Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Overinvestment increases inequality (greater tail concentration of income).
Model computations showing that exponential returns amplify income at the top; comparative statics indicate inequality measures rise with greater investment/technology under lognormal wage assumption.
Overinvestment increases measured GDP (output).
Comparative statics in the theoretical model linking higher private investment/technology adoption to higher aggregate output; model shows positive effect on measured GDP despite welfare loss possibilities.
The exponential returns to skill and technology create strong private incentives for agents to escalate skill (education) investment toward the high tail of the distribution (an educational arms race).
Equilibrium analysis and comparative statics in the theoretical model showing that marginal returns to additional investment are increasing toward the distribution tail, producing higher optimal private investment at the top relative to social optimum.
When wages follow a lognormal distribution, technological progress makes wages increase exponentially in both skill and technology.
Analytical derivation in the paper's economic model that assumes a lognormal wage distribution and specifies wages as an exponential function of skill and a technology parameter; result follows from model algebra (no empirical data).
The paper proposes a research agenda prioritizing interoperable, ethical‑by‑design platforms; metrics to measure social equity impacts; and adaptation of global standards to local institutional capacities.
Explicit list of three prioritized research directions provided in the paper, derived from the systematic synthesis of the 103 items.
High‑income examples (e.g., Estonia, Singapore) demonstrate mature integration of digital/AI systems in e‑government, urban mobility, and e‑health.
Empirical case examples drawn from the reviewed literature and institutional reports cited in the review; specific country examples (Estonia, Singapore) repeatedly referenced as mature adopters.
Research priorities include developing robust measures of AI adoption and using causal methods (difference-in-differences, synthetic controls, RDD, IV) to estimate effects of AI and regulation on productivity, employment, and inequality.
Methodological recommendations in the report based on identified evidence gaps and normative evaluation of empirical priorities.
The American Artificial Intelligence Initiative emphasizes R&D and innovation leadership, standards development, workforce readiness, and fostering 'trustworthy AI' (transparency, fairness, accountability).
Primary source policy documents from the U.S. American Artificial Intelligence Initiative reviewed in the report.
Concrete legislative recommendations include amendments to the EU AI Act, Consumer Rights Directive, and Digital Services Act to operationalize model-level transparency and user choice rights.
Policy design: drafted candidate amendments tailored to existing EU instruments presented in the paper.
The paper introduces a Predictive Skill Gap Intelligence Hub — an AI-driven platform that combines macro- and micro-level indicators with probabilistic growth models and intelligent skill-synthesis to proactively forecast regional and sectoral labor demand–supply gaps.
Description of system architecture and modeling approach in the paper (methods section). No numerical evaluation metrics or datasets provided for this descriptive claim.
Priority investments should target computational infrastructure, local model validation capacity, and training for clinicians and data scientists to increase adoption and trust in synthetic-data–supported AI.
Implementation and capacity-building analyses from the reviewed literature highlighting gaps in infrastructure, validation capability, and human capital; recommendation-based evidence rather than new empirical trials.
Vendor support, warranties, and service-level agreements (SLAs) are important for clinical adoption and liability management.
Policy and implementation literature, industry reports, and stakeholder feedback synthesized in the paper highlighting the role of vendor contractual commitments in adoption decisions.
Proprietary systems lead on reliability, maintenance, and validated integrations with clinical systems.
Literature synthesis including vendor case studies, deployment reports, and stakeholder surveys indicating more mature productization and validated integrations for proprietary offerings.
Open-source deployment options (e.g., on-premises) reduce data-sharing exposure and improve privacy.
Aggregated evidence from deployment reports and technical papers describing on-premises and local inference architectures; industry analyses of data governance tradeoffs.
Open-source models provide greater transparency and inspectability, enabling better auditability and explainability.
Systematic literature synthesis of peer-reviewed studies, industry reports, and case studies comparing open-source and proprietary systems; comparative analysis highlights inspectability of open-source code/models. No new primary experiments reported.
Coordinated policy reform, targeted infrastructure investment, workforce training, and equity-focused implementation are strategic priorities to realize AI’s potential in Indonesian healthcare.
Consensus recommendations drawn from the narrative synthesis, thematic analysis, and Delphi consensus studies included among the 42 supplementary documents and the broader 2020–2025 literature body.
Recommended research priorities for economists include measuring how adoption changes task mixes and wages, quantifying verification/remediation costs, estimating productivity gains net of security/IP costs, and studying market dynamics from centralized model providers.
