Evidence (5539 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Adoption
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Most moderators tested in the analyses have a considerable influence on the relationship between AI use and business performance.
Moderator analyses reported in the meta-analysis (unspecified number of moderators) across the sample of reviewed studies (n=85).
Digital transformation reshapes labor markets.
Paper asserts effects on labor markets (skills demand, employment patterns); the abstract lacks details on labor market data, sample sizes, or econometric analyses used to substantiate this claim.
AI, blockchain, and big data analytics affect productivity, investment strategies, labor markets, and regulatory frameworks.
Stated in the paper as impacts analyzed; the abstract does not specify the data, methods, or scope used to measure these impacts.
Digital transformation through artificial intelligence (AI), blockchain technology (BT), and big data (BD) analytics reconfigures economic mechanisms at both micro- and macroeconomic levels.
Paper-level analytic claim referencing impacts of AI, blockchain, and big data; detailed empirical methodology and sample information not described in the abstract.
This mainstream narrative about what AI is and what it can do is in tension with another emerging use case: entertainment.
Authors' conceptual argument contrasting dominant productivity-oriented narratives with observed/emerging entertainment uses; no quantified data in the excerpt.
A consistent finding is that implementation outcomes are determined by institutional conditions rather than algorithmic performance.
Synthesis across the 81 reviewed sources indicating recurring patterns where institutional factors (governance, reimbursement, workforce, regulations) drive implementation success more than raw algorithmic accuracy. Specific studies supporting this pattern are not named in the abstract.
The fast spread of artificial intelligence (AI) in U.S. organizations has radically altered the managerial decision-making process.
Statement based on a conceptual research design and integration of interdisciplinary literature (literature review). No empirical sample or quantitative data reported.
In digital tourism, there is both substitution potential (virtual experiences, demand management) and rebound risks that may offset emissions reductions.
Sectoral case synthesized from peer-reviewed studies and reports on digital tourism and travel demand (review-level evidence; no single empirical sample size).
Sustainable infrastructure and energy-transition analyses must account for hydrogen value chains and the substantial energy footprint of digital systems (data centers and AI workloads).
Review of sectoral studies on hydrogen supply chains and studies estimating energy use of data centers and AI workloads (review synthesis; specific lifecycle analyses and energy-use studies referenced in paper).
The convergence of green finance and computing — especially automated ESG assessment — expands monitoring capacity but also amplifies measurement divergence and greenwashing risks.
Review of literature on automated ESG tools, sustainable finance, and computational assessment methods (synthesis of empirical and conceptual studies; no single sample size reported).
AI and digitalization are restructuring labor markets, producing wage polarization and rents, with outcomes mediated by labor-market institutions.
Review of labor-market literature on AI/digitalization effects (aggregate synthesis of empirical studies and theoretical papers; review does not report an aggregated sample size).
AI adoption rates differ across countries and firm sizes.
Descriptive/empirical comparisons using AI diffusion indicators and firm-level data from the four named Central and Eastern European countries; heterogeneity by firm size reported.
AI productivity effects are not direct but conditional on organizational readiness.
Empirical analysis of firm-level data covering Serbia, Croatia, Czechia, and Romania combined with AI diffusion indicators; conditional/interaction analysis implied by framing (paper reports that productivity effects depend on organizational factors).
Although the asymmetry in who benefits does not preclude beneficial entry, it raises strategic issues for deployment of AI technology in multiagent settings.
Interpretation and discussion in the paper based on the mixed-population results and observed payoff asymmetries; this is a normative/strategic claim rather than an empirical measurement.
In some parameter regimes, non-adopters may benefit disproportionately from the cooperation induced by adopters (i.e., non-adopters can free-ride on adopter-induced coordination).
Parameter-regime analysis of mixed populations reported in the paper indicating payoff asymmetries between adopters and non-adopters; specific parameter ranges and quantitative results are not provided in the abstract.
AI drives changes in economic growth.
The paper synthesizes theoretical and empirical arguments from the literature about AI's role for economic growth; the review itself does not present new growth accounting or causal estimates.
AI influences income and wage disparity.
Review discussion of research linking technological change and differential wage/income outcomes; no original econometric analysis or dataset presented in this paper.
AI adoption affects productivity levels.
Discussion and synthesis of existing economic literature on AI and productivity included in the review; the paper does not report primary empirical estimates or a quantified effect size.
Smaller models augmented with curated Skills can match the performance of larger models without Skills (model–skill tradeoff).
