The Advice Process

In the AI Story

Orange organizations resist the advice process because its rule appears to invite chaos. If advice need not be followed, what prevents bad decisions? The answer, documented across every organization that practices it, is that the process of articulating a decision clearly enough to seek advice on it, and then listening to perspectives that differ from one's own, produces a quality of understanding that solitary decision-making or hierarchical approval cannot match. The decision-maker arrives at a richer understanding of the decision through the process of seeking advice, and that richer understanding produces better decisions on average.

At Morning Star, the advice process governs decisions from equipment purchases to hiring to significant capital investments. An employee considering a $500,000 machinery purchase does not need managerial approval. She needs to consult the people who will operate the machinery, the people whose work will be affected, and the people with financial expertise who can evaluate whether the investment makes sense. She then makes the decision herself, having absorbed the advice but not being bound by it. The decisions are faster than hierarchical approval — they don't queue at manager's desks — and they are often better, because they incorporate more context and perspective than any single manager could hold.

AI transforms the advice process by introducing a new category of advisor. Claude or an equivalent system can provide technical analysis, comparative research, financial projections, and regulatory information instantly — advice that would previously have required finding and interviewing human experts. This is a genuine gain: the decision-maker arrives at the human advice conversations with a stronger technical foundation, allowing the human conversations to focus on what machines cannot provide. But the gain carries a risk: the ease of machine consultation can displace the discipline of human consultation, hollowing out the process while preserving its form.

The advice process practiced well in the AI age combines machine expertise with human reframing. The decision-maker consults AI for the technical dimensions and then engages human colleagues for the dimensions that require stakes, perspective, and the willingness to challenge the question rather than merely answer it. Practiced badly, the AI-augmented advice process generates a comprehensive analysis, presents it to colleagues as a fait accompli, and treats the human consultation as a checkbox. The form is preserved; the substance is hollowed.

Origin

The advice process was formalized by organizations operating in self-management long before Laloux documented it. Morning Star's Chris Rufer and Holacracy's Brian Robertson have both articulated versions, and the underlying principle — seek input from those with stake and expertise, then decide — appears across many cooperative and self-managed traditions.

Laloux's contribution was to name the practice and situate it developmentally within the Teal framework, allowing practitioners in other organizations to recognize what they were doing (or failing to do) and to refine it. The phrase "advice process" has since entered the vocabulary of self-management practitioners across industries.

Key Ideas

Anyone decides. The mechanism distributes decision authority to the people with the most context and stake.

Two advice categories. Those with expertise and those affected must be consulted.

Advice is required, not followed. The decision-maker remains responsible; the consultation is mandatory; the following is not.

Process transforms understanding. The act of articulating and seeking advice improves the decision beyond what advice alone would provide.

AI augments but cannot replace. Machine consultation enriches the technical foundation; human consultation provides reframing and stakes.

Debates & Critiques

Critics argue that the advice process requires a level of personal responsibility and social maturity that cannot be assumed across all employees, producing a selection effect that limits the pool of people who can work effectively in such structures. Proponents respond that the selection effect is real but that the resulting organizations consistently outperform hierarchical alternatives — which suggests the trade-off favors the advice process even if not all workers thrive in it.