Enhancing Organizational Resilience: Sustainable Development Scenarios Incorporating Disaster Impacts and AI Tools

Abstract

The intensification of human activities and the escalating impact of climate change have increased the probability of disasters, making it important to develop sustainable development scenarios that consider potential disaster consequences. However, disasters are indirectly represented in the 17 Sustainable Development Goals (SDGs) and often overshadowed by other topics. This study focuses on disaster effects in the context of sustainable development. We conducted a sociological survey with 30 respondents from Lithuanian companies, half of which were large manufacturing enterprises, and found that only 37% had encountered sustainable development and disaster management issues, with a similar proportion actively developing related scenarios. Although external stakeholders often participate, 57% of the respondents indicated that their company acts independently on these matters. Large companies rated their disaster preparedness higher (3.5/5) than SMEs (2.9/5) or micro-companies (2.8/5). Rapid response and liquidation of disaster consequences was deemed more important (4.5/5) than risk management and resilience-building scenarios (3.8/5). Using correlation and mutual information analyses, we uncovered linear and non-linear dependencies, showing that psychological stress among employees negatively correlates with the importance assigned to SDGs during disasters. Additionally, we demonstrated how generative AI tools, such as ChatGPT, can transform survey results into tailored scenarios. These findings provide practical insights and methodologies for enhancing organizational resilience and sustainability, even with limited resources.

1. Introduction

In recent years, the world has faced an unprecedented combination of global crises: intensifying climate change, pandemic risks, geopolitical conflicts, and economic stagnation. This makes the need for resilient and sustainable development more urgent than ever. These overlapping challenges, often described as a “polycrisis”, have especially severe implications for developing countries, where they hinder progress towards poverty reduction and sustainable development and exacerbate existing vulnerabilities [1].

The United Nations’ 17 Sustainable Development Goals (SDGs) provide a comprehensive framework for addressing the global challenges [2]. However, within this framework, the risks posed by disasters—both natural and man-made—are often addressed indirectly and may be overshadowed by other critical issues such as economic growth, health, and education. Some research results showed dramatic escalations in both natural and technological disasters over the decades [3]. Recent events have demonstrated that disasters can severely disrupt progress towards these goals, highlighting the need to integrate disaster risk management directly into sustainable development planning [4,5].

While general instruments that increase catastrophe reactions are unquestionably important [6], scenario analysis plays a crucial role in planning and managing the elimination of the consequences of disasters and emergencies [4]. However, developing sustainable development scenarios that account for disaster impacts remains a labor-intensive and resource-demanding process. This challenge is particularly acute for organizations in developing countries, which often lack the necessary resources to conduct thorough scenario analyses and incorporate long-term perspectives into disaster risk management [7]. Additionally, the complexities of long-term changes and multiple actors make disaster risk reduction inherently challenging, requiring the incorporation of uncertainties and dynamic factors into risk assessments [5]. As a result, there is a significant gap in the ability of these organizations to prepare for and respond to crises effectively.

This research aims to address these challenges by identifying and analyzing the critical factors that influence sustainable development scenarios in the context of disaster risk. Through a combination of a literature review and sociological surveys conducted among organizational employees, this study explores the key drivers of disaster resilience and their interactions. Furthermore, the study investigates the potential of leveraging modern artificial intelligence tools, such as large language models and conversational agents, to streamline the scenario development process. By demonstrating how AI can be used to enhance scenario planning, this research offers innovative solutions for resource-constrained organizations, as pursued in other research [8], where conversational AI in the form of a chatbot was utilized for disaster risk reduction.

The remainder of this paper is structured as follows. In Section 2, we provide the background of the research. Section 3 describes the methodology of the research. Section 4 presents the results obtained from the survey and application of the analysis methodology. Finally, Section 5 and  Section 6 are dedicated to the discussion and conclusion. Additionally, we provide Appendix A, Appendix B and Appendix C with ChatGPT prompts and a full correlation coefficient heatmap.

2. Background of the Research

In an era characterized by escalating uncertainties due to climate change, pandemics, and geopolitical tensions, scenario planning has become an indispensable tool for organizations aiming to navigate complex future potentials and enhance resilience. Scenario planning involves creating plausible and detailed narratives regarding how the future might unfold, enabling organizations to anticipate potential challenges and opportunities [9].

2.1. Scenario Planning in Organizational Strategy

The use of scenarios as an effective tool for adapting the strategic beliefs of the managers of an organization provides a new perspective in organizational cognition. Vecchiato [9] highlights that, although scenarios have been extensively praised, there is still limited understanding of their effects on managers’ mental models (representations of reality) and their impact on strategic investment decisions under uncertainty. This gap suggests a need for further research into how scenario planning influences the decision-making processes within organizations.

