US dataswitch to UK
Insurance Sales Agents
customizing insurance programs to suit individual customers, conferring with clients to obtain and provide information when claims are made on a policy and interviewing prospective clients to obtain data about their financial resources and needs. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: performing administrative tasks, such as maintaining records and handling policy renewals. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, calling on policyholders to deliver and explain policy, stays yours. The tools change, the responsibility doesn't.
Your week, as this page understands it
Sell life, property, casualty, health, automotive, or other types of insurance. May refer clients to independent brokers, work as an independent broker, or be employed by an insurance company. The job title says “insurance sales agents”. The real job is the part underneath: calling on policyholders to deliver and explain policy. 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 insurance sales agents is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is calling on policyholders to deliver and explain policy, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 28%
- changing shape
- 22%
- staying human
- 50%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 44 out of 100 (38–51 allowing for uncertainty): partial exposure, across 19 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 insurance sales agents 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.
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.
- One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
- 3 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
6 tasksTasks 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.
Performing administrative tasks, such as maintaining records and handling policy renewals
This is reading one thing and writing another: administrative tasks in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform administrative tasks, such as maintaining records and handling policy renewals.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Record keeping and renewal processing follow set steps on data already in the system, which software handles routinely.
The five ratings: output a model can produce 4/4 · 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.
Calculating premiums and establishing payment method
This is reading one thing and writing another: premiums in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Calculate premiums and establish payment method.” (O*NET task statement)
How this row was scored
Exposure score: 74 out of 100 (70–78 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: Premiums come from published rating tables and formulas, and payment setup is standard, so systems calculate both reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Contacting underwriter and submitting forms to obtain binder coverage
This is reading one thing and writing another: underwriter in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Contact underwriter and submit forms to obtain binder coverage.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 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: Submitting forms to an underwriter to bind cover is routine, structured paperwork that systems already handle.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Developing marketing strategies to compete with other individuals or companies who sell insurance
This is reading one thing and writing another: strategies in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop marketing strategies to compete with other individuals or companies who sell insurance.” (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: Marketing plans draw on widely documented tactics and competitor information, so AI can produce a solid draft strategy.
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
5 tasksTasks 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.
Selecting company that offers type of coverage requested by client to underwrite policy
The software now makes the first pass at company, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Select company that offers type of coverage requested by client to underwrite policy.” (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; someone qualified has to answer for it.
The rating behind it: Comparison software already matches requested cover to carriers, though which insurer will actually take the risk is partly local knowledge.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conferring with clients to obtain and provide information when claims are made on a policy
The software now makes the first pass at clients, 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 · CoreSource: “Confer with clients to obtain and provide information when claims are made on a policy.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 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: Collecting and passing on claim information follows a documented process, though people making a claim often want a person's reassurance.
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 2/4 · how much data exists 3/4.
Ensuring that policy requirements
The software now makes the first pass at policy requirements, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Ensure that policy requirements are fulfilled, including any necessary medical examinations and the completion of appropriate forms.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 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: Tracking outstanding forms and requirements is checklist work software does well, although arranging medical exams involves the real world.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Staying human
8 tasksTasks 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.
Explaining features, advantages and disadvantages of various policies to promote sale of insurance plans
The rules require a named, qualified person to answer for features, advantages and disadvantages of various policies, and that person cannot be a piece of software.
importance 4 · CoreSource: “Explain features, advantages, and disadvantages of various policies to promote sale of insurance plans.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (29–49 allowing for uncertainty): low exposure, medium confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Policy features are documented and easy to explain, though regulated advice needs an authorised adviser.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Seeking out new clients and developing clientele by networking to find new customers and generating lists of prospective clients
The value here is that a specific person handles new clients and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Seek out new clients and develop clientele by networking to find new customers and generate lists of prospective clients.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 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: Prospect lists can be built automatically, but winning clients through networking means meeting people and building personal relationships.
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 3/4 · how much data exists 2/4.
Calling on policyholders to deliver and explain policy
The rules require a named, qualified person to answer for policyholders, and that person cannot be a piece of software.
importance 4 · CoreSource: “Call on policyholders to deliver and explain policy, to analyze insurance program and suggest additions or changes, or to change beneficiaries.” (O*NET task statement)
How this row was scored
Exposure score: 15 out of 100 (8–22 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Visiting policyholders to walk through cover and make changes needs a licensed agent, personal trust and often being there.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 3/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Show the other 9 tasks
Planning and overseeing incorporation of insurance program into bookkeeping system of company
shifting to AIThis is reading one thing and writing another: incorporation of insurance program in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Plan and oversee incorporation of insurance program into bookkeeping system of company.” (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: Mapping insurance costs into a bookkeeping system is documented accounting setup work that AI can plan and draft.
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.
Explaining necessary bookkeeping requirements for customer to implement and provide group insurance program
shifting to AIThis is reading one thing and writing another: necessary bookkeeping requirements in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Explain necessary bookkeeping requirements for customer to implement and provide group insurance program.” (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: The bookkeeping steps for running a group scheme are written down, so AI can explain them clearly to a customer.
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.
Installing bookkeeping systems and resolving system problems
changing shapeThe software now makes the first pass at systems, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · SupplementalSource: “Install bookkeeping systems and resolve system problems.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 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: Setting up and fixing bookkeeping software is well-documented configuration work, though some setups still need someone 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 1/4 · how much data exists 3/4.
Monitoring insurance claims to ensure they are settled equitably for both the client and the insurer
changing shapeThe software now makes the first pass at insurance claims, 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 · SupplementalSource: “Monitor insurance claims to ensure they are settled equitably for both the client and the insurer.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 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: Claim progress and settlement figures are tracked in systems, but arguing a fair outcome for the client often needs a person.
