Will AI replace Logisticians?

Not as a job, but it is already doing parts of the work. Across the 83 official task statements scored for Logisticians (United States, SOC 13-1081), 74% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 66 out of 100 (range 61–72, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Logisticians” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Enter logistics-related data into databases” (93/100, very high); “Develop or maintain freight rate databases for use by supply chain departments to determine the most economical modes of transportation” (93/100, very high); “Track product flow from origin to final delivery” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Logisticians” stay human?

About 8% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Interview key staff or tour facilities to identify efficiency-improvement, cost-reduction, or service-delivery opportunities” (6/100, minimal); “Maintain and develop positive business relationships with a customer's key personnel involved in, or directly relevant to, a logistics activity” (10/100, minimal); “Conduct environmental audits for logistics activities, such as storage, distribution, or transportation” (25/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Logisticians” do about AI?

Start from the ledger rather than the headline: 74% of this job's weighted core work is exposed, and roughly 8% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Logisticians calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 83 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Logisticians

maintaining databases of logistics information, analyzing logistics data and developing logistic metrics. If that's your week, this page is about your job.

The honest answer

Most tasks in this job are the kind AI has learned to do: tracking product flow from origin to final delivery. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 74%
changing shape: 18%
staying human: 8%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 66 out of 100 (61–72 allowing for uncertainty): high exposure, across 83 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for logisticians is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

It would be a lie to soften that; maintaining and developing positive business relationships with a customer's key personnel is what this work rebuilds around. The routes below start from it.

Your week, as this page understands it

Analyze and coordinate the ongoing logistical functions of a firm or organization. Responsible for the entire life cycle of a product, including acquisition, distribution, internal allocation, delivery, and final disposal of resources. The job title says “logisticians”. The real job is the part underneath: maintaining and developing positive business relationships with a customer's key personnel. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But logisticians is not one task. It is 83 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is maintaining and developing positive business relationships with a customer's key personnel, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 74%
changing shape: 18%
staying human: 8%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

63 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Maintaining databases of logistics information
This is reading one thing and writing another: databases of logistics information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain databases of logistics information.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping a logistics database accurate and current is structured data work software handles well with occasional human correction.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying cost-reduction or process-improvement logistic opportunities
This is reading one thing and writing another: cost-reduction in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Identify cost-reduction or process-improvement logistic opportunities.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Spotting cost and process improvements in operational data is pattern-finding software does well, with a specialist confirming what is practical.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Recommending improvements to existing or planned logistics processes
This is reading one thing and writing another: improvements in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Recommend improvements to existing or planned logistics processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Recommending process improvements from performance data is analysis software drafts well, with a specialist judging what is workable.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing reports on logistics performance measures
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare reports on logistics performance measures.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Performance reports pull set measures from the system and add standard commentary.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

13 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Developing an understanding of customers' needs and taking actions to ensure that such needs are met
The software now makes the first pass at an understanding of customers' needs, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Develop an understanding of customers' needs and take actions to ensure that such needs are met.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Order history and service records reveal much of what a customer wants, but reading unspoken concerns and acting on them still needs a person.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Preparing logistic strategies or conceptual designs for production facilities
The software now makes the first pass at logistic strategies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Prepare logistic strategies or conceptual designs for production facilities.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Software can draft facility logistics concepts, but turning them into workable designs needs engineering judgment about the specific site.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing logistics performance with customers against targets
The software now makes the first pass at logistics performance, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Review logistics performance with customers against targets, benchmarks, and service agreements.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The performance analysis is straightforward for software, but sitting down with a customer over missed targets calls for a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Staying human

7 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Maintaining and developing positive business relationships with a customer's key personnel
The value here is that a specific person handles positive business relationships and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Maintain and develop positive business relationships with a customer's key personnel involved in, or directly relevant to, a logistics activity.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Keeping key customer contacts on side depends on personal trust built over time, so software can only help draft the messages around it.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 1/4.
Protecting and controlling proprietary materials
The ratings behind this row put proprietary materials well outside what today's tools can do on their own.
importance 4 · Core
Source: “Protect and control proprietary materials.” (O*NET task statement)
How this row was scored
Exposure score: 33 out of 100 (26–40 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Access rules and handling paperwork can be automated, but physically securing proprietary materials and answering for them stays with staff.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Performing managerial duties, hiring and training employees and overseeing facility needs or requirements
The value here is that a specific person handles managerial duties and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Perform managerial duties such as hiring and training employees and overseeing facility needs or requirements.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Job adverts, screening notes and training material can be drafted by software, but hiring people and running a facility needs someone there.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Show the other 73 tasks

