Redefining Organizational Resilience and Success: A Natural Language Analysis of Strategic Domains, Semantics, and AI Opportunities

Abstract

Organizational resilience and long-term success have become essential capabilities in turbulent and uncertain environments. This study redefines these concepts by applying a natural language analysis to a corpus of 1597 peer-reviewed publications retrieved from Web of Science and Scopus. The methodology adopts a three-level framework: first, a thematic clustering of literature into strategic domains; second, a semantic comparison of classical and emerging terms; and third, the mapping of artificial intelligence (AI) opportunities. The results identify five overarching domains: Health and Wellbeing; Organizations, HR and Leadership; Strategy, Innovation, and Culture; Education, Knowledge and Communities; and Society, Environment and Development. These domains illustrate how resilience and success are addressed at micro, meso, and macro levels. Semantically, the discourse expands from traditional notions such as robustness, risk management, and performance towards more human-centered, systemic, and digitally enabled perspectives. The study further highlights how AI functions both as a methodological tool and as a strategic enabler, with applications ranging from predictive health analytics and leadership support systems to foresight tools and sustainability monitoring. The findings contribute to organizational resilience theory and offer practitioners actionable pathways to strengthen resilience and competitiveness in the face of volatility, uncertainty, complexity, and ambiguity.

1. Introduction

Management and organizational studies have long been preoccupied with the issues of organizational resilience and long-term success. Generally, resilience is defined as the capacity to anticipate, withstand, adapt to, and recover from shocks [1], whereas success is frequently defined by long-term viability, competitiveness, and performance [1,2]. Although these research streams have evolved concurrently, their vocabularies and evaluative criteria only partially overlap [3]. Recent disruptions, including supply chain shocks [4], global health crises [5], and rapid digitalization [6], have reignited interest in the definition and operationalization of resilience and success. This has led to inquiries regarding the feasibility of classical structures in today’s dynamic environments [3].

Resilience and success continue to be the subject of ongoing discussion. According to certain studies, resilience is a necessary condition for long-term success and sustained competitiveness [7]. Occasionally, potential tensions are identified, such as an “efficiency paradox,” which is a situation in which an organization’s adaptive capacity is compromised by efficiency-driven optimization [8]. As a dynamic source of competitive advantage, innovation, and renewal, emerging work positions resilience not only as a safeguard against disruption but also as a source of renewal [1]. In existing scholarship, organizational resilience has been interpreted through two complementary but distinct lenses [9]. The first, often termed resilience as safeguard, emphasizes protection, stability, and recovery—the organization’s ability to absorb shocks, preserve core functions, and return to equilibrium after disruption. The second, resilience as renewal, reflects a more dynamic and transformative orientation, focusing on learning, reinvention, and the capacity to emerge stronger after adversity. This study acknowledges both perspectives: resilience as safeguard underpins short-term continuity, while resilience as renewal connects to long-term adaptation and success. Integrating these views allows for a more comprehensive understanding of how resilience contributes to sustainable organizational performance in a volatile environment [10]. These advancements indicate that resilience and success are becoming more closely linked; however, the precise methods by which they are considered in concurrence remain unexplored. From a theoretical standpoint, this comparison contributes to bridging two traditionally separate research streams—resilience as a defensive capability and success as a performance-oriented construct. By examining their co-evolution, the study clarifies how adaptive and learning-based mechanisms connect short-term continuity with long-term competitiveness, thus advancing integrative theories of organizational resilience. The importance of systematic analyses to elucidate the definition, operationalization, and connection of the two conceptions across various streams of literature is emphasized by this gap [3].

Large-scale, systematic analyses of scientific discourse are now feasible due to improvements in artificial intelligence (AI), particularly natural language processing (NLP). In addition to manual synthesis, it is possible to explore extensive repositories of literature to identify emerging terms that either challenge or complement established definitions, trace conceptual shifts over time, and uncover prevalent topics [11]. AI is not only an object of inquiry (e.g., an enabler of organizational resilience and performance) but also a methodological instrument that can reveal the field’s evolution by processing vast amounts of text.

Recent scholarship has started to explore the intersection between AI, organizational resilience, and long-term success. Systematic reviews [12] emphasize that AI can strengthen resilience by enhancing risk management, predictive capabilities, and decision-making under uncertainty, while also noting the challenges of over-reliance on technology. Conceptual frameworks such as RAIPOF [13] have positioned AI as a strategic enabler of adaptive governance, particularly in healthcare organizations. Similarly, reviews of organizational behavior and teamwork [14] highlight how hybrid human–AI decision-making and emotionally intelligent teams can foster antifragile organizational capabilities. On the empirical side, quantitative studies [15] and case-based research [16] confirm that AI adoption positively influences resilience and performance, especially during crises such as the COVID-19 pandemic. Complementary evidence from performance-oriented studies [17] shows that AI adoption consistently improves organizational productivity, efficiency, and competitiveness. More recent frameworks have expanded this connection. For instance, the adaptive resilience loop [18] emphasizes the feedback between sensing and learning phases in AI-supported resilience, while the digital twin resilience [19] approach integrates predictive analytics with real-time organizational adaptation. Similarly, the RAIPOF framework [13] extends adaptive governance by linking AI-enabled foresight to organizational renewal. Together, these perspectives reinforce the timeliness of the present analysis and its contribution to the ongoing redefinition of resilience in the AI era.

While these contributions provide valuable insights, the literature remains fragmented across contexts, methods, and levels of analysis. Most existing studies focus either on specific sectors (e.g., healthcare, services), particular dimensions of resilience (e.g., risk management, financial slack), or conceptual frameworks that are not empirically validated at scale. To address these gaps, this article advances the field by offering a large-scale, systematic, and integrative analysis of how resilience and success are articulated in academic discourse. Specifically, it employs an empirical, three-level analysis to examine the way in which resilience and success are conceptualized and discussed in a diverse array of peer-reviewed publications [7,20]. In the initial level (thematic), the analysis employs data-driven topic modeling to organize the literature into distinct topics. Subsequently, it aggregates these topics into higher-order domains, thereby identifying potential gaps and concentrations of scholarly attention. At the second level (semantic), it analyzes the language employed to define resilient and successful organizations, juxtaposing a lexicon of classical terms with emerging concepts that have recently gained prominence. The third level (applicative) identifies opportunities related to AI that have been discussed in the literature and interprets how these applications relate to success and resilience in the identified domains.

The methodology integrates unsupervised topic modeling, semantic analysis, and hierarchical aggregation to achieve a balance between granularity (the identification of distinct topics) and interpretability (the synthesizing of topics into broader domains) [21,22]. Transformer-based document embeddings are employed to extract 20 topics into five integrative domains in the investigation. Consequently, the semantic layer quantifies the presence of classical terms and identifies emerging terms that are reshaping the discourse on resilience and success. Lastly, a list and context of AI-related opportunities are provided in the literature, spanning from the macro to the micro levels.

