Evidence (4175 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
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For non‑tech firms seeking to enhance operational efficiency through digitalization, optimizing internal power structures in response to technological shifts can improve firm performance.
Policy/managerial recommendation based on the study's empirical findings linking digitalization, decentralization, and productivity using China's listed firms data (2009–2020).
Digital technologies operate as an external contingency for non‑tech firms, requiring structural decentralization to align organizational structure with technological shifts.
Theoretical proposition and interpretation of empirical findings in the paper; framed as a contribution to organizational structure theory rather than a separate causal test.
Many non‑technology firms' existing organizational structures fail to accommodate data‑driven digital technologies, creating a need for strategic adaptation to integrate these technologies into business operations.
Argument and literature synthesis presented in the paper motivating the study; descriptive characterization rather than a directly tested empirical claim in the reported analyses.
Shifting power allocation (decentralization to subsidiaries) driven by digitalization significantly enhances firm productivity.
Further (post‑hoc / additional) analyses reported in the paper linking measured shifts in internal power allocation to improvements in firm productivity using the sample of China's listed companies (2009–2020).
The decentralizing effect of digitalization is stronger for firms operating in environments of higher uncertainty.
Moderation analyses in the paper using public data on China's listed companies (2009–2020) showing interaction between digitalization and environmental uncertainty on decentralization outcomes.
The decentralizing effect of digitalization is more pronounced for companies with greater business diversification.
Moderation tests reported in the study using the same dataset of China's listed companies (2009–2020) examining interaction between digitalization and business diversification on subsidiary empowerment.
Firms with higher levels of digitalization tend to decentralize decision‑making authority to their subsidiaries.
Empirical analysis using public data from China's listed companies between 2009 and 2020; paper reports multiple measures of digitalization and tests the relationship between firm digitalization and subsidiary empowerment (decentralization).
The paper documents best practices for iteratively generating tests to capture existing system behavior before model-assisted refactoring.
Methodological contributions in the paper: recommended workflow and practices for iterative test generation to lock down behavior prior to refactoring.
The described workflow constrained refactoring changes and enabled model-assisted refactoring under developer supervision, with proposed code changes validated by passing tests.
Methodological description in the paper: iterative test generation to capture existing behavior, then model-assisted refactoring with developer oversight and test-based validation.
The generated tests achieved up to 78% branch coverage in critical modules.
Measured branch coverage reported in the case study for critical modules after running the generated tests.
Using coding models, we generated nearly 16,000 lines of reliable unit tests in hours rather than weeks.
Single case study reported in the paper: automated unit test generation using coding models; reported aggregate output of generated tests and a qualitative time comparison (hours vs weeks).
AI agents autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement.
Definitional framing provided by the authors describing the technical/functional characteristics of 'AI agents' as used in the paper.
The provider's foundational compliance task is an exhaustive inventory of the agent's external actions, data flows, connected systems, and affected persons.
Authors' recommendation/practical conclusion derived from the regulatory mapping (prescriptive guidance rather than empirical measurement).
We propose a twelve-step compliance architecture and a regulatory trigger mapping connecting agent actions to applicable legislation.
Paper asserts it includes a proposed 12-step compliance architecture and a mapping between agent actions and regulatory triggers (explicit step count provided).
We present a practical taxonomy of nine agent deployment categories mapping concrete actions to regulatory triggers.
Paper states it includes a taxonomy comprising nine deployment categories (explicit count provided).
This paper provides the first systematic regulatory mapping for AI agent providers integrating (a) draft harmonised standards under Standardisation Request M/613 to CEN/CENELEC JTC 21 as of January 2026, (b) the GPAI Code of Practice published in July 2025, (c) the CRA harmonised standards programme under Mandate M/606 accepted in April 2025, and (d) the Digital Omnibus proposals of November 2025.
Author claim about the paper's contribution and scope (novelty/first-of-its-kind mapping integrating specified standards and documents).