Entering logistics-related data into databases
shifting to AI
This is reading one thing and writing another: logistics-related data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Enter logistics-related data into databases.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Entering logistics data into databases is routine keying that software already handles faster and more accurately than by hand.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing or maintaining freight rate databases for use by supply chain departments to determine the most economical modes of transportation
shifting to AI
This is reading one thing and writing another: freight rate databases in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Develop or maintain freight rate databases for use by supply chain departments to determine the most economical modes of transportation.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building and updating freight rate tables is structured data work that software maintains more consistently than by hand.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Tracking product flow from origin to final delivery
shifting to AI
This is reading one thing and writing another: product flow in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Track product flow from origin to final delivery.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Following a shipment from origin to delivery runs on tracking data that software already collects and interprets automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Maintaining logistics records in accordance with corporate policies
shifting to AI
This is reading one thing and writing another: logistics records in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain logistics records in accordance with corporate policies.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping logistics records in line with company policy is structured filing work software handles accurately and consistently.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Computing reporting metrics, such as on-time delivery rates, order fulfillment rates or inventory turns
shifting to AI
This is reading one thing and writing another: metrics in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compute reporting metrics, such as on-time delivery rates, order fulfillment rates, or inventory turns.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: On-time delivery, fill rates and stock turns are set formulas applied to system data, which software calculates automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Assessing the environmental impact or energy efficiency of logistics activities
shifting to AI
This is reading one thing and writing another: the environmental impact in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Assess the environmental impact or energy efficiency of logistics activities, using carbon mitigation software.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Carbon and energy assessments are calculations run through software, so the work is largely automatic once activity data exists.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Remotelying monitor the flow of vehicles or inventory
shifting to AI
This is reading one thing and writing another: monitor the flow of vehicles in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Remotely monitor the flow of vehicles or inventory, using Web-based logistics information systems to track vehicles or containers.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Watching vehicle and container movements through web tracking systems is screen-based monitoring that software already does continuously.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Entering carbon-output or environmental-impact data into spreadsheets or environmental management or auditing software programs
shifting to AI
This is reading one thing and writing another: carbon-output in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Enter carbon-output or environmental-impact data into spreadsheets or environmental management or auditing software programs.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keying environmental data into spreadsheets or auditing software is routine entry work that software already automates.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring industry standards, trends or practices to identify developments in logistics planning or execution
shifting to AI
This is reading one thing and writing another: industry standards, trends or practices in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Monitor industry standards, trends, or practices to identify developments in logistics planning or execution.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tracking industry standards and practice is reading and summarising published material, which software does quickly and thoroughly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Reviewing global, national or regional transportation or logistics reports for ways to improve efficiency or minimize the environmental impact of logistics activities
shifting to AI
This is reading one thing and writing another: global in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Review global, national, or regional transportation or logistics reports for ways to improve efficiency or minimize the environmental impact of logistics activities.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading published transport and logistics reports for useful ideas is summarising work software does quickly and thoroughly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Routing or rerouting drivers in real time with remote route navigation software
shifting to AI
This is reading one thing and writing another: drivers in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Route or reroute drivers in real time with remote route navigation software, satellite linkup systems, or global positioning systems (GPS) to improve operational efficiencies.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Live rerouting is what navigation and satellite systems are built to do, though a person still speaks to the driver.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Developing and implementing technical project management tools
shifting to AI
This is reading one thing and writing another: technical project management tools in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop and implement technical project management tools, such as plans, schedules, and responsibility and compliance matrices.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Plans, schedules and compliance matrices follow known formats, so software can build them from project details with light checking.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reporting project plans, progress and results
shifting to AI
This is reading one thing and writing another: project plans, progress and results in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Report project plans, progress, and results.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning project data into plans, progress updates and results write-ups is document work software produces to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing proposals that include documentation for estimates
shifting to AI
This is reading one thing and writing another: proposals in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop proposals that include documentation for estimates.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing proposals with costed estimates from internal figures is document work that software drafts well, with a specialist checking the numbers.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Supporting the development of training materials and technical manuals
shifting to AI
This is reading one thing and writing another: the development of training materials in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Support the development of training materials and technical manuals.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing training material and technical manuals from existing source documents is one of the things software does most reliably today.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying informed of logistics technology advances and applying appropriate technology to improve logistics processes
