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Firms that pair AI and Big Data with strong governance tend to reduce market uncertainty in crises, but benefits are uneven across industries and hinge on regulation and data maturity; misapplied models or blind automation can amplify volatility.
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An Empirical Study on the Impact of the Integration of AI and Big Data on Market Uncertainty in the Context of Economic Turbulence · YiLv Ge · Fetched Mar 15, 2026
AI adoption raises Tunisian SMEs' financial and operational performance, but the returns concentrate in firms with stronger finance and technical skills; weak formal institutions do not significantly blunt these gains, implying firms use adaptive informal mechanisms.
low correlational Full text semantic_scholar
Structural Constraints as Moderators in the Ai–performance Relationship: Evidence from Smes in an Emerging Economy · Fatma Cherif · Fetched Mar 15, 2026
AI often acts as cheap, scalable prediction that can boost output where firms can rewire work and data flows, yet organizational frictions, data dynamics and implementation costs frequently blunt its economic impact; crucially, social‑science research so far provides limited, rigorous guidance for policymakers preparing for very rapid or systemic AI‑driven transitions.
medium review_meta Summary semantic_scholar
The Economic Impacts of Artificial Intelligence: A Multidisciplinary, Multi-book Review · K. Bryan · Fetched Mar 15, 2026
AI could funnel income to a few asset-owning firms and hollow out the labor tax base; the Article urges raising capital‑gains taxes on AI‑intensive firms now and preparing to switch to a consumption tax if labor’s share collapses.
low commentary Summary semantic_scholar
Taxing AI · Assaf Harpaz · Fetched Mar 15, 2026
Digital tools can accelerate green innovation and cut emissions—but only with clean power, enforceable auditability and upgraded workforce skills; absent these complements, AI-driven digitalization risks higher electricity use, measurement divergence and widening inequality.
medium review_meta Full text semantic_scholar
The synergy of digital innovation and green economy: A systematic review of mechanisms, challenges, and adaptive strategies in the post-Al era · R. Santos, María Isabel Casal Reyes · Fetched Mar 15, 2026
Women across Europe are substantially underrepresented in advanced digital tasks — about a 15 percentage-point average gap that becomes a sharp 'digital glass ceiling' at the highest digital-intensity jobs. Observable characteristics explain only roughly 30% of the gap, implying workplace organisation, task allocation and promotion practices — not just STEM supply — must be addressed (Ireland shows an especially large male concentration in advanced digital roles).
medium correlational Summary semantic_scholar
Squandered skills? Bridging the digital gender skills gap for inclusive growth in Ireland – A comparative European perspective · Adele Whelan, Luke Brosnan, S. McGuinness · Fetched Mar 15, 2026
Firms that signal stronger data-driven decision-making in their disclosures tend to perform better internationally; the gains operate through sustainability-focused value-creation channels and are larger in competitive markets and in firms with significant foreign or state ownership.
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The data-driven decision-making, sustainable value creation, and international firm performance: Micro-level evidence based on AI language models · Miao Xu, B. Lu · Fetched Mar 15, 2026
AI boosts organizational decision-making only when paired with human judgment and institutional design; treating algorithms as replacements risks poorer outcomes. Firms must design task allocation, feedback, interpretability, and accountability systems so algorithmic strengths amplify—not displace—managerial sense-making.
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Reframing Organizational Decision-Making in the Age of Artificial Intelligence: A Conceptual Review of Human–AI Augmentation · Dr. Pratik B. Upase · Fetched Mar 15, 2026
Generative AI is not just a tool but a new organizational element: startups are forming 'hybrid decision architectures' in which algorithmic outputs and human judgment recursively co-produce decisions, remaking job roles, routines and managerial practices; these configurations offer productivity complementarities but require new hiring, training and governance to institutionalize.
