The three root causes of failure
- The agent is not grounded in actual business data. A language model given a system prompt describing company processes is not an enterprise AI agent — it is a text generator. Without a direct, reliable connection to live business data (invoices, purchase orders, financial positions), the agent hallucinates plausible-sounding but factually wrong answers for the company’s actual context.
- The scope is too broad. “Automate finance” is not an agent use case. “Handle three-way-match AP exceptions for invoices between $5,000 and $50,000” is. Bounded, rule-governed, high-frequency processes with clear exception taxonomies are where agents earn their keep. Gartner calls the opposite pattern “agent washing.”
- There is no human-in-the-loop governance. Agents that post financial transactions without human approval are not production-ready in enterprise finance. The agent must classify and route — the human approves. Without this governance layer, either the CFO blocks the deployment or audit problems follow.
What SAP-grounded agents do differently
- Grounded in live SAP data: calls SAP’s published APIs — not training data. When it reads an AP invoice it reads the actual document via the SAP Document API. The data is real, current, and auditable.
- Bounded scope by design: SAP processes are documented, transactional, and exception-classified. The agent works within SAP’s process boundaries — automating exception-handling, not inventing new workflows.
- Built-in governance: every agent action is logged in BTP AI Core runtime. Approval workflows are configured in SAP Build Process Automation before any posting is triggered.
The difference between a working SAP AI agent and a failed AI programme is not the quality of the AI model. It is the specificity of the process, the reliability of data grounding, and the presence of human approval governance.