shifting to AI
This is reading one thing and writing another: informed of logistics technology advances in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Stay informed of logistics technology advances and apply appropriate technology to improve logistics processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading up on new logistics technology and summarising what is worth adopting is exactly the kind of research software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Redesigning the movement of goods to maximize value and minimize costs
shifting to AI
This is reading one thing and writing another: the movement of goods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Redesign the movement of goods to maximize value and minimize costs.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Redesigning how goods move is an optimisation problem software handles well, with an experienced planner checking it against real constraints.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Performing system lifecycle cost analysis and developing component studies
shifting to AI
This is reading one thing and writing another: system lifecycle cost analysis in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Perform system lifecycle cost analysis and develop component studies.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Lifecycle cost analysis and component studies are calculations on known figures, which software performs quickly to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing contractual commitments, customer specifications or related information to determine logistics or support requirements
shifting to AI
This is reading one thing and writing another: contractual commitments, customer specifications or related information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review contractual commitments, customer specifications, or related information to determine logistics or support requirements.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading contracts and specifications to pull out logistics requirements is document work software handles accurately with a reviewer checking.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing or validating documentation on automated logistics or maintenance-data reporting or management information systems
shifting to AI
This is reading one thing and writing another: documentation in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare or validate documentation on automated logistics or maintenance-data reporting or management information systems.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Preparing and checking system documentation is drafting work software does well, with someone verifying it matches the live system.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying or developing business rules or standard operating procedures to streamline operating processes
shifting to AI
This is reading one thing and writing another: business rules in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Identify or develop business rules or standard operating procedures to streamline operating processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing business rules and standard procedures from how a process actually runs is drafting work software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing or maintaining cost estimates
shifting to AI
This is reading one thing and writing another: cost estimates in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop or maintain cost estimates, forecasts, or cost models.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cost models and forecasts are built from historical figures using standard methods, which software applies faster than working by hand.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Determining logistics support requirements, such as facility details, staffing needs or safety or maintenance plans
shifting to AI
This is reading one thing and writing another: logistics support requirements in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Determine logistics support requirements, such as facility details, staffing needs, or safety or maintenance plans.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Working out staffing, safety and maintenance requirements follows documented method, so software drafts it with a specialist confirming.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing or interpreting logistics data involving customer service
shifting to AI
This is reading one thing and writing another: logistics data involving customer service in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze or interpret logistics data involving customer service, forecasting, procurement, manufacturing, inventory, transportation, or warehousing.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Interpreting data across forecasting, inventory and transport is analytical work software produces to a standard analysts accept after checking.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing logistics technology or information for effective and efficient support of product
shifting to AI
This is reading one thing and writing another: logistics technology in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Provide logistics technology or information for effective and efficient support of product, equipment, or system manufacturing or service.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Supplying logistics information and technology guidance is research and analysis work software produces to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Evaluating effectiveness of current or future logistical processes
shifting to AI
This is reading one thing and writing another: effectiveness of current or future logistical processes in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Evaluate effectiveness of current or future logistical processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Judging how well logistics processes work is analysis of operational data, which software does well with a reviewer.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Applying logistics modeling techniques to address issues
shifting to AI
This is reading one thing and writing another: logistics modeling techniques in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Apply logistics modeling techniques to address issues, such as operational process improvement or facility design or layout.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Logistics modelling is a well-established computational method that software applies quickly once the operational data is available.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying opportunities for inventory reductions
shifting to AI
This is reading one thing and writing another: opportunities in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Identify opportunities for inventory reductions.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Finding where stock can safely be reduced is a data analysis task software performs well with a planner confirming.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing or maintaining payment systems to ensure accuracy of vendor payments
shifting to AI
This is reading one thing and writing another: payment systems in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Develop or maintain payment systems to ensure accuracy of vendor payments.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking vendor payments against agreed rates is rule-based reconciliation that software performs accurately at scale.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Determining packaging requirements
shifting to AI
This is reading one thing and writing another: requirements in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Determine packaging requirements.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Choosing packaging to suit a product and its journey follows documented rules software applies well with a check.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Writing or revising standard operating procedures for logistics processes
