How SAP Teams Can Decide Which AI Agent Actions Still Need Approval

AI agents in SAP environments are starting to carry out work as well as recommend it, and every step they take without a person’s sign-off is a decision the organization made on purpose. Most SAP customers are making that decision slowly. The useful question is which steps can run without approval today, and what evidence would justify extending that to more of them.

SAP’s Joule Assistants Work Within Existing Approval Rules

SAP says its Joule Assistants operate within the authorizations and audit trails a customer already has, and that each customer decides how much autonomy each assistant gets. In SAP systems, authorization roles define who can approve what, and workflows set thresholds and escalation paths.

Customers place a high value on this kind of control. In SAPinsider’s June 2026 research, a survey conducted in April and May, role-based access control aligned with SAP authorizations was the most important governance requirement for AI, at 49%.

Review Needs Scale With the Consequences of a Mistake

The more harm a mistake can cause, the more human review an action should keep. When an AI assistant drafts a message, a person reads it before anything is sent, so a mistake in the draft costs little. An agent allowed to release a payment or change a production order acts without that read-through, and a wrong result becomes a payment already made or a production plan already changed.

Customers raise a related concern: in SAPinsider’s June 2026 research, the accuracy and reliability of AI outputs in critical processes was the most common worry, cited by 63% of respondents. For its part, SAP is making human-in-the-loop governance central to agents that operate in critical ERP processes such as payroll, financial close, procurement, and supply chain.

Autonomy Expands as the Track Record Builds

Autonomy grows in steps, and each step rests on how the agent has performed so far.

SAP describes a staged path in which customers run agents with a human in the loop, verify outputs and inspect actions, then grant more autonomy as confidence increases. Supervised runs show where an agent is dependable and where it still needs a person, and that performance record gives leaders a factual basis for moving a step from approval-based to independent.

Few organizations yet have the monitoring and audit trails that building that record depends on. Only 21% of the 3,235 IT and business leaders in 24 countries that Deloitte surveyed report a mature governance model for agentic AI. Deloitte gives real-time monitoring of agent behavior, audit trails that capture the full chain of agent actions, and clear boundaries on which decisions agents can make independently as examples of what that includes.

What This Means for SAPinsiders

Write limits into roles and approval workflows. Check that thresholds and escalation paths sit in the authorizations and workflows an agent runs under before it operates unattended. That keeps agent actions inside limits the business has already agreed to.

Rank processes by what a wrong result would cost. List the processes where agents could act and note what a mistake would mean in each, from a reversible inconvenience to a regulatory, financial, or physical consequence. Teams that do this can start autonomy where mistakes are cheapest and keep review where they are not.

Keep an evidence record for each step. Record where agents perform reliably and where they still need a person, step by step. Leaders gain a factual basis for widening autonomy one step at a time.