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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Marketing relies on predictive analytics and conversational interfaces.
Thematic claim in the abstract summarizing the roles of AI in marketing drawn from the reviewed literature.
Human resources applications of AI focus on recruitment and workforce planning.
Specific thematic finding reported in the abstract from the literature synthesis of included studies.
Artificial intelligence enhances analytics, automates routine tasks, personalizes interactions, and supports decision-making.
Aggregate finding reported in the abstract based on thematic synthesis of the reviewed literature (160 articles).
There are convergent patterns of AI adoption in human resources, marketing and customer services, logistics, and finance.
Synthesis claim from the systematic review of the 160 included peer‑reviewed articles as reported in the abstract.
This tension reveals a pattern we call 'bounded delegation': developers wanted AI to absorb the assembly work surrounding their craft, never the craft itself.
Interpretive result from the paper's qualitative thematic analysis of survey responses (n=860), labeled by the authors as the 'bounded delegation' pattern.
Developers wanted systems enforcing explicit authority scoping, provenance, uncertainty signaling, and least-privilege access throughout.
Reported constraints and desiderata from the thematic analysis of survey responses (n=860).
Developers wanted systems that embed quality signals earlier in their workflow to keep pace with accelerating code generation.
Thematic findings from the paper's human-in-the-loop, multi-model council-based analysis of survey responses (n=860).
Using a human-in-the-loop, multi-model council-based thematic analysis, we identify 22 AI systems that developers want built across five task categories.
Qualitative analysis method described in the paper applied to the survey responses (n=860); result reported as identification of 22 desired AI systems organized into five categories.
For listed firms, AI patents command a robust market-value premium in both countries.
Firm-level analysis linking AI patenting to market valuation for listed firms in both countries (regression or valuation analysis implied by statement).
China surpasses the United States in recent annual AI patent counts.
Time-series patent count comparison using classifier-applied corpora (paper reports that recent annual counts are higher for China than the U.S.).
There is broad convergence in AI patenting intensity and subfield composition between the United States and China.
Comparative analysis of AI patenting intensity and subfield composition across the two patent corpora (US 1976-2023, China 2010-2023) reported in paper.
Applying the classifier to granted U.S. patents (1976-2023) and Chinese patents (2010-2023), we document rapid growth in AI patenting in both countries.
Application of classifier to full corpora of granted U.S. patents (1976-2023) and Chinese patents (2010-2023); time-series counts of AI patents reported.
The classifier generalizes well to Chinese patents based on citation and lexical validation.
Validation analyses described as citation-based and lexical validation applied to Chinese patents (paper states generalization to Chinese patents via these validation methods).
Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score.
Reported classifier evaluation metrics (precision, recall, F1) presumably on held-out test data; comparison stated against the existing USPTO approach.
We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO's AI Patent Dataset.
Methodological description in paper: fine-tuning PatentSBERTa on manually labeled USPTO AI Patent Dataset (manually labeled training data and model fine-tuning stated).
The results demonstrate the importance of considering interacting systems of AI agents when doing both capabilities and safety research.
Authors' interpretation/generalization based on experimental findings comparing multi-agent organizations and single agents across tasks and settings.
Organisations should invest in customisation capabilities for AI recruitment tools, implement comprehensive change management strategies, and maintain robust post-hire evaluation procedures.
Authors' recommendations derived from thematic findings and participant perspectives across two firms (qualitative synthesis of n = 22 interviews).
AI functioned optimally as an augmentative technology rather than as a replacement for human decision-makers in recruitment.
Findings: participants across the two case firms described AI being most effective when augmenting human judgment rather than replacing it (interviews n = 22).
AI significantly enhanced efficiency through process standardisation and automation.
Findings based on participant accounts in thematic analysis (interviews n = 22) describing process optimisation and automation benefits.
The Principle of Maximum Heterogeneity reveals a convergence of complex phenomena across fields onto simple underlying design principles with important predictive value for future distributed production systems.
Synthesis claim in the paper arguing cross-field convergence and predictive value based on the theoretical model and conceptual examples; no empirical validation or forecasting trials reported.