shifting to AI
This is reading one thing and writing another: standard operating procedures in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Write or revise standard operating procedures for logistics processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing and revising standard operating procedures is drafting from known process detail, which software produces to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing procedures, such as distribution or inventory management, to ensure maximum efficiency or minimum cost
shifting to AI
This is reading one thing and writing another: procedures in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review procedures, such as distribution or inventory management, to ensure maximum efficiency or minimum cost.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reviewing distribution and inventory procedures for waste is document and data review software drafts to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing ongoing analyses in areas
shifting to AI
This is reading one thing and writing another: analyses in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Provide ongoing analyses in areas such as transportation costs, parts procurement, back orders, or delivery processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Ongoing analysis of transport costs, back orders and delivery performance is repeatable data work software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Managing systems to ensure that pricing structures adequately reflect logistics costing
shifting to AI
This is reading one thing and writing another: systems in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Manage systems to ensure that pricing structures adequately reflect logistics costing.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking that pricing structures reflect real logistics costs is a calculation on internal data software performs well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing or maintaining models for logistics uses
shifting to AI
This is reading one thing and writing another: models in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Develop or maintain models for logistics uses, such as cost estimating or demand forecasting.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cost estimating and demand forecasting models use established statistical methods that software builds and maintains well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Interpreting data on logistics elements
shifting to AI
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Interpret data on logistics elements, such as availability, maintainability, reliability, supply chain management, strategic sourcing or distribution, supplier management, or transportation.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Interpreting supply, reliability and transport data is analytical work software produces to a standard analysts accept after checking.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Applying analytic methods or tools
shifting to AI
This is reading one thing and writing another: analytic methods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Apply analytic methods or tools to understand, predict, or control logistics operations or processes.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Applying analytical methods to predict and control logistics operations is computational work software performs quickly and reliably.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing logistics data, using methods, data mining, data modeling or cost or benefit analysis
shifting to AI
This is reading one thing and writing another: logistics data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze logistics data, using methods such as data mining, data modeling, or cost or benefit analysis.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Data mining, modelling and cost-benefit analysis are computational methods software applies well once the data is available.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Evaluating the use of inventory tracking technology
shifting to AI
This is reading one thing and writing another: the use of inventory tracking technology in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Evaluate the use of inventory tracking technology, Web-based warehousing software, or intelligent conveyor systems to maximize plant or distribution center efficiency.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing tracking and warehousing technologies against operational needs is research and analysis software drafts to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing logistic metrics, internal analysis tools or key performance indicators for business units
shifting to AI
This is reading one thing and writing another: logistic metrics in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop logistic metrics, internal analysis tools, or key performance indicators for business units.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing metrics and dashboards from existing operational data is well-documented work that software drafts to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Evaluating the use of technologies
shifting to AI
This is reading one thing and writing another: the use of technologies in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Evaluate the use of technologies, such as global positioning systems (GPS), radio-frequency identification (RFID), route navigation software, or satellite linkup systems, to improve transportation efficiency.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Weighing up tracking and navigation technologies is comparison research software drafts well from vendor and operational information.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing comprehensive supply chains that minimize environmental impacts or costs
shifting to AI
This is reading one thing and writing another: comprehensive supply chains in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Design comprehensive supply chains that minimize environmental impacts or costs.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing a lower-impact supply chain is a modelling exercise software supports well, with a specialist testing the assumptions.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing or documenting reverse logistics management processes to ensure maximal efficiency of product recycling
shifting to AI
This is reading one thing and writing another: reverse logistics management processes in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Develop or document reverse logistics management processes to ensure maximal efficiency of product recycling, reuse, or final disposal.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing reverse logistics procedures for returns, reuse and disposal is documentation work software drafts to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Creating models or scenarios to predict the impact of changing circumstances
shifting to AI
This is reading one thing and writing another: models in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Create models or scenarios to predict the impact of changing circumstances, such as fuel costs, road pricing, energy taxes, or carbon emissions legislation.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Scenario modelling of fuel costs, taxes and road pricing is arithmetic on published rates, which software handles quickly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing logistical facility or capacity planning analyses for distribution or transportation functions
shifting to AI
This is reading one thing and writing another: logistical facility in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Provide logistical facility or capacity planning analyses for distribution or transportation functions.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Capacity and facility planning analyses are calculations on throughput data, and software produces them to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing or documenting procedures to minimize or mitigate carbon output resulting from the movement of materials or products
