Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Overall, findings highlight that AI serves as a revolutionary (transformative) tool rather than merely a replacement tool for employment—changing the nature of human work rather than simply disengaging it.
Synthesis conclusion in the paper drawing on the literature review and the authors' empirical results indicating task reallocation and changing job content.
The paper argues for equal technology governance as a necessary policy response to AI's labor market effects.
Policy recommendations discussed in the paper that call for equitable governance of AI; based on literature synthesis and empirical findings.
The analysis raises policy implications emphasizing reskilling and education to address AI-driven changes in the labor market.
Policy discussion section summarized in the paper; draws on empirical findings and literature to recommend reskilling/education.
Moderate AI usage is associated with employment growth.
Part of the U-shaped relationship reported in the paper's empirical results; described qualitatively in the abstract/summary.
Secondary empirical evidence from Colombia's EDIT manufacturing survey (N=6,799 firms) shows that management practice quality amplifies the return to technology investment (interaction coefficient 0.304, p<0.01).
Secondary empirical analysis of EDIT manufacturing survey data; sample size reported as N = 6,799 firms; regression interaction term reported as coefficient 0.304 with p < 0.01.
We endogenize the augmentation function as phi(D, W), where W is a five-dimensional workplace design vector (AI interface design, decision authority allocation, task orchestration, learning loop architecture, psychosocial work environment), and prove that human-centric design is profit-maximizing when the workforce's augmentable cognitive capital exceeds a critical threshold.
Theoretical model and formal proof presented in the paper (analytical derivation of phi(D,W) and threshold condition).
There is a need for energy-efficient AI development to align technological progress with sustainable energy consumption.
Policy recommendation based on the paper's empirical findings that AI adoption increases firm-level electricity demands in the short run; normative argument rather than a directly tested empirical claim.
The AI-related widening of the electricity output growth gap is stronger among manufacturing firms, non-state-owned firms, small firms, low-tech firms, and low-energy-consumption and low-pollution firms.
Heterogeneity/subgroup analyses across firm characteristics (ownership type, size, sector, technology intensity, baseline energy use and pollution levels) showing larger estimated effects in the listed subgroups. Specific subgroup sample sizes and coefficients not reported in the summary.
The effect of AI adoption on the electricity output growth gap is more pronounced for firms operating in highly competitive industries.
Heterogeneity analysis by industry competition intensity (likely via industry-level measures of competition); interaction regressions showing larger estimated effects in more competitive sectors. Sample/subgroup sizes not specified in the summary.
The effect of AI adoption on widening the electricity output growth gap is more pronounced for firms located in economically advanced regions.
Heterogeneity analysis by regional economic development level using the firm-level electricity consumption dataset; stratified or interaction regressions showing larger estimated effects in more advanced regions. Exact subgroup sizes not provided in the summary.
The main result (initial widening of electricity growth gap) is robust to alternative variable definitions, exclusion of firms relying on outsourced AI services or non-AI adoption samples, and controls for endogeneity.
Robustness checks reported in the paper: alternative variable definitions, sample restrictions (excluding outsourced-AI-reliant firms and non-AI samples), and application of endogeneity control methods (e.g., instrumental variables or panel fixed effects). Exact methods and sample sizes not specified in the summary.
AI adoption initially widens the corporate electricity output growth gap at the firm level in China.
Empirical analysis using unique firm-level data on corporate electricity consumption in China; econometric estimation comparing electricity output growth between AI-adopting firms and non-adopting peers (panel/firm-level analysis). Sample size not stated in the summary.
Strong governance and advanced digital infrastructure are critical for realizing AI’s potential as a sustainable technology—governance-driven digital transformation is important for achieving sustainable growth.
Interpretation and policy implication drawn from the empirical findings that GQI and DII mitigate the AI→CO2 relationship in the 104-country panel analysis (2000–2023) employing GMM and 2SLS.
The environmental impact of AI is stronger in energy-inefficient and AI-advanced contexts.
Heterogeneity analysis in which the AI→CO2 effect is reported as larger for energy-inefficient countries and for countries in more advanced stages of AI diffusion (same 104-country panel, 2000–2023).
Adoption of AI currently contributes to higher CO2 emissions.
Empirical panel analysis of 104 countries over 2000–2023 using two-step system GMM and two-stage least squares (2SLS) estimations; AI adoption variable positively associated with country-level CO2 emissions in the reported regressions.
To optimize agentic AI integration and ensure responsible innovation across financial services, interdisciplinary, longitudinal research and robust governance frameworks are needed.
