Evidence (5267 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Adoption
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Software development accounts for 67% of all agent tools.
Categorisation of the 177,436 monitored agent tools by task domain (O*NET mapping) yielding 67% in software development.
We evaluated 177,436 agent tools created from 11/2024 to 02/2026 by monitoring public Model Context Protocol (MCP) server repositories.
Empirical monitoring of public MCP server repositories; dataset of 177,436 agent tools collected over the period 11/2024–02/2026 (as stated in paper).
The framework provides a roadmap for coordinated response across educational institutions, government agencies, and industry to ensure workforce resilience and domestic leadership in the emerging agentic finance era.
Authors' proposed integrated roadmap (prescriptive recommendation; no empirical testing or outcome measurement reported in the provided text).
We develop a comprehensive government policy framework including: 1) Federal AI literacy mandates for post-secondary business education; 2) Department of Labor workforce retraining programs with income support for displaced financial professionals; 3) SEC and Treasury regulatory innovations creating market incentives for workforce development; 4) State-level workforce partnerships implementing regional transition support; and 5) Enhanced social safety nets for workers navigating career transitions during the estimated 5-15 year transformation period.
Author-presented policy framework and recommendations (policy design proposals and an asserted 5–15 year transformation timeframe; no empirical evaluation reported).
We propose a multi-layered integration strategy for higher education encompassing: 1) Foundational AI literacy modules for all business students; 2) A specialized "Agentic Financial Planning" course with hands-on labs; 3) AI-augmented redesign of core courses (Investments, Portfolio Management, Ethics); 4) Interdisciplinary project-based learning with Computer Science; and 5) A governance and policy module addressing regulatory compliance (NIST AI RMF, SEC regulations).
Proposed curricular framework presented by the authors (recommendation/proposal, not empirically tested within the paper).
The ultimate competitive edge lies in an organization's ability to treat AI not as a standalone tool, but as a core component of sustainable, long-term corporate strategy.
Concluding normative claim in the paper; presented as an interpretation/synthesis rather than supported by cited empirical evidence in the abstract.
Successful global expansion is no longer predicated solely on physical presence but on the deployment of scalable, localized AI models that navigate diverse regulatory, linguistic, and cultural landscapes.
Argumentative claim in the paper describing a strategic determinant for global expansion; no empirical sample or quantified outcomes presented in the abstract.
AI hyper-personalizes customer engagement.
Declarative claim in the paper about AI's effect on customer engagement personalization; no experimental or observational data reported in the abstract.
AI acts as an internal engine for operational agility by compressing R&D cycles.
Claim made in the paper asserting R&D cycle compression due to AI; no empirical data, sample size or quantitative measures provided in the abstract.
The strategic focus has transitioned from mere process automation to autonomous orchestration, where multi-agent systems independently manage complex, cross-border operations and real-time decision-making.
Analytic statement from the paper describing an observed/argued shift in strategic focus; no empirical methodology or sample reported.
Organizations leverage agentic workflows and domain-specific intelligence to catalyse strategic innovation and facilitate global expansion in the digital era.
Conceptual claim in the paper describing how organizations use specific AI capabilities; no empirical design or sample described in the abstract.
The rapid evolution of Artificial Intelligence (AI) has shifted from a disruptive trend to the fundamental operating layer of the modern enterprise.
Statement/assertion in the paper (conceptual/positioning claim); no empirical method, sample size, or statistical analysis reported in the abstract.
The analysis provides a transparent measurement framework and baseline statistics for tracking the emerging shift from AI discussion to action-oriented, agentic deployments in finance.
Methodological contribution claim: presentation of an auditable dictionary-and-context approach plus reported baseline statistics (percentages by year).
Autonomy evidence focuses on regions with higher control density, consistent with governance maturity serving as a prerequisite for action-taking deployments.
Comparative text-as-data analysis showing agentic/autonomy references concentrated in disclosure windows with higher measured controls density; interpretive claim linking this pattern to governance maturity as a prerequisite.
Agentic disclosures are absent in 2021–2023, appear in 2024 (0.4% of firm-years), and increase in 2025 (1.6% of firm-years), indicating a late but accelerating diffusion phase.