Three directing research questions (RQ) are thus addressed:

• RQ1 (thematic): How does the literature on resilient and successful organizations cluster into broader domains, and what are the dominant topics that make up this literature?
• RQ2 (semantic): Which classical terms are intrinsic to the definitions of organizational resilience and success, and which emerging terms are influencing these definitions?
• RQ3 (applicative): In which domains are these opportunities distributed, and when and how is AI discussed as an enabler of resilience and success?

The following sections offer a thorough examination of the data and methods employed, present the results of the three-level analysis, and discuss the implications for both theory and practice. The discussion and conclusions also encompass limitations and recommendations for future research.

2. Materials and Methods

2.1. Data Collection

Figure 1 provides a summary of the dataset’s collection and filtering process, while

Table A1. Dataset collection and filtering results.

Source	Initial Record	Final Retained	% of Total
WoS	368	368	23%
Scopus	1329	1229	77%
Total	1697	1597	100%

reports the final distribution of retained records by source (refer to Appendix A).

2.1.1. Databases

ISI Web of Science (WoS) and Scopus were the two primary bibliographic databases employed. WoS was chosen due to its selective indexing of core journals, which guarantees a high level of reliability and quality. Scopus was selected due to its comprehensive coverage of disciplines, particularly in emerging and applied fields. The combination of the two allowed the construction of a corpus that is both selective and inclusive, thereby achieving a balance between depth and breadth.

2.1.2. Search Strategy and Keywords

The searches were conducted in September 2025 and used both American and British spelling variations to target precise expressions of success and resilience in organizational contexts:

• “resilient organization” OR “resilient organisation”
• “successful organization” OR “successful organisation”

The target concepts were explicitly addressed in the publications by applying these searches to the titles, abstracts, and author keywords. The dataset encompassed the entire historical span of the indexed literature, thereby reflecting both early conceptualizations and recent developments, as no temporal restrictions were imposed. This dual-query approach guaranteed that the resilience and success studies were comparable while also optimizing retrieval by incorporating linguistic variants.

2.1.3. Inclusion and Exclusion Criteria

For analytical coherence, the following filters were used:

• Inclusion criteria: English-language publications that are classified as journal articles, reviews, book chapters, or proceedings papers, provided that they at least include a title and abstract.
• Exclusion criteria: publications lacking abstracts, duplicate entries across databases, and non-English records (they were excluded from text mining and clustering but retained for descriptive bibliometric statistics).

Through our emphasis on these document types, we achieved a balance between empirical contributions (articles, proceedings papers) and synthetic or conceptual works (reviews, book chapters), both of which are pertinent to the comprehension of resilience and success.

2.1.4. Final Dataset

The initial combined searches yielded nearly 1700 records across the two databases. After deduplication and language filtering, the final dataset comprised 1597 distinct English-language publications. About 90% of these were abstracts, which were the main data for semantic and clustering analyses.

The dataset demonstrates a well-balanced mix of document types, with the majority being journal articles, supported by a substantial number of reviews, and a smaller but still significant number of book chapters and conference proceedings papers. The field’s ongoing development through empirical studies and conference outputs, as well as its maturity, are both reflected in this distribution. This combined dataset establishes a strong foundation for the subsequent semantic and bibliometric analyses.

To contextualize the corpus, Figure 2, Figure 3 and Figure 4 illustrate the publication trends over time. Figure 2 presents the total yearly output from Scopus and Web of Science, together with the individual series. The overall field expands slowly until the early 2000s, followed by steady growth and a marked acceleration after 2010. Figure 3 and Figure 4 separate the two query streams—“resilient organization(s)” and “successful organization(s)”—to highlight their distinct yet convergent trajectories. Research on resilient organizations (Figure 3) emerges in the mid-1990s and increases sharply after 2010, mirroring the transition from defensive to adaptive and AI-enabled understandings of resilience discussed in Section 4.1. In turn, studies on successful organizations (Figure 4) appear earlier but exhibit a similar exponential rise after 2010, consistent with the conceptual shift toward human-centered, processual, and digitally enabled perspectives. Both series display minor peaks around 2015–2020, followed by slight fluctuations, suggesting a phase of thematic diversification and consolidation rather than decline.

2.2. Data Processing

To convert raw bibliographic data (titles and abstracts) into structured semantic knowledge, the analytical pipeline was developed. It integrated domain-level aggregation through agglomerative clustering, topic modeling with BERTopic, and preprocessing [23,24]. By employing this multi-step methodology, the analysis was able to capture both thematic patterns and conceptual variations that were more intricate.

2.2.1. Preprocessing Techniques

The initial stage entailed the preparation of the textual data for semantic modeling [25]. All titles and abstracts were combined into a single piece of text, which made the most of the background information for each document. Standard NLP operations were implemented within the PyCharm2025.2.4 integrated development environment (IDE) by employing the spaCy library (version en_core_web_sm, Python 3.10):

• Normalization and lowercasing to eliminate orthographic inconsistencies.
• Stopword removal, filtering out functional words (e.g., the, and, of) that carry limited semantic value.
• Lemmatization, reducing words to their canonical form (e.g., organizations → organization), which helps unify lexical variants.
• Token filtering, excluding punctuation, numbers, and non-alphabetic strings.

These modifications (illustrated in Figure 5) enhanced the dataset’s quality and adhered to the established NLP conventions for semantic modeling. As an outcome, each document was represented as a lemmatized, clean sequence of content words that were prepared for embedding.

2.2.2. Topic Modeling with BERT

The second stage extracted latent themes from the corpus using BERTopic [26]. Unlike classical probabilistic models such as Latent Dirichlet Allocation (LDA) [27], BERTopic integrates transformer-based embeddings with clustering, making it particularly suitable for heterogeneous and multidisciplinary datasets.

The Sentence-BERT model (all-MiniLM-L6-v2) was employed to encode each text, resulting in dense semantic representations. UMAP was used to project the embeddings into a five-dimensional semantic space, which compresses global distances while maintaining local neighborhood structure. The reduced embeddings were then subjected to topic extraction, which produced 21 coherent topics (20 substantive topics and one noise cluster). Each subject was identified by its most representative terms, which served as the foundation for subsequent aggregation. Internal validation was performed to confirm that the five-dimensional reduction offered the best trade-off between topic cohesion and separation. Comparative tests with three- and ten-dimensional embeddings indicated lower interpretability, supporting the selected configuration.

2.2.3. Agglomerative Clustering Methodology

Agglomerative hierarchical clustering [28] was used to organize the 21 topics into more general thematic domains in order to transcend individual topics. The clustering was conducted using Ward’s linkage criterion [29], which minimizes within-cluster variance, and Euclidean distance as a similarity metric.

The number of clusters was determined through internal validity assessment, primarily based on the Silhouette coefficient, which evaluates both cohesion (compactness within clusters) and separation (distinctiveness across clusters) [28]. After careful consideration, the five-cluster configuration was chosen as the preferred solution due to its ability to strike a balance between interpretability and statistical quality. This solution guaranteed that the final domains were conceptually meaningful and methodologically validated, thereby facilitating the conceptual mapping of organizational success and resilience across the literature. Topic labels were then assigned through inspection of top keywords and representative abstracts, refined through expert review to ensure conceptual clarity and domain consistency.

