Evidence (3062 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Human Ai Collab
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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).
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.
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.
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.
Training improved exam scores by 0.27 grade points relative to optional access without training (p = 0.027).
Intent-to-treat comparison between the optional-access-with-training arm and the optional-access-without-training arm in the randomized trial (n = 164); reported effect size = +0.27 grade points and p-value = 0.027.
A brief, targeted training increased voluntary LLM use from 26% (optional access without training) to 41% (optional access with training).
Randomized experiment with 164 law students assigned to three arms (no access, optional access, optional access + ~10-minute training). Observed adoption rates in the two optional-access arms were 26% (untrained) vs. 41% (trained).
A research agenda prioritizing empirical evaluation, model transparency, and rigorous impact assessment is required to translate conceptual promise into measurable public value.
Explicit recommendation in the blurb identifying research priorities; not an empirical claim but a proposed course of action.
Illustrative vignettes show AI in action: logistics optimization for trade, AI models for national fiscal decision-making, and algorithmic job-acceleration for individual labor market navigation.
Reference to specific case vignettes contained in the book; these are illustrative scenarios rather than empirical case studies with measured outcomes.
Ten defining policy questions structure the book’s approach, turning abstract AI capabilities into operational policy choices.
Descriptive claim about the book's organization; verifiable by inspecting the book's table of contents (no external empirical data).
Keyword-style queries persist even among experienced users.
Analysis of query types across experience levels in the Asta dataset showing continued presence of keyword-style queries among users labeled as experienced.
Prior research has primarily focused on automating user actions through clicks and keystrokes, this paradigm overlooks human intention, where users value the ability to explore, iterate, and refine their ideas while maintaining agency.
Literature characterization and conceptual argument presented in the paper's introduction (qualitative claim based on authors' synthesis of prior work and user values).
Macroeconomic effects remain hard to observe because of a 'productivity J-curve': firms often must invest in organizational changes first and only later realize measurable financial/productivity gains from AI.
Conceptual synthesis supported by firm-level case studies and empirical papers in the reviewed literature indicating implementation lags; the brief frames this as an interpretation of mixed short-run macro evidence rather than a single causal estimate.
AI coding agents can resolve real-world software issues, yet they frequently introduce regressions, breaking tests that previously passed.
Stated as background/motivation in the paper; references general observations about agent behavior and prior work (no specific dataset/sample cited in the provided excerpt).
Organisational rules, regulatory constraints, and transparency requirements materially shape micro-level human–AI interactions and can alter adoption incentives and accountability outcomes.
Conceptual governance argument linking institutional constraints to human–AI design choices; theoretical reasoning, no empirical policy evaluation provided.
Potential productivity gains from automating routine informational tasks are conditional: net gains depend on managerial capacity to integrate AI outputs into systemic decision-making and on governance structures.
Conceptual conditional claim derived from integration of systems thinking and algorithmic optimisation literatures; no empirical measurement of productivity effects.
Information-processing and optimisation tasks exhibit clear substitution pressure (are most automatable), whereas relational and normative tasks remain complementary to human labour.
Theory-driven claim combining managerial role analysis with general automation/complementarity logic from AI economics; conceptual prediction without empirical quantification.
Human–algorithm architectures can take three forms—augment (assist), displace (replace), or reconfigure (redistribute) cognitive tasks—and their design depends on organisational design, regulation, and decision-structure rules.
Taxonomic conceptualization derived from cross-framework analysis; prescriptive mapping rather than empirical classification; no sample.
Interpersonal coordination roles (disturbance handler, liaison, leader) retain strong human elements (influence, ethics, legitimacy) that are difficult to fully algorithmise.
Conceptual argument based on the nature of relational and legitimacy-based tasks within Mintzberg’s framework and limits of algorithmic substitution; theoretical only.
Entrepreneurial and disturbance-handling roles become hybrid decision zones requiring integrated strategic and computational reasoning (modelling, simulation, anomaly detection plus contextual interpretation and values-based trade-offs).
Analytical synthesis of role demands and computational affordances; cross-framework analysis producing a hybrid strategic–computational characterization; no primary data.
Roles that rely on relational intelligence, ethical judgement, and influence (leader, liaison, figurehead, negotiator) remain primarily strategic but are increasingly supported by predictive and diagnostic analytics.
Role-specific effects derived from cross-framework conceptual mapping (Mintzberg roles × computational thinking); theoretical argumentation rather than empirical measurement.
AI systematically reconfigures managerial work by augmenting, displacing, or reconfiguring cognitive tasks across Mintzberg’s ten managerial roles.
Conceptual synthesis and comparative role mapping integrating Mintzberg’s ten managerial roles with Senge’s Five Disciplines and computational thinking; theoretical analysis only (no primary empirical data; no sample).
Commercial platforms' incentives may not align with public-interest verification, so economic policies (transparency mandates, data portability, competition policy) can reshape incentives and improve information ecosystems.
Policy implication drawn from the study's analysis of platform governance and incentive misalignment, supported by interviews and documents discussing platform interactions.
Platforms selectively adopt automated tools for triage, detection, and monitoring while keeping human judgment central to verification.
Interviews and workflow analyses indicating selective automation (for triage/monitoring) combined with human-led verification steps.
Each platform (Akeed, Teyit, Factnameh) adapts its scope and tactics according to national constraints.
