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AI and digital investments can speed up government services and improve targeting, but benefits rarely materialize at scale without fixing infrastructure, skills and institutional incentives. Lasting public‑sector productivity gains depend as much on procurement, interoperability and governance reforms as on the technology itself.
low review_meta semantic_scholar
Digital Transformation and AI Adoption in Government: Evaluating the Productivity Gains, Implementation Barriers, and Governance Risks · Kofi Asante Aninakwah · Fetched Mar 12, 2026
When wages are lognormal, tiny skill or AI upgrades pay off exponentially at the top, prompting costly arms races in education and AI deployment; such overinvestment lifts GDP and widens inequality but can leave society worse off as private costs outweigh social gains.
n/a theoretical semantic_scholar
Janus-Faced Technological Progress and the Arms Race in the Education of Humans and Chatbots · Wolfgang Kuhle · Fetched Mar 12, 2026
AI adoption reallocates work and concentrates wage gains: routine cognitive jobs fall 2.3% per standard-deviation in adoption while complex problem-solving and interpersonal roles rise 1.8%. Wage gains accrue to the top quintile (+3.8%) as middle‑income wages slip (−1.4%), though countries with robust ALMPs and portable benefits experience much smoother transitions.
medium quasi_experimental semantic_scholar
Artificial Intelligence and Labor Market Transformation: Employment Effects, Wage Inequality, and Policy Responses in the Era of Generative AI · A. T. D. · Fetched Mar 12, 2026
A machine‑learning system is reported to raise retained food after harvest by 3.42% on Indian farms at no extra cost and claims near‑perfect prediction (R²=0.999); however, opaque data provenance and absent out‑of‑sample or field validation make the result fragile and potentially misleading.
low correlational semantic_scholar
AI in food inequality: Leveraging artificial intelligence to predict food waste in agriculture and post-harvesting · Akshit Erukulla, Swanish Baweja, Amol Sriprasadh, A. Paul, Aaron Sethi · Fetched Mar 12, 2026
Foreign direct investment can boost jobs and productivity in Sub‑Saharan Africa, but gains are uneven: when FDI is concentrated in extractive or capital‑intensive activities or when institutions and skills are weak, it often produces enclave growth and widens wage inequality; whether AI embodied in foreign investment helps or harms depends critically on sector, absorptive capacity and policy design.
medium review_meta semantic_scholar
Foreign Direct Investment, Labor Markets, and Income Distribution in Sub-Saharan Africa: A Conceptual Review · Isaac Armah, Patience Yamoah · Fetched Mar 12, 2026
AI that lowers coordination costs lets managers supervise more workers, boosting output and compressing hierarchies while widening manager–worker pay gaps; whether gains are shared or concentrated into superstars hinges on who captures the coordination rents.
n/a theoretical semantic_scholar
AI as Coordination-Compressing Capital: Task Reallocation, Organizational Redesign, and the Regime Fork · Alex Farach · Fetched Mar 12, 2026
Calibrated dynamics for China suggest AI capital spurs both investment and wages: AI drives physical-capital accumulation and boosts the wage bill, while feedback from capital and labor onto AI is weak; equilibria are stable rather than cyclic.
low theoretical semantic_scholar
Governance of Technological Transition: A Predator-Prey Analysis of AI Capital in China's Economy and Its Policy Implications · Kunpeng Wang, Jiahui Hu · Fetched Mar 12, 2026
Managers and school administrators who trust AI make quicker, more evidence-aligned decisions and run more data-driven organizations; building trust through transparency, reliability demonstrations and training appears to unlock measurable managerial and institutional performance gains.
low correlational semantic_scholar
Algorithmic Trust and Managerial Effectiveness: The Role of AI-Driven Decision Culture in Digital Organizations and Educational Institutions · Kunal Samanta, S. Singh · Fetched Mar 12, 2026
A cross-model audit of 70+ LLMs on ~26,000 real queries finds outputs have become strikingly similar, creating an 'Artificial Hivemind' that erodes stylistic and cognitive diversity; firms must invest in governance, process redesign and multi-model strategies to avoid correlated errors and loss of creative value.
