Depersonalize the Order

In the AI Story

The distinction between depersonalizing and repersonalizing is critical for AI governance. Algorithmic systems that claim to 'depersonalize' decisions — to remove human bias, human politics, human ego from the process — may actually strip away the human context that gives decisions their meaning and value. The manager who defers to AI output as if it were situational necessity is not repersonalizing authority; she is depersonalizing it in the sense Follett rejected, substituting the machine's framing for the collective reading of persons engaged with the work.

Repersonalization requires that everyone involved study the situation together rather than deferring to hierarchy or algorithm. The AI becomes a shared resource for reading the situation, not a substitute for the reading. The junior developer contributes what she knows about the codebase. The customer service representative contributes what she knows about user complaints. The senior architect contributes decades of accumulated judgment. The AI amplifies each contribution by extending its reach into patterns and possibilities the individual could not have seen alone. Authority derives from the collective reading enriched by AI, not from the AI's synthesis of what should be done.

The test of repersonalization is whether persons remain active in the decision or whether they have been reduced to ratifying outputs the system has produced. An organization that rubber-stamps AI recommendations has not distributed authority; it has concentrated authority in the AI's training data and framing assumptions while preserving the appearance of human decision. An organization that uses AI to enrich the collective reading has distributed authority while keeping persons genuinely engaged with the situation they are jointly addressing.

The principle carries immediate operational weight. When a team member says 'The AI recommends X,' the next question should be: what does the situation actually require, and what does each person who knows part of the situation contribute to our collective reading? The AI's recommendation is an input, sometimes a powerful one. It is not a substitute for the integrative process through which persons working together on genuine problems produce outcomes that transform both the problem and the people.

Origin

The concept emerged from Follett's 'Giving of Orders' paper and was refined in subsequent lectures when audiences misunderstood her as recommending the elimination of personal judgment. She spent the late 1920s and early 1930s clarifying that the principle required more personal engagement, not less — engagement rooted in the work rather than in hierarchy.

Key Ideas

Depersonalize the order, repersonalize the decision. Take authority from position; restore it to persons engaged with the work.

Both directions of authority. Under the law of the situation, the employee can issue orders to the employer — the situation is the common reference.

AI can falsely depersonalize. Algorithmic output can substitute for collective reading, stripping context while appearing to remove bias.

Repersonalization enriches reading. AI becomes a resource for the integrative process rather than a substitute for it.

The test is who remains engaged. Persons ratifying outputs is concentration; persons contributing to readings is distribution.