Evidence (4793 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 |
Productivity
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The article clarifies theoretical relationships and gaps between Material Passports, Digital Product Passports, and Digital Building Logbooks.
Theoretical analysis and synthesis section of the SLR where the authors compare concepts and identify overlaps and gaps among MPs, DPPs, and DBLs.
Correlation and illustrative regression results confirm the absence of an immediate statistical relationship between AI adoption and productivity at the aggregate level.
Both correlation analysis and an illustrative regression model applied to Eurostat aggregate data for 2021–2024; regression presented as illustrative (not necessarily causal); model specification details and robustness checks not given in the summary.
Labour productivity did not show a stable association with AI diffusion in Slovakia over the analysed period.
Correlation analysis between AI adoption indicators and labour productivity measures for Slovakia using harmonised Eurostat data (2021–2024); detailed coefficient estimates and significance levels not provided in the summary.
Diverse decision-making AI from different developers will commonly compete for finite shared resources in everyday devices (examples: charging slots, relay bandwidth, traffic priority).
Motivating background statement in the paper (observational/argumentative; examples drawn from real-world deployment contexts rather than reported experiment data).
There is an arithmetic crossover point between these regimes: it occurs where opposing tribes that form spontaneously first fit inside the available capacity.
Mathematical analysis in the paper deriving a capacity-based threshold (crossover) marked by whether spontaneously formed opposing tribes can be accommodated by available capacity.
When resources are abundant, the same ingredients (model diversity, individual RL, tribe formation) drive system overload to near zero.
Empirical and mathematical results in the paper showing that abundance of resources reduces overload to near zero under the same agent-population conditions.
The study explores the influence of AI on HRM practice specifically within top IT companies.
Scope statement in the paper: empirical study involved HR professionals from various (described as top) IT firms. The summary does not supply the list of companies or sampling criteria.
Top management support does not have a direct influence on AI Adoption in the sampled firms.
PLS-SEM results from the 207-firm survey showing a non-significant direct path from top management support to AI Adoption (as reported in the paper).
Effort expectancy does not have a direct influence on AI Adoption in the sampled firms.
PLS-SEM results from the 207-firm survey showing a non-significant direct path from effort expectancy to AI Adoption (as reported in the paper).
In the sentiment-analysis task, those individual differences do not produce human–AI complementarity: the joint performance of humans and AI did not exceed that of either alone.
Empirical finding reported from the preregistered sentiment-analysis experiment showing no complementarity effect (joint human-AI performance ≤ best individual performance). (Statistical tests and sample size not included in the excerpt.)
We conducted a systematic review and meta-analysis of the literature on AI/HR analytics and organizational decision making, using 85 publications and grounding the work in theories of algorithm-automated decision-making (AST) and matching/hybrid models (STS).
Paper's methods: systematic review and meta-analysis; sample = 85 publications; theoretical framing explicitly stated as AST and STS.
Macroeconomic fiscal moderation remains empirically unvalidated.
Synthesis conclusion from the review noting an absence of empirical evidence that Agentic AI produces macroeconomic fiscal moderation; i.e., no validated studies showing broad fiscal relief effects were identified in the reviewed literature.
By 2024 the RL-FRB/US model produced a federal budget deficit similar to the baseline: RL-FRB/US model: -1,767 trillion $ vs. FRB/US model: -1,758 trillion $.
Reported fiscal balance (federal budget deficit) simulation outputs for 2024 from comparative model runs in the paper.
Self-generated (model-authored) Skills provide no average benefit.
Comparison of three evaluation conditions (no Skills, curated Skills, self-authored Skills) across SkillsBench. Averaged pass-rate deltas show that model-authored Skills do not increase average pass rate relative to baseline; analysis used 7,308 trajectories over 86 tasks and 7 agent–model configurations.
AI will not cause permanent mass unemployment at the aggregate level.
Analytical argument and literature synthesis using labor-economics theory (Skill-Biased Technological Change and structural transformation). No primary microdata, no stated empirical identification strategy or sample size in the paper (methodology appears to be theoretical and sectoral synthesis).
Empirical evaluation is needed on how AI-induced productivity gains translate into aggregate demand and labor absorption.
Identified research priority in the paper, based on theoretical uncertainty about demand-side labor absorption and lack of conclusive empirical evidence.
AI will not mechanically cause permanent mass unemployment at the aggregate level.
Theoretical framing and synthesis of existing empirical findings across task-based and macro studies; no single new dataset provided (paper draws on literature and conceptual models).
