Evidence (4131 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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"Quantum code" in this review is defined as program artifacts (Qiskit code, OpenQASM); quantum error-correcting code (QEC) generation was excluded.
Inclusion/exclusion criteria specified in the review explicitly limited scope to program artifacts such as Qiskit and OpenQASM and excluded QEC-focused works.
A structured scoping review (Hugging Face, arXiv, provenance tracing; Jan–Feb 2026) identified 13 generative systems and 5 supporting datasets relevant to quantum circuit / quantum code generation.
Structured search of Hugging Face model/dataset listings, arXiv literature, and provenance tracing conducted between January and February 2026; results yielded 13 systems and 5 datasets (sample counts reported in the review).
This work is conceptual/theoretical and reports no original empirical dataset; it explicitly calls for mixed-methods empirical validation (case studies, field experiments, longitudinal studies), measurement development, and multi-level data collection.
Explicit methodological statement in the paper describing its nature as a theoretical synthesis and listing empirical needs; no empirical sample provided.
Typical methods used are deep learning for property prediction and representation learning, protein-structure modelling tools, generative models for de novo design, NLP for knowledge extraction, and ADME/Tox in silico models integrated with traditional computational chemistry.
Methodological survey in the paper listing these approaches and examples of their application.
Commonly used data types in AI-driven drug discovery include biochemical/binding assay data, protein structural data, HTS results, ADME/Tox and PK datasets, omics/phenotypic readouts, and scientific literature/patents.
Cataloguing of data sources used across studies and company pipelines described in the paper.
AI became widely adopted in pharmaceutical discovery during the 2010s, driven by greater compute, larger datasets, and advances in deep learning.
Historical overview and trend analysis in the paper referencing increased compute availability, growth in public and proprietary datasets, and the rise of deep-learning publications and tools over the 2010s.
The available evidence consists mainly of promising empirical studies and case studies, but there are few long-run, generalized ROI or productivity estimates; results are heterogeneous across therapeutic areas.
Self-described limitation of the narrative review: heterogeneity of study designs and outcomes precluded pooled quantitative estimates and long-run ROI assessment.
AI applications span the full drug development pipeline, including target discovery, in silico screening and de novo design, preclinical safety models, clinical trial design and patient selection/monitoring, and post-marketing surveillance.
Comprehensive literature synthesis across preclinical, clinical, and post-marketing sources in the narrative review summarizing documented uses across these stages.
Current evidence is illustrative rather than systematic; there is a lack of long-run, quantitative measures of AI’s effect on late-stage clinical outcomes in the literature reviewed.
Explicit methodological statement in the paper: study is an expert/opinion synthesis and narrative review with no new causal econometric estimates or primary experimental data.
Suggested metrics for researchers and investors to monitor include R&D cycle time, cost per IND/NDA, proportion of projects using AI, success rates at development stages, market concentration measures, and investment flows into AI-enabled biotech vs incumbents.
Recommendations made in the Implications section as metrics to watch; no empirical tracking or baseline measures provided.
Limitations of the analysis include limited empirical validation of archetypes or impacts and potential selection bias toward prominent firms and technologies.
Explicit limitations stated in the Data & Methods section of the paper.
The paper is an editorial/conceptual synthesis rather than a primary empirical study: it uses qualitative analysis and illustrative examples, and reports no new quantitative estimates.
Explicit statement in the Data & Methods section of the paper describing document type, approach, evidence base, and limitations.
Ethical oversight and governance (addressing bias, consent, downstream risks) are critical constraints that must be addressed for AI to generate sustained benefits.
Normative synthesis referencing common ethical concerns; no empirical evaluation of oversight mechanisms in the paper.
Transparency and auditability for model behavior, provenance, and decisions are essential for trustworthy deployment and regulatory acceptance.
Policy and governance synthesis drawing on regulatory dynamics; no empirical study of regulatory outcomes included.
Rigorous model validation and reproducibility across datasets and settings are necessary constraints for successful AI deployment.
Normative claim in the editorial based on reproducibility concerns in ML and biomedical research; no reported validation trials within the paper.
The paper is primarily discursive and invitational: it opens a dialogue and proposes a research agenda rather than providing definitive empirical answers.
Stated methodological stance and limits: conceptual/philosophical analysis, interdisciplinary literature synthesis, qualitative/illustrative examples, and explicit note of no systematic empirical evaluation.
