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Organizational psychologists

Official title Industrial-Organizational Psychologists (SOC 19-3032)

Also called I-o psychologist, Industrial-organizational psychologist, People scientist, Workforce scientist, Talent research scientist

In brief

In May 2025, BLS counted 790 US organizational psychologists, with median pay of $193,950 a year. Of the BLS counts since 2003, the highest was 1,710, in May 2009. The earliest figure here is an estimate of 10 from 1917, not measured the same way as today's count. The last full technology era for organizational psychologists: People analytics platforms and O*NET digital infrastructure, 2010 to 2020.

What the sources say

Official outlook

BLS projects employment to grow 6.5% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Organizational psychologists: jobs and pay since 1917 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-3032

Chicago

Future History. "Organizational psychologists: jobs and pay since 1917." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-3032.

BibTeX

@misc{futurehistory_19_3032, author = {{Future History}}, title = {Organizational psychologists: jobs and pay since 1917}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-3032}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1925195019752000now
2026
Known then as Applied Psychologist / Economic Psychologist (pre-WWI consulting era)
Known then as Industrial Psychologist (military testing and interwar consulting era)
Known then as Industrial and Business Psychologist (APA Division 14 founding era)
Known then as Industrial and Organizational Psychologist (APA renaming era)
Known then as Industrial-Organizational Psychologist (SIOP independence era)
Known today as Industrial-Organizational Psychologist (SIOP independence era)
Projected to still be called Industrial-Organizational Psychologist (SIOP independence era)
Data from the May 2025 survey
US Employment
790
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$193,950
Est.

≈ $188,977 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1913

Hugo Munsterberg publishes "Psychology and Industrial Efficiency" at Harvard -- the founding text of industrial psychology as a named field. Munsterberg argues that psychological methods can identify which workers are best suited to which jobs, how to improve learning and training, and how to increase motivation. The book is widely reviewed and generates demand for consulting services. Munsterberg was German-born and faced significant anti-German sentiment during WWI; he died in 1916 before the war ended, and his influence on the field was largely mediated through American followers including Walter Dill Scott and Walter Van Dyke Bingham.

Beat · 1927

Elton Mayo and colleagues begin the Hawthorne Studies at Western Electric's Hawthorne Works in Cicero, Illinois (1927-1932). The studies begin as an investigation of lighting effects on worker productivity but produce a landmark finding: workers increase productivity when they know they are being observed, regardless of the specific working conditions changed. The "Hawthorne effect" becomes the first well-documented phenomenon in what would become organizational psychology. Mayo's work shifts the field's focus from the individual worker's cognitive capacity (the WWI testing model) toward social relationships, group dynamics, and the meaning of work -- the seeds of organizational psychology.

Beat · 1945

The American Psychological Association reorganizes after merging with the American Association of Applied Psychology. Division 14 (Industrial and Business Psychology) is created, giving industrial psychologists an organizational home within APA for the first time. At the 1947 APA meeting in Detroit, 80 fellows and 50 associates are counted as charter members -- 130 people in what is now a field with over 10,000 SIOP members and tens of thousands of practitioners worldwide. The new division adopts as its purpose, in language that has not changed significantly since: "the application of scientific principles to problems of human beings in organizations."

Beat · 1964

Title VII of the Civil Rights Act (1964) and the Civil Rights Act's creation of the Equal Employment Opportunity Commission transform industrial psychology from a productivity science into a legal compliance discipline. Griggs v. Duke Power Co. (1971) establishes that neutral-appearing selection procedures that produce adverse impact on protected groups violate Title VII without demonstrated job-relatedness. The Uniform Guidelines on Employee Selection Procedures (1978) codify the 4/5 rule and validation requirements. I-O psychologists become indispensable to employers who need defensible selection systems -- not because employers are suddenly more ethical, but because the litigation cost of indefensible systems is now very real.

Beat · 1973

APA renames Division 14 from "Industrial Psychology" to "Industrial and Organizational Psychology," formally acknowledging that the field has expanded beyond individual assessment and selection to include group processes, organizational structure, leadership, and culture. The renaming reflects nearly two decades of growth in organizational behavior research -- Kurt Lewin's group dynamics work, Frederick Herzberg's motivation-hygiene theory (1959), Douglas McGregor's Theory X/Theory Y (1960), and the emergence of organizational development (OD) as a distinct consulting practice. The "O" in I-O was real: practitioners were increasingly designing change interventions, facilitating team effectiveness, and diagnosing organizational health, not just validating selection tests.