Authors' conclusions and recommendations based on the identified findings and gaps in the reviewed literature.
Diverse architectural models such as multi-agent systems and cloud-based frameworks enable scalable, adaptive agentic AI deployments in financial services.
Synthesis of architecture-focused studies and framework descriptions within the reviewed literature (architectural benchmarking across papers).
Findings reveal substantial productivity gains and operational efficiencies predominantly in banking and investment.
Systematic review synthesizing multidisciplinary qualitative, quantitative, and bibliometric studies of agentic AI applications in financial services published up to mid-2024 (review-level synthesis).
The ManagerWorker two-agent pipeline (expensive text-only manager + cheaper worker with repo access) can substitute expensive execution by using expensive reasoning in the manager and cheaper execution in the worker.
System design description plus empirical results on 200 SWE-bench Lite instances showing parity in success rates between a strong-manager/weak-worker pipeline and a strong single agent while using fewer strong-model tokens.
A minimal review-only manager loop adds only 2 percentage points over the baseline, whereas structured exploration and planning by the manager add 11 percentage points, demonstrating that active direction (not mere reviewing) produces most of the benefit.
Ablation-style comparison of pipeline variants on the 200-instance SWE-bench Lite evaluation: review-only manager loop versus manager with structured exploration and planning; reported improvements in percentage points.
A strong manager directing a weak worker achieves a 62% success rate on software-engineering tasks, matching a strong single agent which achieves 60%, while using a fraction of the strong-model token usage.
Empirical evaluation on 200 instances from SWE-bench Lite across five pipeline configurations and model pairings; measured task success rates and token usage for manager-worker pipelines versus single-agent baselines.
Overall, the HCT is a robust, accurate, and transparent alternative to the AI-as-advisor approach, offering a simple mechanism to tap into the wisdom of hybrid crowds.
Overall conclusion drawn from the empirical comparisons across datasets and analyses described in the paper (summary statement in abstract).
Using signal detection theory, the paper finds that the HCT outperforms the AI-as-advisor approach because people cannot discriminate well enough between correct and incorrect AI advice.
Analysis in the paper applying signal detection theory to the empirical results (as stated in abstract).
The HCT also performed better in almost all cases in which the AI offered an explanation of its judgment.
Empirical results on the subset of four datasets with AI explanations (abstract reports HCT performed better in 'almost all' of these cases).
The HCT outperformed the AI-as-advisor approach in all datasets.
Empirical comparisons reported across the 10 datasets (statement in abstract that HCT 'outperformed' in all datasets). Specific performance metrics not provided in abstract.
The study points to the need for longitudinal, experimental, or platform-log-based designs to establish causality and measure heterogeneity across platforms.
Authors' methodological recommendations and proposed empirical agenda built on limitations of their cross-sectional survey (N = 450) and literature gaps.
Policy and practice interventions (media literacy, platform design changes, mandated diversity, etc.) are recommended to increase informational diversity and mitigate polarization.
Policy recommendations derived from study findings and literature discussion; not evaluated experimentally in the paper (authors propose interventions as implications).
Algorithmic recommendation (structural) and user selective consumption (behavioural) jointly reinforce ideological positions in digital spaces.
Interpretation based on observed associations between selective exposure and polarization plus reported heterogeneity in perceived algorithmic influence from the N = 450 survey; authors frame results as indicating interacting structural and behavioural mechanisms.
Higher levels of selective exposure are positively associated with increased ideological polarization.
Correlational analyses (reported associations / regression-style tests) using survey measures of selective exposure and measures of opinion/political polarization in the same cross-sectional sample (N = 450).
A large majority of respondents reported frequent exposure to content aligned with their preexisting views (widespread echo chambers / filter bubbles).
Quantitative cross-sectional survey of N = 450 active social media users; self-reported measures of content consumption and indicators of selective exposure; descriptive statistics showing most respondents frequently encounter ideologically consonant content.
An AI agent given revealed-preference data predicts subjects' choices more accurately than an AI agent given stated-preference prompts.
Online experiment in which subjects provided written instructions (prompts) and revealed preferences via choices in a series of binary lottery questions; AI agents were given either the revealed-preference data or the stated-preference prompts and their prediction accuracy on subjects' choices was compared.
Under economy-wide deployment, the share of computer-vision-exposed labor compensation that is cost-effectively automatable rises sharply (relative to the firm-level 11% estimate).
Model counterfactuals or calibration scenarios comparing firm-level deployment vs economy-wide deployment; qualitative statement that share increases substantially.