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Hybrid decision architectures: exploring how facilitated AI access reconfigures startups' organizational structures and decision-making · Douglas Mac Cord, Carla Ramos, G. F. A. Monteiro · Fetched Mar 15, 2026
AI and HR analytics are linked to modest productivity gains on average (r = 0.28), but returns vary sharply with data quality, governance and industry; opaque algorithms and weak labor safeguards risk discrimination, privacy loss and the commodification of workers.
medium review_meta Full text semantic_scholar
ALGORITHMIC DETERMINISM VERSUS HUMAN AGENCY: A SYSTEMATIC REVIEW AND META-ANALYSIS OF ARTIFICIAL INTELLIGENCE AND HR ANALYTICS IN ORGANIZATIONAL DECISION-MAKING · Dr. Fouzia Bedad, Dr. Khaled Mokhtari, Dr. Amina Sammache · Fetched Mar 15, 2026
A modest, private-sector–style AI governance standard could curb risks from AI use in military decision-making while reshaping defense procurement and innovation incentives. Although it raises short-term compliance costs that favor larger contractors, standardized certification and audit markets may lower long-run integration costs and influence international competitive dynamics.
n/a commentary Full text semantic_scholar
AI governance for military decision-making: A proposal for managing complexity · Osonde A. Osoba · Fetched Mar 15, 2026
Top IT firms report that AI tools in HR cut time-to-hire and improve match quality, turning HR from back-office processing into analytics-driven strategy; but benefits are concentrated at data-rich firms and are offset by algorithmic bias, privacy concerns, integration costs and skills gaps.
medium review_meta Summary semantic_scholar
AI-Driven Decision Making and Digital Recruitment: Transforming HRM in Leading IT Companies  · Prashasth Bhushan · Fetched Mar 15, 2026
How researchers pay participants shapes what we learn about human–AI teamwork: inconsistent or misaligned incentives bias measured effort, trust and accuracy, so the authors propose a practical Incentive‑Tuning Framework to calibrate and transparently report pay schemes across studies.
medium review_meta Full text semantic_scholar
Incentive-Tuning: Understanding and Designing Incentives for Empirical Human-AI Decision-Making Studies · Simran Kaur, Sara Salimzadeh, U. Gadiraju · Fetched Mar 15, 2026
Personalized AI explanations can boost human–AI team performance, but only when tasks let humans and models play complementary roles; in controlled experiments personalization delivered complementarity in an engineered geography task but not in a standard sentiment-analysis task.
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Who Needs What Explanation? How User Traits Affect Explanation Effectiveness in AI-Assisted Decision-Making · Torrence S Farmer, Chien-Ju Ho · Fetched Mar 15, 2026
Well-structured prompts materially boost autonomous agents’ performance—improving accuracy, speeding task completion and reducing errors—while standardized prompt frameworks noticeably improve multi-agent coordination; firms that invest in prompt engineering can raise automation productivity and lower coordination costs.
medium rct Full text semantic_scholar
Prompt Engineering for Autonomous AI Agents: Enhancing Decision-Making and Task Coordination in Dynamic Environments · Rana Sami Ullah Khan, Sumayya Bibi, Asad Latif, Maria Soomro, Mahpara · Fetched Mar 15, 2026
AI is a general-purpose economic force that raises productivity and creates new organizational forms, but it also reshapes tasks, labor demand, and market power, requiring fresh research agendas and policy responses.
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Model-Driven Hybrid AI Framework for End-to-End Autonomous Decision-Making in Drug Development · Hyunjung Lee, Hyeonseok Kang, W. Jung, Hyojin Cho, Sungwoo Goo, Hwi-yeol Yun, Mi... · Fetched Mar 15, 2026
AI systems detect gastrointestinal bleeding lesions with high accuracy in retrospective studies and can speed clinician review, but there is scant prospective evidence that these diagnostic gains translate into better patient outcomes or cost-effectiveness, leaving economic value uncertain.
