AI Product: Limited Delegation and Judgment Points

How far should we let an AI decide in a product cycle? A framework of "limited delegation" proposes to expand the machine's role where exploration is reversible, to make the analysis inspectable, and to tighten human control at the point of commitment. Judgment Points make these limits operational and clearly assign responsibility.

Naming the Commitment Holder and Making Analysis Traceable

Teams are encouraged to organize deliberate transfers: specify what the AI can do, document an inspectable analysis, tighten authority at the right moment, and designate who holds the commitment. At the Judgment Point, the team must be able to justify the evidence mobilized, the residual uncertainty, the active assumptions, the alternatives considered, and the person or group responsible. The phrase "human in the loop" is insufficient because it does not specify which human, when, with what authority, or based on what evidence. Responsibilities differ between a designer reviewing generated options and an executive validating a portfolio investment. They also differ between a researcher verifying an AI synthesis and a product manager deciding to extend a quarterly investment.

Questions Structure the Distribution of Responsibilities

Four questions structure the delegation. What happens if the team makes a mistake: the more costly and irreversible the error, the less delegation is appropriate. For example, adjusting the wording of a survey is reversible, while choosing a segment to receive a year of investment is not. Who remains responsible: the person who must defend the outcome needs sufficient access to evidence and reasoning; a simple approval offers little protection if it is not substantiated. Is the work inspectable: the origin, assumptions, and uncertainty must be accessible, including counter-evidence and sample limitations. Finally, is it about generation, analysis, or commitment: this quick test determines the extent of tolerable delegation.

Distributing Authority Based on Generation, Analysis, and Commitment

Generation supports the broadest latitude: the AI can write interview questions, propose flows, summarize observations, and suggest prototype paths, which remain starting points. In analysis, the machine can compare evidence, spot patterns, identify trade-offs, and test reasoning as long as everything is inspectable. Commitment concentrates the stakes: deciding what to build, allocating a budget, modifying a customer experience, making a promise, or accepting a risk. The AI can illuminate these choices without becoming the silent owner. In practice, machine authority should decrease as impact and responsibility increase; asking for ten onboarding variations is not comparable to deciding which customers lose a feature.

Avoiding AI-Imposed Frameworks That Lock in Direction

Risk can arise well before the final action, when the AI sets the framework. The same mass of feedback can point to low perceived value, confusing packaging, inadequate onboarding, or product-segment mismatch. Once the label is chosen, other explanations become less visible, and incentives make the trajectory difficult to reverse. A final human approval does not guarantee a decision truly driven by humans if categorization and narrowing of alternatives occurred upstream. Before acting, it is essential to explore plausible interpretations, identify the evidence that would test the chosen framework, and examine what the synthesis has flattened or excluded.

Human Decisions Mark the Automated Process

The Discovery Judgment Framework does not require manually completing every discovery task. It provides Judgment Points at each stage where a clear decision must be made before proceeding. Before this milestone, the AI can collect evidence, structure information, identify patterns, propose alternatives, formulate hypotheses, perform trade-off comparisons, and simulate scenarios. This milestone corresponds to the transition from AI-assisted exploration to human involvement.

Limited Authority Rather Than Autonomy, and Concrete Next Steps

The co-pilot versus agent opposition is deemed insufficient; the challenge is to define which decisions the machine is allowed to make during the work. Microsoft Research reports projects where developers consider 22 systems respecting limits of authority, provenance, uncertainty, and access. A system can sift through hundreds of interview notes without deciding on investment, propose interface directions without choosing those sent to customers, or draft an experience without deciding to allocate engineering time. The concept of limited authority constitutes the relevant unit. It is not about reducing the use of AI, but about clarifying the prerogatives: multiplying the options considered, examining the evidence, testing hypotheses, and extracting patterns, all while avoiding that expanded capabilities subtly translate into an extension of decision-making power. For the next workflow, it is important to identify the first moment of commitment, determine in advance the expected evidence at this stage, and the person responsible for the decision, so that the organization can explain, justify, and take responsibility for its choices.