Will AI replace Financial and Investment Analysts?

Not as a job, but it is already doing parts of the work. Across the 26 official task statements scored for Financial and Investment Analysts (United States, SOC 13-2051), 50% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 48 out of 100 (range 43–54, 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 “Financial and Investment Analysts” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Draw charts and graphs, using computer spreadsheets, to illustrate technical reports” (93/100, very high); “Evaluate and compare the relative quality of various securities in a given industry” (83/100, very high); “Monitor developments in the fields of industrial technology, business, finance, and economic theory” (83/100, 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 “Financial and Investment Analysts” stay human?

About 38% 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: “Develop and maintain client relationships” (3/100, minimal); “Assess companies as investments for clients by examining company facilities” (5/100, minimal); “Collaborate with investment bankers to attract new corporate clients” (8/100, minimal). 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 “Financial and Investment Analysts” do about AI?

Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 38% 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 Financial and Investment Analysts 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 26 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

Financial and Investment Analysts

drawing charts and graphs, conducting financial analyses related to investments in green construction or green retrofitting projects and interpreting data on price, yield, stability. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: drawing charts and graphs, using computer spreadsheets, to illustrate technical reports is work AI now does quickly and cheaply, and developing and maintaining client relationships is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 12%
staying human: 38%

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

Whole-job exposure score 48 out of 100 (43–54 allowing for uncertainty): partial exposure, across 26 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 financial and investment analysts 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.

Which half fills your week decides your exposure. Moving toward the second half is a real, doable plan.

Your week, as this page understands it

Conduct quantitative analyses of information involving investment programs or financial data of public or private institutions, including valuation of businesses. The job title says “financial” or “investment analysts”: officially one job, two names. The real job is the part underneath: developing and maintaining client relationships. 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 financial and investment analysts is not one task. It is 26 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is developing and maintaining client relationships, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 12%
staying human: 38%

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.

Shifting to AI

13 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.

Drawing charts and graphs, using computer spreadsheets, to illustrate technical reports
This is reading one thing and writing another: charts in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Draw charts and graphs, using computer spreadsheets, to illustrate technical reports.” (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: Making charts and graphs from report data in a spreadsheet is about as routine as computer work gets.
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.
Evaluating and comparing the relative quality of various securities in a given industry
This is reading one thing and writing another: the relative quality of various securities in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Evaluate and compare the relative quality of various securities in a given industry.” (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: Comparing securities within an industry uses abundant public data and documented methods.
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.
Monitoring developments in the fields of industrial technology
This is reading one thing and writing another: developments in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Monitor developments in the fields of industrial technology, business, finance, and economic theory.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 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: Monitoring developments in business, finance and economic theory is reading and summarizing 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 4/4.
Creating client presentations of plan details
This is reading one thing and writing another: client presentations of plan details in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Create client presentations of plan details.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 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: Turning existing plan details into a client presentation is largely assembly work, with an adviser checking what goes out.
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.

Changing shape

3 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.

Presenting oral or written reports on general economic trends
The software now makes the first pass at oral, 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 not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Present oral or written reports on general economic trends, individual corporations, and entire industries.” (O*NET task statement)
How this row was scored
Exposure score: 51 out of 100 (44–58 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 written report is straightforward, but presenting to an audience still calls for a person in the room.
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 4/4.
Evaluating capital needs of clients and assessing market conditions to inform structuring of financial packages
The software now makes the first pass at capital needs of clients, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Evaluate capital needs of clients and assess market conditions to inform structuring of financial packages.” (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: Capital needs assessment mixes documented analysis with client-specific facts only the client can supply.
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 1/4 · how much data exists 3/4.
Specializing in green financial instruments
The software now makes the first pass at green financial instruments, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Specialize in green financial instruments, such as socially responsible mutual funds or exchange-traded funds (ETF) that are comprised of green companies.” (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: Green fund knowledge is documented, but specialising means building judgement and a name in that niche.
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 1/4 · how much data exists 3/4.

Staying human

10 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.