shifting to AI
This is reading one thing and writing another: procedures in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Develop or document procedures to minimize or mitigate carbon output resulting from the movement of materials or products.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Documenting procedures to cut transport emissions is writing from published methods and internal data, which software drafts well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reorganizing shipping schedules to consolidate loads
shifting to AI
This is reading one thing and writing another: schedules in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Reorganize shipping schedules to consolidate loads, maximize vehicle usage, or limit the movement of empty vehicles or containers.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Consolidating loads and cutting empty running is a scheduling optimisation software solves faster than manual planning.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Comparing locations or environmental policies of carriers or suppliers to make transportation decisions with lower environmental impact
shifting to AI
This is reading one thing and writing another: locations in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Compare locations or environmental policies of carriers or suppliers to make transportation decisions with lower environmental impact.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing carrier locations and environmental policies is research and comparison work software assembles well for a decision.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Participating in the assessment and review of design alternatives and designing change proposal impacts
shifting to AI
This is reading one thing and writing another: the assessment in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Participate in the assessment and review of design alternatives and design change proposal impacts.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Assessing what a design change does to logistics is analysis software can draft, but the deciding detail sits in private design files.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Developing specifications for equipment, tools, facility layouts or material-handling systems
shifting to AI
This is reading one thing and writing another: specifications in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop specifications for equipment, tools, facility layouts, or material-handling systems.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Specifications follow standard formats software can draft, though the deciding details come from the particular site and equipment.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Determining feasibility of designing new facilities or modifying existing facilities
shifting to AI
This is reading one thing and writing another: feasibility of designing new facilities in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Determine feasibility of designing new facilities or modifying existing facilities, based on factors such as cost, available space, schedule, technical requirements, or ergonomics.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Feasibility work is analysis software can draft, but the deciding facts about space, cost and ergonomics are specific to the site.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Determining requirements for compliance with environmental certification standards
shifting to AI
This is reading one thing and writing another: requirements in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Determine requirements for compliance with environmental certification standards.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Certification requirements are published, so software can set out what must be met, though a certifier signs the result off elsewhere.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Directing availability and allocation of materials
shifting to AI
This is reading one thing and writing another: availability in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Direct availability and allocation of materials, supplies, and finished products.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Allocation decisions follow rules and forecasts that software applies quickly and consistently.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Directing and supporting the compilation and analysis of technical source data necessary for product development
shifting to AI
This is reading one thing and writing another: the compilation in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Direct and support the compilation and analysis of technical source data necessary for product development.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Compiling and analysing technical source data suits software well, with someone steering what gets gathered and why.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Providing project management services, including the provision and analysis of technical data
shifting to AI
This is reading one thing and writing another: project management services in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Provide project management services, including the provision and analysis of technical data.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Analysing technical data and running project paperwork suits software, though steering a project through its people still needs a manager.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Proposing logistics solutions for customers
shifting to AI
This is reading one thing and writing another: logistics solutions in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Propose logistics solutions for customers.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Putting a logistics solution together for a customer is largely analysis and writing, though presenting it involves an account contact.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Contacting potential vendors to determine material availability
shifting to AI
This is reading one thing and writing another: potential vendors in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Contact potential vendors to determine material availability.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking material availability with vendors is routine enquiry work that automated messaging and supplier portals largely cover.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Contacting carriers for rates or schedules
shifting to AI
This is reading one thing and writing another: carriers in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Contact carriers for rates or schedules.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Getting rates and schedules from carriers is routine enquiry work now largely handled by pricing systems and automated messaging.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Communicating with or monitoring service providers
shifting to AI
This is reading one thing and writing another: or monitoring service providers in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Communicate with or monitor service providers, such as ocean carriers, air freight forwarders, global consolidators, customs brokers, or trucking companies.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Chasing carriers, forwarders and brokers is mostly routine messaging software can handle, though awkward exceptions still get a phone call.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conducting logistics studies or analyses
changing shape
The software now makes the first pass at logistics studies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Conduct logistics studies or analyses, such as time studies, zero-base analyses, rate analyses, network analyses, flow-path analyses, or supply chain analyses.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Network, rate and flow analyses are calculations software handles, although time studies still mean watching work happen on site.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring inventory transactions at warehouse facilities to assess receiving