Author recommendations based on identified gaps in the empirical literature synthesized by the paper.
Recommended policy levers include data-governance rules, provenance and watermarking standards, liability frameworks, copyright clarifications, competition policy, and taxes/subsidies to internalize externalities.
Policy recommendations synthesized from legal, regulatory, and economic literatures within the review; presented as qualitative guidance rather than tested policy interventions.
A structured three-stage framework (input/process/output) clarifies where different risks and regulatory rules apply to generative audiovisual systems.
Framework presented in the paper as a conceptual synthesis of reviewed literatures; supported by cross-references to legal, technical, and ethical sources within the review.
The paper introduces IJOPM’s Africa Initiative (AfIn) to support Africa-based OSCM research, outlining motivation, objectives, review process, and researcher support mechanisms.
Descriptive account within the paper (administrative/initiative description rather than empirical evidence).
Cognitive interlocks include concrete mechanisms such as policy-enforced gates, automated verification thresholds, role-based checks, and mandatory rebuttal workflows to force verification before outputs are trusted or deployed.
Design details and enumerated mechanisms within the Overton Framework as presented in the paper; no implementation case studies reported.
The Overton Framework is an architectural remedy that embeds 'cognitive interlocks' into development environments to enforce verification boundaries and restore system integrity.
Prescriptive architectural proposal described in the paper (design specification and principles); presented conceptually without empirical validation.
High‑frequency sensor and satellite data, processed with AI, improve precision in measuring yields, input use, and environmental externalities, enhancing the quality of economic impact evaluations and policy targeting.
Methodological and validation studies using high‑resolution satellite imagery and field sensors that show improved measurement accuracy versus traditional survey methods; referenced empirical demonstrations in the literature.
The paper proposes specific metrics and empirical follow-ups (e.g., generation-to-verification throughput ratios, defect accumulation rates, time-to-acceptance for machine-generated artifacts, incident rates attributable to unverified AI outputs) to validate the model.
Explicit recommendations and measurement proposals listed in the paper; no empirical implementation provided.
The paper’s own drafting began via casual AI conversation, presented as an illustrative case supporting the model.
Author-reported anecdote (N=1; the paper's drafting process).
Recommended next steps include building and calibrating ABMs with agent heterogeneity, prototyping technical implementations of token verification (proof-of-query receipts, cryptographic attestation), and red-teaming for spoofing/evasion.
Paper's research & policy next-steps and operational recommendations; no implementation results included.
Chain-of-Thought prompts/internal reasoning simulate richer, multi-step decision processes in agents compared with conventional single-step decision rules.
Methodological description: use of CoT prompts/internal reasoning to model multi-step deliberation in agents. This is a documented implementation detail and conceptual claim in the paper.
The framework replaces static, rule-based agent decision-making with LLM-powered cognitive agents that perceive environment signals, deliberate using Chain-of-Thought, and act—without hand-coded behavior rules.
Model architecture description: each agent is an LLM-driven cognitive unit implementing the PDA loop; explicit statement that behavior is not hand-coded but emerges from language-model deliberation. This is a design/implementation claim rather than an empirical result.
Enhanced gross‑flows estimation using longitudinal microdata can better track transitions (job-to-job, upskilling, unemployment spells) and measure occupational churn and reallocation.
Established econometric practice cited in paper; recommendation to use panel/admin microdata (CPS longitudinal supplements, LEHD/LODES, UI records); no new empirical results but aligns with standard methods.
Team Situation Awareness (shared perception, comprehension, projection) remains a useful analytic anchor for HAT even with agentic AI.
Conceptual analysis mapping Team SA components onto agentic AI interactions; literature review of Team SA utility in HAT contexts.
DAR produces ten falsifiable propositions explicitly mapped to measurement constructs, making the framework empirically testable.
Derivation and listing of ten testable propositions in the paper, each linked to observable measures and prioritized by feasibility. Theoretical derivation, no empirical tests provided.
Common uses of AI among practitioners include generating code snippets, suggesting fixes, accelerating routine tasks, surfacing design patterns or documentation, and scaffolding prototypes.
Practice-focused qualitative data from interviews and workflow analysis at Netlight; authors list these use-cases as commonly reported by practitioners; frequency counts not provided.
Practitioners use AI primarily as a practical assistant (coding, debugging, prototyping, knowledge retrieval) rather than as a fully autonomous developer.
