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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Successful human–AI collaboration requires a human-centric approach that balances technological advancement with workforce development, ethical governance, and organizational support.
Study conclusion/recommendation based on survey findings (perceptions of opportunities and challenges) and analytical results (correlation/regression).
Human–AI collaboration reduces employees' routine workload.
Respondent perceptions collected via the structured questionnaire and analyzed with descriptive statistics and regression in SPSS.
AI-based systems support better decision-making by providing data-driven insights, allowing employees to focus on higher-level cognitive and strategic activities.
Survey responses (structured questionnaire) analyzed with SPSS (correlation and regression analyses) reporting perceived support for decision-making.
Human–AI collaboration significantly enhances workplace efficiency and productivity by reducing routine workload and improving accuracy and speed in task execution.
Primary data from employees in AI-enabled organizations collected via a structured questionnaire (5-point Likert); analyzed with SPSS using descriptive statistics and regression analysis.
By reframing reskilling as a shared, supported, and bounded process, AI-driven change can foster long-term career resilience, professional identity renewal, and sustainable human–AI integration.
Conceptual conclusion/implication drawn by the authors from the proposed model and recommendations; no empirical validation included in the paper.
The paper advances a set of sustainable, collective strategies—such as role-linked learning, protected learning time, skill prioritization, and phased AI adoption—to interrupt the reskilling loop and redistribute adaptive demands across organizations.
Prescriptive/theoretical recommendations proposed by the authors; no empirical evaluation or trial evidence presented.
The study highlights the importance of reskilling and education reforms to ensure inclusive labor market outcomes in the era of AI-driven transformation.
Authors' policy recommendation based on their empirical findings from the survey (n=320) and SEM analysis; presented as a conclusion/recommendation rather than a quantified empirical result.
The model explained 49% of variance in wage dynamics (R^2 = 0.49).
SEM model statistics reported for the survey-based model (n=320); R-squared for wage dynamics = 49%.
The model explained 45% of variance in skill transformation (R^2 = 0.45).
SEM model statistics reported for the survey-based model (n=320); R-squared for skill transformation = 45%.
The model explained 52% of variance in employment patterns (R^2 = 0.52).
SEM model fit/variance-explained statistics reported for the survey-based model (n=320); R-squared for employment patterns = 52%.
Mediation analysis confirmed that skill transformation plays a significant mediating role linking AI adoption with wage distribution/outcomes.
Mediation analysis within the SEM framework applied to the survey data (n=320); authors report a significant mediation effect (no numeric indirect effect reported in the summary).
Mediation analysis confirmed that skill transformation plays a significant mediating role linking AI adoption with employment outcomes.
Mediation analysis within the SEM framework applied to the survey data (n=320); authors report a significant mediation effect (no numeric indirect effect reported in the summary).
Skill transformation significantly affected wage dynamics (β = 0.55, p < 0.001).
Structural equation modeling (SEM) on the same sample (n=320); reported standardized path coefficient β = 0.55 with p < 0.001.
Skill transformation significantly affected employment patterns (β = 0.58, p < 0.001).
Structural equation modeling (SEM) mediation/causal-path analysis on the survey (n=320); reported standardized path coefficient β = 0.58 with p < 0.001.
AI adoption significantly influenced wage dynamics (β = 0.61, p < 0.001).
Structural equation modeling (SEM) on the same survey sample (n=320); reported standardized path coefficient β = 0.61 with p < 0.001.
AI adoption significantly influenced skill transformation (β = 0.67, p < 0.001).
Structural equation modeling (SEM) on the same survey sample (n=320); reported standardized path coefficient β = 0.67 with p < 0.001.
AI adoption significantly influenced employment patterns (β = 0.63, p < 0.001).
Structural equation modeling (SEM) on primary survey data from n=320 employees across IT, banking, manufacturing, education, and service sectors; reported standardized path coefficient β = 0.63 with p < 0.001.
