Evidence (3308 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Skills Training
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The article specifies how different AI modalities alter activity demands and facet configurations.
Analytical mapping within the conceptual framework described in the paper (theory-driven specification; no empirical testing reported in the abstract).
The paper clarifies how the framework was theoretically derived and distinguishes it from competency models, KSAO approaches, ability requirement scales, work analysis, psychometrics, and skills-based HRM.
Theoretical exposition and comparative discussion presented in the paper (conceptual analysis; no empirical comparison reported in the abstract).
These five dimensions are decomposed into nineteen sub-dimensions and sixty facets, each interpretable through four progressive mastery levels.
Framework taxonomy and granularity as specified in the paper (explicit counts given in the conceptual model; no validation sample).
The ATHENA framework is organized around five interdependent dimensions: cognition, conation, knowledge, emotion, and sensorimotor resources.
Descriptive specification of the framework's structure as presented in the paper (conceptual delineation; no empirical measurement reported).
ATHENA proposes an intermediate analytical layer through facets specified at developmental mastery levels to connect activity-based work analysis with recruitment, learning design, internal mobility, and strategic workforce planning under task volatility.
Framework design and proposed application described in the paper (conceptual/theoretical proposal; no empirical testing reported in the abstract).
This article introduces ATHENA (Advanced Tool for Holistic Evaluation and Nurturing of Abilities), a facet-based framework that reconceptualizes competence as an emergent, context-bound configuration of mobilizable human resources rather than a stable entity attached to job titles.
Primary contribution described in the paper: a theory-building, conceptual framework presented and explained (no empirical validation reported).
Artificial intelligence (AI) increasingly changes work at the level of tasks, activity sequences, decision criteria, and human–tool interaction.
Conceptual claim supported by theoretical argumentation in the paper (theory-building; no empirical sample or quantitative analysis reported in the abstract).
Human resource management (HRM) remains predominantly organized around competency and occupation-based representations that implicitly presume relative stability in work content.
Statement in the paper's introduction/theory section; presented as a literature-grounded theoretical observation in this theory-building article (no empirical sample reported).
Social implication: AI may contribute to wage differences across occupations by enhancing productivity in certain roles, so equitable access to skills and training is important to distribute benefits.
Discussion in the paper linking observed AI-wage associations to potential productivity effects and recommending equitable skill and training access.
Policy implication: occupations with higher exposure to AI tend to exhibit higher wages, suggesting the importance of skill upgrading and targeted workforce policies.
Interpretation and policy discussion based on the observed positive association between AI exposure and wages in the occupation-level analysis.
Findings are broadly consistent when using an instrumental variable (IV) approach.
Paper reports results from an IV estimation strategy on the same occupation-level data, which produce broadly consistent associations between AI exposure and wages.
The positive association between AI exposure and wages holds across the wage distribution.
Quantile regression analysis applied to the occupation-level dataset (671 occupations) showing the pattern across different quantiles of the wage distribution.
The positive association between AI exposure and wages is robust across different model specifications.
Reported consistency of results across multiple regression specifications on the same occupation-level dataset; robustness checks described in the paper.
There is a positive and statistically significant association between AI exposure and wages.
Cross-sectional regression models with robust standard errors estimated on occupation-level data combining wage information and an AI exposure index for 671 occupations; models control for employment size and occupational characteristics.
The paper maps classical teaching moves and AI-supported interventions to each step of the six-move model to make it usable.
Authors' mapping contribution described in the paper (conceptual/resource contribution; no validation reported in the excerpt).
Placement rule: secure the first hard attempt and the final unaided check, scaffold with guarded AI in between.
Authors' prescriptive rule derived from their framework and interpretation of evidence; no empirical validation provided in the excerpt.
The authors propose a six-move learning frame (Prime, Probe, Point, Attach, Strengthen, Test) as a graspable way for educators to place AI in instruction.
Authors' proposed instructional framework described in the paper (conceptual contribution, no empirical validation presented in the excerpt).
