Evidence (8974 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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The review ends with policy recommendations to address barriers and to facilitate increased public–private partnership (PPP) aimed at increasing health access in India.
Statement in the paper summarizing its policy recommendations; based on authors' synthesis of reviewed literature and conclusions.
AI, Blockchain, and 5G have great potential for transforming healthcare in India.
Forward-looking claim in the review summarizing technological potential as reported in the literature; presented as potential rather than demonstrated effect (no empirical effect sizes given).
These technologies can optimize workforce output in constrained healthcare contexts.
Review assertion synthesizing qualitative and quantitative literature describing impacts on workforce productivity/output; no specific sample size reported in excerpt.
These technologies can increase clinical effectiveness.
Claimed potential in the review based on prior studies (synthesis of evidence; no single quantified trial/sample provided in excerpt).
These technologies can be used to enhance operational effectiveness in healthcare organisations operating under severe constraints.
Review claims and discussion of use-cases/ways technologies may improve operations; based on synthesis of qualitative and quantitative studies (no single trial/sample reported).
Healthcare technology is considered a key organizational-efficiency enhancer, particularly in traditional [healthcare] settings addressing escalating health needs.
Synthesis statement from the review summarizing prior papers that view technology as improving organizational efficiency in traditional settings; method = literature review (qualitative/quantitative studies synthesis).
India has a vast population, meaning a vast market for healthcare technology adoption.
Statement in paper's introduction/abstract asserting India's large population makes it a large market; based on literature review/contextual framing (no primary sample size reported).
The capacity to create, maintain, and control digital agents becomes a new axis of international inequality, potentially devaluing the demographic dividend of developing countries and revising the logic of comparative advantages.
Geoeconomic theoretical analysis in the paper; no cross-country empirical analysis demonstrating changed comparative advantages presented.
The institutional architecture of modern societies (pension systems, taxation models, etc.) is built on assumptions that are systematically undermined by the rise of an agentic economy, necessitating a revision of fiscal and social models, including discrete taxation of algorithmic employment.
Normative and theoretical analysis linking institutional assumptions to agentic economy dynamics; no empirical policy evaluation or fiscal simulation results reported.
The agent energy profile (AEP) is introduced as a measure of annual energy consumption per unit of cFTE, allowing energy-based comparisons between algorithmic and human cognitive labour.
Methodological/conceptual proposal in the paper; no empirical measurements or energy accounting dataset provided.
The paper proposes a quantitative identification of algorithmic agents via the category of cognitive full-time equivalent (cFTE), enabling comparison of algorithmic and human productivity within a unified analytical framework.
Methodological proposal (definition and proposed use of cFTE) presented in the paper; no empirical validation or implementation sample reported.
The ontological status of technology is transforming from a productivity-enhancing tool to an autonomous participant in economic processes, forming a hybrid factor of production that combines characteristics of both capital and labour.
Theoretical analysis and conceptual framing in the paper; no empirical factor decomposition or production-function estimation provided.
Institutionalising digital agent registration could transform 'shadow demographics' into formal 'algorithmic demographics'.
Policy/theoretical proposition in the paper (institutionalisation as a mechanism); no empirical pilot or legal implementation evidence reported.
The concept of 'shadow demographics' describes a growing algorithmic population that expands in parallel with the stagnation or decline of the human population.
Conceptual definition and theorised dynamics in the paper; no empirical counts or longitudinal measurements of algorithmic population provided.
The expanding role of digital agents in production and market processes creates the preconditions for a gradual decoupling of demographic dynamics from economic growth.
Argumentative/theoretical exposition in the paper; no empirical panel or cross-country time-series evidence reported in the text provided.
AI-based digital agents can be interpreted as functional equivalents of economic actors.
Theoretical and conceptual argument presented in the paper (conceptual interpretation; no empirical sample or quantitative validation reported).
The authors conclude that these findings have implications for responsible and perceptible genAI use in hiring contexts.
Authors' conclusions/recommendations based on the interview findings and analysis.
Participants reported only marginal efficiency gains from genAI despite a seemingly seismic shift in how recruiting happens.
Self-reports from 22 interviewed recruiting professionals indicating small/marginal efficiency improvements.
Individual recruiters also felt compelled to adopt genAI because of the personal need to boost productivity.
Qualitative interview responses (n=22) reporting individual-level productivity motivations for using genAI.
Recruiters often felt compelled to adopt genAI to combat applicant use of AI.
Interview data from 22 recruiting professionals reporting adoption motivations tied to applicants' AI use.
When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making.
Normative statement presented in the paper (literature/theoretical recommendation), no empirical data reported to support this recommendation within the study.
AI adoption leads to a statistically significant expansion of white-collar employment (reallocation toward higher-skilled occupations).
Difference-in-differences analysis using employer–employee administrative records linked to survey adoption timing, showing significant increases in white-collar employment shares in adopter firms.
Using a difference-in-differences framework, AI adoption increases profitability.
Difference-in-differences (DID) estimation using survey adoption data linked to administrative balance-sheet profitability measures.
Using a difference-in-differences framework, AI adoption increases labour productivity.
Difference-in-differences (DID) estimation linking survey-reported adoption timing to administrative balance-sheet measures of labour productivity.
Adoption is concentrated among larger and more knowledge-intensive firms, as well as among firms with higher labour costs.
Cross-sectional analysis of survey linked to administrative balance sheets and employer–employee records showing higher prevalence of reported AI use for firms with larger size, higher knowledge intensity, and higher labour costs.
Nearly 30 per cent of firms plan to adopt AI within the next two years.