2.3. Semantic Analysis

The semantic analysis was designed to complement topic discovery by examining the vocabulary through which resilience and organizational success are described. This stage was implemented in three consecutive steps: (i) identification of classical terms, (ii) extraction of emerging terms, and (iii) validation of semantic categories. Figure 6 displays examples of these three steps in a structured way. It shows how representative terms were processed, analyzed, and interpreted. Analyses were conducted on the preprocessed corpus of titles and abstracts, with the exception of Topic 0 (miscellaneous/noise).

2.3.1. Identification of Classical Resilience and Success Terms

The first step focused on identifying the established vocabulary historically associated with organizational resilience and success. To achieve this objective, a lexicon of classical terms was compiled from prior conceptual and review studies. This lexicon encompasses resilience-specific notions (e.g., resilience, robustness, redundancy), success-oriented notions (e.g., performance, growth, profitability), and attributes that are typically anticipated to bridge both categories (e.g., efficiency, agility, flexibility, sustainability).

Rather than relying solely on theoretical assumptions, these terms were systematically validated against the corpus. Through string-matching procedures that included normalization for multi-word variants and orthographic differences, the frequency of each candidate term was quantified in abstracts and titles. In addition, embedding-based similarity scores (Sentence-BERT, cosine similarity) [29] were implemented to verify that these terms were consistently present in semantically coherent contexts within the corpus.

This dual methodology guaranteed that the classical vocabulary was empirically grounded in the dataset and derived from the literature. The resultant collection of validated terms served as a foundation against which emerging concepts could be compared in the future.

2.3.2. Extraction of Emerging Terms

To identify prominent unigrams and bigrams across the global corpus and within each of the aggregated domains, a statistical term-weighting approach was employed, utilizing Term Frequency–Inverse Document Frequency (TF-IDF) [30]. The classical lexicon and generic background words (e.g., organization, management, study) were excluded from the list of candidate terms.

Then, the terms were lemmatized, duplicates were merged, and multi-word phrases were kept if their weight showed that they had important conceptual meaning. This process enabled the identification of emerging vocabulary that expands or reframes the established lexicon, for example, emphasizing dimensions such as human-centeredness, knowledge, digitalization, or systemic processes.

2.3.3. Validation of Semantic Categories

The coherence of emerging terms was verified through the application of two complementary procedures. Initially, a co-occurrence analysis was conducted to determine whether emerging terms were consistently located near classical terms within each domain, thereby guaranteeing conceptual relevance [31]. Secondly, a validation based on embedding was performed by encoding both classical and emerging terms with Sentence-BERT embeddings and calculating cosine similarity [29,32]. Terms that exceeded a similarity threshold were assigned to conceptual categories that were related to them (e.g., knowledge → learning capacity, process → adaptability).

Depending on their semantic alignment, emerging terms were classified as oriented toward resilience, success, or both as a result of these validations. The Section 3 provides a detailed analysis of these orientations, as well as their frequency distributions.

3. Results

3.1. Level I—Thematic Analysis: Topics and Clusters

3.1.1. Topic Modeling Results

The BERT-based topic modeling produced a set of 20 coherent topics that collectively map the intellectual landscape of resilience and organizational success research. An additional topic (Topic 0), referred to as the noise cluster, was generated by the model but was excluded from the analysis, as it aggregated generic terms without a clear thematic identity. This group contained documents that could not be semantically assigned to a consistent topic due to low contextual similarity scores. Such clusters are common in transformer-based topic modeling and typically capture texts with weak thematic signals, ambiguous terminology, or heterogeneous scopes that differ from the main analytical focus.

In this study, the noise cluster included approximately 12% of the total records and consisted primarily of abstracts with insufficient organizational framing. Representative examples involved papers on ecological resilience in coral reef ecosystems, psychological coping and mental health recovery after trauma, or household-level disaster preparedness. Although these studies use the concept of resilience, they do so in contexts unrelated to organizational management, performance, or strategic success. As a result, their inclusion would have diluted the conceptual boundaries of the analysis and introduced semantic noise into clustering and co-occurrence metrics.

While the algorithm assigns documents to topics based on semantic similarity, the interpretative step enables us to attach meaningful labels and understand their substantive content.

Table 1. Narrative description of the 20 topics identified through BERT-based topic modeling.

Topic ID	Suggested Label	Narrative Description
1	Healthcare and Patient Care	Resilience in healthcare systems, with focus on patient safety, medical treatment, and hospital performance.
2	Workforce Diversity and Inclusion	Organizational success linked to diversity, inclusion, and workforce adaptability.
3	Risk and Safety Management	Classical resilience perspective centered on risk prevention, safety procedures, and hazard control.
4	Organizational Culture and Entrepreneurship	Role of cultural values and entrepreneurial orientation in fostering innovation and adaptability.
5	Leadership and Governance	Leadership styles and governance models as central mechanisms for building resilience and success.
6	Business Processes and Customer Orientation	Adaptive business processes and customer-focused strategies as enablers of long-term competitiveness.
7	Human Resource Management	HR practices, workforce policies, and talent development as contributors to organizational resilience.
8	Quality and Operational Excellence	Lean practices, supply chain reliability, and continuous improvement as resilience mechanisms.
9	Sustainability and Stakeholder Orientation	Integration of sustainability and stakeholder engagement into organizational strategies.
10	Innovation and Ambidexterity	Balancing exploration and exploitation for sustained innovation and success.
11	Strategic Management and Planning	Long-term strategic vision, planning, and growth alignment as resilience enablers.
12	Networks and Community Collaboration	Role of partnerships, collaboration, and networks in strengthening resilience.
13	Employee Engagement and Psychological Resilience	Coping, job satisfaction, and engagement strategies that sustain individual and organizational resilience.
14	Sport, Events, and Community Resilience	Community resilience through social events, sports, and urban activities that foster cohesion.
15	Knowledge and Universities	The role of universities, research projects, and institutional knowledge in resilience building.
16	Knowledge Management and Learning	Knowledge sharing, transfer, and organizational learning as sources of adaptability.
17	Education and Training	Educational systems, teachers, and training practices as foundations of resilience.
18	Change Management and Adaptability	Organizational change processes and adaptive capacity during transitions.
19	Creativity and Culture	Creativity, motivation, and organizational culture as drivers of innovation and success.
20	Agricultural and Environmental Resilience	Rural, agricultural, and environmental factors as dimensions of resilience and sustainability.

summarizes these topics in narrative form, highlighting their main orientation and conceptual contribution. A more detailed account—including the full list of representative keywords and the exact number of articles associated with each topic—is provided in

Table A2. Detailed distribution of topics, with article counts and top representative keywords.