Cross-size performance comparisons reported across seven agent–model configurations showing that certain smaller model + curated-Skill pairings achieve pass rates comparable to larger model baselines without Skills. Analysis uses the SkillsBench trajectories (7,308 total) to support tradeoff claims.
Knowledge industries exhibit strong complementarities with AI but also face task-level automation (e.g., routine analysis) that changes job content.
Literature synthesis on AI adoption in knowledge sectors and task-based mapping showing both complementarities and partial task substitution.
Services show mixed effects: routine clerical and customer-service tasks are vulnerable, while personalized, creative, and relational services are less so.
Task-level synthesis of service-sector automation exposure studies and conceptual analysis of task complementarities in relational services.
Manufacturing faces high automation potential for routine production tasks but also opportunities in advanced manufacturing and robotics maintenance.
Cross-sectoral analysis and literature on automation in manufacturing; theoretical task mapping indicating routine task exposure and emergence of maintenance/advanced roles.
Wage polarization is likely: middle-skill wages will be compressed while high-skill wages rise; some low-skill service roles may persist or expand.
Synthesis of skill-biased technological change literature and task substitution/complementarity arguments; paper references empirical patterns of polarization in prior studies.
Firms with better data infrastructure and higher initial IT investment will adopt AI faster, potentially widening performance gaps across firms and industries.
Theory-informed assertion and literature synthesis; no empirical heterogeneity analysis is specified in the abstract.
Complementarity between AI and skilled accountants may raise wages for analytical roles while compressing demand for routine clerical roles, contributing to wage polarization.
Prediction grounded in economic theory and prior literature; the paper does not report direct wage-change estimates in the abstract.
AI will automate routine accounting tasks, reducing demand for low-skill bookkeeping work while increasing demand for higher-skilled roles (data interpretation, advising, oversight), creating occupational reallocation and upskilling needs.
Projection based on task-based labor economics literature and the paper's synthesis; not supported by specific longitudinal labor-market estimates in the abstract.
Generative AI can play a bounded, auditable role as multilingual, low‑bandwidth learning support, but must be governed to avoid digital gatekeeping and should be excluded from eligibility screening, risk scoring, or automated decision‑making.
Analytical assessment of AI's potential roles and risks in training delivery; governance prescriptions based on policy and risk reasoning rather than empirical AI evaluations in the corridor.
Proposition 3: Rights‑based effectiveness requires measurable capability outcomes and institutional follow‑through (beyond information transfer).
Normative and governance analysis based on gap mapping and the paper's empirical agenda; not tested with outcome data in this study.
Training can be treated as migration-governance infrastructure that functions simultaneously as a capability intervention (actionable navigation, contract comprehension, safe help‑seeking), a labour‑market signal when aligned with TVET/human-capital planning, and a potential gatekeeping node if access, assessment, and accountability are weak.
Conceptual reframing supported by policy analysis and governance gap mapping; no empirical validation provided in the paper.
Europe, Japan, and South Korea occupy intermediate positions between China and the United States in terms of AI–robotics integration and actor composition.
Comparative country-level decomposition of patent series and actor-type shares (1980–2019) reported in the paper; metrics for integration and actor composition place these regions between the stronger China pattern and the more market-driven U.S. pattern.
AI can enable new revenue streams (platforms, personalized pricing, automation-as-a-service) and increase market concentration, producing 'winner-takes-most' dynamics that raise profit rates for leading adopters and compress margins for laggards.
Literature synthesis on platforms and winner-take-all effects applied to AI; conceptual argument without firm-level causal testing in the paper.
AI adoption exerts downward pressure on routine labor costs while raising capital and recurrent costs (R&D, computing infrastructure, data, cybersecurity); higher fixed and lower marginal costs favor scale and incumbents with access to data and capital.
Conceptual cost-structure analysis drawing on automation and platform literature; no microdata or empirical cost estimates presented.
AI is a Schumpeterian general-purpose technology that can increase aggregate productivity potential but will do so unevenly across firms and sectors, producing heterogeneous effects on profitability.
Theoretical application of general-purpose technology and Schumpeterian literature to AI; literature-based claims without original empirical validation in the paper.
Firms' profitability and sustainability are shaped both by technological adoption (which can raise productivity and market power) and by structural pressures (trade wars, labor relations, supply constraints) that can erode margins.
Synthesis of firm-level implications from innovation and political-economy literatures; no firm-level causal estimates presented in the paper.
Contemporary crises change firms' cost structures (logistics, inputs, financing) and revenue prospects (demand shifts, market access).