Same 2024 firm-level survey asking about planned AI adoption within two years, linked to administrative records.
AI-mediated expert networks are an emerging phenomenon that existing coordination theories fail to account for.
Mentioned as an example in the abstract to motivate theoretical gap; no empirical data or sample provided.
GitHub Copilot exhibits 'recursive value creation' as an example of an emerging organizational phenomenon enabled by GenAI.
Illustrative example named in the abstract; no empirical measurement or sample reported within the abstract.
UCF provides a theoretical foundation for understanding organizational coordination when GenAI transforms cognitive constraints from scarce to abundant resources.
Position paper asserts UCF as foundational theory for coordination under transformed cognitive constraints; conceptual argument only.
Three emergent organizational forms illustrate UCF principles: cognitive meshworks (coordinated through competence synthesis), algorithmic ecosystems (achieving emergent optimization), and hybrid intelligence collectives (operating through cognitive complementarity).
Conceptual typology and illustrative examples in the position paper; no reported empirical measurement or sample.
We introduce unbounded cognitive fusion (UCF) as a new theoretical framework explaining coordination through cognitive synthesis rather than price signals or authority structures.
Theoretical proposal and framing within the paper; conceptual development rather than empirical validation.
Generative artificial intelligence (GenAI) fundamentally alters [traditional organizational coordination] assumptions by augmenting human cognitive capabilities across organizational boundaries.
Position paper argumentation and conceptual reasoning presented in the abstract; no empirical data or sample reported.
The results offer practical insights for evaluating intelligent transformation strategies in the context of Industry 5.0 and data-driven industrial development.
Authors' stated implications and conclusions drawing on empirical findings and the proposed framework.
Integrating AHP–EWM weighting and FCE aggregation within the Feltham–Ohlson valuation structure provides an interpretable quantitative mechanism linking AI adoption, operational capability development, and enterprise value creation.
Methodological exposition and discussion in the paper; authors describe interpretability and linkages enabled by the integrated methods.
Higher PI is accompanied by enhanced production and service performance.
Empirical associations shown between PI and production/service performance indicators in the panel data analysis.
Higher PI is associated with improved data governance conditions.
Empirical results in the paper reporting positive relationships between PI and indicators of data governance quality.
Higher PI is accompanied by accumulation of knowledge-based human capital.
Authors report empirical associations between PI and proxies for knowledge-based human capital accumulation in the panel analysis.
Higher PI is associated with increased digital technology investment intensity.
Empirical findings reported in the paper linking PI to greater intensity of digital technology investment in the sample firms.
The Process Performance Index (PI) is positively associated with firm profitability.
Empirical regression results on the panel data indicating a positive association between PI and measures of firm profitability.
The Process Performance Index (PI) is positively associated with abnormal earnings.
Empirical regression results using the panel dataset (3,515 firms; 20,076 firm-year observations) reporting a positive association between PI and abnormal earnings.
A Process Performance Index (PI) is constructed to measure AI-enabled operational capability across resource allocation efficiency, coordination effectiveness, and production performance dimensions.
Authors describe construction of PI using multi-dimensional indicators and the AHP–EWM weighting plus FCE aggregation procedure.
This study proposes a data-driven evaluation framework that integrates the Feltham–Ohlson enterprise value assessment with a multi-level performance evaluation framework (hybrid AHP–EWM weighting and Fuzzy Comprehensive Evaluation aggregation) to quantify the impact of AI on industrial process performance and enterprise value creation.
Methodological description in the paper: authors describe integrating Feltham–Ohlson valuation with AHP–EWM weighting and FCE aggregation to form a unified evaluation framework.
New tendencies in managerial AI research and practice include explainable AI, human–AI collaboration, knowledge management, enterprise analytics, and algorithmic management.
Descriptive finding from the paper's literature synthesis (topics emphasized in the review); no quantitative prevalence or counts provided in the abstract.
Machine Learning and Deep Learning enhance employee productivity, business intelligence, process mining, and data-driven decision-making by enabling prediction, perception, and adaptive learning solutions.
Claim synthesized in the review from multiple studies identified via PRISMA screening; abstract does not list the number or identity of underlying empirical studies.
AI-based technologies can greatly enhance managerial efficiency by automating repetitive activities, improving resource allocation, enabling intelligent scheduling, and supporting predictive modelling and strategic planning.
Summary conclusion from the paper's literature review (PRISMA methodology referenced); no quantitative meta-analytic effect sizes provided in abstract.
Machine Learning, Artificial Intelligence, and Deep Learning are tools that can optimize managerial decisions, enable intelligent automation, streamline workflows, and improve organizational performance.
Synthesis claim from the paper's PRISMA-based literature review (no numeric sample size reported in the abstract).
Policy frameworks, reskilling initiatives, and institutional adaptations are required to ensure inclusive technological progress.
Prescriptive conclusion presented in abstract based on the review and synthesis; no empirical validation or sample sizes provided in abstract.
AI simultaneously generates demand for higher-order problem solving, emotional intelligence, and human-AI collaboration skills.
Explicit finding reported in abstract from the review of interdisciplinary literature; no quantified effect sizes or sample sizes provided in abstract.
The majority of AI’s effect on potential GDP in the period under review was due to increased labor productivity and the optimization of existing processes.
Attribution/decomposition within the scenario analysis of aggregated industry data indicating productivity and process-optimization channels as principal contributors.
Artificial intelligence has become a significant factor in the growth of Russia’s potential GDP.
Findings reported from the scenario analysis and aggregated industry data reviewed in the paper and syntheses of Russian analytical sources.