Empirical counts/percentages reported from the assembled panel; per-year denominators are 500 firm–year observations (500 firms per year).
We implement an auditable dictionary-and-context approach that flags agentic references and then quantifies the surrounding 'controls density' (governance and safety language) within the same local disclosure window.
Methods description: dictionary-and-context text-as-data approach and a quantified 'controls density' metric applied to filings.
We assemble a balanced panel of 2,500 firm–year observations (500 firms per year) from 2021–2025.
Stated dataset construction in the paper: balanced panel across years with 500 firm–year observations per year, total 2,500 firm–years.
Agentic artificial intelligence (AI) systems can execute actions rather than merely generate content.
Conceptual/definitional statement in the paper framing agentic AI as systems that execute actions (not an empirical test).
Transparency’s effectiveness in promoting data-sharing is amplified by, and dependent upon, user trust; fostering trust in AI may be a more vital prerequisite for data-sharing than implementing transparent designs.
Synthesis of experimental findings (N=240): transparency increased willingness only among users with pre-existing trust; null effect of transparency alone on actual sharing; authors conclude that trust moderates transparency effects and recommend focusing on trust-building.
Immediate sharing decisions were largely driven by intuitive System 1 processing rather than deliberative evaluation (System 2).
Interpretation of the pattern in experimental data (N=240): high, similar sharing rates across conditions despite differing stated willingness-to-share and measured privacy concerns; authors attribute this to dual-process dynamics (System 1 driving immediate behavior).
The positive effect of transparency on willingness to share was contingent on pre-existing user trust in AI, particularly for white-box systems.
Moderation analyses reported from the experiment (N=240): interaction between transparency (white-box vs black-box) and measured pre-existing trust in AI showed increased willingness-to-share only among users with higher trust, with the effect most pronounced for white-box systems.
We conducted a pre-registered online experiment (N=240) where participants interacted with a fictional sleep-optimization app and were randomly assigned to scenarios where data was processed by either a human expert, a transparent white-box AI, or an opaque black-box AI.
Pre-registered online experimental design described in paper; random assignment to three processing-entity conditions (human, white-box AI, black-box AI); sample size reported as N=240; measured outcomes included actual data-sharing and willingness to share, plus trust and privacy concerns.
EcoThink offers a scalable path toward a sustainable, inclusive, and energy-efficient generative AI Agent.
Concluding claim in the paper asserting broader impact and scalability of the proposed method (position/interpretive claim based on reported results).
Extensive evaluations were performed across 9 diverse benchmarks.
Statement in the paper that evaluations were run on 9 benchmarks (as stated in the abstract).
EcoThink employs a lightweight, distillation-based router to dynamically assess query complexity, skipping unnecessary reasoning for factoid retrieval while reserving deep computation for complex logic.
Methodological description of the proposed framework in the paper (design/architecture claim).
EcoThink reduces inference energy by up to 81.9% for web knowledge retrieval.
Experimental result reported in the paper (maximum observed reduction for the web knowledge retrieval benchmark, as stated in the abstract).
EcoThink reduces inference energy by 40.4% on average across 9 diverse benchmarks.
Experimental evaluations reported in the paper across 9 benchmarks comparing inference energy of EcoThink versus baseline (as stated in the abstract).
The proposed system and findings have policy-relevant implications for policymakers and fiscal institutions, improving their ability to name (identify) and react to potential instabilities.
Paper discussion claims implications for policymakers and fiscal institutions based on the proposed framework and synthesized empirical findings; specific policy-impact evaluations are not provided in the excerpt.
This paper proposes a novel framework that uses machine learning and news data to create a regulatory early-warning mechanism for predicting and mitigating fiscal risk.
Paper text describes a proposed framework combining machine learning with news streams; described as a methodological contribution (conceptual design/architecture). No numeric evaluation or sample size reported in the provided excerpt.
Integrating AI into financial ecosystems can strengthen both economic and climate resilience, provided that regulatory frameworks, ethical AI practices, and capacity-building measures are simultaneously addressed.
Paper's concluding recommendation based on combined qualitative and quantitative findings from the three case studies and the 1,500 interviews; framed as conditional policy guidance in the abstract.
Predictive AI models can facilitate climate-resilient decision-making in agriculture.
