Evidence (4560 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 |
Productivity
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We built an AI-based multi-agent system to support automatic migration of TensorFlow-based deep learning models into JAX-based ones.
System implementation and description in the paper; demonstration on real-world code migration tasks in a hyperscaler environment (qualitative description in abstract).
The productivity channel raises corporate cash flows and is equity-bullish.
Model mechanism described in the paper: productivity effects of AI increase corporate cash flows which, within the model, produce an equity-bullish effect on the ERP/valuations.
Efficient conversion of R&D into technological barriers is key to avoiding the 'AI trap'; new energy vehicle firms should prioritize R&D efficiency, translate innovation into stable returns, and maintain sound financial conditions.
Paper's conclusion/recommendation derived from empirical findings (2013–2023 sample) linking R&D conversion/patent transformation and intelligent equipment output to reduced financial risk from AI dependence.
Strong knowledge or intelligent equipment output and effective patent transformation mitigate the financial risks associated with AI dependence.
Moderation and heterogeneity tests reported in the paper using the same sample (listed NEV and automobile manufacturers, 2013–2023) indicate these factors reduce the adverse effect of AI dependence on financial safety.
The dataset, contexts, annotations, and evaluation harness are released publicly.
Paper states that dataset, contexts, annotations, and evaluation harness are released publicly (release / open-source claim).
A structured 2,000-token diff-with-summary prompt outperforms a 2,500-token full-context prompt (enriched with execution context, behaviour mapping, and test signatures) across all 8 models.
Direct prompt/context-size comparison across the 8 models on SWE-PRBench; reported that the 2,000-token diff-with-summary prompt yields better performance than the 2,500-token full-context prompt with extra enrichments.
The LLM-as-judge framework used for evaluation is validated at kappa = 0.75.
Inter-judge validation reported in paper (agreement metric kappa reported as 0.75). Specific validation sample size not stated in the excerpt.
Pull requests are drawn from active open-source repositories, filtered from 700 candidates using a Repository Quality Score.
Dataset curation procedure reported: initial pool of 700 candidate repositories/PRs filtered by a Repository Quality Score to produce the final benchmark.
We introduce SWE-PRBench, a benchmark of 350 pull requests with human-annotated ground truth for evaluating AI code review quality.
Dataset construction described in paper: benchmark contains 350 pull requests with human annotations. Pull requests drawn from active open-source repositories and filtered from 700 candidates using a Repository Quality Score.
The paper concludes by articulating expected outcomes for management practice and proposes a research agenda calling for future mixed-methods validation of the framework.
Stated conclusion and explicit call for mixed-methods validation; no validation results provided in this paper.
The review derives constructs, hypothesized links among them, and governance implications for managing and institutionalizing workplace AI.
Paper reports that reviewed sources were used to derive constructs and governance implications; this is a conceptual derivation rather than empirical testing.
The framework and synthesis can be used to diagnose patterns of disengagement and pilot-to-production failure in corporate AI initiatives.
Proposed analytical structure derived from literature synthesis and conceptual mapping; intended as a diagnostic tool but not empirically validated within this paper.
The paper integrates adoption frameworks (TAM and TOE) with evidence on human-AI interaction to produce a scaling-oriented conceptual framework for diagnosing disengagement and pilot-to-production failures.
Comparative conceptual analysis and framework building based on reviewed literature; no new empirical validation reported.
Integrating technological, human, and organizational capabilities is important to maximize the benefits of AI in smart manufacturing.
Conclusion based on thematic patterns in interviews, observations, and document analysis from purposively sampled supply chain and production professionals; identified as an implementation implication.
Firms adopting AI-driven forecasting and inventory strategies can achieve higher operational agility, better strategic resource alignment, and maintain a competitive advantage in dynamic manufacturing contexts.
Synthesis and implications drawn from thematic analysis of interviews, site visits, and documents from purposively sampled industry practitioners; presented as study conclusions rather than quantitatively tested outcomes.
AI supports sustainability initiatives within manufacturing operations.
Thematic analysis of practitioner interviews and organizational documentation where respondents linked AI-based forecasting/inventory optimization to sustainability outcomes (e.g., waste reduction).
AI improves supply chain coordination among partners and internal functions.
Interview and document-based thematic findings from purposively sampled supply chain managers and industry experts reporting enhanced coordination following AI adoption.
AI contributes to operational resilience in manufacturing supply chains.
Qualitative evidence from interviews and organizational documents indicating that AI-enabled forecasting and inventory controls improve firms' ability to adapt to disruptions; thematic analysis produced resilience as a reported benefit.
Organizational readiness, skilled personnel, data quality, and robust technological infrastructure are critical factors influencing AI effectiveness.
Recurring themes identified via thematic analysis of semi-structured interviews with supply chain and production professionals, corroborated by observational site visits and organizational documents from purposive sample.
