Many companies automate operational workflows such as scheduling, service execution, and resource allocation. As long as conditions are predictable and stable, operations run automatically.

But as soon as exceptions occur – capacity constraints, service-level deviations, unexpected incidents, or cost-relevant trade-offs – automation stops and manual coordination is required.

AI can analyze situations and suggest actions, but it cannot decide or execute.

With a BOB, companies define binding rules for when operational actions are allowed.

For example:

• Allocate resources within predefined capacity limits
• Only if service-level agreements are met
• Only if cost and risk thresholds are respected
• Only if no operational or safety constraints are violated
• Only if escalation conditions are not triggered

AI evaluates real-time operational data, capacity, and constraints. The BOB decides whether the operational action is allowed.

If the rules are fulfilled, execution proceeds automatically. If not, it is blocked.

Automation no longer stops at operational exceptions. Operational decisions are executed autonomously within defined limits.

• Higher automation across day-to-day operations
• Faster response to incidents and changes
• Less manual coordination and firefighting
• More reliable and predictable execution

This is the difference between automating operational workflows and automating execution decisions.