AI agents - i.e. AI systems that autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement - are being deployed at scale across enterprise functions ranging from customer service and recruitment to clinical decision support and critical infrastructure management.
Author assertion in the paper's introductory framing; no empirical sample size or quantified deployment statistics provided in the excerpt.
Rather than indiscriminate collection of context-relevant data, researchers and practitioners should adopt interactional practices to embed generative AI systems more appropriately into users' contexts of use.
Normative conclusion/provocation drawn from the paper's empirical findings and analysis of failure modes; presented as a recommendation (not an empirical effect; based on qualitative synthesis).
Users deploy concrete strategies to address failures of generative AI systems to account for context.
Empirical observations from interviews describing user-devised workarounds and strategies; qualitative cases/examples (sample size not provided).
The study introduces 'career reconfiguration' as a framework explaining intra-role task transformation, extending existing career mobility and job transition theories.
Theoretical/conceptual contribution presented in the paper (framework proposition; not an empirical effect).
Mediation analysis confirms that training and organizational support significantly mediate the relationship between AI adoption and career shifts.
Mediation analysis reported in the study (method stated; no mediation coefficients or sample size provided in abstract).
Together, these variables explain 61% of the variance in adaptive outcomes (R² = 0.61).
Multiple regression model summary reported in the paper (R-squared value provided; sample size not stated).
Readiness to change is a significant predictor of career adaptation (beta = 0.298, p = 0.011).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Openness to technology is a significant predictor of career adaptation (beta = 0.367, p = 0.003).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Organizational support is a significant predictor of career adaptation (beta = 0.389, p = 0.005).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Skills training is the strongest predictor of career adaptation (beta = 0.412, p = 0.002).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
By elucidating the mechanisms and trade-offs inherent in AI-human collaboration, this work lays a robust foundation for future research on adaptive decision systems.
Authors' forward-looking claim in the abstract that their synthesis clarifies mechanisms/trade-offs and thus supports subsequent research; based on their review and framework.
By synthesizing these paradigms, this research advances the theoretical understanding of hybrid decision-making systems and provides actionable insights for organizations navigating complex and AI-driven environments.
Authors' stated contribution based on the conceptual synthesis of the literature and the proposed framework (as reported in the abstract).
The framework introduces four distinct paradigms of AI-human collaborative decision-making: adaptive intuitive decision, programmed algorithmic decision, interpretive analytical decision and integrative hybrid decision.
Authors' conceptual taxonomy reported in the abstract, produced from synthesis of the reviewed literature (627 articles).
We developed a novel conceptual framework that identifies two critical dimensions, AI-human dynamics and decision typologies, that shape decision outcomes.
Authors' reported conceptual synthesis derived from the systematic review/bibliometric analysis of the 627 articles.
The agentic-specificity classification helps organizations distinguish challenges that require novel approaches from those that are addressable with established practices.
Authors' proposed classification (agentic-specific vs. carried-over/amplified) intended as a practical decision aid; derived from the coding and comparative analysis.
The taxonomy provides a diagnostic framework for identifying priority barrier dimensions and understanding cross-dimensional amplification mechanisms.
Authors present a taxonomy derived from the review and claim it can be used diagnostically by organizations; supported by the coded barrier classification and STS mapping.
In the short run, with fixed human capital, wages, and job boundaries, AI raises productivity by reducing the time required to perform steps.
Model distinction between short-run (fixed job design and skills) and long-run horizons; short-run optimization shows AI reduces expected execution times for steps, thereby raising productivity.
Aggregating heterogeneous firms that deploy a commonly available AI technology yields an aggregate production function that admits a constant elasticity of substitution (CES) representation with three inputs: aggregate manual labor, aggregate AI-assisted labor, and aggregate capital.
Theoretical aggregation argument drawing on Houthakker (1955) and Levhari (1968), deriving a macro-level CES representation from a microfounded algorithmic cost function defined by firms' joint optimization over AI deployment and job design.