changing shape
The software now makes the first pass at inventory transactions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Monitor inventory transactions at warehouse facilities to assess receiving, storage, shipping, or inventory integrity.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Inventory integrity checks run largely on system data, though confirming what is actually on the shelves means going to the warehouse.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Explaining proposed solutions
changing shape
The software now makes the first pass at proposed solutions, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Explain proposed solutions to customers, management, or other interested parties through written proposals and oral presentations.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software writes strong proposal documents, but presenting them to customers and answering challenges on the spot is still a person's job.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Planning, organizing and executing logistics support activities, such as maintenance planning, repair analysis and testing equipment recommendations
changing shape
The software now makes the first pass at logistics support activities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Plan, organize, and execute logistics support activities, such as maintenance planning, repair analysis, and test equipment recommendations.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Maintenance and repair plans can be drafted from records, though executing support activities and choosing test equipment draws on hands-on knowledge.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing plant distribution centers
changing shape
The software now makes the first pass at plant distribution centers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Design plant distribution centers.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Software can produce distribution centre layouts, but real building constraints and equipment realities need a designer on the ground.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collaborating with other departments as necessary to meet customer requirements
changing shape
The software now makes the first pass at other departments, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Collaborate with other departments as necessary to meet customer requirements, to take advantage of sales opportunities or, in the case of shortages, to minimize negative impacts on a business.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software can flag a shortage and prepare the case, though agreeing what other departments will do happens between colleagues.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Directing team activities, establishing task priorities, scheduling and tracking work assignments, providing guidance and ensuring the availability of resources
changing shape
The software now makes the first pass at team activities, establishing task priorities, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Direct team activities, establishing task priorities, scheduling and tracking work assignments, providing guidance, and ensuring the availability of resources.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Scheduling and tracking assignments can be automated, yet setting priorities for a team and guiding people remains human work.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Managing subcontractor activities, reviewing proposals, developing performance specifications and serving as liaisons between subcontractors and organizations
changing shape
The software now makes the first pass at subcontractor activities, reviewing proposals, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Manage subcontractor activities, reviewing proposals, developing performance specifications, and serving as liaisons between subcontractors and organizations.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software can check subcontractor proposals and draft performance specifications, though acting as the go-between with people relies on judgment and relationships.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Directing the work of logistics analysts
changing shape
The software now makes the first pass at the work of logistics analysts, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Direct the work of logistics analysts.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software can allocate and review analytical work, but leading a team of analysts day to day rests with a manager.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Conferring with logistics management teams to determine ways to optimize service levels
changing shape
The software now makes the first pass at logistics management teams, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Confer with logistics management teams to determine ways to optimize service levels, maintain supply-chain efficiency, or minimize cost.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software can prepare the options and figures, but agreeing service levels and cost trade-offs happens in conversation between managers.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Managing the logistical aspects of product life cycles
staying human
The ratings behind this row put the logistical aspects of product life cycles well outside what today's tools can do on their own.
importance 3 · Core
Source: “Manage the logistical aspects of product life cycles, including coordination or provisioning of samples, and the minimization of obsolescence.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tracking obsolescence and planning sample supply is data work, but getting physical samples where they are needed involves people.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Arranging for sale or lease of excess storage or transporting capacity to minimize losses or inefficiencies associated with empty space
staying human
The value here is that a specific person handles sale and stands behind it. That is earned, not computed.
importance 2 · Supplemental
Source: “Arrange for sale or lease of excess storage or transport capacity to minimize losses or inefficiencies associated with empty space.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Software can spot spare capacity and list it, but agreeing a sale or lease comes down to negotiating with a buyer.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Conducting environmental audits for logistics activities
staying human
This work happens in the physical world: environmental audits, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Conduct environmental audits for logistics activities, such as storage, distribution, or transportation.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Environmental audits mean visiting storage and transport sites, though much of the analysis and the write-up can be automated.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Interviewing key staff or tour facilities to identify efficiency-improvement
staying human
This work happens in the physical world: key staff, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Interview key staff or tour facilities to identify efficiency-improvement, cost-reduction, or service-delivery opportunities.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Touring a site and drawing people out in interviews means being there, so software can only help prepare questions and write up notes.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $82,320a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 251,040in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: product flow in, a record out. The rows above are exactly that shape: tracking product flow from origin to final delivery and maintaining databases of logistics information. What it cannot do is be trusted in person, which is what positive business relationships run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

