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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Wage growth for occupational groups with high exposure to automation lags markedly behind that of low-exposure groups.
Heterogeneity analysis across occupational exposure groups using CFPS panel data comparing wage growth trajectories for high- vs low-exposure occupations.
Existing research has significant shortcomings in terms of local empirical evidence, micro task mechanisms, and the impact of cutting-edge AI.
Critical appraisal in the paper's discussion of gaps identified through the systematic literature review; no single-study sample size.
Skill mismatch constitutes the core contradiction of labor force transformation.
Interpretive conclusion from the literature review asserting that mismatches between worker skills and job/task requirements are central to the labor-market effects of AI.
Despite the growing prevalence of human-AI decision making, the human-AI team’s decision performance often remains suboptimal, partially due to insufficient examination of humans’ own reasoning.
Motivating claim stated in the paper's introduction/abstract (appears to be based on broader literature and motivation rather than a new empirical test in this paper).
AACT also triggers higher cognitive load.
Reported measurement of cognitive load in the same house price prediction case study comparing AACT to traditional AI support (details and sample size not provided in abstract).
STARA may widen inequalities across occupational groups and cohorts—particularly affecting low- and medium-skill occupations—by fragmenting or limiting career paths and reducing institutional supports.
Concerns and literature synthesis in the editorial citing prior work on inequalities and occupational differences (e.g. Zajko, 2022 and other cited studies).
AI-based career planning platforms and digital portfolio/performance trackers can embed biases, amplify pressures for self-optimisation, provide only generic recommendations, and risk promoting a narrow view of what constitutes a desirable career.
Conceptual concerns and literature cited in the editorial (Bankins et al., 2024a and other referenced works); argued as potential unintended consequences rather than direct evidence from a single large empirical study.
Algorithmic gatekeeping in promotion and evaluation processes can privilege certain behaviours or skill sets while limiting transparency and equity in career advancement.
Editorial synthesis referencing recent work (e.g. Hillebrand et al., 2025) and conceptual concerns raised in the literature.
STARA is displacing routine tasks and potentially entire roles, particularly in occupations where automation and robotics can substitute standardized work processes.
Synthesis of existing literature cited in the editorial (e.g. Bahadure et al., 2024; Oosthuizen, 2019, 2022; Singh and Chandra, 2026; Singh et al., 2026).
Pure implementations of the data mesh paradigm frequently underdeliver because teams inherit new responsibilities without the platform maturity, tooling, or coordination mechanisms to exercise them effectively.
Argument/observation presented in the paper as rationale for proposing a new architecture (anecdotal/experience-based reasoning rather than reported empirical trial).
Enterprise data platforms face an enduring tension between domain self-service and holistic governance (a flexibility-versus-control trade-off).
Conceptual statement in the paper describing the problem motivating the work (literature/architectural framing).
There is a ceiling effect where excessive linguistic expansion yields diminishing marginal utility.
Empirical observation reported in the abstract that overly expanding linguistic output leads to diminishing marginal gains; presumably derived from analysis of the dataset and evaluation framework.
Limited data, resource constraints and skill gaps significantly influence the pace and form of AI adoption in SMEs.
Synthesis of barriers identified across multiple studies in the 2016-2024 literature (review-level claim without a single quantitative estimate).
Ethical concerns—especially algorithmic bias—and the need for human oversight remain essential for ensuring positive financial outcomes.
Argument and synthesis from the reviewed literature highlighting ethical risks and recommended governance (conceptual and empirical discussions across studies).
SMEs face barriers to AI adoption such as limited data, skill shortages, and high implementation costs.
Review synthesis of barriers reported in multiple studies from 2016-2024 (no pooled quantitative prevalence reported).
Existing AI education, AI literacy, and human-AI collaboration frameworks remain centred on prompting, task execution, and productivity support and are poorly equipped to address this tacit layer of expert cognition.
Argumentative critique in the paper drawing on conceptual analysis and review of prevailing frameworks; no empirical evaluation or sample reported.
Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating the staffing models AI was expected to transform.
Stated as an observational/introductory claim in the paper (no empirical data or sample size reported to support the general trend).
AI may influence society broadly via ethical issues, economic inequality, and social adaptation challenges.
Paper lists ethics, economic inequality, and social adaptation as societal-level areas affected by AI (abstract). Presented as thematic concerns reviewed in the paper; no empirical estimates included in the provided text.
AI-driven automation is associated with job loss.
The paper lists automation and job loss among the areas it examines (abstract). The provided text frames job loss as a potential negative ramification but does not report primary empirical estimates or sample sizes.
The impact of EPU on ETM is relatively moderate in intensity but more persistent compared with the impact from AI.
GARCH-Conditional Quantile Regression (persistence measures reported in the study summary; exact metrics/sample size not provided).
The risk spillover from AI to ETM is characterized by high volatility and strong extremeness.
GARCH-Conditional Quantile Regression results showing AI→ETM spillover features (method reported; sample size not stated).
A GARCH–conditional quantile regression model reveals asymmetry of risk spillovers: the intensity of upside risk spillovers is far greater than downside ones.
GARCH-Conditional Quantile Regression (GARCH-CQR) applied to volatility and tail-risk dynamics among AI, EPU and ETM (method reported; sample size/time-series length not stated).
The cross-quantilogram indicates that the negative predictive effect of EPU on ETM is mainly concentrated in periods of policy stability.
Cross-quantilogram analysis applied to EPU and ETM time-series, with quantile-specific predictive effects identified (method reported; sample size not stated).
The nonparametric quantile causality test shows a unidirectional causal relationship from EPU to China’s education and training market (ETM).
Nonparametric quantile causality test applied to time-series data on EPU and ETM in China (method reported; sample size not stated).
Organizations implementing AI without responsible transition mechanisms may worsen workforce anxiety, skill obsolescence, inequality, and trust erosion.
Paper's theoretical/conceptual assertion about risks of poorly-managed AI adoption; no empirical validation reported in the excerpt.
The International Monetary Fund estimates that nearly 40% of global employment is susceptible to AI, with exposure rising to 60% in advanced economies owing to cognitive task-oriented jobs.
Cited IMF estimate reported in the paper (reference to an IMF analysis; no sample size given in the excerpt).
Regardless of apparent performance advances in AI technology, human and environmental factors of the organization may substantially attenuate — or even negate — the effective productivity benefits.
Conceptual argument in the paper; theoretical reasoning and literature synthesis (no primary empirical data reported in the abstract).
Adopting AI in organizational practice does not guarantee productivity gains, because human and environmental factors critically moderate the relationship between AI deployment and realized productivity improvements.
Position paper's conceptual argument presented in the abstract; no empirical sample or quantitative study reported.
AI-driven efficiency pressures in IT services may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers.
Abstract cites high-reliability sector evidence (Reuters 2026a; Nasscom) to support this sector-specific claim; no sample size provided in abstract.
Labor-market segmentation and digital capability gaps in India create distributional vulnerabilities.
Abstract cites Indian official statistics and household/labor surveys (PLFS, HCES, MoSPI–NSO) and integrates sector evidence; no specific sample size reported in abstract.
Refined exposure measures imply widespread task transformation rather than uniform job destruction, with accelerated skill change as a central risk for vulnerable workers.
Abstract cites labor-market analyses and ILO (2025) as the basis for refined exposure measures and conclusions; no sample size stated in abstract.
Global frameworks warn that uneven readiness may produce a 'Next Great Divergence' between countries.
Cited global reports in abstract (UNDP 2025, WTO 2025, OECD 2026) which are summarized as issuing this warning; no primary data sample size reported in paper abstract.
Persistent adoption gaps among groups suggest unequal access to AI-enabled productivity.
Abstract references global reports (OECD, WEF, UNDP, WTO) and sector evidence indicating adoption gaps; no numerical sample size given.