Purchasing investments for companies in accordance with company policy
The rules require a named, qualified person to answer for investments, and that person cannot be a piece of software.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Purchase investments for companies in accordance with company policy.” (O*NET task statement)
How this row was scored
Exposure score: 36 out of 100 (29–43 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Placing actual investment orders has to be done by a licensed and registered person.
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 0/4 · how much data exists 3/4.
Advising clients on aspects of capitalization
The rules require a named, qualified person to answer for clients, and that person cannot be a piece of software.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Advise clients on aspects of capitalization, such as amounts, sources, or timing.” (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: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Capitalisation advice can be drafted from documented practice, but clients expect a qualified person accountable for it.
The five ratings: output a model can produce 2/4 · 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 2/4.
Determining the prices at which securities should be syndicated and offered to the public
The rules require a named, qualified person to answer for the prices, and that person cannot be a piece of software.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Determine the prices at which securities should be syndicated and offered to the public.” (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: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Pricing a public offering depends on live investor demand and carries regulatory responsibility.
The five ratings: output a model can produce 2/4 · 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 2/4.

Show the other 16 tasks

Informing investment decisions by analyzing financial information to forecast business
shifting to AI
This is reading one thing and writing another: investment decisions in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (68–76 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: Forecasting from financial and economic information uses plentiful published data and established methods.
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 4/4.
Performing securities valuation or pricing
shifting to AI
This is reading one thing and writing another: securities valuation in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Perform securities valuation or pricing.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (68–76 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: Valuing securities uses standard methods and market data that are widely available and well documented.
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 4/4.
Monitoring fundamental economic, industrial and corporate developments by analyzing information from financial publications and services
shifting to AI
This is reading one thing and writing another: fundamental economic, industrial and corporate developments in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Monitor fundamental economic, industrial, and corporate developments by analyzing information from financial publications and services, investment banking firms, government agencies, trade publications, company sources, or personal interviews.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (66–74 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: Most of this is reading published sources, though personal interviews still bring information nobody has written down.
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 4/4.
Analyzing financial or operational performance of companies facing financial difficulties to identify or recommend remedies
shifting to AI
This is reading one thing and writing another: this work in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Analyze financial or operational performance of companies facing financial difficulties to identify or recommend remedies.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Analysing a struggling company's numbers and suggesting remedies is analysis over financial statements and operating data.
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.
Conducting financial analyses related to investments in green construction or green retrofitting projects
shifting to AI
This is reading one thing and writing another: financial analyses in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Conduct financial analyses related to investments in green construction or green retrofitting projects.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Financial analysis of green building projects is spreadsheet work using published cost and incentive data.
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.
Employing financial models to develop solutions to financial problems or to assess the financial or capital impact of transactions
shifting to AI
This is reading one thing and writing another: financial models in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Employ financial models to develop solutions to financial problems or to assess the financial or capital impact of transactions.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Building and running financial models to test transaction impacts is well documented spreadsheet work.
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.
Interpreting data on price, yield, stability, future investment-risk trends, economic influences and other factors affecting investment programs
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 not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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 price, yield and risk data is pattern work over abundant, well documented market information.
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.
Preparing all materials for transactions or execution of deals
shifting to AI
This is reading one thing and writing another: all materials in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare all materials for transactions or execution of deals.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Assembling transaction paperwork follows standard document sets and templates.
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.
Preparing plans of action
shifting to AI
This is reading one thing and writing another: plans of action in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare plans of action for investment, using financial analyses.” (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: Investment action plans follow documented methods, though a responsible person signs them off.
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.
Recommending investments and investment timing
staying human
The rules require a named, qualified person to answer for investments, and that person cannot be a piece of software.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Recommend investments and investment timing to companies, investment firm staff, or the public.” (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; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Recommending specific investments to others is regulated advice that a licensed person must give.
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 2/4 · how much data exists 3/4.
Collaborating on projects with other professionals
staying human
The value here is that a specific person handles projects and stands behind it. That is earned, not computed.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Collaborate on projects with other professionals, such as lawyers, accountants, or public relations experts.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (12–20 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Working alongside lawyers, accountants and others on a deal runs on live coordination between people.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Conferring with clients to restructure debt
staying human
The rules require a named, qualified person to answer for clients, and that person cannot be a piece of software.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Confer with clients to restructure debt, refinance debt, or raise new debt.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (9–17 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Debt restructuring talks with a client turn on trust and negotiation with the people involved.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Supervising, training or mentor junior team members
staying human
The value here is that a specific person handles mentor junior team members and stands behind it. That is earned, not computed.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Supervise, train, or mentor junior team members.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (9–17 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Mentoring junior colleagues depends on a working relationship with those particular people.
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 2/4.
Collaborating with investment bankers to attract new corporate clients
staying human
The rules require a named, qualified person to answer for investment bankers, and that person cannot be a piece of software.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Collaborate with investment bankers to attract new corporate clients.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Winning new corporate clients rests on relationships and reputation between specific people.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 1/4.
Assessing companies as investments for clients by examining company facilities
staying human
This work happens in the physical world: companies, in a real place. Software cannot follow it there.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Assess companies as investments for clients by examining company facilities.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (1–9 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Assessing an investment by examining the company's facilities means going there and seeing them.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Developing and maintaining client relationships
staying human
The value here is that a specific person handles client relationships and stands behind it. That is earned, not computed.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Develop and maintain client relationships.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: A client relationship is the work itself and needs a person on both sides of it.
The five ratings: output a model can produce 0/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 4/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $102,740a 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: 361,980in 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: the relative quality of various securities in, a record out. The rows above are exactly that shape: drawing charts and graphs, using computer spreadsheets and evaluating and comparing the relative quality of various securities in a given industry. What it cannot do is be trusted in person, which is what client 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: drawing charts and graphs, using computer spreadsheets, to illustrate technical reports is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 50% of this job's task weight sits in rows the software is already learning, 12% in rows that change shape rather than disappear, and 38% in rows it is nowhere near. That is the position, measured across 26 scored tasks. It is not a forecast about you.

