Evidence (4892 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).
Browse by theme
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 |
Org Design
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The near-term value of Agentic AI does not lie in full autonomy or workforce reduction, but in controlled partial autonomy for simple and medium complexity business processes.
Central argumentative claim/recommendation in the paper (theoretical justification; no empirical study or sample size reported).
The effectiveness of AI in strategic core functions is contingent upon the human–AI interface.
Stated as a conditional claim in the paper—AI effectiveness depends on the quality of the human–AI interface; no empirical quantification provided in the summary.
The U-shaped pattern is concentrated in software-based AI applications rather than supporting hardware.
Heterogeneity/subgroup analyses in paper that separate software-based AI applications from supporting hardware and find the non-linear pattern concentrated in software applications.
Spline regressions, the Lind–Mehlum U-test, an instrumental-variable analysis using leave-one-out peer AI investment, and entropy balancing all support the non-linear (U-shaped) pattern.
Robustness and identification methods reported in paper: spline regressions, Lind–Mehlum U-test for U-shape, IV using leave-one-out peer AI investment, and entropy balancing.
There is a U-shaped association between AI investment and internal control deficiency (ICD) risk.
Main empirical finding reported in paper based on analyses of 41,725 firm-year observations; supported by spline regressions and Lind–Mehlum U-test.
Board composition, particularly the presence of female and minority directors, impacts AI adoption.
Statement in abstract reporting an analysis linking board composition variables (female and minority directors) to AI adoption outcomes in the dataset.
Defining query difficulty is one of the hardest problems in deployment engineering.
Statement/assertion in the paper (introductory claim); no specific empirical measurement in the abstract.
Trust is conceptualized as network-mediated expectation stabilization in the embodied finance framework.
Theoretical claim in the framework articulating trust as stabilized through network interactions among humans, machines, and platforms; no empirical data.
The proposed framework—the machine–platform–crowd triangle—reframes agency, trust, and value as emergent properties rather than institutional attributes.
Conceptual framing and argumentation within the paper; synthesis of theory to reconceptualize agency, trust, and value; no empirical testing reported.
Du et al. (2026) find that information-based team faultlines can enhance proactive behavior via deep information processing, while AI adoption moderates and mitigates the negative effects of social-based faultlines on team cooperation.
Information-processing theoretical framing and empirical analysis reported in the paper (study type and sample size not specified in the excerpt).
Liao et al. (2026) identify multiple equifinal pathways to high performance in digit-oriented spin-offs (parent-oriented, independent-oriented, ambidextrous-oriented configurations) using fuzzy-set qualitative comparative analysis (fsQCA).
fsQCA analysis reported in the paper (methodological approach described; sample not specified in excerpt).
A store-level policy learned from logged marketplace data selects a discrete multiplier that shifts the dispatch optimizer's tradeoff between delivery quality and batching efficiency.
Methodological description: store-level policy trained from logged data that outputs a discrete multiplier to alter optimizer objective weights; stated design and training approach in paper (no numerical evaluation details provided in the excerpt).
Implementation success depends heavily on data quality, workflow redesign, interpretability, governance, and procurement alignment.
Synthesis of factors identified across included studies and supporting regulatory/industry documents as important determinants of successful deployment.
However, evidence is uneven: many studies are simulation-based.
Review observation from the synthesis of the 35 included studies noting study designs (simulation prevalence noted but not numerically specified).
There is a significant U-shaped relationship between AI application and employees' job insecurity: moderate AI application reduces insecurity, whereas excessive application heightens it.
Empirical analysis of cross-sectional self-reported questionnaire data collected from employees (411 valid responses) using regression-type analyses reported as showing a significant U-shaped relationship between AI application intensity and job insecurity.
Both risk perception and guilt play a role in GenAI adoption (they are relevant predictors of employees' intention to continue using the technology).
Empirical finding reported from the vignette experiment linking risk perception and guilt to GenAI adoption intention (paper states 'highlight the role of both risk perception and guilt in GenAI adoption').
The effect of embeddedness (GenAI being integrated into internal software environments) on employees depends on the presence of organizational authorization.
Reported empirical result from the vignette experiment indicating an interaction effect between embeddedness and organizational authorization (text states 'the effect of embeddedness depends on the presence of organizational authorization').
This research employed a vignette experiment to investigate how the embeddedness of GenAI and organizational authorization impact employees' negative emotion (specifically guilt) and risk perception.