The exposed part of your job is the biggest part, and I am not going to dress that up: tracking product flow from origin to final delivery is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 74% of this job's task weight sits in rows the software is already learning, 18% in rows that change shape rather than disappear, and 8% in rows it is nowhere near. That is the position, measured across 83 scored tasks. It is not a forecast about you.

What you have that the software does not is maintaining and developing positive business relationships with a customer's key personnel, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.

This week: one thing

Sit on the machine's side of the desk. Pick one real piece of product flow you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.

What you end up holding: a written list of the machine’s mistakes, in your handwriting
How long it takes: an evening, or an hour if you pick one job rather than one client

If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of product flow, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is tracking product flow from origin to final delivery” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.

Over the next 12 months

Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of maintaining and developing positive business relationships with a customer's key personnel you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to logisticians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was data scientists: only about 3% of its durable work is work you already do and it is under the same pressure this job is. I am not going to pretend that is comfortable news: 74% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “maintain and develop positive business relationships with a customer's key personnel involved…” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Data Scientists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide project management services, and their equivalent is to supervise the work of data management project staff. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 84% of its own task list already scores in the top exposure band (75/100 in this release), so the same software is eating it.
Look at that job’s page anyway →
Industrial Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already recommend improvements to existing or planned logistics processes, and their equivalent is to incorporate new manufacturing methods or processes to improve existing operations. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Transportation, Storage, and Distribution Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already contact carriers for rates or schedules, and their equivalent is to negotiate with carriers, warehouse operators, or insurance company representatives for services and preferential…. Across both published task lists that is about 1% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 74% of its task weight, across 83 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: maintaining and developing positive business relationships with a customer's key personnel is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Management consultants and business analysts is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

In UK official statistics this job is counted as Management consultants and business analysts, Quality assurance and regulatory professionals and Business and financial project management professionals. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Two honest options, and no deadline on either

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

A nearby route

There's no Space built for logisticians yet.

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

The closest match is Microsoft 365 Report Builders, a community for people who build business reports in Excel, Power Query and Power BI without a data team behind them. It overlaps with the part of your job that is growing: the supply-chain reporting layer - clean data, defined measures, dashboards that refresh - not the operations and not the contracts. If that overlap isn't you, the free route below covers the same ground.

Try Microsoft 365 Report Builders free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

After the trial it is a paid community, and you get identical data either way. If the overlap above is not your job, the moves above cost nothing and stand on their own.

Noted, and thank you. We’ll email you if a Space for logisticians launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Logisticians?: Not as a job, but it is already doing parts of the work. Across the 83 official task statements scored for Logisticians (United States, SOC 13-1081), 74% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 66 out of 100 (range 61–72, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Logisticians” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Enter logistics-related data into databases” (93/100, very high); “Develop or maintain freight rate databases for use by supply chain departments to determine the most economical modes of transportation” (93/100, very high); “Track product flow from origin to final delivery” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Logisticians” stay human?: About 8% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Interview key staff or tour facilities to identify efficiency-improvement, cost-reduction, or service-delivery opportunities” (6/100, minimal); “Maintain and develop positive business relationships with a customer's key personnel involved in, or directly relevant to, a logistics activity” (10/100, minimal); “Conduct environmental audits for logistics activities, such as storage, distribution, or transportation” (25/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Logisticians” do about AI?: Start from the ledger rather than the headline: 74% of this job's weighted core work is exposed, and roughly 8% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Logisticians calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 83 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.