Personal Financial Advisors

Business & Financial Operations

AI exposure

  • Data source: BLSPublished: 2026-08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.350

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.57

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 7% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Explain to clients the personal financial advisor's responsibilities and the types of services to be provided.

O*NET Task ID 20186

1.0
Analyze financial information obtained from clients to determine strategies for meeting clients' financial objectives.

O*NET Task ID 12904

0.5
Answer clients' questions about the purposes and details of financial plans and strategies.

O*NET Task ID 12905

0.5
Interview clients to determine their current income, expenses, insurance coverage, tax status, financial objectives, risk tolerance, or other information needed to develop a financial plan.

O*NET Task ID 12907

0.5
Implement financial planning recommendations, or refer clients to someone who can assist them with plan implementation.

O*NET Task ID 12909

0.5
Prepare or interpret for clients information, such as investment performance reports, financial document summaries, or income projections.

O*NET Task ID 12913

0.5
Guide clients in the gathering of information, such as bank account records, income tax returns, life and disability insurance records, pension plans, or wills.

O*NET Task ID 12914

0.5
Contact clients periodically to determine any changes in their financial status.

O*NET Task ID 12915

0.5
Devise debt liquidation plans that include payoff priorities and timelines.

O*NET Task ID 12917

0.5
Open accounts for clients, and disburse funds from accounts to creditors as agent for clients.

O*NET Task ID 12918

0.5
Meet with clients' other advisors, such as attorneys, accountants, trust officers, or investment bankers, to fully understand clients' financial goals and circumstances.

O*NET Task ID 12916

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information

Recent Changes Related to This Occupation

Mar 2026: Gupta & Kumar (arXiv, Mar 2026) list Personal Financial Advisors at an Agentic Task Exposure (ATE) score of 0.42 by 2027 in the San Francisco Bay Area (Tier 1), rising to 0.46 by 2030. With 55.9% of business and financial operations workers teleworking at least some hours, the paper argues exposure follows the employer's region rather than where the worker lives.

[Source: arXiv 2604.00186 (Gupta & Kumar, 2026)]

These summaries were written by AI Changing Work from the source linked with each one, and any figures in them are given as AI Changing Work summarised them; they can differ from the source's own wording and from figures shown elsewhere on this page, so check the source before relying on them. AI Changing Work matched this page's occupation to an O*NET occupation and chose, by its own judgment, case by case, which summaries relate to that O*NET occupation; a summary appearing here does not mean that its source names this occupation.