Beat · 1982

SIOP incorporates as an independent organization, separating from APA Division 14 in response to tension over APA's attempts to regulate I-O practice and the division's desire for more autonomy in setting professional standards. The independence move was partly about scientific integrity (I-Os resisted APA's clinical-model credentialing framework being applied to organizational consulting) and partly about governance (SIOP wanted to control its own membership requirements and publications). SIOP retains its Division 14 affiliation with APA as a dual structure but operates independently. The 1982 date marks the field's formal professional maturity: it is now a self-governing applied science with its own standards, journal (Journal of Applied Psychology predates this; SIOP's own publications would follow), and identity distinct from clinical psychology.

Beat · 2023

NYC Local Law 144 goes into enforcement on July 5, 2023. Any employer or employment agency that uses an automated employment decision tool (AEDT) in New York City hiring or promotion must: (1) conduct an annual bias audit by an independent auditor before using the tool; (2) publish the audit results; and (3) notify candidates that an AEDT is being used. The law creates a direct compliance market for I-O psychologists who can conduct adverse impact analyses and produce audit reports meeting the NYC Department of Consumer and Worker Protection's published requirements. SIOP members report billings of $10,000-$50,000 per AEDT audit engagement. The EU AI Act's employment provisions, staggered for enforcement through 2027, extend similar requirements across the European Union.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace organizational psychologists? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+6.5%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-3032 (Industrial-organizational psychologists): 4.0 thousand jobs in 2025, projected 4.3 thousand in 2035, a change of +0.3 thousand (+6.5%). Matrix employment counts all jobs in the occupation, including self-employed workers (80.3% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
58.9%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-3032.00 Industrial-Organizational Psychologists. Shown: human-rated β = 0.589, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.356 and γ (all LLM-assisted) 0.822; GPT-4-rated β 0.489. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for organizational psychologists

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereAnalyze people-analytics data to drive workforce planning and organizational decisions: using platforms (Visier, Culture Amp, Workday Analytics) to model headcount needs, attrition risk, internal mobility patterns, and compensation equity

Analyze people-analytics data to drive workforce planning and organizational decisions: using platforms (Visier, Culture Amp, Workday Analytics) to model headcount needs, attrition risk, internal mobility patterns, and compensation equity; applying statistical methods (regression, survival analysis, machine learning) to people datasets; interpreting AI-generated predictive attrition and flight-risk scores; and translating statistical findings into strategic recommendations for HR leadership and the C-suite.[12],[10]

Where your edge is

People analytics platforms have automated the data aggregation, dashboard visualization, and basic predictive modeling that previously required weeks of I-O analyst time: use them aggressively. The platforms surface patterns; they do not contextualize them within the organization's business strategy, merger history, or workforce composition changes. Predictive attrition models frequently produce low base-rate precision-recall tradeoffs on small workforce segments that executives misinterpret as actionable individual-level predictions. The I-O's differentiating contribution is: statistical interpretation sophistication (effect sizes, confidence intervals, base-rate sensitivity of ML predictions), causal inference awareness (correlation in observational HR data ≠ causal lever), and organizational context that turns a Visier dashboard into a defensible strategic recommendation.

AI is sitting alongside you hereConduct research synthesis and applied research to inform people practices: using AI-powered literature synthesis tools (Semantic Scholar, Consensus, OpenEvidence) to accelerate evidence reviews on interventions (team effectiveness, psychological safety, diversity and inclusion program efficacy, AI tool validity), designing field studies or quasi-experiments to evaluate HR intervention effectiveness within the organization, and translating research findings into executive briefings and practitioner guidance.