Reported as a finding from the Thailand AI-supported smart agriculture finance case study, supported by qualitative evidence and (implied) predictive-model-driven finance decisions noted in the abstract.
Women exhibit higher adoption and savings patterns on AI-enabled financial platforms.
Abstract reports gendered impacts derived from 1,500 semi-structured customer interviews plus account-activity data across the three case studies, noting higher adoption and savings for women.
AI-enabled platforms reduce vulnerability to climate-related income shocks.
Abstract claims findings that AI-enabled platforms reduce vulnerability to climate-related income shocks based on case studies (including smart agriculture finance in Thailand), interviews and transaction/loan data analysis.
AI-enabled platforms promote savings behavior among customers.
Abstract reports findings based on mixed-methods: qualitative interviews (1,500) and quantitative account-activity analysis indicating increased savings behavior on AI-enabled platforms.
AI-enabled platforms significantly improve credit access for low-income and rural customers in the case-study contexts.
Quantitative analysis of transaction records and loan repayment histories combined with qualitative insights from 1,500 interviews across three case studies (M-KOPA, TymeBank, and smart agriculture finance in Thailand) as described in the abstract.
Policymakers should pursue integrated policies linking energy transition, macroeconomic stability, and digital innovation to preserve the United States' technical supremacy in AI.
Normative recommendation based on the paper's empirical findings (WQR/WQC on 2013Q1–2024Q4 US data) showing links between energy policy, macro determinants, and AI investment.
Stable energy policy, continuous economic growth, and improved global integration are significant for promoting AI development in the United States.
Policy implication drawn from empirical associations found using WQR/WQC on US quarterly data (2013Q1–2024Q4), where renewable energy, growth, trade openness, and globalisation positively associate with AI investment and energy policy uncertainty exhibits nonlinear effects.
Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC) effectively capture distributional asymmetries and time–frequency dynamics in the relationships between macro/policy determinants and AI investment.
Methodological claim supported by the paper's use of WQR and WQC on the 2013Q1–2024Q4 US quarterly dataset; results are reported across quantiles and scales (as stated).
Globalisation positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Trade openness positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Economic growth positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Renewable energy consumption positively influences AI investment in the United States.
Empirical analysis using Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC) on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
AlphaFold represents an 'oracle' breakthrough in AI for scientific discovery.
Cited as an example of an algorithmic breakthrough that changed a specific scientific subtask (protein structure prediction). The paper frames AlphaFold as a milestone in the history reviewed; no new experimental data presented.
The resulting policy matrix includes R&D funding, regulatory sandboxes, public procurement incentives, and tax relief, tailored to each stage of technological evolution.
Paper presents a policy matrix produced by the study listing these instruments mapped to maturity stages; no quantitative evaluation of impact reported in text provided.
To validate and prioritise policy instruments, Delphi rounds with domain experts and Analytic Hierarchy Process (AHP) weighting are employed.
Paper reports use of Delphi method and AHP for validation and prioritization; methodological description without reported number of experts or rounds.
A technology maturity classification categorises innovations into emerging, developing, and mature stages, forming the basis for strategic policy matching.
Paper defines a maturity classification (emerging/developing/mature) and indicates it is used to match policy instruments; categorical description provided, no quantitative validation details in text provided.
Temporal mapping and citation networks reveal distinct technology maturity patterns, which are visualised using S-curve and hype cycle models.
Paper describes use of temporal mapping and citation network analysis and visualization via S-curve and hype cycle models; methodological description without quantitative sample-size details.
Technologies such as AI-driven healthcare, quantum communication, hydrogen energy, and smart educational AI are identified as key domains of convergence.
Paper reports these domains were identified via the applied analytic framework and multi-source data triangulation; no numeric counts/sample sizes provided.
The study applies advanced techniques such as LDA topic modelling, BERT-based clustering, and co-citation analysis to detect innovation trajectories.
Paper states these specific analytic techniques were applied (method description).
The research leverages large AI models and multi-source data—including global patent databases (WIPO, USPTO, Lens.org), scientific literature corpora, and industry intelligence platforms (CB Insights, Qichacha).
Paper statement of data sources and use of large AI models; methodological description (no sample sizes reported).