The study offers actionable insights for leaders seeking to balance innovation, capability development and ethical governance in AI-enabled workplaces while sustaining human interpretive authority, accountability and responsibility over time.
Implications and recommendations derived from the study's qualitative findings (28 interviews) and interpretive synthesis.
AI reshapes contemporary work by augmenting, rather than substituting, human roles.
Qualitative semistructured interviews with 28 managers and professionals from 12 organizations across technology, finance and knowledge-intensive services in Europe and Asia; thematic and interpretive analysis supported by organizational document review.
The model identifies simple measures/conditions that characterize when productivity paradoxes and skill polarization arise.
Theoretical derivations and analytical characterizations within the model yielding threshold conditions and measures parameterizing when paradoxical outcomes occur (model-based; no empirical validation).
Sustainable progress requires collaborative integration of humans and machines, rather than replacement.
Normative conclusion/recommendation stated in the paper based on study findings (argument for augmented intelligence over replacement).
This research presents the innovative Marketing Intelligence Operations (MIO) Framework and a practical AI Adoption Readiness Scorecard, enabling leaders to manage the operational balance between transformative efficiency improvements and human capital vulnerability.
Paper states that it introduces a new framework and a practical scorecard as deliverables of the research (descriptive claim about the paper's contributions).
AI-integrated Marketing Intelligence Operations (MIO) quantitatively improves campaign Return on Investment (ROI) by 47%.
Reported as an empirical result from the paper's mixed-methods study (the paper states use of audits, surveys, and NLP analysis to evaluate MIO outcomes).
LLMs can help generate more correct and functional code compared to participant-generated solutions.
Comparative analysis of generated solutions reported in the paper (no sample-size for solutions explicitly stated in the abstract). The paper states LLM-assisted solutions were more correct/functional.
Qualitative analysis of participants' interactions and interviews revealed four different human-LLM collaboration modes supporting various problem-solving strategies.
Qualitative analysis of interaction logs and retrospective interviews from the study participants (N=20) reported in the paper; identification of four collaboration modes described.
We conducted a within-subject study followed by retrospective interviews with programmers (N=20).
Stated methods in the paper: within-subject experimental design plus retrospective interviews; sample size explicitly given as N=20.
Organizations classified as 'Proactive Integrators' can reduce the risk of obsolescence by up to 53%.
Subgroup finding reported in the study (reduction estimate for organizations labeled 'Proactive Integrators'); specific subgroup sample not provided in abstract.
AI-assisted engineering teams can achieve a 24% increase in productivity.
Empirical finding reported by the study, derived from the mixed-methods analysis (survey of 320 orgs, Delphi with 40 experts, and case studies of 5 industries as described in abstract).
Entities that strategically implement AI can enhance their innovation cycles by up to 30%.
Statement in paper (presented as a forecast/estimate; no specific study or sample detailed in abstract).
There is a 15%–22% wage premium for workers demonstrating AI-augmentation capabilities.
Reported range across synthesized empirical studies documenting wage differences associated with demonstrated AI-augmentation capabilities.
The study draws policy implications for EU Cohesion programming and Sustainable Development Goals 4, 8, 9, 10, and 17.
Paper explicitly states policy implications and links to specific SDGs in its conclusions.
External technology partnerships, targeted education, and economic incentives operate as enablers [of AI adoption], all mediated by social and human capital availability.
Thematic analysis of interview data identifying these factors as enabling AI adoption, with mediation by social/human capital.
The socially optimal adoption speed and retraining capacity are complements: stronger institutions (larger retraining capacity) raise the optimal adoption speed.
Comparative-static result from the social-planner optimization in the dynamic model showing positive cross-partial effect between retraining capacity and optimal adoption speed.
Faster adoption produces a larger discouraged stock.
Analytical comparative-static result from the dynamic model linking adoption speed to the size of the discouraged (permanently exited) worker stock.
Faster AI adoption compresses the displacement window without reducing total displacement.
Analytical result from a dynamic theoretical model in which displaced routine workers enter a retraining pipeline with finite capacity (model derivation and comparative statics). No empirical sample reported.
