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).
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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 |
Org Design
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Policy implications derived from the literature include interventions spanning labor transition (reskilling/transition support), competition regulation, and digital governance.
Narrative synthesis of policy recommendations across the 78 studies and institutional reports included in the SLR.
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.
Although SMEs anchor employment and output across Sub‑Saharan Africa, their uptake of AI lags global benchmarks, and prevailing explanations emphasize capital, infrastructure, and institutional voids while overlooking leadership competencies.
Background/introductory claim made by the authors to motivate the study (presented as context rather than an empirical finding from this study).
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).
Firm profitability shows a "J-curve" as firms move from no adoption to deep adoption.
Reported relationship between adoption intensity and firm-level profitability (authors' empirical comparison/regression of profitability across adoption categories).
Adoption is slowly accelerating among non-technology firms but very aggressive adoption in the technology sector which accounts for two-thirds of deeply integrated enterprise adoption.
Reported sectoral breakdown and temporal trend in adoption (authors' sector analysis of SEC 10-K–based adoption measure; statement that tech sector comprises two-thirds of deep adopters).
Traditional jobs based on manual work are transforming into collaborative management and exception-handling roles that demand new cognitive and ethical skills from employees.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No specific sample size reported.
AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding.
Synthesis of cross-domain empirical studies and theoretical arguments showing differential AI performance by task type (routine/data-rich vs. experience-dependent/contextual).
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.
Practitioners sharply disagree about how coding agents change code review: whether review becomes the bottleneck, whether human review remains necessary, and whether agents erode the understanding that review once built.
Synthesis of practitioner discourse at scale via collected grey-literature (engineering blogs and Reddit threads) and a coded sample; claim summarizes observed disagreement in practitioner sources.
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.
The paper identifies four systemic tensions generated by embodied AI adoption: openness versus control; scaling versus local fit; automation ambition versus reliability constraints; and monetization versus trust.
Explicit listing of four tensions in the abstract as theoretical findings (conceptual analysis).
Data generated through physical use of embodied AI travels beyond the adopting firm (i.e., data flows cross firm boundaries).
Explicit conceptual claim in the abstract about data movement across ecosystems (theoretical observation).
Embodied AI implies a double learning loop: a closed learning loop inside the adopting firm (transforming situated use into operational feedback and workflow changes) and an external learning loop across the ecosystem of technology providers, component suppliers, software firms, platform orchestrators, and users.
Conceptual model/argument presented in the abstract describing intra-firm and inter-organizational learning loops (theoretical development).
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.
The net effect of AI on work is better described as displacement than wholesale elimination.
Author's conceptual argument and synthesis of literature/reports (qualitative argumentation in the paper).
Other refugee groups saw meaningful gains in job placements, but increases were concentrated among males and in low-skilled jobs, with only limited effects for females.
Subgroup difference-in-differences analyses by origin group, gender, and skill level using administrative placement data.
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).
A 2025 forecasting study of experts reveals an apparent disconnect between expectations of significant AI capability improvements and modest near-term economic projections.
2025 forecasting study / expert elicitation involving 69 leading economists and 52 AI experts, plus additional expert panels; comparison of experts' expectations about AI capability progress versus their near-term economic projections.
From a sociomaterial perspective, auditor reconfiguration depends both on the evolution of technological capabilities (material agency) and on professionals' engagement and adaptation (social agency).
Theoretical framing and interpretive synthesis in the SLR of 43 studies; application of sociomateriality theory to the empirical patterns identified in the literature.
The introduction of AI reconfigures the auditor’s role through an ongoing, dynamic process: as technology evolves, organizational practices and arrangements transform, rebalancing functions and responsibilities between auditors and tools.
Interpretive synthesis from the SLR of 43 studies using a sociomateriality theoretical lens; cross-study observations about changing tasks, responsibilities and human–machine interactions.
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).
Despite task-level gains, GenAI produces uneven or limited firm-level productivity effects in many settings.
Review synthesizing discrepancies between task-level experiments and firm-level outcome studies, and discussion of conversion frictions in the paper.
Generative AI (GenAI) should not be treated as a standalone productivity shock; its economic value depends on the interaction between model capability, task fit, human-AI calibration, organizational complementary assets, and regional digital infrastructure.
Conceptual framework developed in this review synthesizing literature from AI research, task-level productivity experiments, general-purpose technology theory, digital economics, and China-focused digital transformation studies; no new firm-level empirical analysis in this paper.
