AI Delegation Index

What work could you hand off to an AI agent?

Loss Prevention Managers

Moderate

AI agents could support several recurring tasks in this job, while people continue to lead decisions and handle exceptions.

Where agents can help most

  1. 1

    Analyze loss trends and recommend controls

    Use an agent to combine retail loss data from stores, spot patterns in theft or fraud, and draft a short list of controls that might help.

  2. 2

    Review cash discrepancies and document fixes

    Use an agent to read exception reports and cash discrepancy notes, compare them with prior issues, and draft a clear summary for store or finance follow-up.

  3. 3

    Summarize evidence for security needs across locations

    Use an agent to compare loss patterns, store risk factors, and current coverage across locations, then draft options for where staff or technology should be placed.

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O*NET-SOC 11-9199.08 · #195 of 923

Result context

How to read this result

Plan and direct policies, procedures, or systems to prevent the loss of assets. Determine risk exposure or potential liability, and develop risk control measures.

National position
#195 of 923 occupations
Top 22% of occupations
Overall AI delegation potential
Moderate
Potential of score-contributing workflows
Strong · 78/100
Meaningful work covered
64%

The overall rating combines how useful the best agent workflows are with how much of the occupation they address. It is not an estimate of job automation or replacement.

Recommended agent uses

3 workflows you could delegate to AI

1

Analyze loss trends and recommend controls

How you could use an agent

Use an agent to combine retail loss data from stores, spot patterns in theft or fraud, and draft a short list of controls that might help. It can prepare recommendations on staffing, scheduling, training, or process changes so you can choose what fits each location.

Where you stay involved

You review the store patterns, pick the controls you want to try, and decide when a case needs a specialist or outside help.

Review level: Medium

2

Review cash discrepancies and document fixes

How you could use an agent

Use an agent to read exception reports and cash discrepancy notes, compare them with prior issues, and draft a clear summary for store or finance follow-up. It can keep a running log of each case, the supporting paperwork, and whether the issue was explained or corrected.

Where you stay involved

You decide whether the discrepancy is resolved, needs more review, or should be sent to finance, compliance, or investigators.

Review level: High

3

Summarize evidence for security needs across locations

How you could use an agent

Use an agent to compare loss patterns, store risk factors, and current coverage across locations, then draft options for where staff or technology should be placed. It can summarize the practical effects of each option so you can plan the next move with managers.

Where you stay involved

You choose the deployment plan, weigh budget and safety limits, and track whether the changes actually reduce exposure.

Review level: Medium

Supporting analysis

Why this occupation's AI delegation potential is Moderate

Underlying methodology score

72 / 100

This technical score determines the qualitative rating; it is not an estimate of the share of the occupation that can be automated.

Importance & frequency71
AI capability83
Digital actionability88
End-to-end leverage80
Safety & reversibility66
Meaningful-work coverage
64%
Physical-work modifier
Limited
Safety modifier
Limited
Qualitative judgment
No material constraint
O*NET task evidence
27 tasks

O*NET 31.0 · methodology 3.3.0. Every workflow passes an action-level physical-execution and protected human-and-veterinary clinical-action gate. Documentation workflows must own a complete digital loop and use digital task evidence only; support-only workflows are disclosed separately and excluded from scoring. Artistic, editorial, normative, and policy-dependent work receives a transparent human-judgment constraint. National ranking within 923 scored O*NET occupations under methodology 3.3.0.

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