At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation.
Calibration and implementation in computer vision; reported firm-level estimate from the framework.
Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks.
Modeling and calibration arguments showing fixed-cost spreading effects increase set of tasks for which automation is cost-effective; qualitative and quantitative comparisons in implementation.
Because higher accuracy is disproportionately costly (convex cost), full automation is often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium.
Theoretical model combined with calibration (scaling laws + task mappings); equilibrium outcomes reported from the framework implementation.
We model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.
The paper develops a theoretical framework / model that treats automation intensity as a continuous decision variable; described as the central modeling approach.
The findings demonstrate that technological innovation strategies, when effectively implemented, provide measurable competitive advantages for banks and offer evidence-based insights for policymakers and practitioners.
Authors' interpretation/conclusion drawing on the reported statistically significant relationships between innovation (product and technological) and competitiveness.
Technological innovation is positively and statistically significantly related to bank competitiveness (simple linear regression result reported).
Simple linear regression reported in the paper testing the hypothesis that technological innovation influences competitiveness; data collected from innovation-focused executives across licensed banks (paper states data from 39 licensed banks).
Product innovation strategy has a positive and statistically significant effect on competitiveness (F(1,134) = 74.983, p < .001).
Bivariate regression analysis reported in the paper with F(1,134)=74.983, p < .001; based on survey data from innovation-focused executives (regression degrees of freedom indicate n≈136 observations).
The results (conceptual/model results) support corporate GenAI policies, leadership development programs, and HR assessment of leader readiness for GenAI-enabled delegation and communication.
Practical implications and recommendations section arguing policy and HR applications based on the conceptual model.
The article introduces an EI-driven trust-calibration framework as an explanatory mechanism showing when generative AI improves leadership effectiveness and when it amplifies managerial errors.
Novel theoretical framework developed in the paper synthesizing EI, trust calibration, and psychological safety to explain boundary conditions of AI in leadership.
The paper provides an operationalization toolkit including measures: GenAI use intensity; delegation quality indices (clarity, boundaries, success criteria); communication quality indices (empathy, tone, transparency); psychological safety markers; and behavioral trust-calibration measures.
Operationalization section in the paper listing suggested indices and markers for empirical measurement.
As a follow-up validation path, the paper proposes a two-wave time-lag design and 180° assessment (leader + subordinates) to reduce common-method bias.
Methodological proposal in the paper describing longitudinal and multi-rater validation approaches.
The paper proposes a 'Package B' rapid empirical design: a randomized online experiment manipulating access to generative AI in core managerial tasks (decision, delegation, team communication), combined with EI measurement and trust-calibration indicators.
Methodology section proposing the rapid randomized online experiment design as the primary empirical test.
Emotional intelligence strengthens the positive impact of generative AI on managerial outcomes when trust is properly calibrated and psychological safety is maintained.
Conceptual model and integrative argument combining EI, trust-calibration, and psychological safety; supported by proposed empirical test design.
The paper conceptualizes human–AI leadership as an integrated managerial competence.
Conceptual modeling presented in the paper integrating EI theory, psychological safety, and trust calibration (theoretical synthesis).
Hukum diharapkan tidak hanya berfungsi sebagai alat perlindungan, tetapi juga sebagai instrumen strategis dalam mengelola transisi menuju masa depan kerja yang lebih inklusif, adil, dan berkelanjutan di era kecerdasan buatan.
Kesimpulan dan rekomendasi normatif penulis berdasarkan analisis perundang-undangan dan literatur yang dikaji.
Pengakuan 'hak atas pengembangan keterampilan berkelanjutan' (right to lifelong learning) penting dan perlu dimasukkan sebagai bagian integral dari perlindungan pekerja di era digital.
Klaim normatif dan rekomendasi kebijakan yang muncul dari studi konseptual dan tinjauan literatur komparatif.
Diperlukan reformasi hukum yang lebih progresif dan adaptif, termasuk penguatan sistem jaminan sosial dan pembaruan kebijakan fiskal untuk menangani dampak AI.
Rekomendasi kebijakan yang disimpulkan dari analisis normatif dan komparatif serta tinjauan literatur dalam penelitian.
Diperlukan dasar hukum bagi penerapan model kompensasi inovatif seperti Universal Basic Income (UBI), pajak otomasi, dan skema distribusi manfaat produktivitas AI.
Rekomendasi kebijakan hasil analisis normatif dan komparatif yang dikemukakan penulis berdasarkan tinjauan literatur.