Evidence (1416 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 |
Effectiveness of AI in tax compliance is contingent on data quality, governance capacity, and organizational readiness.
CIMO-structured synthesis of contextual factors across the 68 reviewed articles highlighting data, governance, and organizational readiness as moderators of AI effectiveness.
Firm-level productivity gains from AI are contingent on complementary organizational investment.
Synthesis finding from the SLR: multiple studies report that complementary investments (e.g., organizational change, worker training, data infrastructure) are necessary for realizing productivity benefits.
The task-based adaptive collaboration model hypothesizes that trust, explainability, and task difficulty moderate the effect of human–AI collaboration on performance.
Statement of hypothesized relationships within the model developed in the paper (theoretical hypotheses rather than reported experimental estimates).
The organizing claim of the theory is that review is the control point through which a coding agent's effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process.
Synthesis of practitioner discourse coded into a causal model derived from the LLM-assisted analysis of 3,100 sampled documents; presented as the central theoretical claim.
The direction of these observed trends (review frequency, merge speed, discussion) flips under different but equally defensible analysis choices.
Authors' sensitivity/robustness checks on the observational GitHub analysis indicating that trend direction depends on analysis choices; reported in abstract without numeric detail.
Coding agents are capable; human oversight is the bottleneck.
Authors' high-level claim/argument in the paper, supported conceptually and motivated by the reported experiment showing reviewer limits.
Agentic AI differs from human organisations because these patterns are not sustained by motivation, identity, trust, employment, socialisation, or moral accountability; they are sustained by context architecture: prompts, memory, traces, schemas, tools, validators, and permissions.
Theoretical argument in the paper contrasting sustaining mechanisms for organisational behaviour; based on conceptual analysis and description of system-level affordances (no sample size reported).
The SCR-enhancing effect of GAI is conditional: it is not automatic but depends critically on alignment between technological deployment and organizational adaptation.
Empirical heterogeneity/conditionality findings from the panel analysis (2017–2024), implying the positive effect of GAI on SCR varies with organizational alignment and adaptation measures.
Key human factors—trust calibration, output-quality sensemaking, expertise depth, feedback latency, cognitive load, and metacognitive skill development—serve as performance-shaping mechanisms within AI-enabled systems.
Presentation of a socio-technical evaluation model synthesizing prior research across several disciplines (conceptual synthesis; no empirical sample reported).
The paper develops a task-to-firm conversion framework explaining why task-level GenAI productivity gains do not automatically translate into firm-level improvements.
Theoretical and conceptual contribution presented in the review, integrating multiple literatures (GPT theory, digital economics, task experiments, China studies).
Existing user-role frameworks (e.g., the BTP User Type Matrix) require adaptation because the workforce is undergoing significant role-specific changes.
Authors' analysis based on 20 expert interviews and a 24-person workshop that uncovered mismatches between current role taxonomies and emergent AI-influenced responsibilities.
The simulation offers a template of how firms ought to reorganize internal promotion ladders when junior positions are significantly automated.
Model-based policy/reorganization recommendation derived from the simulation results; presented as guidance for firm-level reorganization rather than an empirically tested organizational intervention in the abstract.
Stronger synchronization can increase collective output but may also increase systemic fragility and reduce mobility.
Analytical results and trade-off analysis in the model showing the effects of synchronization on collective output, fragility, and mobility; theoretical deduction without empirical sample.
The paper formalizes four mechanism theorems explaining the overhead-pressure dynamics: overhead non-additivity, augmentation-saved-time pathways, innovation-premium amplification, and human-AI dyad attribution uncertainty.
Presentation of four mechanism theorems within the paper (theoretical/mathematical exposition rather than direct empirical tests).
The ICH framework predicts three distinct augmentation regimes (determined by combinations of A and C) with distinct policy implications.
Theoretical classification derived from the model; conceptual prediction presented in the paper.
Organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions.
Conceptual argument supported by synthesis of empirical studies in the systematic review (68 peer-reviewed empirical studies).
Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems.
Statement based on literature synthesis in the paper; theoretical framing and review of empirical studies (systematic review).
The relationship between AI use levels and corporate carbon emission intensity exhibits a significant inverted U-shaped curve: at early stages AI adoption may increase emissions, but beyond a critical point further AI use significantly reduces emissions.
Empirical two-way fixed effects (TWFE) analysis on provincial panel data from China, with robustness checks; the paper reports a statistically significant inverted U-shaped relationship.
The endurance budget is dormant on premium 3,000-P/E TLC at datasheet prices and binding on the commodity QLC/eMMC (~1,000 P/E) that cheaper edge robots run.
Comparative statement based on device endurance specifications cited in the paper (3,000 P/E for TLC vs ~1,000 P/E for QLC/eMMC) and cost/pricing considerations; presented as boundary conditions for when the endurance budget matters. No empirical sample size reported.
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.
National AI development can be interpreted as a controlled balance between information injection and entropy dissipation.
Theoretical mapping using HCLM; paper presents this dynamical framing and definitions of the two processes; no empirical sample.
Significant advancements in smart technology, AI, robotics and algorithms (STARA) are changing how organisations design and implement work for the current and future workforce.
Statement in the editorial supported by references to prior literature and reviews (e.g. Brougham and Haar, 2018; Raisch and Krakowski, 2021; Tang et al., 2023; Ulfert et al., 2024; Yam et al., 2023). This paper is an editorial/literature-synthesis rather than a primary empirical study.