Topic ID	No. of Articles	Top Keywords (Representative)
0	199	resilience, resilient, organizational, organization, crisis, build, study, research, business, management
1	139	care, health, patient, medical, healthcare, hospital, community, successful, physician
2	130	organization, employee, diversity, workforce, work, inclusion, women, study
3	110	risk, safety, resilience, management, security, system, approach, information
4	87	culture, entrepreneur, organizational, business, entrepreneurial, success, firm, company
5	84	leadership, leader, organization, successful, change, behavior, study
6	83	customer, business, process, service, quality, system, information, management
7	83	human, resource, HR, management, employee, performance, manager, practice
8	73	lean, quality, supply, maintenance, system, chain, product, manufacturing
9	64	sustainability, stakeholder, sustainable, digital, transformation, new, military
10	63	innovation, process, ambidexterity, new, technology, product, firm, industry
11	61	strategy, management, strategic, planning, business, performance, environment, competitive
12	58	network, community, social, organization, group, action, cooperative, virtual
13	58	employee, work, job, engagement, satisfaction, motivation, relationship, performance
14	54	sport, event, city, destination, tourism, planning, urban, exhibition
15	53	library, project, university, knowledge, program, information, communication, technical, student
16	52	knowledge, management, transfer, organization, KM, intellectual, capital, OLC
17	47	student, teacher, education, educational, school, pedagogical, learning, principal
18	46	change, organization, adaptability, transformation, transition, scheme, implementation
19	32	creativity, creative, reward, motivation, organizational culture, idea, market orientation
20	21	water, farmer, agricultural, rural, development, research, supradisciplinary

(Appendix A), ensuring transparency and replicability of the results.

The 20 topics were grouped into higher-order categories using agglomerative hierarchical clustering to capture conceptual structures that go beyond individual topics. The resulting dendrogram, depicted in Figure 7, illustrates the iterative merging of topics into five overarching domains.

3.1.2. Cluster Analysis Across Five Domains

The semantic proximity of topics guided the aggregation into five higher-order clusters, which represent broader domains of research on organizational success and resilience.

Figure 8 illustrates the keyword co-occurrence network, where terms from the 20 topics are visualized according to their semantic associations and color-coded by domain. This representation highlights how sector-specific terms (e.g., patient, hospital, care in health; leadership, governance in HR; innovation, ambidexterity in strategy) are embedded in larger thematic structures. The network also reveals bridging terms that link domains, such as knowledge and process.

Table 2. Mapping of the 20 topics into five overarching domains.

Domain	Associated Topic IDs
I—Health and Wellbeing	Topics 1, 20
II—Organizations, HR and Leadership	Topics 2, 9, 18
III—Strategy, Innovation and Culture	Topics 4, 10, 11, 19
IV—Education, Knowledge and Communities	Topics 12, 14, 17
V—Society, Environment and Development	Topics 3, 5, 6, 7, 8, 13, 15, 16

presents the mapping of the 20 topics into the five overarching domains. For transparency, detailed statistics on the number of articles and AI-related contributions per domain are reported in

Table A3. Detailed distribution of articles across the five domains.

Domain	No. of Articles	AI-related Articles	AI Share (%)
I—Health and Wellbeing	197	11	5.58
II—Organizations, HR and Leadership	395	31	7.85
III—Strategy, Innovation and Culture	541	63	11.65
IV—Education, Knowledge and Communities	190	15	7.89
V—Society, Environment and Development	75	9	12

(Appendix A).

Health and wellbeing, the initial domain, encompasses subjects such as patient care, occupational health, employee wellbeing, and healthcare systems. At the organizational and societal levels, it highlights the importance of health and safety as the cornerstones of resilience. In a total of 197 articles, 11 (5.58%) pertain to AI applications, including digital wellbeing tools and predictive health analytics. Within this domain, Topics 1 and 20 capture complementary aspects of resilience in healthcare settings. Studies in Topic 1 address human-centered and organizational learning dimensions, showing how hospitals and public health institutions strengthen adaptive capacity through crisis management and knowledge-based improvement [33,34]. Conversely, Topic 20 emphasizes the technological and systemic side of resilience, where AI and robotics enhance foresight, coordination, and operational continuity during crises such as the COVID-19 pandemic [35,36]. Together, these findings illustrate how health-related resilience integrates preventive preparedness with digitally enabled adaptation, reinforcing both human and technological foundations of sustainable healthcare systems.

With 395 articles, the second domain, Organizations, HR, and Leadership, incorporates themes of workforce diversity, organizational adaptability, leadership, and change management. While AI-related contributions (31 articles, 7.85%) concentrate on HR analytics, talent management, and AI-driven leadership support systems, leadership, and HR practices are continuously portrayed as facilitators of resilience and long-term success. Within this domain, Topics 2, 9, and 18 capture distinct but interconnected organizational dimensions. Topic 2 emphasizes workforce development and healthy leadership, addressing how organizations can foster employee wellbeing and resilience through supportive environments and adaptive management practices [37,38]. Topic 9 focuses on organizational adaptability under uncertainty, illustrating coping strategies and digital transformation as mechanisms for navigating volatile, uncertain, complex, and ambiguous (VUCA) contexts [39,40]. Topic 18 centers on organizational transformation and planned change, demonstrating how innovation, design thinking, and structured change processes contribute to sustainable organizational success [41,42,43]. Collectively, these contributions highlight the central role of human-centered leadership and adaptive capability as foundational drivers of resilient organizations.

The third domain, Strategy, Innovation, and Culture, is the largest cluster, with 541 articles. It underscores the importance of strategic foresight, innovation, ambidexterity, and organizational culture, particularly in the context of how organizations balance exploration and exploitation to ensure resilience and competitiveness. A significant proportion of the studies (63 articles, 11.65%) incorporate AI, particularly in predictive strategy, data-driven innovation, and AI-supported cultural transformation. Within this domain, Topics 4, 10, 11, and 19 reflect complementary dimensions of strategic resilience. Topic 4 focuses on the ethical and cultural foundations of organizational success, showing how emotionally intelligent leadership and shared values contribute to sustainable performance [44,45]. Topic 10 emphasizes ambidextrous innovation practices and the cultivation of innovation ecosystems that balance stability with adaptability [46,47]. Topic 11 advances a strategic management perspective by linking organizational control, environmental awareness, and learning mechanisms that enable long-term alignment between vision and performance [48,49]. Finally, Topic 19 explores creativity and communication as essential drivers of innovation and renewal, demonstrating how collaborative leadership and open idea exchange sustain adaptability in dynamic contexts [50,51]. Together, these perspectives highlight that organizational resilience and success are deeply rooted in the integration of cultural cohesion, strategic agility, and creative capacity.

The fourth domain, Education, Knowledge, and Communities, comprises 190 articles that address community resilience, education, training, and knowledge management. It highlights the role of learning systems, networks, and collaboration in building adaptive capacity. AI-related research (15 articles, 7.89%) is gaining strength, with potential applications in community monitoring, knowledge extraction, and adaptive learning platforms. Within this domain, Topic 12 centers on the role of knowledge networks and digital collaboration in organizational resilience, with studies highlighting how open-source communities and social network analysis facilitate information exchange and collective problem solving in complex systems [52,53]. Topic 14 extends this discussion to the community level, showing how participatory frameworks and civic engagement—particularly in the context of large-scale events—support inclusive development and enhance social capital [54,55]. Finally, Topic 17 focuses on the educational dimension, examining how digital pedagogy, teamwork, and context-sensitive leadership foster learning continuity and institutional adaptability during disruption [56,57]. Collectively, these perspectives demonstrate that education and collaboration are not only enablers of knowledge diffusion but also essential foundations of resilient, learning-oriented organizations and communities.