Platform-level descriptions derived from interviews with staff/editors and analysis of platform outputs and workflows for each of the three organizations.
Fact-checking platforms in Jordan (Akeed), Turkey (Teyit), and Iran (Factnameh) face similar operational constraints—censorship, limited access to data, and difficulties engaging audiences—but respond with different strategies shaped by local politics.
Comparative interpretive analysis based on document analysis of platform outputs/guidelines and semi-structured interviews with staff, editors, and stakeholders from the three platforms (Akeed, Teyit, Factnameh).
Hybrid norms combined with AI platforms lower coordination costs and may encourage more decentralized or platform‑based organizational structures, changing the premium on co‑location.
Theoretical integration of organizational economics and digital platform literature; supported by conceptual examples but no firm‑level causal analysis in the paper.
Differential access to informal learning and sponsorship in hybrid settings can produce long‑term human‑capital inequalities; AI-based mentoring and visibility tools may partially mitigate these gaps but risk biased recommendations if trained on skewed data.
Synthesis of literature on mentorship, social capital, and algorithmic bias; illustrative case examples rather than empirical evaluation of AI mentoring systems.
Geographic dispersion plus AI-enabled remote hiring can widen the labor supply for firms, potentially compressing wages for some roles while raising returns to digital-collaboration skills.
Economic reasoning and literature review on remote hiring and labor supply effects; the paper offers conceptual arguments rather than presenting empirical wage-impact estimates.
Automation of routine tasks may shift task content toward relational and creative work, areas where hybrid arrangements influence social capital accumulation.
Theoretical argument combining automation literature with sociological perspectives on social capital; no direct empirical measurement or longitudinal data in the paper.
Hybrid work complicates traditional productivity metrics, making AI-driven analytics and monitoring tools more attractive but creating trade-offs between measurement accuracy, privacy, and employee trust.
Conceptual argument synthesizing literature on measurement, monitoring, and AI tools; no empirical evaluation of specific tools or datasets in the paper.
Sustaining productivity and organizational culture under hybrid arrangements depends crucially on leadership practices—trust, communication, and fairness—and on inclusive policies that explicitly manage equity, well‑being, and flexibility.
Comparative case illustrations and management literature integration; recommendations derived from secondary sources and theoretical argumentation rather than controlled empirical testing.
Dispersed work alters identity construction, belonging, and social cohesion; digital interactions reshape workplace rituals and norms.
Sociological literature synthesis and qualitative case illustrations emphasizing identity and ritual processes; no longitudinal or quantitative measures provided in the paper.
The paper proposes an 'algorithmic workplace' framework emphasising hybrid agency (agents composed of humans plus GenAI), decentralised decision processes, and erosion of rigid managerial boundaries.
Conceptual synthesis derived from thematic mapping, co‑word analysis and interpretive discussion of the mapped literature; framework presented as the article's conceptual contribution.
Passive AI use produced an initial increase in enjoyment/satisfaction that reversed once participants returned to manual work.
Pre-registered experiment (N = 269) measured enjoyment/satisfaction before and after return to manual work; passive-copy condition showed short-term increases in enjoyment/satisfaction that declined after returning to manual tasks.
Ethics is distinct from and prior to law: legal codification cannot fully capture the primordial ethical demand.
Philosophical engagement with Derrida and Levinas; normative argumentation and conceptual examples. No empirical validation of precedence.
Legal norms and technical reforms are necessary but incomplete: they must remain responsive to a primordial, non-codifiable ethical obligation that structures how responsibility is perceived and allocated in practice.
Conceptual analysis drawing on Derrida and Levinas; argument supported by illustrative cases across three domains (care robotics, AVs, algorithmic governance). No empirical measurement of legal efficacy.
AI feedback may either augment teacher productivity (complementarity) or substitute for routine teacher feedback tasks (substitution), with unclear net labor impacts.
Workshop deliberations among 50 scholars highlighting competing theoretical scenarios; no causal labor-market evidence provided.
Human experts will likely shift roles from sole decision-makers to adjudicators, challengers, and validators of AI-generated arguments, changing required skills toward critical evaluation and dialectical oversight.
Conceptual labor-market projection; no empirical labor studies or surveys presented.
Productivity gains from partial automation may be offset by negative externalities (incorrect legal outcomes, appeals, reputational damage) that impose social and private costs not captured by narrow productivity measures.
Theoretical economic analysis and illustrative case vignettes describing error propagation; no empirical quantification of externalities.
Market demand will likely split between providers offering generative convenience with liability exposure and providers offering certified/verified, explainable tools at a premium, creating a two-tier market.
Market-structure analysis and illustrative projections; no empirical market data or sample size.
Reported monetary supervision cost was low (~$200) for this project, but the paper cautions that general equilibrium effects and scaling may change costs as demand for supervisors rises.
Paper provides reported supervision cost (≈$200) for the single project and includes a caveat about external validity and scaling; cost is self-reported and contextualized by authors.
Policymakers must weigh productivity gains from higher autonomy against increased systemic risk and governance costs; optimal allocation will vary by sector (high-consequence systems justify stricter human oversight; lower-consequence tasks may tolerate more autonomy).
Normative policy analysis and cost–benefit reasoning; sector-differentiated triage framework proposed (no quantitative welfare or sectoral optimization performed).