The principles derived (including the Principle of Maximum Heterogeneity) can be used as a blueprint for constructing ideal distributed production systems; demonstrated by suggesting specific redesigns for compute systems executing large-scale AI.
Paper includes suggested redesigns for compute systems as demonstrations of the blueprint; these are proposed designs/illustrative applications rather than empirically validated interventions or trials.
The Principle of Maximum Heterogeneity applies recursively across all layers of nested production systems.
Theoretical claim within the paper arguing recursive applicability across nested system layers (e.g., neurons, firms, ecosystems); supported by conceptual reasoning and model exposition rather than empirical multi-layer tests.
The communication topology determines the spatial scale over which heterogeneity spreads in distributed production systems.
Model-based theoretical argument in the paper linking topology to the spatial scale of heterogeneity; illustrated conceptually and via examples but not via empirical sample testing.
Principle of Maximum Heterogeneity: any distributed production system optimising for performance will converge on an increasingly heterogeneous configuration.
Statement of a derived principle from the paper's model (theoretical derivation/argument); demonstration via model reasoning and examples rather than empirical testing; no sample size reported.
A small set of underlying laws generates the complex dynamics observed across fields (biology, economics, neuroscience, computing).
Theoretical argument and synthesis across disciplines within the paper; no empirical or experimental sample size reported.
The Distributed Production System model captures how agent heterogeneity, resource constraints, communication topology, and task structure jointly determine the productivity, efficiency, and robustness of distributed systems across biology, economics, neuroscience, and computing.
Presentation of a unified theoretical model (Distributed Production System) in the paper; conceptual/mathematical development and cross-disciplinary argumentation; no empirical sample size reported.
A simple regret-based payout rule is proposed that satisfies three out of the four Shapley axioms and also lies in the core.
Constructive proposal in the paper with accompanying theoretical/axiomatic analysis showing compliance with three Shapley axioms and proof of core-membership.
Convexity (in the homogeneous-agent case) implies a non-empty core that contains the Shapley value and ensures both stability and fairness of payout allocations.
Theoretical implication shown in the paper: proof that convexity leads to non-empty core and that the Shapley value belongs to the core under the stated conditions.
For identical (homogenous) agents with fixed action sets, the induced TU game is convex under mild algorithmic conditions.
Theoretical result/proof provided in the paper under assumptions of homogenous agents and fixed action sets and certain algorithmic conditions.
Participants in the treatment conditions showed greater positive belief change about the AI across the session.
Pre/post measures of participant beliefs collected during the field experiment (N=388) showing larger positive shifts among those assigned to treatment conditions versus controls.
A cognitive scaffolding intervention (partnership training that reframed AI as a thought partner) was associated with higher individual document quality at the top of the distribution.
Field experiment with 388 employees comparing cognitive scaffolding to other conditions; reported improvements concentrated at the top of the individual document-quality distribution.
Organizations that strategically invest in blended, context-rich, and partnership-based development programs position themselves for sustainable competitive advantage in an increasingly automated marketplace.
Normative recommendation supported by the paper's synthesis of theory and practice (organizational development, adult learning, workforce development); no empirical effect sizes or sample-size-based evaluation provided.
Forward-thinking organizations are redesigning learning architectures to cultivate irreplaceable human capabilities that complement rather than compete with AI systems.
Synthesis of literature from organizational psychology, adult learning theory, and workforce development practice cited in the paper; presented as descriptive statement about current organizational practice rather than based on a reported empirical study with sample size.
Corporate and academic learning ecosystems will converge (necessary convergence of corporate and academic learning ecosystems).
Conceptual synthesis and argumentation in the paper referencing workforce development practice and organizational development research; no quantitative measures or sample size reported.
Human skills (critical thinking, adaptive decision-making, interpersonal acumen) will be elevated to core competency status as AI automates technical tasks once considered core competencies.
Argument and synthesis presented in the paper drawing on organizational psychology, adult learning theory, and workforce development practice; no empirical sample size or statistical tests reported (conceptual/literature-based claim).
A four-phase implementation roadmap translates the MIGT into actionable enterprise programs.