Alternatives to one-size-fits-all chatbots—such as pluralistic system design, task-specific tools, and institutional safeguards—would better mitigate social and economic harm.
Prescriptive recommendations based on the paper's analysis; not supported by empirical trials or quantified evaluations within the paper.
Users maintain a moderate level of trust in AI even when their decisions diverge from those of AI.
Reported descriptive/analytic finding from the experiment with 59 pre-service teachers indicating measured trust remained at a moderate level in inconsistent decision conditions.
The proportion of consistent decisions significantly moderates the impact of AI-assisted decision-making paradigms on users' confidence levels.
Moderation analysis reported in the study (N=59); authors indicate that proportion of consistent human-AI decisions significantly moderates the effect of AI-assisted decision-making paradigm on confidence.
Consistency between human and AI decisions significantly enhances task performance.
Within-subject consistency manipulation in the experimental sample of 59 pre-service teachers; authors report significant positive association between proportion of consistent decisions and measured task performance.
Consistency between human and AI decisions significantly enhances users' confidence.
Within-subject manipulation of human-AI consistency in the study (N=59); authors report a significant positive effect of consistency on users' confidence in the measured models.
Consistency between human and AI decisions significantly enhances users' trust in AI.
Within-subject manipulation of human-AI consistency in the experiment with 59 pre-service teachers; authors report a significant positive effect of consistency on trust measured and tested in their models.
When human-AI decision consistency is taken into account, AI-assisted decision-making paradigms influence task performance indirectly through a sequential psychological pathway involving users’ confidence and their trust in the AI.
Same experimental sample (N=59), structural equation modeling reported a significant indirect (mediated) pathway from AI-assisted paradigms → users' confidence → trust in AI → task performance; moderation by human-AI consistency was considered.
Policy responses must therefore move beyond predicting job loss to supporting workers in navigating newly emerging, and often counterintuitive, mobility pathways.
Policy recommendation derived from the paper's simulation findings and theoretical interpretation that automation reorganises tasks/skills and creates new mobility pathways; presented in the abstract as an implication.
AI-driven automation sustains occupational roles through emerging complementarity rather than substitution.
The authors' simulated tracing of changes in shares of skills reallocated to machines (using AI exposure measure) and observed patterns interpreted as complementarity that help sustain roles; stated in abstract as a primary theoretical interpretation.
Despite substantial task erosion, most occupations retain residual skills that enable adaptation rather than extinction.
Simulation of task removals (332 tasks) across 736 occupations showing that occupations typically maintain remaining skill bundles sufficient for adaptation; reported as a structural finding in the abstract.
AI automation moderates a broader range of cognitive and social skills, creating new bridges across heterogeneous domains.
Simulation results using the AI-driven cognitive automation exposure measure applied to O*NET task data, showing erosion/moderation patterns across cognitive and social skills and resulting cross-domain connectivity in the occupational network.
Automation increases skill overlap between occupations, promoting structural integration within the occupational network.
Result from the authors' simulations based on O*NET task-to-occupation mappings and the two exposure measures (routine and AI-driven automation); simulated task removals and analysis of resulting skill overlap/occupational network structure.
Overall, GenAI coding assistants can increase developer productivity, although these gains depend strongly on context.
Synthesis of meta-analytic result showing a pooled positive effect on productivity (g = 0.33) together with reported substantial heterogeneity across settings and moderator analyses.
GenAI assistance produces a statistically significant, moderate positive effect on developer productivity (Hedges' g = 0.33, 95% CI [0.09, 0.58]).
Meta-analysis pooling k = 27 effect sizes (from n = 23 studies) using Hedges' g; the paper reports the pooled estimate and 95% confidence interval.
Proactive feedback produces post-intervention gains in Joint Visual Attention (JVA) and Joint Mental Effort (JME).
Within-subject empirical study with 26 dyads reporting post-intervention increases in JVA and JME measures following proactive feedback.