Use of AI raises needs for traceability, explainability, and continuous validation to maintain compliance and avoid error propagation in curricular decisions.
Paper's AI governance recommendations (prescriptive), referencing general AI risk principles rather than empirical study.
There is no evidence of nonlinearities in the relationship between digital trade and urban house prices (the effect is linear across the sample).
Explicit tests for nonlinearity reported in the econometric analysis (details of test specification not provided in the summary).
Realising DT value requires upfront investment in sensors, integration, standards, and skills; economic viability depends on contract structures and how gains are allocated between investors, owners, contractors, and operators.
Synthesis of cost/benefit discussions and case descriptions in the reviewed literature; policy and procurement examples referenced.
Under truthful bidding, the decentralised price-based market matches a centralised value-optimal benchmark (i.e., decentralised allocation equals centralised value-optimal allocation).
Paper presents both a theoretical argument (mechanism properties under quasilinear utilities and discrete slices) and empirical validation in simulation by comparing decentralised outcomes to a centralised value-optimal baseline across configurations in the ablation study.
No clear evidence that project phase systematically shifts sentiment perception.
Project-phase indicators were collected each round and included in correlation and repeated-measures analyses; no consistent, systematic association between project phase and sentiment labeling was found.
Predictors of negative labeling are weak and at best trend-level (e.g., task conflict shows only weak/trend-level association with negative labels).
Correlation analyses and GEE models testing multiple predictors (mood states, life circumstances, team dynamics including task conflict) on negative vs other labels; effects for negative labeling were small and lacked robustness.
Experiments used realistic channel and beamforming datasets reflecting varying elevation angles and dynamic LEO link conditions.
Dataset description in the paper states use of realistic channel and beamforming data including varying elevation angles and dynamic links; no dataset size or public dataset identifiers provided in the summary.
There is a need for causal studies (randomized pilots, phased rollouts) to quantify net welfare effects including patient trust, equity, legal risk, and long-run labor impacts.
Authors' recommendation based on gaps identified in the mixed-methods evidence and acknowledged limitations around causal identification and long-term measurement.
Under the current estimated parameters, dynamics converge toward equilibria—implying convergent, policy-mediated adjustment rather than endogenous cyclical instability.
Inference from stability classification (stable-node equilibria) and model dynamics simulated or linearized around equilibria using 2016–2023–estimated parameters.
Equilibrium points of the estimated three-stock system are classified as stable nodes (no persistent endogenous cycles under the estimated parameters).
Stability analysis: equilibria computed from estimated parameters and local stability assessed via Jacobian eigenvalues; eigenvalues indicate stable nodes.
Liability for harm from AI remains unresolved; current regulatory frameworks (notably in the EU) continue to emphasize human responsibility and require conformity and clinical validation.
Regulatory and legal analyses, with emphasis on European Union device regulation and liability principles, as reviewed in the paper.
On-Premise RAG matches commercial (cloud) RAG on standard quantitative retrieval and generation metrics.
Empirical comparative analysis using standard retrieval/generation benchmarks comparing three systems (zero-shot baseline, GPT RAG cloud, Open-source On-Prem RAG) under representative SME workloads; specific metric names and sample sizes not reported in the summary.
Research priorities include causal studies on productivity gains from AI, firm‑level adoption dynamics, sectoral labor reallocation, long‑run general equilibrium effects, and heterogeneous impacts across regions and demographic groups.
Set of empirical research recommendations drawn from gaps identified in the literature review and limitations section; not an empirical claim but a prioritized research agenda based on secondary evidence.
Growth‑accounting frameworks and measurement approaches must be updated to capture AI/robotics as intangible and embodied capital, including quality improvements and spillovers.
Methodological argument grounded in literature on measurement challenges and examples of intangible capital; no new measurement exercise or empirical re‑estimation is provided in the paper.
Backtesting the proposed models against historical technological transitions (e.g., ATMs, robotics) and recent AI adoption episodes can validate model performance.
Recommended validation strategy; paper does not report backtest results but prescribes holdout/pseudo‑counterfactual experiments and calibration with administrative outcomes.
Scenario modelling in the reviewed literature typically uses counterfactual simulations with different adoption speeds, policy responses, and initial conditions to bound possible employment, wage, and productivity trajectories.
Description and citations of scenario-modelling practices by think tanks and organisations (TBI, IPPR, IMF) and academic work referenced; evidence is methodological and report-based.
NLP/LLM pipelines are used to extract tasks and skills from free-text job ads and to map those tasks to AI capabilities.