Ultimately, the fifth domain, Society, Environment, and Development, has the highest AI share (12.00%) despite having the smallest volume (75 articles). It emphasizes financial resilience, social responsibility, environmental adaptation, and sustainability. AI-related studies (9 articles) in this cluster address sustainable development monitoring, environmental modeling, and AI-enabled risk assessment. Topic 3 focuses on risk and safety, highlighting proactive risk management and the protection of critical infrastructures as foundations of organizational preparedness [58,59]. Topic 5 examines leadership and governance under uncertainty, illustrating how mindfulness, ethical decision-making, and humility strengthen collective resilience and integrity [60,61]. Topic 6 explores business processes and customer orientation, emphasizing BPM maturity and customer journey design as drivers of sustainable performance and stakeholder trust [62,63]. Topic 7 centers on human resource transformation, addressing how digital HR tools, BI, and AI applications enhance workforce agility and decision-making [64,65]. Topic 8 advances operational excellence, showing how total productive maintenance and IT service management frameworks support reliability and continuous improvement [66,67]. Topic 13 reflects the human and behavioral side of resilience, where leader–member exchange, communication, and CSR-driven identification foster employee engagement and organizational wellbeing [68,69]. Topic 15 illustrates the growing role of knowledge-based collaboration through crowdsourcing and data-driven teamwork as mechanisms for informed decision-making and innovation [70,71]. Finally, Topic 16 addresses knowledge management and learning, identifying critical success factors of KM systems and their contribution to competitive advantage and adaptive capacity [72,73]. Collectively, these contributions depict how societal resilience integrates governance, innovation, human capital, and knowledge systems—supported increasingly by AI technologies—toward sustainable and adaptive development.

Cluster analysis shows that resilience and organizational success are examined at three different levels: macro (society, sustainability, environment), meso-level (organizations, processes, strategy), and micro-level (health, employees, leadership). AI is found in all five domains, but its adoption is not uniform. It is most prevalent in societal sustainability (Domain V) and strategic innovation and culture (Domain III), while it is still in its infancy in the areas of health and wellbeing (Domain I) and education and communities (Domain IV). According to this distribution, AI is both bringing together traditional fields and creating new opportunities for resilience studies.

3.2. Level II—Semantic Analysis: Classical and Emerging Terms

Moving from the broad thematic level to the semantic level, it is important to look at not only where resilience and success are studied but also how they are talked about and defined. This part compares the traditional words that have been linked to resilience and success with new ideas that change and expand on them.

3.2.1. Classical Terms

The semantic analysis suggests that the corpus maintains a clear distinction between terms that are primarily associated with resilience and those that are more closely tied to organizational success, while also illustrating a set of attributes that operate across both orientations.

Figure 9 illustrates that resilience-specific vocabulary underscores the ability to endure shocks and recover. This vocabulary includes terms such as resilience (197 occurrences), risk management (61), robustness (8), vulnerability (31), and less frequently, adaptive capacity, absorptive capacity, redundancy, and recovery capacity. In contrast, success-specific terminology emphasizes long-term viability and competitiveness. Performance (314) is the most frequently used concept, followed by growth (94), competitiveness (38), profitability (28), and additional concepts such as value creation, sustainable success, market share, customer orientation, and strategic vision.

A smaller number of terms emerge as shared characteristics between these two poles: sustainability (including sustainable success, 10 occurrences), agility (41), flexibility (35), and efficiency (44). These characteristics serve as conceptual bridges, as they are pertinent to both the performance orientation of success and the survival orientation of resilience. Their contextualized meanings are summarized in

Table 3. Definitions of shared classical terms in resilient and successful organizations.

Characteristics	In Resilient Organizations	In Successful Organizations
Efficiency	Capacity to maintain functionality with minimal resource loss during crises.	Optimization of resources and processes to achieve superior performance.
Agility	Rapid operational response to unexpected disruptions.	Speed in seizing opportunities and competitive advantages.
Flexibility	Ability to adjust structures and processes to absorb shocks.	Capacity to reconfigure operations to capture market shifts.
Sustainability	Ensuring resilience through long-term balance of resources and ecological systems.	Integrating social, environmental, and economic goals into business strategy.

.

The complete frequency counts for all identified classical terms are provided in

Table A4. Frequency of classical terms in the corpus.

Term	Frequency (n)
Performance	314
Resilience	197
Growth	94
Risk management	61
Efficiency	44
Agility	41
Competitiveness	38
Flexibility	35
Vulnerability	31
Profitability	28
Value creation	12
Sustainable success	10
Robustness	8
Market share	4
Adaptive capacity	3
Absorptive capacity	2
Customer orientation	2
Redundancy	2
Strategic vision	1
Recovery capacity	1

(Appendix A), offering transparency regarding their distribution across the corpus.

3.2.2. Emerging Terms—Global Perspective

Additionally, the analysis identified a series of emerging concepts that reframe the discussion of resilience and success in organizations, in addition to the persistence of classical terms. In contrast to the classical vocabulary, which prioritized robustness, performance, and competitiveness, these new terms indicate dimensions that are more dynamic, human-centered, knowledge-based, and digitally enabled.

Four concepts are particularly prominent at the global level: knowledge, work and employees, data and information, and processes. A fifth concept that is becoming more prominent is development, which is a more expansive version of the traditional idea of growth.

• Work and employees align more closely with resilience, emphasizing human protection and wellbeing.
• Data and information and processes are tied to organizational success, as they enable competitiveness and long-term efficiency.
• Knowledge and development emerge as bridging terms: knowledge supports adaptive capacity (resilience) and continuous improvement (success), while development extends growth by linking organizational progress to sustainability.

These orientations and contributions are summarized in

Table 4. Emerging terms, their orientation, and interpretive contribution.

Emerging Term	Relations to Classical Concepts	Orientation	Interpretive Contribution
Knowledge	Extends learning capacity	Common	Knowledge as an asset and enabler of adaptive capacity
Work and Employees	Absent from classical terms	Resilience	Human-centered resilience and success
Data and Information	Absent from classical terms	Success	Digitalization, AI, and evidence-based decision-making
Processes	Implicit in adaptability	Success	Continuous systemic adaptation and innovation
Development	Broader than growth	Common	Long-term, sustainable, and societal orientation

.

To further illustrate the relationship between classical and emerging terms, Figure 10 extends the Venn diagram presented earlier by incorporating the newly identified concepts. The diagram highlights two key insights. First, the persistence of classical terms confirms the stability of the established conceptual core in the resilience–success literature. Second, the placement of emerging terms in the shared or domain-specific zones demonstrates how the field is evolving: resilience is increasingly linked to human-centered concepts (work and employees), success is being reframed through digital and systemic enablers (data, information, processes), and the bridging zone is enriched by knowledge and development, which connect adaptive capacity with sustainable progress.