The collection includes a mix of methodological papers, empirical applications demonstrating ecological insight, and translational work focused on policy or conservation practice.
Study-types categorization provided in the paper (descriptive tally/characterization of the kinds of contributions in the collection).
Methods in the collection span from automated image and signal processing for routine tasks to integrated modelling that couples ecological theory with data‑driven methods.
Methods-scope summary in the paper describing the range of AI/ML approaches used across the collection (descriptive across studies).
The collection uses large ecological observational datasets such as camera‑trap imagery, sensor streams, biodiversity surveys, and other high‑volume ecological monitoring data.
Data & methods section listing the data types represented across the reviewed papers (descriptive inventory of dataset types used in the collection).
AI-adopting firms do not increase capital expenditures following adoption.
Firm-level capex analysis showing no significant change in capital expenditures for adopters versus nonadopters post-adoption in the paper's empirical framework.
Descriptive statistics, reliability tests, regression analysis, and structural equation modelling (SEM) were employed to analyse the relationships between AI adoption and entrepreneurial outcomes.
Methods section reporting use of descriptive statistics, reliability tests, regression analysis, and SEM to evaluate relationships between AI adoption and measured outcomes.
The study used a quantitative research design and collected data from 350 entrepreneurs and managers of small and medium-sized enterprises (SMEs) who had adopted AI in their business operations.
Methods section of the paper specifying a quantitative design and a sample size of 350 AI-adopting SME entrepreneurs/managers.
The study used portfolio-level analysis to compare the financial outcomes of portfolios constructed using AI-driven ESG indicators with those based on conventional ESG ratings.
Methodological statement in the paper: portfolio-level analysis and comparative design. The summary does not specify the number of portfolios, asset universes, time frame, or construction rules.
Foi realizada etnografia organizacional orientada ao SCF, com roteiro e triangulação de evidências.
Método qualitativo divulgado no resumo: etnografia organizacional com roteiro e triangulação; o resumo não fornece número de organizações, duração ou amostragem.
Foi construído e validado um instrumento psicométrico (escala SCF-30) e calculado um índice 0–100, com modelagem por Equações Estruturais (SEM) e testes de confiabilidade/validade.
Descrição metodológica explícita no resumo: construção e validação da escala SCF-30, uso de SEM e testes de confiabilidade e validade. O resumo não detalha estatísticas, amostra ou resultados numéricos.
O SCF é operacionalizado por três vetores centrais: Percepção de Complexidade (PC), Aversão ao Risco Institucional (AR) e Inércia Cultural (IC).
Estrutura conceitual e operacional apresentada no artigo; especificação explícita dos três vetores como componentes do construto SCF.
The paper explains the main legal frameworks that currently regulate AI in India, as well as proposals for future legislation.
Author's legal and policy analysis / document review of existing statutes and proposed laws (qualitative review). No quantitative sample size; based on review of legal texts and policy proposals cited in the article.
DDDM was quantified using AI language models, specifically BERT and ChatGLM2-6B.
Methodological description in the paper stating that BERT and ChatGLM2-6B were leveraged to quantify the extent of DDDM (implementation details, training/data specifics, and sample not provided in the excerpt).
This research examined three countries (China, the United States, and Germany) using panel vector autoregressive (panel VAR) and difference-in-differences (DID) methods to assess how technology and public policy interventions affect emissions reductions.
Study design reported in the paper: sample of three countries (China, US, Germany) and application of panel VAR and DID methods; specific time period and sample size not provided in the summary.
The study examines 268 Chinese cities from 2010 to 2023 and integrates theoretical analysis with empirical testing to study AI innovation's employment effects.
Study description specifying sample size (268 cities), period (2010–2023), and combined theoretical and empirical approach.
The conceptual model for the study is grounded in the Resource-Based View (RBV) and the Technology-Organization-Environment (TOE) framework.
Theory section of the paper: model development explicitly references RBV and TOE as theoretical foundations for selecting determinants and mediators.
The data were analysed using partial least squares structural equation modeling (PLS-SEM).
Methods section: PLS-SEM specified as the primary analytical technique for hypothesis testing and mediation analysis.
Data were collected via a cross-sectional survey of 312 senior managers across diverse UK industries.
Study methods: described sample = 312 senior managers from multiple UK industries; cross-sectional survey instrument and sampling reported in methods section.