AI reduces excess inventory levels in manufacturing firms.
Thematic findings from interviews, site visits, and documents from industry experts and practitioners who reported decreased excess inventory following AI-driven forecasting and inventory optimization.
AI reduces stockouts in manufacturing supply chains.
Practitioner accounts and organizational document evidence from purposive qualitative sampling and thematic analysis indicating fewer stockouts associated with AI-driven forecasting and inventory controls.
AI adoption reduces operational inefficiencies in manufacturing processes.
Thematic analysis of qualitative data (semi-structured interviews, site observations, organizational documents) from purposively sampled industry practitioners reporting reductions in inefficiencies after AI implementation.
AI supports proactive decision-making among supply chain and production stakeholders.
Qualitative reports from interviews and document review with supply chain managers, production planners, and industry experts; thematic analysis identified proactive decision-making as a theme associated with AI use.
AI enables adaptive inventory management in manufacturing operations.
Findings from thematic analysis of semi-structured interviews with supply chain managers, production planners, and industry experts, plus observational site visits and organizational documents (purposive sampling).
AI technologies enhance forecasting accuracy in smart manufacturing.
Qualitative evidence from purposive sample of supply chain managers, production planners, and industry experts gathered via semi-structured interviews, observational site visits, and organizational documents; analyzed using thematic analysis.
Our dataset is available at https://guide-bench.github.io.
Paper's statement providing a URL for dataset access.
Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop).
Motivating claim in the paper's introduction/abstract, based on prior work and the authors' argument about potential application domains.
Providing user context significantly improved the performance, raising help prediction by up to 50.2pp.
Experimental comparison reported in the paper showing differences in Help Prediction performance with and without provided user context; reported improvement magnitude of up to 50.2 percentage points.
GUIDE defines three tasks - (i) Behavior State Detection, (ii) Intent Prediction, and (iii) Help Prediction that test a model's ability to recognize behavior state, reason about goals, and decide when and how to help.
Paper's benchmark/task definitions describing three evaluation tasks and their goals.
GUIDE consists of 67.5 hours of screen recordings from 120 novice user demonstrations with think-aloud narrations, across 10 software.
Paper's dataset description: dataset construction of screen recordings, number of demonstrations, duration, participant expertise (novice), and inclusion of think-aloud narrations across 10 software.
Automatic speech recognition (ASR) has shown increasing potential to assist in the transcription of endangered language data.
Background claim in the paper, referring to advances in ASR and prior work suggesting utility for endangered-language transcription; stated as motivation rather than a novel empirical finding in this paper.
We train an ASR model that achieves a character error rate as low as 15%.
Reported quantitative evaluation of the trained ASR model on the constructed Ikema dataset (character error rate = 15%). Exact evaluation protocol, test set size, and train/test split not provided in the abstract.
We construct a {\totaldatasethours}-hour speech corpus from field recordings.
Stated in paper as an outcome of the authors' data-collection and corpus-construction effort from field recordings; no numeric value resolved in the provided text (placeholder present).
With calibrated oversight that aligns accountability to real-world risks, AI can secure the profession’s future.
Normative/prognostic claim in the Article (argument that appropriate governance will preserve or strengthen the legal profession).
With calibrated oversight that aligns accountability to real-world risks, AI can improve service quality in legal services.
Normative/prognostic claim in the Article (argument that governance plus AI yields quality improvements). No empirical effect sizes reported in the excerpt.
While the risks of AI are real, they must not eclipse the opportunity: with calibrated oversight that aligns accountability to real-world risks, AI can expand access to legal services.
Normative claim and projected benefit argued by the authors (theoretical/argumentative; no empirical evidence in excerpt).
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.
A Metacognitive Co-Regulation Agent (in CRDAL) assists the Design Agent in metacognition to mitigate design fixation, thereby improving system performance for engineering design tasks.
Mechanistic claim supported by the paper's experimental results on the battery pack design problem showing CRDAL outperforming SRL and RWL; detailed measures of fixation reduction not provided in the excerpt.
The CRDAL system navigated through the latent design space more effectively than both SRL and RWL.
Empirical analysis on the battery pack design task comparing latent-space trajectories/exploration between CRDAL, SRL, and RWL; details on how 'more effectively' was quantified and sample size are not provided in the excerpt.
The CRDAL system achieves better design performance without significantly increasing the computational cost compared to SRL and RWL.
Empirical claim based on experiments on the battery pack design problem comparing computational cost across CRDAL, SRL, and RWL; exact computational metrics and sample size not provided in the excerpt.
In the battery pack design problem examined here, the CRDAL system generates designs with better performance compared to a plain Ralph Wiggum Loop (RWL) and the metacognitively self-assessing Self-Regulation Loop (SRL).
Empirical comparison on a battery pack design task between CRDAL, SRL, and RWL reported in the paper; exact number of test instances or runs not stated in the excerpt.