medium descriptive semantic_scholar
The Artificial Hivemind: Rethinking Work Design and Leadership in the Age of Homogenized AI · Jonathan H. Westover · Fetched Mar 12, 2026
AI complaint-management tools markedly speed triage and standardize case classification, raising operational efficiency and routine-patient satisfaction; however, privacy risks, algorithmic bias, system-integration hurdles and empathy losses mean hybrid human-plus-AI models with strong governance are essential for sustained benefits.
medium quasi_experimental semantic_scholar
The Role of Artificial Intelligence in Healthcare Complaint Management: Implications for Organizational Performance, Patient Experience, and Service Governance · Mohammad Ayan, Ayushi Singh, Khushbu Singh, S. Sarfaraz, Ajit Pal Singh · Fetched Mar 12, 2026
Organizations that implement formal, company-wide risk management turn risk control into a strategic advantage, improving stability, decision-making and stakeholder trust; applied to AI projects, such practices can reduce model failure, speed adoption and create competitive edge.
low review_meta semantic_scholar
The Role of Risk Management as an Organizational Management Strategy: A Literature Study · Ameilia Nurfadhilah, Fitria Aulia Cahyaningrum, Ahmad Dairobi, Janu Ilham Saputr... · Fetched Mar 12, 2026
AI-enabled financial analytics turn bookkeeping into decision engines that raise capital efficiency and speed risk response; the biggest gains accrue to firms with integrated data and cloud infrastructure, while opaque models and uneven adoption create governance and competition concerns.
n/a descriptive semantic_scholar
Next-Generation Financial Analytics Frameworks for AI-Enabled Enterprises · Gaurav Walawalkar, Titilayo Elizabeth Oduleye, Micheal Olumuyiwa Adesuyi, Adaora... · Fetched Mar 12, 2026
AI 'co‑pilots' will soon be standard in developer tools, automating routine coding, testing and maintenance while boosting productivity; firms that combine these tools with skilled staff and organizational processes will capture most gains, widening advantages for well-resourced teams.
low descriptive semantic_scholar
How AI Will Transform the Daily Life of a Techie within 5 Years · Ripunjoy Sarkar · Fetched Mar 12, 2026
A seven-stage operational algorithm helps SMEs deploy AI responsibly: a pilot shows a gradient-boosting forecasting model — embedded with explainability and human-in-the-loop controls and supported by MLOps — outperforms the baseline and raises managerial trust, though findings come from a single firm and require broader validation.
low descriptive semantic_scholar
ALGORITHM FOR IMPLEMENTING AI IN THE MANAGEMENT LOOP OF SMES: FROM PROBLEM FORMULATION TO DAILY OPERATION · S. V. Savin · Fetched Mar 12, 2026
A blueprint for human–AI complementarity: firms that invest in team composition, shared mental models, attention/orchestration, and continuous training can achieve team performance that exceeds humans or AI alone; without these sociotechnical investments, AI’s productivity gains will be limited and uneven.
n/a theoretical semantic_scholar
Toward a science of human–AI teaming for decision-making: A complementarity framework · Cleotilde Gonzalez, Kate Donahue, Daniel G Goldstein, Hoda Heidari, Mohammad S. ... · Fetched Mar 12, 2026
Explainability helps, but only with rules and design: readable, actionable explanations increase trust and accountability in high‑risk AI applications only when paired with human‑centered design, clear governance and auditability; without institutional safeguards, explanation efforts can fail or backfire.
low review_meta semantic_scholar
Explainable AI in High-Stakes Domains: Improving Trust, Transparency, And Accountability in Automated Decision-Making · Lalithareddy Badam · Fetched Mar 12, 2026
AI forecasting and ERP integration unlock supply-chain gains only with organizational change; technology alone delivers limited benefit without executive commitment, cross-functional processes and strong data practices.