Improvements in AI quality generate non-linear effects on labor demand and wages because firms' cost-minimizing AI deployment and job designs change discretely at particular AI quality thresholds (microfoundation for the productivity J-curve).
Theoretical analysis of discrete switches in the cost-minimizing arrangement as AI success probability and execution times change; characterization of threshold effects and discussion linking to the J-curve phenomenon (model results and comparative statics).
Adjacency to AI-executed steps increases the likelihood that a given step is executed by AI (local complementarities): a step is more likely to be AI-executed in occupations where its neighboring steps are also AI-executed.
Empirical comparisons of conceptually similar steps across occupations paired with workflow adjacency information and realized AI execution outcomes from Anthropic’s Economic Index; statistical tests reported in the paper.
AI-executed steps co-occur in contiguous chains rather than being randomly scattered across a production workflow.
Empirical analysis linking O*NET tasks to human assessments of AI exposure (Eloundou et al., 2024), realized AI execution outcomes from Anthropic’s Economic Index (Handa et al., 2025), and GPT-generated workflow orderings for occupations; statistical tests comparing observed contiguity to random/scaled baselines reported in the paper.
Instrumenting AI use cases with treatment assignment suggests each additional AI use case prompted by treatment leads to approximately 26% higher revenue.
Instrumental variable analysis using randomized treatment as instrument for number of AI use cases in the 515-firm sample; outcome measured as revenue.
Instrumenting AI use cases with treatment assignment suggests each additional AI use case prompted by treatment leads to 0.85 more completed tasks.
Instrumental variable analysis using randomized treatment as instrument for number of AI use cases in the 515-firm sample; outcome measured as completed tasks.
Revenue and investment gains are largest at the 90th percentile and above, suggesting AI expands the upper range of what firms achieve.
Quantile/upper-tail analysis of revenue and investment outcomes in the randomized sample (515 firms); reported concentration of gains at the 90th percentile+.
Treated firms generate 1.9x higher revenue compared to control firms.
RCT with 515 firms; revenue reported by firms during and after the accelerator; comparison of mean revenues between treated and control groups.
Treated firms are 11 percentage points (18%) more likely to acquire paying customers.
RCT with 515 firms; customer acquisition measured in weekly reports / traction outcomes; treatment vs control comparison.
Treated firms complete 12% more tasks.
RCT with 515 firms; weekly progress reports used to measure tasks completed; comparison of completed tasks between treatment (255) and control (260) groups.
The additional AI use cases discovered by treated firms are concentrated in product development and strategy-related domains.
Analysis of categorized AI use cases reported in weekly progress reports from the randomized accelerator sample (515 firms); comparison of functional distribution of use cases between treated and control firms.
Treated firms discover 2.7 additional AI use cases (a 44% increase).
Randomized field experiment in a 3-month accelerator; sample of 515 high-growth startups, 255 treatment and 260 control; weekly progress reports capturing AI use cases; treatment delivered case-study workshops prompting broader search for AI use cases.
Organizations and policymakers that treat work-time policy as foundational economic planning will better position their economies to harness AI's benefits while mitigating systemic instability.
Policy-prescriptive conclusion based on cross-disciplinary analysis; no empirical trial or quantification offered in the summary.
Work-time reduction can distribute productivity gains more equitably.
Argument supported by examination of historical work-time transitions and pilot programs referenced in the article; no empirical effect sizes or sample details in the summary.
Coordinated reduction in working hours helps maintain aggregate demand.
The paper's synthesis of historical transitions and pilot programs and argument about distribution of productivity gains; no quantitative evidence or sample sizes provided in the summary.
Gradual, policy-led reduction in standard working hours can preserve employment.
Claim based on examination of historical work-time transitions, contemporary pilot programs, and cross-sector implementation strategies referenced in the paper; no specific studies or sample sizes cited in the summary.
Platforms should implement AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of online content platforms.
Policy/recommendation derived from the paper's empirical findings on consumption preferences, producer behaviors, and the moderating role of distribution algorithms.