The agents exhibited substantially different behaviors and computational costs.
Overall observation from the two runs: distinct behavioral patterns (silent reinterpretation vs explicit restarts), different execution times, and differing computational actions (optimization introduced by Codex).
Task accuracy, monetary cost, and edge energy consumption are tightly coupled in hybrid MAS design.
Claim made in the abstract and investigated empirically by adapting MAS architectures and measuring power, cost, and performance trade-offs.
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)'.
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.
AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).
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.
We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives.
Explicit enumeration of proposed moderating factors in the paper (conceptual identification rather than empirical measurement).
Completion time itself is not sufficient to characterize efficiency gains.
Authors' inferential conclusion in the abstract based on observed dissociation between completion time (no difference) and subjective effort (lower with AI) in their preregistered study (N = 1237).
Labor-market adjustment to generative AI is a process of organizational reconfiguration, in which firms reshape both hiring demand and the task architecture of work.
Synthesis/conclusion drawn from the paper's empirical findings (decomposition results, heterogeneity analyses).
Artificial Intelligence (AI) has caused massive changes in nature of workplaces in healthcare sector.
Asserted in paper's introduction and supported by a scoping review (PRISMA-ScR) of 29 peer-reviewed empirical studies published 2020–2025.
Acceleration in the Generate/Take Action phase translates into durable performance only when Analyze/Prioritize is de-biased by individuals and teams, and Measure/Review converts results into reusable knowledge with appropriate inference discipline.
Thematic conclusions from the 17 interviews and cross-case analysis (Gioia methodology) identifying conditional relationships across stages of the seven-stage growth pipeline.
AI changes the traditional relationship between learning and performance: in AI-intensive environments, learning must be supported by systems that coordinate knowledge and build intelligence rather than relying on learning alone.
Authors' synthesis and interpretation of their cross-sectional mediation results (AIDLC → KO → OI → IP) and comparison with prior management models.
AI alters strategizing practices (Strategy-as-Practice) by making strategy processes continuous and AI-augmented rather than episodic and purely human-driven.
Conceptual synthesis of Strategy-as-Practice literature; theoretical claim about process change to continuous, AI-augmented strategizing; no empirical sample.
AIO’s decarbonization effects vary systematically across climate risk, industry competition, and AI exposure (heterogeneity analyses).
Authors state they performed heterogeneity/subgroup analyses showing systematic variation in the AIO–decarbonization relationship by climate risk, the degree of industry competition, and firms' AI exposure.
Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success.
Model coefficients/feature effect analyses and nonlinear diagnostics from the fitted models.
Augmented work agency is shaped by whether applications are generative or non-generative, by employees' experiences of anxiety and technostress, and by micro-politics through which teams negotiate AI use and AI ethics.
Thematic findings from semistructured interviews (28 participants) and document review identifying these factors as shaping agency in practice.
The analysis uncovers three central tensions shaping AI-mediated work: autonomy versus orchestration; capability versus dependency; and experimentation versus ethics.
Recurring themes identified through qualitative interviews (28 participants) and document review; interpretive synthesis presented in findings.
AI integration transforms managerial practices, workforce identities and organizational coordination.
Thematic and interpretive analysis of semistructured interviews with 28 managers/professionals across 12 organizations and review of organizational documents.
These AIECI benefits were contingent on complementary conditions—particularly data quality, governance, managerial interpretation, and integration of intelligence outputs into operating decisions.
Cross-case pattern-matching across five analytical dimensions (intelligence source, AI mechanism, decision domain, economic implication, boundary condition) identifying recurring contingencies in the four firms' archival evidence.
The dominant explanation for the gap locates it in model capability; instead, software-engineering capability emerges from a model-harness-environment system where a runtime substrate (the harness) mediates how an agent observes a project, acts on it, receives feedback, and establishes that a change is complete.
Conceptual argument and reframing presented in the paper (abstract). The paper formalizes this perspective rather than reporting a large-scale empirical test in the abstract.
The research challenges for this vision stem from a broader flexibility–robustness tension that requires moving beyond the on-the-fly paradigm to navigate effectively.
Analytical claim in paper identifying a design trade-off (flexibility vs. robustness) as the core challenge motivating the proposed shift; no empirical demonstration provided.
Integrating Generative AI into agile development processes has potential benefits and limitations for planning efficiency.
High-level conclusion based on the controlled experiment with GitLab Duo and qualitative participant feedback discussed in the paper.
Model routing can mitigate the cost of agentic tool use, but existing routers are designed for chat completion rather than tool use.
Argument/positioning in the paper and literature discussion (no specific empirical test reported for existing routers in this statement).
AI will affect public administration.
Report introduction describing a section focused on how AI will affect public administration; based on expert synthesis rather than reported empirical study.
This workload-buffering effect (governance improving performance while reducing fatigue) contradicts the usual framing of governance as pure overhead.
Interpretation and comparison of empirical manufacturing results against prior framing in literature (qualitative claim within the paper). No sample size provided.
Modeled joules per correct answer varies by a factor of 6.2 across endpoints.
Modeled energy estimate combined with task accuracy to compute joules per correct answer across 78 endpoints.
Fluent users adopt a fundamentally different interactional mode: they iterate collaboratively with the AI, refining goals and critically assessing outputs, whereas novices take a passive stance.
Qualitative and quantitative analysis of the same 27,000 annotated WildChat transcripts, with annotations describing interactional mode and user behavior (iteration, goal refinement, critical assessment vs. passivity).