Described methods and citations (Xu et al., 2025; Hampole et al., 2025); evidence is methodological application of transformer-based models to job-ad text in recent studies.
Methods increasingly apply advanced NLP and large language models (BERT, LSTM, GPT-4) to parse job descriptions, map skills/tasks, and predict automation risk.
Cited methodological examples in the paper (Xu et al., 2025; Hampole et al., 2025) and discussion of common pipelines using transformer-based models to extract tasks from free-text job ads and to map tasks to AI capabilities; evidence is methodological and based on recent studies rather than a single benchmarked dataset.
Some functional domains show varying maturity: for example, procurement has more applied work compared with other functions.
Reviewer observation from the systematic search and screening across 2020–2025 literature noting uneven distribution of empirical/ applied studies across functions.
A centralized policy engine for access control, data handling rules, and change management is a necessary control point in the reference pattern.
Prescriptive recommendation in the paper supported by best-practice synthesis and case anecdotes; no direct empirical comparison of centralized vs federated policy engines provided.
Research gaps include the need for standardized evaluation metrics, robustness- and consistency-focused XAI methods, domain-informed explanation frameworks, and longitudinal/clinical impact studies.
Recommendations section of the review synthesizing recurring deficits across papers and proposing priorities.
Recommendation for research and modeling: economic models of AI markets should incorporate institutional regime types (centralized vs decentralized), enforcement uncertainty, and legitimacy effects as parameters affecting data access costs, R&D productivity, and market concentration.
Normative recommendation based on the comparative typology and inferred mechanisms from the document analysis; not empirically validated within the study.
Theoretical contribution: the paper extends modular coordination theory by treating openness–security trade‑offs as layered, adaptive institutional processes embedded in political regimes and 'legitimacy economies.'
Argumentative/theoretical development in the paper grounded in document analysis and literature on coordination and legitimacy.
Providing optional LLM access without training did not increase average exam scores versus no LLM access.
Intent-to-treat comparisons across randomized arms reported in the study: comparison of optional-access-without-training arm to no-access arm showed no average score improvement (sample n = 164).
Realizing AI’s potential for circular-economy and energy-efficiency goals requires coordinated interventions across environmental regulation, digital infrastructure, and workforce skill formation.
Policy interpretation drawn from heterogeneity results (regulation and infrastructure amplify AI effects) and the identified labor-market mechanism (skill composition matters); recommendation rather than direct causal estimate.
The benefits of AI-enabled e-commerce and automated warehousing are conditional on complementary policies (competition policy, data governance, workforce reskilling, automation oversight) to manage concentration, privacy, distributional effects, and safety.
Policy-analysis synthesis supported by sensitivity checks in scenario analyses and discussion of governance risks; recommendations informed by observed distributional and market-concentration patterns in the case material.
AI’s net impact on employment to date is modest — no clear evidence of mass unemployment.
Systematic literature review/meta-synthesis of 17 peer‑reviewed publications (published 2020–2025). Aggregate assessment across those studies found no consistent empirical support for large-scale, economy-wide unemployment attributable to AI to date.
Given current constraints, AI's current role is primarily to improve operational efficiency within the legacy petroleum system rather than to drive fundamental structural economic change.
Synthesis of quantitative and qualitative findings in the paper concluding that operational gains are not sufficient to produce structural reallocations without broader policy reforms.
Human-in-the-loop governance is a practical lever to align GenAI productivity with environmental efficiency.
Interpretation of the experimental results: findings that certain prompt-based governance (operational constraints/decision rules) reduced footprint while preserving outputs, leading to the recommendation (argumentative claim).
Inference efficiency and system level optimisation are growing rapidly in the Green AI literature.
Temporal / thematic analysis of literature cited in the paper's mapping (asserted growth; no growth rates or counts provided in abstract).
Exposing codebase-specific verification mechanisms may significantly improve the performance of externally trained agents operating in unfamiliar environments.
Paper suggests that providing access to repository-specific verification (tests, static analysis) could improve externally trained agents based on observed advantage for models that used validation tools.
Iterative verification helps achieve effective agent behavior.
Paper infers from analysis (models using iterative verification achieved better performance) that iterative verification contributes to effective agent behavior.
Experts (pooled) forecast annualized GDP growth rising to around 4% under a 'rapid' AI progress scenario.
Conditional survey forecasts elicited under a described 'rapid' AI capabilities scenario (abstract summarizes pooled expert forecasts across groups). Exact sample sizes not provided in excerpt.