Conduct research synthesis and applied research to inform people practices: using AI-powered literature synthesis tools (Semantic Scholar, Consensus, OpenEvidence) to accelerate evidence reviews on interventions (team effectiveness, psychological safety, diversity and inclusion program efficacy, AI tool validity), designing field studies or quasi-experiments to evaluate HR intervention effectiveness within the organization, and translating research findings into executive briefings and practitioner guidance.[13],[14]

Where your edge is

AI literature synthesis tools (Semantic Scholar, Consensus) now compress a targeted literature review from hours to minutes: use them systematically. The I-O's differentiating contribution is methodological sophistication: distinguishing RCT evidence from observational study findings, evaluating whether effect sizes in lab studies generalize to organizational field settings, and identifying where the literature has a publication bias problem (diversity training ROI, for instance, has contested effect sizes across well-designed studies). Research synthesis tools summarize existing literature; they cannot design a field study or quasi-experiment, identify appropriate statistical controls for observational HR data, or evaluate the internal validity of a proposed research design. I-Os who integrate AI research tools with rigorous methodological training become faster without becoming less rigorous.

AI is sitting alongside you hereDesign and execute employee engagement and org-health diagnostic surveys, developing theoretically grounded survey items (or customizing vendor instruments such as Glint/Viva Insights or Culture Amp), managing survey administration and communication strategy, interpreting AI-surfaced engagement themes and sentiment analysis from open-text responses, identifying drivers of engagement and attrition through regression and machine learning analysis, and presenting findings with evidence-based recommendations to HR leadership and executive teams.

Design and execute employee engagement and org-health diagnostic surveys, developing theoretically grounded survey items (or customizing vendor instruments such as Glint/Viva Insights or Culture Amp), managing survey administration and communication strategy, interpreting AI-surfaced engagement themes and sentiment analysis from open-text responses, identifying drivers of engagement and attrition through regression and machine learning analysis, and presenting findings with evidence-based recommendations to HR leadership and executive teams.[15],[16],[12]

Where your edge is

Culture Amp and Glint/Viva Insights now automate the data aggregation, benchmarking, and basic theme identification that previously consumed weeks of I-O analyst time. The AI-surfaced themes and suggested "focus areas" are a useful starting point, but they embed assumptions about what engagement dimensions matter and how open-text sentiment maps to actionable HR interventions that require I-O interpretation to evaluate critically. The distinctive I-O contribution is: (1) asking whether the survey instrument measures what it claims to measure (construct validity) and whether the engagement drivers identified by the platform actually predict the outcomes the organization cares about (criterion validity); (2) identifying where AI-benchmarked scores are misleading because the comparison group is not comparable to this organization's workforce composition; (3) reading the political landscape of the organization to distinguish survey findings that reflect measurement artifacts from those that reflect real workforce dynamics. The AI does the arithmetic; the I-O does the diagnosis.

Where organizational psychologists go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Human Resources Managers

I-O psychologists with enterprise consulting or embedded People Analytics experience are well-positioned to transition into HR Manager or VP People roles, particularly in organizations that are building out AI-powered people analytics capabilities and need a leader who can evaluate vendor claims, drive research-backed people practices, and navigate the AI hiring compliance landscape. The transition leverages the I-O's quantitative and assessment expertise while adding operational scope (managing HR teams, owning the employee lifecycle end-to-end, budget responsibility). The slightly lower CRI (but see 11-3121 for the curated figure) reflects that HR Management is broader and includes more routine administrative functions that are more AI-augmented. The career path is well-established: Google's "People Operations" model explicitly drew I-O psychologists into HR leadership roles, and this pattern has diffused broadly through tech and large enterprise HR. Target roles: Director of People Analytics, VP People Science, Head of Talent Intelligence.

What you'd add
  • · HR operations literacy: HRIS administration (Workday, SAP SuccessFactors), compensation benchmarking, benefits administration, employee relations case management, the operational muscle that I-O consulting backgrounds often lack
  • · People management: building and leading an HR team, performance management of direct reports, organizational influence in a functional leadership role
  • · Business partnership: translating I-O research findings into executive-ready business cases; operating as a strategic HR business partner vs. a technical consultant
  • · Employment law: deeper working knowledge of Title VII, ADEA, ADA, FMLA, state employment law, beyond the UGESP / EEOC adverse impact context familiar from I-O practice
  • · AI governance: formal familiarity with NYC LL144, EU AI Act, EEOC AI guidance, I-Os' existing compliance expertise becomes a leadership differentiator in HR manager roles
What it takesSome new skills to pick up
Tools of the era

How has the work of organizational psychologists changed since 1901?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Paper-and-pencil surveys and time-motion studies (Taylor era)

    The first I-O practitioners worked with two principal instruments: Frederick Taylor's time-motion study stopwatch and clipboard (to measure and standardize physical labor) and paper-based advertising response surveys (to measure persuasion and consumer choice). Walter Dill Scott's early work on advertising psychology relied on surveys and experiments conducted with printed materials. Hugo Munsterberg's laboratory studies of worker selection used reaction-time apparatus and written tests. There was no standardized psychometric tradition yet; each practitioner devised their own instruments. The Binet-Simon intelligence scale (1905, revised 1908) was the first widely circulated standardized test, but its application to workplace selection was not yet established.