What you have that the software does not is developing and maintaining client relationships, 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 the relative quality of various securities 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 the relative quality of various securities, 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 drawing charts and graphs, using computer spreadsheets, to illustrate technical reports” 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 developing and maintaining client relationships 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 financial and investment analysts (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was financial managers: only about 13% of its durable work is work you already do. Your own job splits about 50/50: 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 “advise clients on aspects of capitalization”, 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.

Financial Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already recommend investments and investment timing to companies, investment firm staff, or the public, and their equivalent is to manage investment funds to maximize return on client investments. Across both published task lists that is about 13% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 13% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Financial Risk Specialists
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “recommend investments and investment timing to companies, investment firm staff, or the public”. Across the whole of both lists that adds up to about 7% of the work in that job the software is not taking.
Why I am not recommending it: Almost none of it is work you already do: about 7% 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: 70% of its own task list already scores in the top exposure band (66/100 in this release), so the same software is eating it. And it is a narrow door: about 63,850 of those jobs against 361,980 of yours (OEWS May 2025), 18% as many seats.
Look at that job’s page anyway →
Personal Financial Advisors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already create client presentations of plan details, and their equivalent is to answer clients' questions about the purposes and details of financial plans and strategies. 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.
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: 50% of its task weight, across 26 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: developing and maintaining client relationships 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 Finance and investment analysts and advisers 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:

Financial managers and directors

In UK official statistics this job is counted as Finance and investment analysts and advisers and Financial managers and directors. 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 financial analysts 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 - it's built for people making the Microsoft reporting stack behave: clean data, defined metrics, refreshes that don't break. That's the model-and-report half of your week. It does not cover valuation, markets or client work, which is most of the rest of it.

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 financial analyst 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 Financial and Investment Analysts?: Not as a job, but it is already doing parts of the work. Across the 26 official task statements scored for Financial and Investment Analysts (United States, SOC 13-2051), 50% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 48 out of 100 (range 43–54, 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 “Financial and Investment Analysts” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Draw charts and graphs, using computer spreadsheets, to illustrate technical reports” (93/100, very high); “Evaluate and compare the relative quality of various securities in a given industry” (83/100, very high); “Monitor developments in the fields of industrial technology, business, finance, and economic theory” (83/100, 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 “Financial and Investment Analysts” stay human?: About 38% 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: “Develop and maintain client relationships” (3/100, minimal); “Assess companies as investments for clients by examining company facilities” (5/100, minimal); “Collaborate with investment bankers to attract new corporate clients” (8/100, minimal). 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 “Financial and Investment Analysts” do about AI?: Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 38% 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 Financial and Investment Analysts 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 26 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

O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
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.
12 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-with-imputed)
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.

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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}
}

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.