A well-engineered tutor (AI) roughly doubled learning.
Reported as part of the paper's summary of causal evidence; specific study details and sample size not given in the excerpt.
Used well, AI can scale feedback, examples, practice, and individualized support.
Asserted by the authors as a general benefit; no specific empirical sample or study detailed in the excerpt.
The allow-or-ban framing is a false dichotomy; the relevant design question is placement.
The paper's conceptual argument / authors' recommendation (no empirical evidence reported in the excerpt).
Proactive transition planning and workforce interventions (systematic retraining, transparent transition planning, strategic capability repositioning, long-term resilience building) can support employee wellbeing and maintain operational continuity during profound economic transformation.
Article presents these interventions as evidence-based organizational responses—synthesized recommendations rather than results from a specific controlled empirical study in the provided excerpt.
Organizations that proactively address AI's workforce implications through systematic retraining, procedural fairness, and adaptive organizational design can better navigate technological disruption.
Synthesis of research-backed organizational responses presented in the article (recommendations drawn from reviewed literature and expert panels); no specific randomized or longitudinal evaluation cited in the excerpt.
Policy frameworks should prioritize human capital development alongside technological integration to prevent AI-driven increases in inequality.
Policy recommendation derived from the empirical findings (observational cross-country associations and heterogeneity by education/development) reported in the paper.
Developing nations face a 'digital divide' where AI adoption coincides with rising income inequality.
Subgroup analysis / heterogeneity results reported for developing vs. developed countries using the compiled World Bank/OECD dataset and OLS / Random Forest methods; the paper states that in developing countries AI adoption is associated with increases in the Gini index.
AI adoption, in isolation, exhibits a positive correlation with the Gini index—suggesting it exacerbates income inequality.
Cross-sectional analysis using a compiled dataset from World Bank and OECD indicators; statistical analysis reported using Ordinary Least Squares (OLS) regression and Random Forest models showing a positive association between AI adoption measures and country Gini coefficients.
The benefits of AI in auditing are more effectively realized when organizational practices support interaction between auditors and AI tools.
Synthesis from the SLR of 43 studies identifying organizational enablers and practice changes that mediate AI outcomes; sociomaterial analysis emphasizing socio-technical interaction.
AI adoption in auditing is driven by efficiency, accuracy, real-time auditing, Big Data analytics and standardization.
Systematic Literature Review (SLR) of 43 studies analyzed through a sociomaterial lens; synthesis across reviewed studies reporting motives and expected benefits for AI use in auditing.
Proficiency in data analysis and creative ideation are increasingly requisite for media roles referencing AI skills.
Coding of skill requirements in the >200 job vacancies showing rising mentions of data analysis and creative ideation alongside AI-related requirements in 2023–2025.
Media professionals are expected to acquire familiarity with emerging tools and demonstrate capabilities in content creation, editing, fact-checking, and generation.
Analysis of job-ad required skills and responsibilities across the >200 vacancies showing repeated expectations for tool familiarity and tasks like creation, editing, fact-checking, and content generation.
The repertoire of available positions is being augmented to include roles such as AI translator, AI editor, AI designer, AI content manager, and AI trainer.
Identification of novel job titles that explicitly include 'AI' in the title within the >200-job vacancy dataset collected through 2025.
By 2025, roles encompassing AI expertise include copywriter, social media manager, public relations specialist, and designer, whereas in 2023 these roles were confined to editors and journalists.
Year-by-year breakdown of job titles in the >200 vacancy sample showing the presence of new role types (copywriter, social media manager, PR specialist, designer) with AI requirements in 2025 compared to predominantly editors/journalists in 2023.
The scope of positions requiring AI competence expanded significantly between 2023 and 2025.
Longitudinal comparison of job titles and required skills across the >200 vacancies showing an increase in the variety of roles listing AI competence from 2023 to 2025.
The demand for AI-competent roles is predominantly for full-time employment.
