Decision Ownership Before Delegation
Seed. This is a planning sketch, not a runnable, tested, or learner-available module.
Relationships
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Primary track: AI Readiness and Risk Assessment Support
Helpful preparation: Friction Mapping -- Understanding Work Before Recommending Solutions
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Stage
Seed. This is a planning sketch, not a runnable, tested, or learner-available module.
Audience
Small-business owners, nonprofit leaders, managers, advisors, facilitators, and future RS assessment practitioners deciding how much authority to give a person, system, or AI-enabled workflow.
Purpose
Clarify who owns decisions before tasks are delegated to people, systems, or AI. Replace the binary question “Should we automate this?” with a staged authority model that identifies what may be observed, assisted, recommended, prepared, or executed—and the evidence and controls required before authority expands.
Candidate Learning Outcomes
Learners may eventually be able to:
- Name the accountable owner for a specific decision loop rather than a whole job title.
- Distinguish model confidence from operational evidence.
- Identify which state, authorization, and constraints should remain outside a probabilistic model.
- Define approval, escalation, rollback, stop, promotion, and demotion conditions.
- Explain why broader autonomy is not the assumed destination.
Core Ideas
- Delegation attaches to a specific decision loop, not to a role in the abstract.
- People are apprenticed into responsibility; AI-supported systems should also earn authority through evidence, controls, and review.
- Observation and recommendation differ from execution.
- Probabilistic systems may interpret, predict, classify, or propose. Records, authorization, limits, and transactions need deterministic controls.
- Human approval is useful only when the reviewer has enough information, time, and authority to disagree.
- Reliability depends on failure shape—not average accuracy alone—including consequence, detectability, reversibility, blast radius, correlation, recovery, and trust cost.
Candidate Delegation Ladder
- Shadow: observe and record what the system would have recommended.
- Assist: retrieve, summarize, organize, draft, or calculate while a human decides.
- Recommend: present a proposed decision with evidence, assumptions, and uncertainty.
- Execute with approval: prepare a specific action for explicit confirmation.
- Bounded execution: act within deterministic limits, logging, and exception handling.
- Expanded delegation: receive broader authority only after sustained evidence and continued monitoring.
The best operating point may remain Assist or Recommend.
Lab Concept
The future module may combine a Decision Ownership Map with a Delegation Ladder and Control Plan. For one decision loop, learners would identify the human owner, proposed AI contribution, allowed and prohibited actions, approved sources of truth, deterministic limits, review, failure detection, repair, escalation, stop condition, and evidence needed to promote or reduce authority.
This is a candidate artifact, not a full lab or worksheet in this pass.
Risks and Boundaries
- Avoid rubber-stamp human approval and accountability laundering through “the AI decided.”
- Include hidden review and cleanup labor in the operating cost.
- Do not use confidence scores as proof of correctness or assume more autonomy is progress.
- Identity, employment, legal commitments, safety, financial transfers, and irreversible public actions need domain review before any authority decision.
Candidate Relationships
- requires: Friction Mapping – Understanding Work Before Recommending Solutions — maps the decision loop and context before authority is assigned.
- requires: Information-Flow Discovery Before Automation — allowed context and sources of truth must be known.
- extends: Becoming an Informed Technology Consumer — turns experiment graduation from a purchase question into an authority question.
- companion_to: Preserve What Already Works — staged delegation protects functioning routines and relationships.
- companion_to: Resilience and Outage Planning — bounded delegation needs a fallback when models, vendors, or integrations fail.
- applies: Trust, But Check the Wires — verification habits become operational control practices.
These are proposed relationships, not current learner prerequisites beyond the stated Friction Mapping dependency.
Readiness Lens
Primary: Governance, privacy, and trust.
Secondary: Evidence, testing, and learning; people and judgment.
Risk themes: authority, reliability, correlated failure, approval quality, rollback, vendor dependency, and accountability.
Status Notes
Seed captured from the v0 brief and expanded with the July 14 work-systems and bounded-delegation ideas. It needs small-business communication and escalation examples, privacy and surveillance review, and domain-specific guidance before any delivery decision.
Review status
- Package lifecycle: Seed
- Learner availability: Planned
- Review needs: Ideation integration; not reviewed
Source: modules/decision-ownership-before-delegation/module.md