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How Do AI-Assisted Diagnostic Tools Impact Clinical Decision-Making And Patient Outcomes In Acute Gastrointestinal Bleeding Cases? : A Comprehensive Systematic Review · Gafrinda Kautsari, Luluk Aflakah, R. Nurlaili · Fetched Mar 15, 2026
Feature-level AI tools—recommendations, chatbots and comparison engines—lift consumer trust and boost purchase intent for electronics, but privacy fears and cognitive costs blunt the gains; transparent, ethical design is therefore a commercial and regulatory priority.
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Role of artificial intelligence on consumer buying behavior: the dual effects of AI-enabled features on decision-making and trust · Rishika Bhojwani, Justin Paul, Rajesh Srivastava · Fetched Mar 15, 2026
LLMs reframe strategic thinking but don't boost foresight: in a randomized 2×2 experiment, both time pressure and LLM assistance changed participants' mental representations in a startup evaluation task, yet neither improved forecasting accuracy—LLM use also increased reported information overload and reduced psychological ownership.
medium rct Summary semantic_scholar
AI-Augmented Strategic Decision-Making Under Time Constraints: An Experimental Study on Mental Representations and Strategic Foresight · Tim Kanis, Justus Emanuel Mann, Jutta Stumpf-Wollersheim · Fetched Mar 15, 2026
Seventeen ways to pair humans with LLMs: the chosen interaction pattern materially alters recommendations, error types and who bears responsibility; in clinical diagnostics different archetypes produce systematic differences in outputs and reliance, implying nontrivial consequences for productivity, liability and adoption.
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Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making · S. Chappidi, Jatinder Singh, A. Krauze · Fetched Mar 15, 2026
Treat analytics as the interpretive bridge: a five-layer Human-AI decision framework argues that combining AI outputs with business-analytics interpretation, managerial judgment and feedback loops can raise decision quality, reduce algorithmic bias and boost employee confidence; however, the framework is conceptual and requires empirical validation to quantify productivity and labor impacts.
low theoretical Full text semantic_scholar
Designing Human–AI Collaborative Decision Analytics Frameworks to Enhance Managerial Judgment and Organizational Performance · Jannatul Ferdousi, Md Shokran, Md Saiful Islam · Fetched Mar 15, 2026
AI and Big Data materially raise audit productivity and accuracy, expanding coverage and improving fraud detection in finance and tech firms; but the gains depend on stronger governance, model transparency and new auditor skills to avoid overreliance on opaque systems.
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Audit 5.0 and the Digital Transformation of Auditing: The Role of Big Data Analytics and Artificial Intelligence in Enhancing Audit Quality and Decision-Making · Isaiah Osemudiamen Okogun, Victor Apatu, Natasha Mwanandimayi, Rutendo Talent Si... · Fetched Mar 15, 2026
In Egypt one in five jobs are at high automation risk, but only one-quarter of affected workers can realistically switch to safer roles using existing shared skills, meaning three-quarters face a structural mobility barrier and need full reskilling; process-oriented skills are the highest-leverage training target.
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Graph-Based Analysis of AI-Driven Labor Market Transitions: Evidence from 10,000 Egyptian Jobs and Policy Implications · A. Dawoud, Sondos Samir, Youssef Nasr, A. Habashy, Aya Saleh, Mahmoud Mohamed, O... · Fetched Mar 15, 2026
AI is boosting productivity by automating routine work and augmenting cognitive tasks, but it is also making jobs more precarious and unequal; policymakers must pair reskilling, social insurance and algorithmic governance to capture benefits and protect workers.
n/a review_meta Full text semantic_scholar
ARTIFICIAL INTELLIGENCE, AUTOMATION, AND THE CHANGING PATTERN OF WORK · S. Tripathi, Dr. Prachi Rode · Fetched Mar 15, 2026
India's IT sector will keep hiring strongly to 2026, but the jobs are changing: demand is concentrating in AI, cloud and cybersecurity specialists and mid-career product roles while traditional volume hiring at service firms contracts. Growth is decentralizing into tier‑2 cities, making large-scale reskilling and regional training essential to fill emerging talent gaps.
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A Study on Hiring Trends In 2026 In India’s Information Technology Sector · Shweta Parkar · Fetched Mar 15, 2026