Additionally, comparative validation was conducted to ensure the robustness of the emerging vocabulary. The semantic proximity between classical and emerging terms was measured through cosine similarity diagnostics, confirming that new expressions such as digitalization, knowledge, and development maintain conceptual coherence with the established constructs of resilience and success. This analysis was complemented by frequency trend inspection (Figure 11), which illustrates the temporal shift from traditional notions of risk and performance toward learning, innovation, and digital transformation after 2010. The figure shows the increasing prominence of human-centered and digitally enabled vocabulary, indicating a conceptual transition from defensive to adaptive and processual perspectives of organizational resilience and success.

3.2.3. Emerging Terms—Domain-Level Distributions

We investigated the distribution of emerging terms across the five domains that were previously identified to capture the contextual specificity of the context. The study finds that resilience and success are not rethought using the same words, but rather words that become more important in certain situations. This contextualization demonstrates how resilience and success are anchored in domain-specific priorities, thereby complementing classical concepts.

Table 5. Orientation of emerging AI-related terms toward resilience, success, or both.

Domain	AI-Related Opportunities	Orientation
I—Health and Wellbeing	employees, health, care, work	Resilience
II—Organizations, HR, and Leadership	leadership, knowledge, leaders	Both
	humans, change	Resilience
III—Strategy, Innovation and Culture	innovation, quality	Success
	process	Both
IV—Education, Knowledge, and Communities	social, communication, students	Resilience
	knowledge, development	Both
V—Society, Environment, and Development	events, sport, water	Resilience
	development	Both

summarizes the orientations of emerging terms—whether they align primarily with resilience, success, or both—across the five domains.

In Domain I (Health and Wellbeing), the terms employees, health, care, and work indicate a significant emphasis on the human aspect of resilience. Resilience is not solely defined by organizational robustness; rather, it is reflected in the context of patient care, workforce, wellbeing, and occupational health. The prominence of employees underscores that organizational survival depends directly on protecting, supporting, and empowering individuals [74].

The vocabulary in Domain II (Organizations, HR, and Leadership) emphasizes leadership, knowledge, change, humans, and leaders. These phrases underscore the dual function of leadership as a stabilizing and transformational force. Humans and change are unmistakably signs of resilience, but knowledge and leadership serve as links between adaptability (resilience) and long-term strategic orientation (success) [75].

Innovation, process, and quality are the focal points of the emerging vocabulary in Domain III (Strategy, Innovation, and Culture). In this context, the emphasis is on success, with innovation and quality being associated with competitiveness and excellence [76]. The process, however, emphasizes resilience as a dynamic capability that is ingrained in routines, demonstrating that even success-oriented terms can maintain adaptive capacity.

Defining resilience as a collective and networked phenomenon, Domain IV (Education, Knowledge, and Communities) employs terms such as communication, students, knowledge, and development. Collaboration and community ties are what make people resilient. Development, on the other hand, connects resilience and success by connecting adaptive learning to bigger progress [77].

Finally, Domain V (Society, Environment, and Development) is distinguished by water, development, sports, and events. These terms reflect the societal and ecological scale of resilience. While water symbolizes environmental adaptation [78], events and sports indicate social cohesiveness and recovery [79]. Once more, development emerges as a concept that bridges the gap between resilience and sustainability and long-term societal success.

Together, the distribution of emerging terms across domains illustrates that resilience and success are contextually constructed. Human capital and resilience are prioritized in health and wellbeing; HR and leadership emphasize both adaptive change and success-oriented leadership; strategy and innovation emphasize systemic processes and competitiveness; education and communities emphasize collective resilience and knowledge; and society directly connects resilience to sustainability and environmental adaptation. This domain-level specificity demonstrates how the vocabulary of success and resilience changes in response to the demands and priorities of various domains.

3.3. Level III—Applicative Analysis: AI Opportunities

The analysis of AI-related contributions indicates that AI has had a varying degree of influence on each of the five domains of organizational success and resilience. According to Table A3 (Appendix A), which presents the quantitative distribution of articles pertaining to AI, Domain III—Strategy, Innovation, and Culture—has the largest shares (11.65%), followed by Domain V—Society, Environment, and Development (12.00%), while Domain I—Health and Wellbeing—has the lowest shares (5.58%). These ratios serve as confirmation that AI is unevenly distributed across various sectors, which is indicative of both sectoral priorities and varying degrees of technological preparedness.

Beyond these numbers, the qualitative analysis highlights how AI contributes to reshaping organizational resilience and success.

Table 6. Orientation of AI-related opportunities toward resilience, success, or both.

Domain	AI-Related Opportunities	Orientation
I—Health and Wellbeing	Predictive health analytics; Digital wellbeing monitoring	Resilience
	Smart occupational health tools	Both
II—Organizations, HR, and Leadership	HR analytics; AI-driven recruitment; Talent management	Success
	Leadership support systems	Both
III—Strategy, Innovation and Culture	Predictive strategy; AI for innovation management	Success
	Foresight tools; Cultural analytics	Both
IV—Education, Knowledge, and Communities	Adaptive learning platforms	Both
	AI-based knowledge extraction	Success
	Community resilience monitoring	Resilience
V—Society, Environment, and Development	Sustainability monitoring	Both
	Environmental modeling; AI-based risk and disaster assessment	Resilience

provides an overview of illustrative opportunities identified in each domain, together with their orientation toward resilience, success, or both.

The focus of AI applications in Domain I (Health and Wellbeing) is on digital wellbeing tools and predictive health analytics. By incorporating wellbeing indicators into broader resilience strategies, organizations can anticipate risks to employee health and monitor stress levels. Although relatively less represented in the corpus, these applications have significant transformative potential in bridging occupational health with organizational performance [80].

In Domain II (Organizations, HR, and Leadership), AI is primarily utilized in the management of human capital. As enablers of adaptability and efficiency, AI-driven recruitment and leadership decision-support tools are emerging [81]. AI technologies support leaders in complex decision-making, monitor workforce diversity, and enhance talent identification, thereby aligning directly with classical enablers of organizational success, such as agility and competitiveness.

The opportunities for AI are most noticeable in Domain III (Strategy, Innovation, and Culture). Predictive strategy, foresight tools, and AI-enabled innovation management are prominently featured, emphasizing the manner in which organizations employ data-driven intelligence to balance exploration and exploitation. In addition, cultural analytics are gaining traction as instruments for evaluating organizational culture and adaptability, rendering this sector the most technologically advanced in its integration of AI with resilience and success [82].

In Domain IV (Education, Knowledge, and Communities), AI research emphasizes adaptive learning platforms and AI-driven knowledge extraction. By fostering both individual and group learning, these technologies help communities and organizations develop their capacity for adaptation [83]. Additionally, resilience monitoring shows how AI can help not only the internal functions of an organization but also the larger networks that organizations work in.