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 2/4 · how much data exists 3/4.
Customizing insurance programs to suit individual customers
staying humanThe rules require a named, qualified person to answer for insurance programs, and that person cannot be a piece of software.
importance 4 · CoreSource: “Customize insurance programs to suit individual customers, often covering a variety of risks.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Matching documented policy options to a customer's risks is comparison work AI handles, though a licensed agent assembles the package.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Interviewing prospective clients to obtain data about their financial resources and needs
staying humanThe value here is that a specific person handles prospective clients and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Interview prospective clients to obtain data about their financial resources and needs, the physical condition of the person or property to be insured, and to discuss any existing coverage.” (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 same decision, made over and over; the value is that a specific person does it.
The rating behind it: Online forms already gather much of this information, but a conversation draws out details customers do not think to mention.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Attending meetings, seminars and programs to learn about new products and services
staying humanThe ratings behind this row put meetings, seminars and programs well outside what today's tools can do on their own.
importance 4 · CoreSource: “Attend meetings, seminars, and programs to learn about new products and services, learn new skills, and receive technical assistance in developing new accounts.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: AI can summarise new product material, but the point of attending is the agent building their own skills and contacts.
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 1/4 · how much data exists 3/4.
Selling various types of insurance policies to businesses and individuals on behalf of insurance companies
staying humanThe rules require a named, qualified person to answer for various types of insurance policies, and that person cannot be a piece of software.
importance 4 · CoreSource: “Sell various types of insurance policies to businesses and individuals on behalf of insurance companies, including automobile, fire, life, property, medical and dental insurance, or specialized policies, such as marine, farm/crop, and medical malpractice.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (16–24 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Selling policies legally requires a licensed agent and usually depends on a trusted relationship with the person buying.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 3/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Inspecting property, examining its general condition, type of construction, age and other characteristics
staying humanThis work happens in the physical world: property, examining its general condition, type of construction, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Inspect property, examining its general condition, type of construction, age, and other characteristics, to decide if it is a good insurance risk.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Judging whether a building is a good risk means walking round it and looking at the construction yourself.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $62,280a 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
- 479,100in 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: administrative tasks in, a record out. The rows above are exactly that shape: performing administrative tasks and calculating premiums and establishing payment method. What it cannot do is be answerable: policyholders need a named person the rules will accept, and software cannot be that person. 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
Your week is splitting in two, and which half fills it is the whole question. Performing administrative tasks, such as maintaining records and handling policy renewals is going; calling on policyholders to deliver and explain policy is not.
So, given all that: 28% of this job's task weight sits in rows the software is already learning, 22% in rows that change shape rather than disappear, and 50% in rows it is nowhere near. That is the position, measured across 19 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (calling on policyholders to deliver and explain policy) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles calling on policyholders to deliver and explain policy, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 insurance sales agents (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was credit counselors: only about 6% of its durable work is work you already do, it is under the same pressure this job is, it pays 16.1% less and there are far fewer of those jobs than of yours. Your own job splits about 28/72: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “explain features, advantages, and disadvantages of various policies to promote sale of…”, and let the exposed end go.
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.
Credit Counselors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already explain features, advantages, and disadvantages of various policies to promote sale of insurance…, and their equivalent is to explain services or policies to clients. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% 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: 53% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it. It is a pay cut, in those words: $52,230 against your $62,280, 16.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 27,770 of those jobs against 479,100 of yours (OEWS May 2025), 6% as many seats.
Sales Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already attend meetings, seminars, and programs to learn about new products and services, learn…, and their equivalent is to attend trade shows and seminars to promote products or to learn about industry…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $124,900 against your $62,280 is 2.00× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 51,790 of those jobs against 479,100 of yours (OEWS May 2025), 11% as many seats.
Actuaries
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already calculate premiums and establish payment method, and their equivalent is to ascertain premium rates required and cash reserves and liabilities necessary to ensure payment…. 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. The pay gap is the market pricing a barrier: $130,000 against your $62,280 is 2.09× (OEWS May 2025 (both)), and you would be crossing it holding about 2% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 26,670 of those jobs against 479,100 of yours (OEWS May 2025), 6% as many seats.
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: 28% of its task weight, across 19 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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 Insurance underwriters 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
In UK official statistics this job is counted as Insurance underwriters. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
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:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for insurance sales agents, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 28% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for insurance sales agents. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for insurance sales agents launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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 Insurance Sales Agents?
- Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Insurance Sales Agents (United States, SOC 41-3021), 28% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 44 out of 100 (range 38–51, band: partial). 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 “Insurance Sales Agents” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Perform administrative tasks, such as maintaining records and handling policy renewals” (81/100, very high); “Develop marketing strategies to compete with other individuals or companies who sell insurance” (75/100, high); “Calculate premiums and establish payment method” (74/100, 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 “Insurance Sales Agents” stay human?
- About 50% 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: “Inspect property, examining its general condition, type of construction, age, and other characteristics, to decide if it is a good insurance risk” (7/100, minimal); “Call on policyholders to deliver and explain policy, to analyze insurance program and suggest additions or changes, or to change beneficiaries” (15/100, minimal); “Sell various types of insurance policies to businesses and individuals on behalf of insurance companies, including automobile, fire, life, property, medical…” (20/100, 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 “Insurance Sales Agents” do about AI?
- Start from the ledger rather than the headline: 28% of this job's weighted core work is exposed, and roughly 50% 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 Insurance Sales Agents 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 19 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
About the data on this page
- One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 3 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 2026-08-05.
- 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.
Using these figures?
Cite this
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}
}Data as of release 2026-q4.1, published . 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.