The Execution Gap: Why AI Agents Stall in Production.
SAP provides the foundation for critical business procEnterprise AI agents can perform impressively in controlled demos, but moving from a successful pilot to reliable production execution is a different challenge.
The model is only one part of the equation. Real business environments bring custom logic, complex workflows, existing system constraints and decisions that require clear accountability.
In this episode of Neptune Talks, we examine why AI agents can struggle when they encounter the realities of enterprise systems, particularly in SAP environments. Years of business decisions are often reflected in custom code, approval paths and process exceptions. Without access to the relevant context and the ability to act within established controls, an agent may be able to recommend the next step without actually completing the work.
We also explore the distinction between generative AI and agentic AI, and why greater autonomy is something organizations need to engineer rather than simply switch on. Connecting AI to business applications, workflows and underlying logic is essential, but so are permissions, escalation paths and accountability. The objective is not to automate every decision. It is to give AI the right level of responsibility within the processes where work happens.
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In this episode
- Why successful AI demos do not necessarily translate into production success
- How custom SAP logic and business process exceptions affect AI execution
- What distinguishes AI assistance from agents that can take action
- Why enterprise autonomy develops through different levels of responsibility
- How governance and escalation help establish safe operating boundaries
- Four questions to ask before funding your next AI pilot
The path to the Autonomous Enterprise starts with closing the execution gap. Listen to the full episode of Neptune Talks to explore what that means for enterprise AI and SAP.