low descriptive semantic_scholar
Optimizing integrated supply planning in logistics: Bridging gaps for enhanced operational efficiency and strategic decision-making · Vutivi Scholarstic Mashaba, Bruno Emwanu · Fetched Mar 12, 2026
Scroll-and-click consent no longer suffices for AI systems: invisible inferences, model updates, and delegated decisions make traditional privacy disclosures ineffective; interdisciplinary design and a new research agenda are needed to create contextual, usable consent that shapes data flows, firm incentives, and market outcomes.
n/a descriptive openalex
Moving Beyond Clicks: Rethinking Consent and User Control in the Age of AI · William Seymour, Florian Alt, Zinaida Benenson, Sophie Grimme, Farzaneh Karegar,... · Fetched Mar 12, 2026
Serious games can unlock farmer uptake of land‑use decision tools by making model assumptions transparent, demonstrating profitability–emissions trade‑offs, and fitting into farm workflows; however, the evidence is preliminary, coming mostly from small pilots and requiring larger field trials and validation to confirm real‑world effects on land use and emissions.
low review_meta openalex
Serious games and decision support tools: Supporting farmer decision making for land use net zero. · Thompson, Chloe, Urquhart, Julie, Maye, Damian, Goodenough, Alice · Fetched Mar 12, 2026
Generative AI promises productivity and innovation in information systems but adoption is hindered by hallucinations, bias and an absence of governance; IS research should move from documenting impacts to actively shaping human–AI co-evolution and adaptive regulatory and organizational practices.
n/a review_meta arxiv
The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas · Aleksander Jarzębowicz, Adam Przybyłek, Jacinto Estima, Yen Ying Ng, Jakub Swach... · Mar 12, 2026
Autonomous scheduling agents that reward cooperative behavior using only aggregate demand signals can substantially improve simulated electricity-load coordination and permit beneficial unilateral adoption; but in some parameter regimes non-adopters capture outsized gains, creating free‑rider risks.
low theoretical arxiv
Hybrid Human-Agent Social Dilemmas in Energy Markets · Isuri Perera, Frits de Nijs, Julian Garcia · Mar 12, 2026
A GPT-5 audit finds similar job and industry recommendations for simulated male and female applicants but uses markedly gendered language — women described as empathetic and relational, men as analytical and leadership-oriented, raising fairness concerns for AI-assisted hiring.
low descriptive arxiv
Gender Bias in Generative AI-assisted Recruitment Processes · Martina Ullasci, Marco Rondina, Riccardo Coppola, Antonio Vetrò · Mar 12, 2026
Coordination and proto-intelligence can arise from simple feedback between agents, incentives and persistent environmental memory without centralized goals; stability hinges on dissipation outweighing reactive amplification, captured in explicit spectral conditions.
n/a theoretical arxiv
How Intelligence Emerges: A Minimal Theory of Dynamic Adaptive Coordination · Stefano Grassi · Mar 12, 2026
Frontier LLMs can articulate when they are failing in high‑stakes, unverifiable decisions but still keep making the same mistakes; across clinical, investment and reputational scenarios seven systems cycled into a refined but persistent 'helicoid' loop that grows more stable under pressure, undermining trust in AI as a reliable partner for irreversible decisions.
low descriptive arxiv
AI Knows What's Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLMs Under High-Stakes Decisions · Alejandro R Jadad · Mar 12, 2026
A centralized Supervisor that adaptively orchestrates modality-specialist tools cuts multimodal query latency and inference cost: on 2,847 benchmark queries the system reports a 72% median reduction in time-to-accurate-answer, 85% less conversational rework, and 67% fewer expensive model invocations versus a matched hierarchical pipeline, while maintaining accuracy parity.
low descriptive arxiv
One Supervisor, Many Modalities: Adaptive Tool Orchestration for Autonomous Queries · Mayank Saini Arit Kumar Bishwas · Mar 12, 2026