Why this question is critical right now
In the first two editions, we showed how the focus in retail is shifting. From visibility to selection. From user interfaces to system logic. From human research to AI-supported decision preparation.
This is where a question arises that many organizations are currently avoiding:
When should a prepared recommendation become an action?
Not technically. But organizationally.
The wrong debate: Autonomy as an either-or
Many discussions surrounding AI revolve around a false dichotomy. Either the human decides. Or the AI decides autonomously.
This logic is misleading.
In practice, AI systems do not emerge as autonomous actors, but as delegated action units with clearly defined boundaries. The real transition is not human versus machine, but implicit versus explicit decision rules.
The crucial question is therefore not: Should AI be allowed to act?
But rather: Which decisions are governed by rules clear enough to be delegated?
Why B2B has a structural advantage
B2B organizations have been working with rules for decades. Budgets, approval levels, contract terms, escalation paths.
What is often perceived as complexity is actually an advantage. Because these rules are the prerequisite for delegating decisions step by step.
B2C often has to define its decision logic first. B2B just needs to make it visible, consistent, and machine-readable.
Three real-world levels of delegation – with examples
Three stable patterns emerge from practical experience. They clearly show where delegation works and where it fails.
1. Preparation without impact
Example: Mechanical engineering
An international mechanical engineering firm uses AI to analyze requests for quotes, identify components, and suggest technical variations. The AI provides structured recommendations, including price and delivery time indicators.
In practice, however, these suggestions are rarely used. Why? Because pricing decisions have historically rested with individual senior engineers, and their decision-making logic was never explicitly documented.
The AI is technically correct, but organizationally ineffective.
The bottleneck is not the technology, but the lack of explicit rules.
2. Preparation with controlled execution
Example: Wholesale / Distribution
A technical wholesaler uses AI for reordering and inventory optimization. The system is authorized to trigger orders independently, provided that:
● defined product groups are involved
● quantities remain below clear thresholds
● suppliers are contractually approved
Anything beyond that is prepared but not executed.
Delegation works here because:
● rules are clearly defined
● responsibilities remain transparent
● deviations are consciously escalated
Many email-to-order and replenishment flows operate at exactly this level. This is currently the most mature and stable state in B2B.
3. Delegated action with auditability
Example: Pharmaceutical procurement
In regulated environments like the pharmaceutical industry, AI systems are used to prepare supplier selection and order approvals. In clearly defined scenarios, the system is permitted to trigger orders independently, provided that:
● suppliers are qualified
● regulatory requirements are met
● every decision is fully documented
The key here is not autonomy, but auditability. It is not the question of "Who made the decision?", but "Who defined that a decision could be made?".
Such setups are rare, but they demonstrate that delegation is possible when governance is considered before automation.
Why governance is not a bottleneck
A common mistake in AI initiatives is the assumption that governance begins after the pilot phase.
In practice, it is the other way around.
Without governance, you don't get delegation; you get uncertainty. Without clear responsibilities, you don't get speed; you get a standstill.
Governance answers three questions that every delegatable decision requires:
● Who defines the rules?
● Who is responsible for deviations?
● How do we learn when something goes wrong?
Only then can a system act without destroying trust.
The role of open standards
Standards like UCP do not solve this problem. They assume that organizations already know:
● which decisions can be delegated
● under what conditions action may be taken
● where clear boundaries are drawn
Without this clarity, any standard remains abstract. With this clarity, standards become true accelerators.
The uncomfortable truth
Many companies do not fail because of AI. They fail because they have never explicitly formulated their own decision-making logic.
AI forces organizations to explain themselves. That is uncomfortable, but necessary.
Three points to consider for decision-makers
- Which decisions are made regularly today without being debated anew each time?
- Which of these could be delegated if they were clearly defined?
- Who is currently responsible for the rules, rather than the execution?
Anyone who can answer these questions is closer to agentic action than many tool demos would suggest.
Final thoughts
AI does not change business by replacing people. It changes it by making decisions visible, comparable, and delegable.
The crucial question is not whether AI will act. But rather:
Who is prepared to take responsibility for delegated decisions?