Stated method in paper: a vignette experiment was used to test effects on guilt and risk perception. (No sample size reported in the provided text.)
The research contrasts tool-shaping (AI behavior/prototype) and mind-shaping (user strategy training) pathways and reports differing effects between them.
Paper presents both a tool-shaping experiment (Study 1) and a mind-shaping experiment (Study 2) and discusses comparative findings across these pathways.
Cognitive flexibility is examined as a moderator (boundary condition) of the interventions' effects.
Paper reports including cognitive flexibility as an individual-differences moderator in analyses across the two studies (moderation analysis planned/reported).
Analysis of recent benchmark evidence including SWE-bench Verified, EvoClaw, and LangChain's multi-agent coordination studies demonstrates both the transformative potential of the agentic paradigm and its current limitations.
Empirical/benchmark analysis referencing SWE-bench Verified, EvoClaw, and LangChain multi-agent studies as sources of evidence; the paper analyzes these benchmarks qualitatively or comparatively (specific sample sizes and quantitative effect sizes not stated in the abstract).
Human and algorithmic actors jointly influence strategic outcomes, motivating the concept of 'hybrid upper echelons' in which executive influence increasingly shifts from making decisions to configuring and governing AI-enabled decision processes.
Theoretical contribution based on integration of management and IS literature in the concept-centric review; proposition of a new conceptual framework ('hybrid upper echelons') rather than primary empirical validation.
AI reconfigures UET through discretion reconfiguration: AI enables delegation and embedding of decision authority, redistributing managerial discretion.
Concept-centric literature review synthesizing studies on delegation/automation of decision authority and managerial discretion (no primary empirical sample reported).
AI reconfigures UET through evaluation reconfiguration: AI partially substitutes human judgment with algorithmic decision logic and thereby shapes how alternatives are evaluated.
Conceptual synthesis from the literature review integrating findings from management and IS studies on algorithmic decision logic and judgment substitution (no primary empirical sample reported).
AI reconfigures upper echelons theory (UET) through cognition reconfiguration: AI mediates information and attention, expanding analytical capacity while introducing new constraints on executive cognition.
Synthesis of management and IS research in a concept-centric literature review; conceptual argument drawing on prior studies about information mediation and attention (no primary empirical sample reported).
AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely.
Conceptual argument supported by literature across finance and related fields (review-level synthesis; no single empirical sample size reported).
Human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance.
Synthesis of literature from finance, management, HCI, and AI showing moderating factors for complementarity (conceptual integration; no unified empirical sample size reported).
The paper's contribution includes an estimand distinction, an inspectable ABM/RL mechanism, and a reproducible artifact demonstrating that transparent behavioral assumptions are sufficient to generate gaming-like boundary dynamics without implying that computable regulation is inherently undesirable.
Author-stated contributions in the abstract describing methodological and reproducibility outputs (estimand distinction, inspectable model, reproducible artifact).
Our findings show qualitative and enduring differences between hyperscaler-based platforms and non-hyperscaler providers.
Stated as a conclusion based on the paper's taxonomy and comparative analysis; phrasing indicates interpretive/qualitative evidence rather than longitudinal empirical demonstration (no temporal sample or size reported in abstract).
Non-hyperscaler providers embody distinct value-creation logics beyond hyperscaler efficiency.
Claim arises from the taxonomy and comparative analysis contrasting hyperscaler-based platforms with non-hyperscaler alternatives; evidence appears qualitative and conceptual as presented in the paper summary (no empirical sample size reported in abstract).
We illustrate this transition through examples in consumer markets, education, news, and coding.
Authors state they use sectoral examples to illustrate the framework; this is a claim about the paper's contents rather than an empirical finding.
We offer a three-stage lens: Augmentation, Automation, and Reconstruction.
Conceptual framework proposed by the authors; presented as a taxonomy in the paper (no empirical validation reported in the excerpt).
There is a suggestive non-linear relationship between embodiment and team performance.
Analysis reported in the paper indicating a non-linear (not strictly monotonic) association between degree of agent embodiment (Box, Avatar, humanoid) and measured team performance; described as 'suggestive' in the abstract, without quantified functional form or statistics included there.
Artificial agents have an uneven impact on team outcomes, with some mixed human–AI teams performing exceptionally well and others markedly worse.
