Forecasting the Future: The Interplay of Artificial Intelligence, Innovation, and Competitiveness and its Effect on the Global Economy
Bibliographic record
Abstract
The study investigates the profound impact of Artificial Intelligence (AI) on various facets of the global economic landscape. Against a backdrop of rapid technological advancements, the study draws on the context of the pivotal IMF report highlighting the transformative potential of AI. The report suggests that AI could modify, replace, or transform about 60% of jobs in advanced economies and a significant proportion in emerging and low-income countries, reflecting a global paradigm shift in employment and economic structures. The core objective of this study is to thoroughly examine the role of AI-driven innovation in organizational competitiveness, its impact on community development and socioeconomic dynamics, and its implications on national economic policies and global economic trends. A quantitative research methodology was employed, involving a structured survey targeting a diverse group of professionals in various industries. The survey was meticulously designed to capture insights into participants' experiences and perceptions regarding AI implementation and its impacts. A total of 642 valid responses from consultants, technology enthusiasts, industry experts, and policymakers provided a robust dataset for analyzing the study's four hypotheses. The research findings reveal that AI integration significantly bolsters organizational competitiveness, echoing the insights from contemporary literature. Higher levels of AI adoption in communities are linked to improved socioeconomic outcomes, albeit with the risk of intensifying existing inequalities. On a national scale, strategies focusing on AI and innovation correlate with enhanced global economic competitiveness. Furthermore, the integration of AI in business processes markedly influences workforce dynamics, necessitating shifts in skill requirements and job roles. In light of these findings, the paper recommends strategic AI integration within businesses, equitable policy frameworks for AI deployment, a focus on AI in national economic strategies, substantial investment in workforce training, and international collaboration in AI development and ethics are imperative for maximizingAI's benefits while mitigating potential risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".