Finally, the growing role of AI in tackling global sustainability challenges is reflected in Domain V (Society, Environment, and Development). These include AI-based risk assessment for climate adaptation and disasters, as well as environmental modeling and sustainability monitoring [84,85]. Despite their limited number, these contributions illustrate the potential of AI to enhance organizational resilience on a societal and ecological scale.

The results indicate that AI is more than just a useful tool; it also opens up new ways of thinking about resilience and success. It serves as a bridge between traditional definitions of resilience and emerging challenges and opportunities, with applications that extend from the micro-level (employee wellbeing, HR practices) to the macro-level (sustainability and environmental adaptation).

While these are all positive changes, adding AI to resilience systems brings up important ethical and governance issues. The extensive use of AI in corporate decision-making raises concerns about accountability, transparency, bias, and data privacy. For example, predictive analytics in HR or wellbeing management may inadvertently perpetuate existing inequalities if the training data are unbalanced, while automated foresight systems may obscure human judgment in strategic planning. Therefore, it becomes imperative to guarantee explainability, equity, and human supervision to preserve credibility and confidence in AI-assisted resilience. When it comes to governance, businesses should make sure that AI tools support the human-centered and adaptive principles that make a company truly resilient. This can be achieved by implementing clear ethical guidelines, continuous auditing systems, and cross-functional review processes.

4. Discussion

4.1. Redefining Resilient and Successful Organizations

The findings suggest that the field maintains a consistent conceptual foundation while channeling its attention toward a collection of emerging concepts. Success is still based on the ability to survive and compete (performance, growth, profitability, competitiveness), whereas resilience is still based on absorbing shocks and recovering (resilience, risk management, vulnerability, redundancy), according to the level contrasts and Venn diagrams (Figure 6 and Figure 7). Simultaneously, the terminology of both constructs is transformed by terms that are not prominent in classical frameworks, such as work and employees, data and information, processes, knowledge, and development. In particular, work and employees link resilience to human safety and wellbeing; data, information, and processes link success to digitally enabled efficiency and routine adaptation; and knowledge and development serve as links between adaptive capacity and longer-term advancement and sustained improvement (Table 3 and Table 4).

Together, these trends point to a change in perspective from considering resilience primarily as a defensive trait to considering it as a dynamic, processual ability that develops alongside success. The prevalence of human-centered terms is indicative of the importance of employee health, inclusion, and engagement in the context of organizational survival. Conversely, the emergence of data- and process-oriented vocabulary implies that success is contingent upon continuous reconfiguration rather than static optimization. The bridging role of knowledge and development indicates that organizations sustain performance when learning mechanisms translate shock responses into improved routines and strategic renewal [86]. The prominence of knowledge and development as bridging terms suggests that resilience and success are connected through processes of continuous learning and capability renewal. Conceptually, these terms act as transition mechanisms—linking short-term recovery to long-term advancement—by transforming experiences of disruption into sources of innovation and growth. This implies that organizational resilience is not merely the maintenance of stability but the institutionalization of learning, positioning knowledge development as the theoretical bridge between reactive and generative forms of resilience.

Based on these insights, a new framework is suggested that combines traditional traits with new focus areas into four capability clusters that work together to strengthen each other:

• Sensing and foresight—continuous scanning for vulnerabilities and opportunities; early warning through metrics and signals; supports timely anticipation rather than post hoc reaction [87].
• Absorption and continuity—buffers, redundancy, safety, and quality routines that limit functional loss during disruptions [88]; preserves core services and stakeholder trust.
• Adaptation and reconfiguration—agility, flexibility, and process redesign (often digitally enabled) that shorten cycle times from detection to change [89]; operationalizes resilience as “change-as-routine.”
• Learning and renewal—knowledge creation, transfer, and development that convert incidents and experiments into improved practices and strategic shifts; links short-term stabilization to long-term success [90].

While the use of NLP represents methodological innovation, the core contribution of this study is theoretical. It advances organizational theory by reframing resilience from a reactive safeguard into a dynamic, multi-capability construct that interlinks with success through sensing, absorption, adaptation, and learning.

This framework clarifies how resilience and success interlock: absorption protects continuity, adaptation restores and improves functioning, sensing guides where to adjust, and learning compounds improvements into durable advantage. Additionally, it explains the observed term orientations: human-centered ideas mainly improve understanding and learning; data and process terms mainly allow sensing and adaptation; and bridging terms link all four groups. Ultimately, organizations that are resilient and successful are distinguished by the integration of these capabilities, which are embodied through human wellbeing, information-rich processes, and cumulative learning, rather than by isolated traits.

It should be noted that these conclusions describe general conceptual tendencies within the academic discourse and do not imply uniform adoption across all organizational contexts. The patterns observed in the corpus reflect aggregated research perspectives rather than direct empirical evidence from specific industries. Therefore, while the identified capability clusters represent a holistic conceptual model, their relative emphasis and manifestation may vary depending on organizational type, sectoral dynamics, and technological maturity.

4.2. AI as an Enabler of Organizational Resilience

AI emerges as both a methodological tool and an organizational enabler. It is most noticeable throughout the corpus in the areas of societal sustainability (Domain V) and strategic innovation and culture (Domain III), where the ability to predict and adapt is increased by environmental modeling, predictive analytics, and foresight tools. In domains more traditionally focused on human capital (Health and Wellbeing, HR and Leadership), AI operates as a complement, enhancing monitoring, recruitment, and leadership decision-making rather than replacing human judgment. The pattern suggests that AI reinforces resilience not as a standalone solution but by amplifying the four resilience clusters identified above: sensing (through predictive analytics), absorption (through risk modeling), adaptation (through AI-enabled reconfiguration), and learning (through knowledge extraction and adaptive platforms).

Sector-specific applications further illustrate the diversity of AI-enabled resilience. In healthcare and wellbeing, predictive diagnostics and stress-monitoring systems enhance sensing and continuity capabilities by anticipating workforce or patient risks. In human resources and leadership, AI-driven recruitment analytics and performance feedback loops improve adaptive reconfiguration and continuous learning. Within strategic and cultural domains, AI-powered foresight and innovation management tools facilitate organizational adaptation and renewal. In education and community settings, intelligent learning platforms and early intervention systems foster knowledge transfer and collaborative resilience. Finally, in societal and sustainability domains, environmental monitoring and disaster-assessment models strengthen absorption and foresight at the system level. These examples demonstrate how AI operates as a domain-sensitive enabler, embedding resilience mechanisms within context-specific structures rather than applying a universal formula.

A conceptual integration (

Table 7. Mapping resilience clusters to business domains and AI capabilities.

Resilience Cluster	Health and Wellbeing	HR and Leadership	Strategy and Culture	Education and Communities	Society and Sustainability
Sensing and Foresight	Predictive health analytics	HR analytics	Strategic foresight tools	Adaptive learning metrics	Environmental monitoring
Absorption and Continuity	Digital wellbeing tools	Leadership support systems	Cultural analytics	Community monitoring	AI-based disaster assessment
Adaptation and Reconfiguration	Smart occupational health tools	AI-driven recruitment	Innovation management	Knowledge extraction	Sustainability modeling
Learning and Renewal	Stress/health feedback loops	Talent development	Data-driven cultural learning	Collaborative learning platforms	Societal development analytics

) can be proposed that maps resilience characteristics × business domains × AI capabilities.