Interpretive synthesis of observed firm-level impacts from pandemic, inflation episodes, and geopolitical events reported in secondary literature (no primary firm-level panel used).
Supply-chain fragilities and trade conflicts (emphasized by Mandel) mediate distributional and macroeconomic outcomes during long waves and crises.
Qualitative historical interpretation and literature references on supply-chain disruptions and trade conflicts (no systematic empirical identification in the paper).
New technological waves—most notably artificial intelligence (AI) and the green transformation—act as Schumpeterian forces that can alter productivity, competition, and profitability.
Conceptual mapping of Schumpeterian innovation-cluster theory to contemporary technologies (literature synthesis; no firm-level causal estimates reported).
Contemporary shocks (COVID-19, global inflation, geopolitical tensions) interact with long-wave mechanisms to reshape firms' cost and revenue structures.
Interpretive application of the comparative framework to recent historical episodes and macro trends; qualitative evidence from literature on pandemic and recent shocks (no primary microdata presented).
Patients classified as high‑risk by CDRG‑RSF had higher TMB, lower NK and CD8+ T cell infiltration, and model‑predicted resistance to Erlotinib and Oxaliplatin but sensitivity to 5‑fluorouracil.
CDRG‑RSF study reported immune deconvolution and TMB comparisons across risk groups and used pharmacogenomic prediction methods to infer drug sensitivity/resistance patterns for high‑risk vs low‑risk groups.
Both DNNs and LASSO correlated well at the individual‑sample level, but linear models (LASSO) struggled to recover cross‑study DEA log2FCs despite good sample‑level fits.
Same cross‑omics comparative study: reported good sample‑level prediction correlations for both model classes, but DNNs more faithfully reproduced differential expression signals across independent studies while LASSO did not recover DEA log2FCs robustly.
Defender returns depend critically on attacker rationality and information-processing; economic/security models should incorporate strategic heterogeneity and bounded rationality for accurate valuation.
Computational sensitivity analyses varying attacker rationality/modeling assumptions with reported impact on metrics (simulations; details of attacker models and number of runs not specified).
Computational results highlight tradeoffs among decoy realism, defender budget, and attacker rationality (attacker model), affecting deception value.
Simulated parameter sweeps varying decoy realism, budget levels, and attacker rationality with reported sensitivity analyses (computational experiments; exact experimental grid not specified).
Compliance costs and audit requirements create regulatory barriers to entry but also incentives for standardized metadata and interoperable systems; policy can encourage open standards to reduce lock-in.
Policy analysis and recommendation in paper (theoretical); no regulatory cost quantification provided.
Algorithmic lesson planning, automated audits, and data-driven competency mapping are natural targets for AI augmentation and can reduce recurring resource burdens but require quality-labelled data, strong governance, and transparency.
Paper's discussion of AI complementarity (conceptual); no implementation trials or performance metrics presented.
Returns to AI and digital investments are heterogeneous across firms and industries, implying adoption barriers and varied productivity impacts.
Across the 145 studies, reported effect sizes and qualitative findings vary by firm characteristics, industry sector, and technology readiness, as summarized in the review.
Impacts of digital transformation on productivity vary substantially by moderators such as digital competencies, organizational culture, leadership, and technology readiness.
Multiple included studies identified these factors as moderators/mediators in their empirical analyses; moderator effects were synthesized in the review.
Levels of familiarity and use of AI tools vary widely by role, discipline, and region.
Quantitative survey items (Likert-scale, multiple-choice) measuring familiarity and use of AI tools; subgroup comparisons (role, discipline, region) using descriptive statistics; thematic support from open-ended responses.
There are large disparities in AI engagement and preparedness across roles (students vs. educators), academic disciplines, and world regions.
Descriptive statistics from the survey comparing subgroups by role, discipline, and region; sample of >600 respondents; measures include self-reported awareness, familiarity, use, and confidence mapped to UNESCO competency frameworks.
Task-based labor effects: GenAI will substitute routine tasks (documentation, triage) and complement complex decision-making; net employment effects are ambiguous and vary by role.
Task-based model of labor and early observational/pilot studies; the paper highlights heterogeneity by specialty and role, but presents no comprehensive empirical employment-impact studies.
GenAI can reduce clinician time per case (productivity gains) but may increase utilization (more tests/treatments) if it lowers thresholds for intervention or aligns with revenue incentives.
Economic reasoning supported by early empirical and simulation work; the paper notes the possibility based on task substitution and induced demand literature; direct causal empirical evidence from large-scale deployments is limited.