope.

Human Needs & Aspirations. The handbook keeps people outside the organisation in view. Teams validate use cases with users and possibly affected individuals, consider accessibility and unequal impacts across populations, and let affected individuals correct their data or challenge decisions. End users are told when they are interacting with AI, and anyone inside or outside SAP can raise concerns anonymously through SpeakOut. Broader public sentiment and aspirations for AI lie outside its scope.

What this framework does not give you. A map of where the organisation stands across its AI strategy; its assessments work use case by use case. Without a clear picture of where it stands, an enterprise cannot tell which moves matter most. A route for the organisation as a whole; its five stages govern individual use cases. Without a route, even a well-chosen destination stays an aspiration. A way to choose your own destination; it supports AI with a net positive impact on users, customers and society. An enterprise that has not stated its own ambition cannot check whether this destination is the right one for it. A view of geopolitical forces such as sovereignty, cross-border data and national AI competition. Sovereignty, cross-border data rules and export controls can change which AI an enterprise can use, and where.

Synthesis. The handbook suits AI governance leads, product managers and engineering leaders in organisations that build or embed AI in their own products, particularly those selling into the EU. Its distinctive contributions are a clear set of red lines and high-risk criteria that include agentic AI, and stage-by-stage checklists that put ethics and regulation into the same lifecycle as security and quality. Readers should note that it is written for SAP teams and partners and refers to SAP’s internal tools, so other organisations will adapt rather than adopt it. Used alongside a strategy framework, it deepens the trust and governance discipline. The Five Forces lens adds a map and route for the strategy as a whole, geopolitical context, competitive positioning, a declared time horizon and how falling execution costs change the value of ideas.

Sources

Tier 1 (scored), recency-gate pass unless noted:

1. SAP AI Ethics Handbook (PDF) – updated May 2026; 45 PDF pages; no text could be extracted automatically, so the link was verified manually (the Sapience team supplied the downloaded PDF, 1 November 2026) and the text read from that copy

Tier 2 (reported in Section 2.75, not scored): none.

Tier 3 (third-party corroboration, not scored): none used. Publication dates are taken from the handbook itself.

Excluded on recency or quality (not scored): none.