AI Quality
Quality for AI extends beyond model accuracy. It includes context, permissions, evaluation, observability, escalation, human accountability and the impact of a wrong action inside a business process.
AI & software quality
Quality is the bridge between innovation and trust.
From global production programmes and platform validation to enterprise AI, agents and engineering excellence.
A quality leadership foundation
Software quality is not a new label added to Andreas' AI work. SAP Labs' executive support letter documents responsibility for production programmes covering mobile and database technologies, quality systems, KPIs, customer validation, regression and new-functionality testing, partner platform validation, product standards and release-readiness recommendations.
The same logic now applies to enterprise AI: innovation cannot be separated from the evidence that it is safe, useful, supportable and accountable in the process where it operates.
Test Automation
Automation creates speed when the organisation knows what must be proven. The objective is not maximum test volume; it is reliable evidence across the highest-consequence paths.
Enterprise AI
Assistants and agents need business context, governed data, clear tools and bounded authority. AI should improve a decision or workflow, not simply add a conversational surface.
Engineering Excellence
Engineering excellence connects architecture, developer productivity, release readiness, security, supportability and customer validation into one operating system.
Digital Transformation
A transformation succeeds when the business changes how it decides and operates — not when a new technical platform reaches go-live.
Featured thought leadership
What is the Autonomous Enterprise?
Enterprise AI needs a new operating model, not another layer of automation.
Read the analysis →Data & Business AI
Where AI creates value — and what it assumes about context, control and ownership.
Read the analysis →SAP Clean Core Strategy
The economics and governance of keeping differentiation without freezing the core.
Read the analysis →The Five Risks of an S/4HANA Transformation
The risks that sit between a technical programme and a business outcome.
Read the analysis →BTP & Extensibility
Where custom logic should live when the core stays clean.
Read the analysis →Transformation Governance
From executive decision to sustained value after go-live.
Read the analysis →Software quality credentials
The public SAP Labs record describes programmes involving teams from 10 to 1,000 people, budgets up to US$7.5 million, customer and partner validation, ISO 9001 and SAP-specific quality criteria, and due-diligence evidence influencing investment decisions ranging from US$10 million to US$1 billion.