Automation Displacement and Augmentation Evidence Review

Review whether specific work should be automated, augmented, redesigned, or retained. Base recommendations on task-level operating evidence and outcome requirements rather than model capability demonstrations or generic labor-saving assumptions.

Inputs:
- Process, task inventory, roles, handoffs, decisions, service outcomes, and accountability: [Process tasks roles and outcomes]
- Volume, time, queue, cost, error, rework, variability, seasonal load, skill, and user-impact evidence: [Baseline workload and performance evidence]
- Pilot scope, adoption, accepted outcomes, AI attempts, reviewer intervention, exceptions, latency, cost, and observed limitations: [AI pilot and operating evidence]
- Safety, legal, privacy, security, quality, bias, customer, employee, and operational failure evidence: [Quality risk and exception evidence]
- Staffing, specialist capacity, training, labor commitments, change readiness, redeployment options, and transition limits: [Workforce capacity and transition constraints]
- Process owner, role owners, people/HR reviewer, legal/privacy reviewer, finance reviewer, and executive decision authority: [Decision rights and accountable owners]

Do not treat time saved per attempt as released capacity. Do not invent job impact, adoption, attrition, productivity, or quality results. Distinguish observed operating evidence from inference. Separate task automation from role elimination, assisted work from autonomous work, theoretical potential from observed operation, and gross time savings from rework, review, exception, coordination, and transition effort.

Review:

1. Decompose work at the decision-relevant level.
   Map tasks, inputs, outputs, variability, tacit judgment, interaction, authority, dependencies, failure consequences, and current owner. Avoid labels so broad that distinct work is hidden.

2. Establish the baseline.
   Record volumes, time, queue, quality, rework, cost, skill, demand variability, and outcome evidence with periods and sources. Mark missing measures.

3. Assess AI operating evidence.
   For each task, compare attempted, accepted unchanged, corrected, rejected, escalated, failed, and unmeasured outcomes. Include setup, prompting, context gathering, validation, review, exception handling, and downstream correction.

4. Determine the appropriate work mode.
   Consider Retain, Assist, Automate bounded task, Automate with review, Redesign process, Consolidate demand, or Stop low-value work. State why the mode fits the evidence and which authority remains with the accountable role.

5. Model capacity and role effects.
   Calculate capacity released only when workload can actually be removed, avoided, absorbed, or redeployed. Identify new work, bottlenecks, skill shifts, supervision, peak capacity, and exception ownership.

6. Assess distributional and transition risk.
   Review who bears errors, monitoring, learning, workload intensification, accessibility impacts, or skill erosion. Identify employment, consultation, legal, or accommodation questions for the responsible reviewers without offering legal conclusions.

7. Design a reversible transition.
   Specify scope, owners, training, shadow period, dual-run if justified, acceptance gates, workload and quality monitoring, escalation, rollback, and a review date. Use the smallest viable change.

8. Make the decision.
   Provide task-level dispositions and a role/process implication; do not generalize beyond supplied evidence.

The process owner approves task redesign, the data or privacy owner approves sensitive-data use, and the workforce or legal reviewer addresses role and employment consequences. For each recommendation, require acceptance evidence with an expected observation, actual observation when measured, outcome guardrail, and reversal condition. The review may recommend a disposition but does not authorize staffing or production changes.

Record approval from the process owner for task redesign and approval from the relevant data, privacy, workforce, or legal owner before consequential changes.

Required deliverable:

# Automation Displacement and Augmentation Evidence Review

## Task and Outcome Map
| Task | Current owner | Required outcome/authority | Variability/judgment | Failure consequence | Baseline evidence |
|---|---|---|---|---|---|

## AI Operating Evidence
| Task | Attempts | Accepted/corrected/rejected | Review/exception effort | Quality/risk | Evidence limit |
|---|---:|---|---|---|---|

## Work-Mode Decisions
| Task | Retain/assist/automate/redesign/stop | Evidence rationale | Authority retained | Required control | Owner |
|---|---|---|---|---|---|

## Capacity and Transition Effects
| Role/team | Work removed | New work | Net capacity evidence | Skill/change need | Risk |
|---|---|---|---|---|---|

## Transition Plan
| Phase | Scope | Evidence gate | Training/ownership | Stop or rollback trigger |
|---|---|---|---|---|

## Decision Summary
- Tasks approved for change:
- Tasks retained:
- Capacity claim supported:
- Claims not supported:
- Required people/legal/privacy reviews:
- Reassessment date:

Completion requires task-level evidence, explicit retained authority, a credible net-capacity bridge, and a reversible transition that does not turn a capability demo into an unsupported workforce decision.