AI may widen capability inequality—inequalities in access to knowledge, digital infrastructure, computational resources, and organizational adoption—thereby shaping income opportunities and socio-economic security for low-income groups.
Argument presented using the paper's socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) and official Indian statistics; no direct empirical sample from this paper reported.
AI adoption contributes to labor market polarization and increases the risk of structural unemployment.
Authors' thematic synthesis of interdisciplinary studies reporting patterns of job polarization and macro/labor market risks associated with AI in manufacturing.
AI disproportionately affects routine and mid-skilled jobs.
Synthesis of literature (2010–2024) reported by the authors indicating disproportionate automation/AI exposure for routine and mid-skilled occupations.
AI adoption in manufacturing has critical implications for human labor, raising concerns about labor displacement.
Authors' systematic literature review (2010–2024) synthesizing interdisciplinary studies discussing labor impacts and displacement risks.
AI adoption presents workforce adaptation challenges.
Reported in the study's literature synthesis and thematic analysis of secondary sources (qualitative review). No sample size reported.
AI adoption raises ethical considerations.
Authors' thematic evaluation of secondary literature identifying ethical issues associated with human-AI collaboration (qualitative synthesis). No sample size reported.
AI adoption presents challenges related to skill gaps.
Thematic findings from peer-reviewed literature and secondary data (qualitative review). No sample size reported.
Simultaneously, there is a structural shortage of qualified personnel and a gap between the education system and the needs of the economy in Uzbekistan.
Synthesis of statistical data, industry reviews, and regulatory/legal document analysis presented in the paper (no primary survey/sample size reported).
Other changes are more nuanced and put the typical career growth opportunities, like receiving feedback from professional networks and promoting leadership and mentorship, at risk.
Qualitative reports from interview participants (n=24) expressing concerns that AI-driven changes may reduce feedback, leadership development, and mentoring opportunities.
This transition proceeds without tools to forecast how individual employees will respond psychologically and behaviorally.
Asserted by the authors as a gap/need; no empirical inventory or systematic review presented in the excerpt to substantiate completeness of tool absence.
Workforce transformations are difficult to forecast and costly to mismanage.
Stated as a general assertion in the paper's introduction; no empirical data, sample, or formal analysis reported in the excerpt.
Student-designed tasks reveal hidden failures in current deep research systems: fluent, source-backed answers can still miss the right query, source, term, or evidence standard.
Qualitative analysis of failure modes from student-designed tasks and system evaluations reported in the paper (examples and discussion of how answers can be fluent and sourced yet incorrect on key criteria).
Evaluation on QuestBench shows that student-designed tasks reveal hidden failures in current deep research systems: across thirteen evaluated systems, the mean question-level pass rate is only 16.85%.
Empirical evaluation reported in the paper: 13 systems evaluated on QuestBench; aggregated mean question-level pass rate reported as 16.85%.
The de-coring and skill-demand changes are concentrated among low entry-threshold, small firms.
Abstract statement reporting heterogeneity: concentration of observed patterns among firms characterized as small and with low entry thresholds.
Both displacement and augmentation exposure are associated with a de-coring pattern: a shallower and more dispersed skill portfolio with within-category importance diverging from share movements.
Empirical description in abstract that both forms of exposure correlate with changes in portfolio depth and dispersion, and with divergence between within-category importance and category shares.
Displacement exposure is negatively associated with the routine cognitive skill share.
Empirical result stated in abstract: negative association between displacement exposure and routine cognitive share, identified using within-firm variation and the constructed exposure measures.
The most significant barriers to AI adoption reported by entrepreneurs are human-centred—talent scarcity, organisational resistance, and change management—rather than technology or cost alone.
Theme 'Barriers and the Adoption Journey' from thematic analysis of interviews (n=16); interviewees repeatedly cited human-centred barriers (talent scarcity, resistance, change management) over purely technical/cost barriers.