  • Army Alpha and Beta group-administered cognitive tests (WWI mass testing era)

    The Army Alpha and Beta tests, designed in 1917 by a committee including Robert Yerkes, Arthur Otis, Lewis Terman, and Walter Dill Scott, were the first group-administered standardized tests used at scale. Army Alpha was a verbal-arithmetic test for literate English speakers; Army Beta used pictorial items for the illiterate and non-English-speaking. More than a million soldiers were tested between 1917 and 1919. The tests established that cognitive measurement could be administered to large groups in a single session -- a practical breakthrough that made workplace selection testing economically feasible. Post-WWI, the Alpha and Beta served as templates for civilian cognitive tests; the Army General Classification Test in WWII (administered to millions of soldiers) extended the same paradigm at even greater scale.

    Effect on the work

    The WWI testing program validated psychological testing as a personnel selection method and produced a cohort of trained practitioners who took their methods into corporate employment. The 1920s saw the founding of the Psychological Corporation (1921) and the growth of industrial consulting practices at large employers. By the late 1920s, AT&T, Standard Oil, and other major corporations had begun using standardized cognitive tests in hiring.

  • Validated personality inventories and structured job analysis (post-WWII psychometrics era)

    The decades following WWII produced the psychometric infrastructure that defined I-O practice for a generation: the Minnesota Multiphasic Personality Inventory (MMPI, 1943), the Edwards Personal Preference Schedule (1954), the California Psychological Inventory (1956), Flanagan's Critical Incident Technique for job analysis (1954), and the establishment of structured job analysis methods linking task requirements to selection criteria. The field formalized its scientific standards: the APA Technical Recommendations for Psychological Tests and Diagnostic Techniques (1954) established the first validity and reliability standards for test publishers. I-Os designed selection batteries for large employers (the AT&T Management Progress Study beginning in 1956 was the landmark longitudinal study of management assessment center methods).

  • Uniform Guidelines and adverse impact analysis (Civil Rights compliance era)

    Title VII of the Civil Rights Act (1964) and the creation of the EEOC transformed I-O psychology from a productivity science into a compliance discipline. For the first time, employers could face federal liability if their selection tests produced adverse impact on protected groups without demonstrated job-relatedness. The Supreme Court's 1971 decision in Griggs v. Duke Power Co. established that even unintentional testing discrimination violated Title VII. The Uniform Guidelines on Employee Selection Procedures (1978) codified the rule (adverse impact threshold), specified validity evidence requirements, and established I-Os as the credentialed practitioners responsible for selection system defense. Every large employer with a testing program needed I-O psychologists who could design, validate, and defend their selection tools in court.

    Effect on the work

    The Civil Rights Act and subsequent litigation created institutional demand for I-O psychologists as compliance experts, not just productivity consultants. Corporate legal and HR functions began hiring I-Os specifically to validate selection systems and respond to EEOC complaints. The field's practitioner base grew substantially through the 1970s and 1980s as employer liability for discriminatory testing became a real litigation risk.

  • Statistical software and meta-analysis (Hunter-Schmidt validity generalization era)

    John Hunter and Frank Schmidt's meta-analytic validity generalization research (beginning with their 1977 paper and culminating in their 1984 and 1990 books) transformed I-O practice by demonstrating that cognitive ability tests showed consistent predictive validity across jobs and organizations -- undermining the then-dominant "situational specificity" assumption that each selection test had to be validated locally. Meta-analysis became the primary I-O scientific method for synthesizing research findings. Statistical software (SPSS, SAS, and later R) made multivariate analysis, confirmatory factor analysis, and structural equation modeling routine for practitioners. The combination of meta-analytic evidence and personal computer software meant an I-O could conduct what previously required a mainframe and months of hand calculation.