Observed performance outcomes across mixed human–AI teams in the escape room experiment, showing high between-team variability; exact sample size and statistical details not provided in the abstract.
Acquiescent silence (resignation-based) is motivationally distinct from defensive (fear-driven) silence.
Theoretical distinction advanced using organisational silence literature (conceptual claim referencing existing theory).
These findings demonstrate the feasibility and current limits of automated expertise mapping.
Synthesis/conclusion based on model performance (e.g., MAE results) and observed limitations reported across evaluations.
AI maturity moderated the effects of governance exposure on adaptation (p ≤ 0.035).
Reported moderation analysis: 'with AI maturity moderating these effects (p ≤ 0.035)'.
The UPCT framework offers a unified explanation for varied phenomena: pandemic resilience patterns, divergent digital transformation outcomes, and emerging risks of AI-driven organizational rigidity.
Synthesis claim by the author asserting explanatory scope of the theoretical framework; no empirical cross-case synthesis or formal validation included.
The paper's Universal Phase Crystallization Theory (UPCT) reconceptualizes organizations as recursive generative cycles (Φ→R→S→Φ′) and asserts organizational existence is better described as E = ΦR rather than E = S.
Theoretical/model claim introduced and developed in the paper; purely conceptual without empirical testing.
Resilience should be redefined not as reserve magnitude (accumulated buffers) but as recoverability of generative relational capacity.
Normative/theoretical redefinition proposed by the paper; no empirical validation provided.
Emotion is a strategic action channel rather than a surface style.
Interpretation based on experimental results (GoEmotions prompting and subsequent analyses) demonstrating that adding emotional framing changes negotiation outcomes in systematic ways.
AI's future impact on employment will depend not only on automation capabilities but also on how responsibly enterprises manage workforce transitions.
Paper's concluding claim synthesizing arguments and proposed governance approach (normative conclusion rather than an empirically tested causal estimate in the excerpt).
AI-induced workforce disruption is not only a labor market issue but also an enterprise governance challenge.
Argument/position advanced in the paper highlighting governance responsibilities for firms implementing AI.
Artificial intelligence, especially generative AI, is transforming enterprise operations by automating tasks, enhancing decision-making, and redefining job roles.
Conceptual statement in the paper describing observed/expected effects of generative AI on enterprise operations (no specific empirical sample or experiment reported in the excerpt).
The workflow was cache-dominant, suggesting that persistent agentic environments may shift the economic unit from cost per token to cost per completed artifact.
Observed high cache-read fraction (82.9% in May subset) and interpretation by authors that caching dominates token usage, leading to the suggestion about economic-unit shifts.
Depending on operational parameters, the most time-efficient way to complete a workflow may undergo a transition between two task-processing regimes: a fully AI-assisted regime and a fully manual regime.
Analytical results derived from the paper's formal queueing model (theoretical/model-based derivation; no empirical sample reported).
AI assistance can generate a deceptive productivity signature: average completion times fall because AI tools typically supply a fast first draft, yet workflow-level performance can deteriorate when a subset of AI errors escapes review and returns as costly downstream rework.
Analytical derivation and discussion based on the paper's queueing model (theoretical/model-based evidence; no empirical sample provided).
Public data from Anthropic's Mythos Preview and Mozilla Firefox collaborations, along with public exploit-market price anchors and vulnerability reward programs, support the argument that the near-term shift is toward increased defender remediation throughput rather than simply more zero-days.
Explicit statement that the paper's argument is based on public datasets: Anthropic Mythos Preview, Mozilla Firefox collaboration records, exploit-market price anchors, and vulnerability reward program information (no sample sizes provided in the abstract).
Defender-side bugonomics already existed in vulnerability research, reward programs, and vendor remediation work; LLM-assisted systems change its scale and distribution.
Descriptive claim supported by references to vulnerability reward programs and vendor remediation practices and by public collaboration data (no numerical sample sizes provided in the abstract).
The near-term shift is not simply more zero-days; it is a move toward broader defender remediation throughput: low-signal candidates become cheaper, evidence-rich remediation become more important, and scarce capacity shifts toward maintainer review and release work.
Synthesis drawing on public data from Anthropic Mythos Preview, Mozilla Firefox collaborations, public exploit-market price anchors, and vulnerability reward program information (no numeric sample sizes provided in the abstract).