This framework illustrates how AI can operationalize resilience capabilities across domains, enabling a multi-level integration of human, organizational, and societal resilience.

4.3. Theoretical and Practical Implications

From a theoretical perspective, this study extends resilience theory by integrating semantic analysis, demonstrating how classical and emerging terms evolve toward a more process-based and AI-enabled understanding. The findings also show that resilience is not solely defensive but increasingly developmental, connecting human, digital, and ecological dimensions in a more holistic framework.

From a practical standpoint, the study provides organizations with a structured map of AI opportunities that are tailored to domain-specific priorities. It suggests that adoption strategies should focus first on augmenting human capacity—such as wellbeing and leadership—while also scaling to strategic foresight and sustainability monitoring. Managers are encouraged to view AI not merely as a tool for efficiency, but as an enabler of resilience that bridges challenges across micro- and macro-levels of organizational life.

The four identified capability clusters could be operationalized as follows. For instance, sensing and foresight can be enhanced by establishing early-warning dashboards and predictive analytics systems. Absorption and continuity can be supported through digital continuity plans and real-time risk monitoring. Adaptation and reconfiguration may be implemented via agile process redesign and AI-assisted decision tools. Finally, learning and renewal can be promoted through knowledge-sharing platforms and cross-functional training initiatives. Together, these practices translate the study’s conceptual framework into actionable strategies for managers seeking to build resilient and adaptive organizations.

In practical terms, these findings can be translated into specific managerial tactics. Organizations can establish resilience dashboards to monitor key vulnerability and performance indicators, implement scenario-based training and simulation exercises to strengthen adaptive responses, and develop cross-functional learning programs to ensure that lessons from disruptions are institutionalized. Furthermore, embedding AI-assisted foresight tools into strategic planning and digital continuity protocols into operational routines can help organizations move from reactive recovery toward proactive resilience-building. These tactics ensure that the conceptual clusters identified in this study are not only theoretical abstractions but actionable levers for organizational transformation.

4.4. Limitations and Future Research

This study also has several limitations. The dataset was restricted to English-language publications indexed in Web of Science and Scopus, which means that gray literature and non-indexed studies were not included.

The reliance on English-language sources also introduces potential linguistic and regional biases. It is common for English-dominant datasets to include too much research from Anglo-American and Western European institutions and not enough from emerging economies or non-English-speaking contexts where resilience and success may be thought of in different ways. Because certain cultural, institutional, or policy nuances are not visible in English-only corpora, this imbalance may restrict the findings’ ability to be applied globally. This issue demands the integration of gray literature, policy documents, and institutional reports, which frequently capture localized understandings and practical applications of organizational resilience, as well as the inclusion of multilingual datasets—such as but not limited to publications in Spanish, Chinese, or French.

Furthermore, the analysis focused on titles and abstracts as the primary textual sources for semantic and topic analysis. While this approach enables large-scale coverage and comparability across disciplines, it inevitably restricts the semantic depth of interpretation. Full-text analyses could capture a richer set of contextual relationships, methodological nuances, and conceptual elaborations that are often absent from abstracts.

However, conducting full-text analyses at this stage would be impractical due to the large size of the corpus (1597 records). Therefore, such an approach is more appropriate for follow-up or domain-specific investigations, in which individual clusters or thematic domains are examined in greater depth. Once the major domains have been identified through large-scale analysis, targeted full-text exploration can be conducted within each cluster—particularly for open-access or institutionally accessible publications—to reveal the detailed mechanisms, contextual factors, and theoretical nuances that shape organizational resilience and success.

Future research can extend this work in numerous directions. First, by incorporating full texts and multilingual corpora, scholars can capture richer and more diverse semantic signals. Second, applying longitudinal topic modeling could reveal how vocabularies of resilience and success evolve. Third, empirical case studies are needed to test how AI-enabled resilience is implemented in real organizational settings, thereby validating the conceptual framework in practice. Finally, integrating complementary methods—such as social network analysis of co-authorship or citation patterns—would provide a broader and more relational perspective of how resilience and success discourses develop across fields.

5. Conclusions

This investigation enhanced comprehension of organizational success and resilience by incorporating thematic, semantic, and applicative analyses. At the thematic level, the literature was categorized into five broad domains: (I) Health and Wellbeing, (II) Organizations, HR and Leadership, (III) Strategy, Innovation and Culture, (IV) Education, Knowledge and Communities, and (V) Society, Environment and Development. These domains reflect the multiple scales at which resilience and success are studied, from individual and organizational levels to broader societal and ecological contexts.

At the semantic level, the results indicate that the field is becoming increasingly enriched by emerging concepts, including knowledge, employees/work, processes, data and information, and development, although classical terms such as resilience, performance, risk management, and competitiveness remain central. These concepts introduce development as a way to bridge the gap between sustainability and competitiveness, reframe resilience as more human-centered and collective, and connect success to digital and systemic enablers.

At the applicative level, the mapping of AI-related opportunities shows that AI is already acting as a catalyst for reconfiguring resilience and success. As evidenced by Domain I’s predictive health analytics and digital wellbeing tools, Domain II’s AI-driven recruitment and leadership support systems, Domain III’s foresight and innovation management, Domain IV’s adaptive learning platforms and knowledge extraction, and Domain V’s sustainability monitoring, environmental modeling, and risk assessment, AI is more than just a tool for operational efficiency; it is a tool that fosters resilience at multiple levels: macro (societal sustainability and ecological adaptation), meso (organizational culture and strategy), and micro (employee health and HR).

The study contributes to the field by dividing resilience and success research into five overlapping domains that can be repurposed on an ongoing basis. In addition, it introduces human-centered, systemic, and digital terms to the classical vocabulary and clarifies the mechanisms of resilience through four capabilities: sensing and foresight, absorption and continuity, adaptation and reconfiguration, and learning and renewal. It also suggests a transparent and reproducible NLP pipeline that can be extended to longitudinal and multilingual analyses.

In summary, the findings indicate that the language of resilience and success is transitioning to a process-based, digital, and sustainability-oriented framework. AI is instrumental in facilitating this transformation by integrating adaptive capacity into all five domains. By depicting this convergence, the research provides a practical roadmap for organizations that aspire to align AI adoption with long-term resilience and success, as well as a theoretical refinement of resilience concepts.

Finally, the study explicitly addressed the three guiding research questions. RQ1 (thematic): The literature was shown to cluster into five integrative domains, spanning micro, meso, and macro levels. RQ2 (semantic): The vocabulary of resilience and success is evolving, with emerging terms enriching and extending classical terms. RQ3 (applicative): AI-related opportunities are unevenly distributed across domains but consistently function as enablers of resilience and success, bridging human-centered, organizational, and societal dimensions.