  • People analytics platforms (Culture Amp, Visier, Glint, Workday) and O*NET digital infrastructure

    The people-analytics revolution of the 2010s gave I-O practitioners data infrastructure they had never previously had at scale. Culture Amp (founded 2009), Visier (founded 2010), and Glint (founded 2015, acquired by LinkedIn/Microsoft) automated the data aggregation, benchmarking, and basic predictive modeling that previously required weeks of analyst time. The O*NET database (launched 1998, continuously updated) provided a freely accessible, research-grade occupational taxonomy that accelerated job analysis and competency modeling projects. I-Os in corporate "People Analytics" functions could run regression-based flight-risk models, compensation equity analyses, and engagement driver analyses that would previously have required specialized research teams.

  • AI hiring tools and algorithmic bias auditing (HireVue, Pymetrics/Harver, NYC LL144, EU AI Act era)

    The proliferation of AI-powered hiring tools -- HireVue video interview AI (since 2014), Pymetrics/Harver game-based assessments, Plum talent scoring, and hundreds of other vendors -- created both a threat and an opportunity for I-Os. The threat: AI tools claimed to do selection science automatically, potentially bypassing the I-O practitioner. The opportunity: AI tools introduced new forms of potential discrimination that required credentialed expert review. NYC Local Law 144 (enforcement from July 2023) requires annual independent bias audits of any AI employment decision tool used in New York City hiring; the EU AI Act (Regulation (EU) 2024/1689, staggered enforcement 2024-2027) classifies employment AI as high-risk, requiring conformity assessments and bias documentation. Both laws created a billable compliance market -- AEDT audits billing $10,000-$50,000 per engagement -- that positions the I-O psychologist as the AI auditor, not the person being audited.

    Effect on the work

    The AI hiring-tool compliance market is the single largest new revenue stream in I-O consulting since the Civil Rights Act created the validation market in the 1970s. SIOP members reported in TIP Vol. 62 (2025) that NYC LL144 auditing had become a significant practice area, with I-Os billing at rates previously reserved for litigation support. The demand for psychometricians who can evaluate AI vendor technical reports, run adverse impact analyses by demographic group, and sign off on algorithmic fairness determinations has meaningfully expanded the senior practitioner market.

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Industrial-Organizational Psychologists employment and pay by year

On record since1901
Latest US employment790 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$193,950 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+6.5% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
191710n/aEst., TxWes Pressbooks Researched contextReport
192950n/aEst., TxWes Pressbooks Researched contextReport
1947130n/aEst., SIOP History Researched contextReport
1956n/a$7,200Est., University of Missouri Libraries Researched contextReport
20012,400$82,000Est., SIOP membership data 2001 (employment) Researched contextEst., BLS Occupational Outlook Handbook (pay) Researched contextReport
20031,330$64,440BLS OEWS May 2003 Official statisticReport
20041,500$71,400BLS OEWS May 2004 Official statisticReport
20051,070$84,690BLS OEWS May 2005 Official statisticReport
20061,140$86,420BLS OEWS May 2006 Official statisticReport
20071,240$80,820BLS OEWS May 2007 Official statisticReport
20081,460$77,010BLS OEWS May 2008 Official statisticReport
20091,710$83,260BLS OEWS May 2009 Official statisticReport
20101,420$87,330BLS OEWS May 2010 Official statisticReport
20111,230$94,720BLS OEWS May 2011 Official statisticReport
20121,030$83,580BLS OEWS May 2012 Official statisticReport
20131,040$80,330BLS OEWS May 2013 Official statisticReport
20141,110$76,950BLS OEWS May 2014 Official statisticReport
2015990$77,350BLS OEWS May 2015 Official statisticReport
20161,020$82,760BLS OEWS May 2016 Official statisticReport
2017920$87,100BLS OEWS May 2017 Official statisticReport
2018780$97,260BLS OEWS May 2018 Official statisticReport
2019630$92,880BLS OEWS May 2019 Official statisticReport
2020780$96,270BLS OEWS May 2020 Official statisticReport
2021610$105,310BLS OEWS May 2021 Official statisticReport
20221,280$139,280BLS OEWS May 2022 Official statisticReport
20231,030$147,420BLS OEWS May 2023 (employment) Official statisticBLS OEWS May 2023 (pay) Official statisticReport
20241,050$109,840BLS OEWS May 2024 Official statisticReport
2025790$193,950BLS OEWS May 2025 Official statisticReport
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