Industry Competitiveness 2035: Four scenarios on how emerging technologies will shape the enterprises of the future
Bibliographic record
Abstract
This report presents the results of a future-oriented research project exploring the future competitiveness of medium-to-large Italian companies, shaped by artificial intelligence and enabling technologies. Its purpose is to provide both insight and inspiration, offering a structured vision of possible futures and their strategic implications. The document opens with an overview of the research context – the Observatory on Trends and Applications of Supercomputing – in which this study is situated. Next, the theoretical foundations of the study are presented. Drawing from the field of Future Studies, this section clarifies the distinctions between forecasting, foresight, and anticipation, and explains the principles and objectives of strategic foresight. The methodology section follows, detailing the diverse techniques employed to achieve the project’s objectives. The research, that involved a panel of visionary experts, combined horizon scanning with scenario planning, allowing for a comprehensive exploration of trends, uncertainties, and transformative drivers that could shape the future competitive landscape. At the heart of the report are four original future scenarios. Each scenario is carefully crafted and analyzed, highlighting the opportunities and threats it presents. These scenarios offer alternative visions of how medium-to-large Italian companies might evolve, emphasizing the variability and uncertainty of the future while providing actionable insights for decision-makers and other stakeholders. Lastly, strategic insights and surprising or overlooked patterns emerged during the study are included in the conclusive section, pointing out interesting suggestions and ideas that could be further developed. This research is not just an exploration of potential futures—it is a guide for strategic thinking, innovation, and preparedness, equipping Italian companies and innovation ecosystems to navigate the challenges and opportunities of the AI-driven economy of tomorrow.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".