# Most enterprise AI conversations start at the wrong layer.
Canonical: https://social-archive.org/tgroenwals/egOMYaXiuc
Original URL: https://www.linkedin.com/posts/gauravbhargavaindia_enterpriseai-sap-aiarchitecture-share-7494947228432613376-oUwu/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAUj9ABA6XwLJ3RcHE4FACjTijtAT4klvY
Author: Gaurav Bhargava
Platform: linkedin
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## Content
Most enterprise AI conversations start at the wrong layer. They start with the model. Which LLM should we use? Which copilot should we deploy? Which agent framework should we choose? But enterprise AI does not create value because a model can generate an answer. It creates value when intelligence can understand business context, interact with enterprise systems and take the right action within the right controls. Consider a simple business question: "Why is this supplier invoice blocked, and what should happen next?" A chatbot can explain information. An enterprise AI capability needs to understand the user, interpret the business context, access the right data, interact with systems and operate within defined permissions. That requires architecture. The seven layers behind enterprise AI are: 1. User Experience Layer This is where humans interact with AI through experiences such as Joule, Copilot and conversational interfaces. A good experience makes AI accessible, but the interface alone does not create enterprise value. 2. Reasoning Layer This is where models, planning and agent frameworks provide intelligence. The goal is not just generating responses. It is enabling AI to reason through business problems and determine possible actions. 3. Context Layer This is what separates enterprise AI from general AI. Knowledge graphs, semantic models and business rules provide the context required for AI to understand how an organisation actually operates. 4. Interaction Layer AI creates value when it can connect with enterprise capabilities. APIs, events and protocols such as MCP allow AI to move from answering questions toward interacting with business processes. 5. Enterprise Systems Layer This is where business execution happens. ERP, HR, procurement, customer and operational platforms contain the processes and transactions that AI needs to understand and improve. 6. Data Layer AI quality depends on the foundation beneath it. Trusted, connected and governed data from platforms such as Datasphere, data lakes and master data solutions determines whether AI produces reliable outcomes. 7. Control Layer Once AI can act, governance becomes part of the architecture. Identity, roles, segregation of duties, auditability and monitoring define what AI is allowed to do and who remains accountable. This is the difference between a chatbot and an enterprise AI capability. A chatbot answers questions. Enterprise AI understands context, connects to systems, follows controls and improves decisions. The executive question is not "which AI model should we buy?" It is "have we designed the architecture that allows AI to understand, decide and act responsibly?" Save this if your organisation is moving from AI experimentation toward enterprise adoption. Repost if your AI strategy focuses on models but has not yet designed the architecture around them. #EnterpriseAI #SAP #AIArchitecture
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