Paper claims to include a four-phase roadmap; this is described as a design/implementation contribution in the excerpt.
A cross-jurisdictional regulatory alignment structure mapping enterprise AI identity governance obligations under EU, US, and Chinese frameworks simultaneously, identifying irreconcilable conflicts and providing a governance mechanism for managing them.
Paper claims to produce a mapping/alignment structure comparing EU, US, and Chinese obligations and to identify irreconcilable conflicts; method not detailed in excerpt.
Machine Identity Governance Taxonomy (MIGT): an integrated six-domain governance framework simultaneously addressing the technical governance gap, the regulatory compliance gap, and the cross-jurisdictional coordination gap that existing frameworks address only in isolation.
Paper presents MIGT as a novel, integrated six-domain framework; described as targeting three specific governance gaps. Evidence cited is the framework design itself (conceptual contribution).
AI-Identity Risk Taxonomy (AIRT): a comprehensive enumeration of 37 risk sub-categories across eight domains, each grounded in documented incidents, regulatory recognition, practitioner prevalence data, and threat intelligence.
Paper claims to have produced the AIRT taxonomy and states its grounding sources (documented incidents, regulatory recognition, practitioner prevalence data, threat intelligence); taxonomy size given (37 sub-categories across eight domains).
A machine-learning research agenda is needed centered on team-level evaluation, privacy-preserving memory layers, scaffolded AI for learning, carbon-aware routing, and pro-agency workflow design.
Prescriptive recommendation in the position paper proposing specific research priorities; no empirical evaluation of these approaches is presented within the paper itself.
Rather than eliminating the office, this shift supports selective co-presence, reserving in-person time for tasks with high tacitness, high coupling, or high relational stakes (including apprenticeship, conflict repair, trust formation, and early-stage synthesis).
Theoretical/qualitative argument about task types best suited for in-person interaction; illustrated by examples (apprenticeship, conflict repair, trust formation, early-stage synthesis); no empirical task-level allocation study presented.
Capabilities that are already widely deployed—transcription, summarization, retrieval, translation, drafting, and code assistance—are the basis for this shift (with bounded agents as an amplifying but not necessary extension).
Descriptive claim citing the prevalence of specific AI capabilities in current deployments; presented as observation in the position paper rather than as a quantified adoption study.
The organizational significance of these systems is not generic automation but the accumulation of artifact capital: durable, queryable, reusable traces such as transcripts, summaries, decisions, tickets, code comments, and retrieval layers.
Argumentative claim in the paper describing a conceptual mechanism ('artifact capital') by which foundation-model features create reusable organizational artifacts; no empirical measurement of artifact capital provided.
The foundation-model stack (NL interaction, multimodal capture, long context, retrieval, transcription, translation, bounded tool use) changes the coordination economics that previously favored daily in-person co-presence.
Conceptual claim supported by descriptions of foundation-model capabilities and their potential to create durable, queryable artifacts; no empirical test or measured coordination-costs reported.
Remote-capable knowledge work should default to AI-enabled flexibility because the workflow-integrated foundation-model stack changes the coordination economics that once favored daily co-presence.
Normative argument in the position paper based on conceptual analysis of coordination economics and the claimed effects of foundation-model features; no empirical sample or quantitative study reported.
Preliminary corroboration is provided by a companion production automation system with eleven operating lanes and 2,132 classified tickets.
Reported companion system operational statistics in the paper (11 lanes, 2,132 tickets).
When iteration was permitted, the final success rate for the structured interactions reached 91.5% (183 of 200).
Reported final success counts/rate in the paper for structured interactions (183 of 200).
Among structured interactions, 110 of 200 were accepted on first pass.
Reported counts in the paper for the structured-interaction group (110 accepted of 200 structured interactions).
Structured context assembly was associated with an improvement in first-pass acceptance from 32% to 55%.
Observational comparison reported in the paper (baseline vs. structured first-pass acceptance rates are given as 32% and 55%).
Structured context assembly was associated with a reduction from 3.8 to 2.0 average iteration cycles per task.
Observational comparison reported in the paper (structured vs. baseline interactions); the paper states the 3.8 to 2.0 cycle figures.