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.
There is a growing reliance on agentic AI systems within the platform context.
Qualitative evidence from the 20 interviews and the 24-participant workshop reporting increased dependence on AI agents for tasks and decision support.
There is increasing automation of operational tasks in the development domain.
Participant reports and workshop discussions from 20 interviews and a 24-person workshop indicating automation of operational activities; qualitative thematic evidence.
The results reveal substantial shifts in day-to-day tasks and roles in the development domain.
Reported findings from 20 expert interviews and a 24-participant participatory workshop; claim based on participants' reported changes to responsibilities and observed themes in the data.
AI is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms.
Qualitative study reported in the paper combining 20 expert interviews and a participatory workshop with 24 participants; findings derive from thematic analysis of participant accounts and workshop outputs.
The intended contribution is an Information Systems framework explaining when AI supports human augmentation and when it produces functional substitution.
Stated intended theoretical contribution in the abstract (proposed framework). This is an intended outcome rather than an empirically demonstrated result in the provided text.
The study investigates both perceived and enacted managerial agency.
Stated measurement targets in the abstract (descriptive of dependent variables). No measurement instruments or sample reported in the provided text.
The research uses a sequential multi-phase design combining experiments and qualitative fieldwork.
Stated methodology in the abstract (methodological claim about study design). No sample sizes or procedural details provided in the excerpt.
The study focuses on how technological design features, including transparency and override flexibility, interact with governance structures such as accountability and incentive systems.
Stated focus of the study in the abstract (descriptive of independent variables and governance moderators). No empirical details or sample reported in the provided text.
This doctoral research examines how AI-enabled decision systems affect human agency in data-driven organizations.
Stated research scope and aim in the paper (descriptive claim about the study's focus). No sample or results provided in the abstract.
Artificial intelligence is increasingly embedded in organizational decision-making, reshaping how managers exercise discretion and responsibility.
Stated as a background/motivation statement in the paper (literature-driven claim in the abstract). No empirical evidence or sample reported in the provided text.
Using survey data from AI startups in Qatar, the study will employ PLS-SEM to examine the relationships between these factors, AI capability, and venture performance.
Methods statement in the paper/abstract indicating planned empirical approach (survey of AI startups; use of Partial Least Squares Structural Equation Modeling). No sample size or empirical estimates provided 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.
AI brand visibility can be measured, differs by platform, and varies strongly by brand maturity.
Synthesis claim supported by cross-platform/brand analyses reported in the paper (Ranqo dataset across multiple AI engines and >100 brands, March–May 2026); empirical results (tiered visibility, citation patterns) underpin the assertion.
The guarded engagement loop framework conceptualizes generative AI adoption as a feedback process in which risk perceptions may shape interaction conditions that, in turn, can influence observed performance and subsequent trust calibration.
Central conceptual claim of the paper; framework articulated by the authors and presented as a set of testable propositions (theoretical contribution rather than empirical finding in the abstract).
Risk salience may shape interaction dynamics with LLMs via a multilevel feedback mechanism called the 'guarded engagement loop', in which risk perceptions shape interaction strategies that influence observed performance and, in turn, recalibrate trust in generative AI systems.
Conceptual framework proposed by the authors, integrating theories from trust in automation, privacy calculus, algorithm aversion, and social amplification of risk; presented as a theoretical model rather than an empirical test.
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).
While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems.
Conclusion drawn from the integrative conceptual framework and the systematic review of 68 empirical studies documenting both benefits and risks in different contexts.
Evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research.
Author's assessment of the literature based on the systematic review (PRISMA) of 68 empirical studies published 2015–2025.
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 paper develops the concept of 'bidirectional dynamics' in digital sovereignties, applying a paradoxical view to interpret institutional control objectives and individual autonomy aspirations as persistent organizational tensions in AI adoption.
Theoretical/conceptual development grounded in the empirical single-case study; concept introduced and motivated by observed tensions in the organization (empirical method details and sample size not provided).
Early digital transformation presents tensions but also synergies between digital sovereignty levels in AI adoption.
Empirical observations from the single-case study of a Nordic public transportation organization during early AI adoption; qualitative examples and analysis (specific methods/sample size not stated).
Generative engine optimization (GEO) should be studied not only as a security risk, but also as an emerging marketing practice that shapes market competition.
Paper's concluding/interpretive statement based on the experimental findings about LLM recommendation dynamics and GEO effects on brand recommendations.