Reported practitioner accounts and observations from the Netlight field study (interviews/observations); examples of tasks AI is used for were documented in the paper; sample limited to experienced consultants at one firm.
Experienced IT professionals at Netlight are already integrating AI tools into everyday development work.
Qualitative field study conducted at Netlight Consulting GmbH using interviews, observations, and analysis of practitioner workflows; single-firm sample (Netlight); exact number of participants not reported.
BERT-family encoders provide superior contextual understanding for sentiment analysis, intent detection, behavioural segmentation, and feature extraction from user signals compared to simpler feature pipelines.
Use of BERT encoders for classification tasks with offline metrics reported such as classification accuracy for intent/sentiment and user embedding quality for segmentation. (Specific datasets and sample sizes are not provided.)
Enablers of value realization are high-quality, integrated data; explicit data governance and metadata; process standardization; clear KPIs; user training and change management; and executive sponsorship.
Consistent findings across standards-based guidance, practitioner reports, and case studies from the 2020–2025 review highlighting these enablers as prerequisites or facilitators of success.
Value pathways enabled by ERP-integrated AI include improved visibility and real-time decisioning, automation of routine tasks, better forecasts and risk detection, and faster exception handling.
Thematic analysis across the reviewed literature (case studies and conceptual papers) identifying recurring mechanisms by which AI produced value in ERP contexts.
Observed AI techniques used in ERP contexts include supervised and unsupervised machine learning, predictive forecasting, anomaly/fraud detection, optimization, and explainable AI.
Systematic review of peer-reviewed articles, technical evaluations, and practitioner reports (2020–2025) documenting the methods applied in ERP and enterprise planning/control systems.
Durable benefits require the co‑evolution of technology, people, and process capabilities rather than technology deployment alone.
Interpretive framing and synthesis of multiple empirical case studies and best-practice guidance included in the 2020–2025 literature review; recurring theme across studies.
Automated equivalency systems require algorithmic oversight features (audit trails, human-in-the-loop checks) to maintain trust and labor-market legitimacy.
Governance recommendation following best practices in algorithmic accountability; not supported by empirical testing of oversight mechanisms in this context.
AI tools (automated document parsing/NLP, translation, equivalency-prediction classifiers, anomaly detection) can scale credential processing and reduce transaction costs and processing time.
Paper cites potential AI capabilities and application areas; the claim is inferential from known AI functionalities, with no implementation benchmark or throughput numbers provided.
Continuous monitoring and observability for performance, compliance, and drift are essential to maintain operational stability and detect model or process degradation.
Prescriptive claim grounded in engineering practice and comparative analysis of failure modes; supported by illustrative deployments; no quantitative evaluation of monitoring impact reported.
Core governance components should include policy enforcement integrated into development and deployment pipelines, risk controls for data/model behavior/automated actions, explicit human-in-the-loop and human-on-the-loop oversight, continuous monitoring/logging/incident-response, and role-based governance structures linking legal, compliance, IT, and business units.
Prescriptive design based on literature synthesis and practitioner experience; described as core components in the proposed reference pattern (conceptual, case-illustrated).
Research needs include empirically measuring prevalence and average loss from prompt fraud incidents, evaluating effectiveness and cost-effectiveness of technical mitigations (watermarking, provenance), and modeling firm-level investment decisions under varying regulatory/insurance regimes.
Authors' recommended agenda for further research based on identified gaps in the paper's qualitative analysis.
All data are openly available at https://www.antscan.info.
Explicit statement of public repository/portal and URL provided in the paper.
The dataset includes metadata such as taxonomic labels, collection/locality data, and links to genome projects where available.
Paper states dataset contents include whole-body volumes/meshes and associated metadata (taxonomic labels, locality, genome links).
The scanning pipeline was optimized and standardized to enable digitizing hundreds to thousands of specimens.
Authors describe an optimized, standardized pipeline and cite the achieved output (2,193 scans) as demonstration.
The project demonstrated a high-throughput application of synchrotron X-ray microtomography for whole-organism digitization at scale.
Combination of method (synchrotron microCT), standardized pipeline, and production of 2,193 scans presented as evidence of high-throughput capability.
Imaging modality used is synchrotron X-ray microtomography (high-resolution 3D imaging).
Method section details use of synchrotron X-ray microtomography for whole-body imaging.
Scans were acquired with standardized parameters to facilitate automated and replicable analysis and benchmarking.
Paper describes a standardized acquisition protocol and pipeline (synchrotron X-ray microtomography) and notes standardized parameters and metadata format.