The experimental sample underlying the statistical tests comprised 20 observations (implied by ANOVA degrees of freedom: df between = 1, df within = 18).
Interpretation of the reported one-way ANOVA degrees of freedom (F(1,18) for multiple outcomes) indicating total N = 20 observations.
Field experiments at the Al‐Ra'id Research Station in Baghdad during the 2025 season compared conventional diesel‐based irrigation with AI‐assisted irrigation using soil moisture sensors, IoT controllers, and predictive weather algorithms.
Reported field experiment design in the paper (Al‐Ra'id Research Station, Baghdad, 2025 season) specifying two treatments: conventional diesel irrigation vs AI-assisted irrigation using soil moisture sensors, IoT controllers, and predictive weather algorithms.
Definitions and scopes of Material Passports vary among authors.
Content analysis of the 46 included studies showing differing definitions and scope treatments for MPs reported by the authors.
Among the included studies, 65% focused primarily on Material Passports (MPs), while 35% addressed MPs within the broader context of a circular economy (CE).
Quantitative categorization of the 46 included studies reported in the paper (percentages attributed to focus areas).
A total of 54 peer-reviewed articles and book chapters were screened from the Scopus database, of which 46 were included for in-depth analysis in April 2025.
Reported screening and inclusion counts from the Scopus search (54 screened, 46 included); date of in-depth analysis given as April 2025.
This article presents a Systematic Literature Review (SLR) following the PRISMA methodology.
Stated methodology in the paper: SLR using PRISMA; literature search performed in Scopus; review process and inclusion/exclusion described (screening and inclusion counts reported).
Future research could strengthen causal identification by exploiting exogenous policy shocks rather than relying solely on matching methods like PSM.
Authors' methodological suggestion for future work, based on limitations of current causal inference strategy (PSM and observational panel regression).
Propensity Score Matching (PSM) and other robustness checks were used to mitigate selection bias and support the causal interpretation of AI's effects.
Paper reports use of Propensity Score Matching in robustness analyses on the panel of A-share-listed design firms (2014–2023).
The paper operationalizes firm-level AI exposure by constructing an AI lexicon via natural language processing and applying text analysis to annual reports and patents to generate enterprise-level AI indicators.
Described methodology: NLP to generate an AI lexicon and text-analysis of annual reports and patents to build AI measures for each listed design enterprise in the 2014–2023 panel.
By integrating dynamic capabilities theory with a micro foundations perspective, the study proposes a conditional model that reframes the essential challenge from technology adoption to organizational adaptation.
Model/theory construction presented in the paper (conceptual integration). This is a methodological/theoretical claim about the paper's contribution; no empirical validation provided.
This study identifies three types of AI triggers that target routines, cognitive frameworks, and resource allocation.
Proposed taxonomy / typology presented in the paper (theoretical classification). The claim is descriptive of the paper's contribution rather than empirically validated.
The adoption and implementation of AI in entrepreneurial firms is an under-studied area of research.
Paper's literature review and motivation statement asserting limited empirical research on AI adoption in entrepreneurial contexts.
The study collected data from 207 entrepreneurial businesses (including SMEs, startups, and knowledge-based businesses) using a structured questionnaire and analyzed the data using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3.
Structured questionnaire administered to a sample of 207 entrepreneurial businesses; analysis conducted with PLS-SEM (SmartPLS 3) as reported in the paper.
This study analyzes 28 papers (secondary studies and research agendas) published since 2023.
Systematic literature review conducted by the authors of secondary studies and research agendas; sample size explicitly reported as 28 papers; timeframe specified as 'since 2023'.
Research has insufficiently modeled joint distributional outcomes and environmental performance, and lacks integrated evaluation of AI-enabled sustainable finance under heterogeneous disclosure regimes.
Review-level identification of methodological gaps across the surveyed literature (authors' synthesis of existing studies and their limitations).
There is a shortage of long-horizon causal evidence on non-linear coupling between digitalization and decarbonization, limiting robust policy inference.
Meta-level assessment in the review noting gaps in existing empirical literature (review authors' synthesis of the field; claim about research availability rather than primary data).
The Act instituted a rigid seven-percent per-country cap that allocates the same number of visas to India (population of 1.4 billion) as to Iceland (population of 400,000).
Statutory per-country cap (7% rule in the INA) combined with publicly available country population figures for India and Iceland; claim about identical allocation follows directly from the 7% rule.