Classification of contract type (full-time vs. part-time/contract) in the >200 job vacancies, with majority labeled as full-time.
Employers increasingly prioritize practical experience with primary tools used for content creation and management.
Content analysis of job-ad required skills showing growing mentions of specific content-creation/management tools across the >200 vacancies sampled (2023–2025).
Employers now prioritize candidates who possess foundational knowledge of AI functionalities and an active interest in the technology.
Thematic coding of job-ad text (requirements sections) from the same sample of >200 vacancies showing recurring language requesting foundational AI knowledge and interest.
Proficiency in AI has transitioned from a supplementary skill to a fundamental competency essential for media professionals.
Content analysis of over 200 media-industry job vacancies referencing AI skills collected across 2023–2025; comparison of job-ad requirement language across years.
For Chinese firms, productivity gains from GenAI are most likely when adoption is supported by cloud infrastructure, data readiness, skilled labor, workflow redesign, and strong digital ecosystems.
Synthesis of China-focused digital transformation studies and literature incorporated into the review; no new China-specific empirical analysis in this paper.
Existing studies show that GenAI can improve software-development tasks.
Synthesis of empirical studies and task-level experiments (e.g., developer assistance tools) reviewed in the paper.
Existing studies show that GenAI can improve consulting tasks.
Cited task-level studies and applied examples in advisory/consulting work synthesized in the review.
Existing studies show that GenAI can improve customer support tasks.
Review of task-level experiments and applied studies in customer support settings reported across the literature synthesized in the paper.
Existing studies show that GenAI can improve writing tasks.
Synthesis of task-level productivity experiments and prior empirical studies on GenAI-assisted writing (literature reviewed in the paper).
There is a need for revised role taxonomies, new governance and oversight functions, and updated design approaches for AI-native enterprise software systems.
Study conclusions drawn from thematic synthesis of data from 20 expert interviews and a 24-person participatory workshop; presented as recommendations based on observed changes and anticipated risks/opportunities.
Human-AI collaboration is expanding in day-to-day development work.
Observed and reported changes from 20 expert interviews and a 24-person participatory workshop; thematic analysis highlighted more frequent and deeper collaborative interactions between humans and AI tools.
Production scale positively moderates both the human capital mechanism and the product R&D mechanism through which AI promotes value chain upgrading.
Moderation analysis in the panel econometric framework using 30-province data (2010–2022) showing that larger production scale strengthens the mediating effects of human capital and product R&D.
AI facilitates value chain upgrading in the equipment manufacturing industry through two channels: enhancing human capital levels and driving product R&D.
Mechanism tests (mediation/ channel analysis) conducted on the 30-province panel (2010–2022) showing empirical support for human capital and product R&D as mediators of AI's effect on upgrading.
AI significantly enhances value chain upgrading in capital-intensive and technology-intensive equipment manufacturing industries.
Industry-type heterogeneity analysis within the 30-province panel (2010–2022) comparing capital-intensive and technology-intensive subsectors; reported statistically significant positive coefficients for these subsectors.
The positive effect of AI on value chain upgrading remains robust after a series of stability tests and when addressing endogeneity concerns.
Stability/robustness tests and endogeneity discussions reported in the paper applied to the same 30-province panel (2010–2022); unspecified robustness procedures and endogeneity treatments mentioned.
AI promotes value chain upgrading in the equipment manufacturing industry.
Panel econometric analysis using data from 30 Chinese provinces over 2010–2022; models report a statistically significant positive coefficient on AI measures; robustness checks reported.
The positive relationship between talent introduction and AI development remains robust after a series of robustness tests and instrumental variable estimations.
Reported robustness checks and instrumental variable (IV) estimations performed on the same panel dataset; results reportedly persist under these alternative specifications.
Talent introduction is associated with higher levels of AI development among firms.
Empirical analysis using panel data of Shanghai and Shenzhen A-share listed companies; reported positive association between talent introduction and firm-level AI development.