AI-Driven Competitive Intelligence in Decision-Making
Notice bibliographique
Résumé
This issue brings together the authors’ research, which reveals important and unique themes in the context of understanding competitive environment, which resonate with modern scientific development trends and the dynamics of change. Scientific research offers an in-depth look at current challenges that shape future research directions.Traditional data analysis, which used to be based on human intuition and limited data interpretation, has been transformed today with the introduction of artificial intelligence and machine learning technologies. In today’s business environment characterized by rapid change and high uncertainty, the integration of artificial intelligence (AI) and sound strategies is an essential prerequisite for competitiveness. Predictive analytics is one of the main areas of application of artificial intelligence in data analysis, when organizations face a high level of complexity, determined by dynamic environmental changes and affecting the strategic decisions of organizations.AI-driven competitive intelligence uses advanced algorithms and predictive analytics tools that can identify market trends and competitive strategies. AI-powered predictive models are particularly effective at identifying hidden patterns in complex data sets, providing companies with insights that would be difficult to uncover using traditional methods (Basu, Aktar & Kumar, 2024). Organizations are increasingly incorporating algorithmic and analytical tools into their operations and strategies, using them not only for automation, but also for generating insights and ensuring strategic alignment. Such an approach significantly improves the quality of decision-making, providing organizations with flexibility and the ability to adapt to a dynamic environment in a timely manner (Smyth C. et al., 2024). In turn, the challenge for organizations is to ensure that the information generated by these systems is transformed into organizational knowledge and practical value that has long-term benefits.Another major trend is the rise of social media as a source of knowledge. Once considered secondary, social platforms have become essential for gaining real-time insights into markets, the success of competitors and partners, and consumer opinions. By effectively integrating these unstructured data streams, organizations can significantly improve their situational awareness and resilience in a rapidly changing environment, as they are able to identify both direct and indirect trends, namely, obvious changes in the market and hidden, contextual relationships that affect consumer behavior, competitor strategies, and market dynamics. However, this approach increases the need for data validation, critical analysis, and high-level analytical skills to effectively manage the diversity of digital information and ensure its accurate interpretation. Consequently, organizations simultaneously face a significant challenge, as the growth in the volume and diversity of such data significantly increases the need for careful data validation, critical analysis, and high-level analytical skills.To fully exploit the potential of AI and big data, interdisciplinary skills are required - the ability to combine technological understanding with strategic thinking and critical evaluation of information. Such an approach ensures that the use of data not only improves organizational efficiency, but also promotes sustainable decision-making based on reliable and interpretable data.We would like to express our gratitude to the authors for their analytical perspectives, sharing of research results and contribution to the creation of this publication.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,014 |
| Communication savante | 0,017 | 0,011 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».