Bayer CropScience deploys both LSS and AI to improve operations, yet their isolated use prevents synergies. Administrative processes, which underpin manufacturing and supply chain activities, often lack integration, governance and realtime decision support. This limits productivity and scalability.

A qualitative research design was applied, including expert interviews, internal documentation, and literature analysis. Integration opportunities, enablers and barriers were identified and structured into a practice-oriented framework. The work also includes an implementation roadmap and guidance for scaling and change management.

The study proposes a three-layer framework that embeds AI into LSS practices: a Data Integration Layer consolidating process and interaction data, an AI Core Layer accelerating DMAIC with waste-scanning and analytics, and a Feedback Layer delivering contextual, role-aware recommendations. Use cases include automating repetitive tasks such as invoice approvals or enhancing root cause analysis. AI acts as an enabler of DMAIC, while human oversight ensures trust and adaptability. The framework provides the client with a scalable, governance-ready blueprint for aligning structured methodology with AI-driven flexibility. It increases efficiency, reduces manual workload and enables continuous improvement across administrative workflows, laying the foundation for sustainable operational excellence.