One of the most notable examples of scenario planning in a corporate context is Shell’s extensive use of scenarios to guide strategic decisions. After an oil crisis, Shell adopted scenarios widely throughout the company, evolving both in scope and process. The initial focus on key variables such as energy demand and oil prices expanded to include economic, political, social, and environmental factors [9]. Notably, Shell’s scenario-based planning approach was a major success, which was recognized by many experts. Thanks to its scenarios, Shell was better prepared for the crisis, and the company recovered more quickly than competitors from such crises as oil embargoes and the growing interventionism of the Russian administration [9]. Shell’s scenario planning process involved several steps:

• Listing predetermined elements and trends in the global energy market.
• Identifying drivers of change in the external environment that could affect the company’s competitive position.
• Exploring likely patterns of evolution for these drivers of change.
• Building the scenarios’ plots and narratives.
• Analyzing the industry and market structure that would prevail in each scenario.
• Identifying emerging sources of competitive advantage.
• Generating and evaluating options for strategic action.

Shell developed scenarios at three levels:

• Global Scenarios: Exploring forces in the global macro-environment, including politics, economy, society, ecology, technology, and demographics.
• Focused Scenarios: Addressing each business sector and geographic area based on the global scenarios.
• Project Scenarios: Investigating specific investment projects with detailed information on competitors, profitability, and risks.

These scenarios enabled Shell to anticipate changes and adapt its strategies accordingly. For instance, in the early 1990s, Shell’s global scenarios focused on drivers such as globalization and liberalization, outlining alternative futures to guide the strategic decisions [9].

However, critiques have emerged regarding the effectiveness of Shell’s scenario planning. Zalik [10] argues that Shell’s scenarios often reflect the company’s preferred outcomes, potentially overlooking less favorable but plausible futures. In “Scenarios to 2025”, developed by Shell, three futures were presented—“Low-Trust Globalization”, “Open Doors”, and “Flags”. From these scenarios, the “Open Doors” scenario was clearly the preferred option, offering stronger economic growth over time [10].

Jefferson [11] further critiques Shell’s scenario planning, highlighting issues such as over-reliance on a few individuals without ensuring adequate succession, logical inconsistencies, and failures to anticipate critical events like the oil price collapse in the mid-1980s. He points out that considering a scenario as “unsustainable and unsuitable” when it accurately describes future events is a logical absurdity. Moreover, Shell’s abandonment of medium-term scenarios compounded the strategic errors in planning.

In response to claims that scenario planning is a “black box”, Jefferson [12] defends the transparency of Shell’s scenario planning process. He argues that the inner workings of scenario development are fully revealed—a “clear box evaluation”—and that evidence of openness is available for those willing to engage with it. He further emphasizes that developing and applying alternative scenarios can help to highlight opportunities and challenges, assess realism, and explore compatibility with the Sustainable Development Goals [13].

These discussions underscore the complexities and challenges inherent in scenario planning within organizations. They highlight the need for robust methodologies that effectively capture uncertainties and inform strategic decisions without bias toward preferred outcomes.

2.2. Scenario Planning for Sustainable Development and Disaster Risk Reduction

Scenario planning has also been applied extensively in the context of sustainable development and disaster risk reduction. The Sustainable Development Goals (SDGs) set by the United Nations provide a comprehensive framework for global development, but integrating disaster risk management into these goals remains a challenge. Even the existing standards for smart community infrastructure cannot meet the new reality requirements [14]; thus, there is a need to adapt them to meet the SDGs.

Scenario planning has been studied by authors who have focused on selected strategic research directions, applying various research methods. One such work is by Ameli et al. [15], which examines how the COVID-19 crisis has significantly impacted the implementation of the 2030 Agenda for Sustainable Development in Iran. Using a fuzzy cognitive map to specify cause–effect links among interdependent SDGs, they analyze the potential effects of the pandemic on SDG achievement. They develop scenarios corresponding to five proposed strategies and test them under different pandemic activation levels. Their findings indicate that applying certain strategies at high activation levels can mitigate the impact of COVID-19 on the SDGs, highlighting the importance of integrating disaster considerations into sustainable development planning.

Similarly, Cernev [16] explore the effects of crossing planetary boundaries and global catastrophic risk (GCR) events on disaster risk reduction efforts and international development targets. Through scenario analysis, they develop distinct futures for humanity and Earth under different levels of GCR and planetary boundary transgressions. They identify scenarios such as “Earth Under Uncertainty”, “Global Collapse”, “Stable Earth”, and “Earth Under Threat”, emphasizing that, without direct action, the world may be on a pathway toward the “Global Collapse” scenario. Their work underscores the need to integrate planetary boundaries and GCR considerations into international development goals and disaster risk reduction frameworks.

Examples of scenarios that study the environment of a company in the context of urban development are relevant to our topic. For example, Fang et al. [17] address the complex interactions between urbanization and the eco-environment in China’s Beijing–Tianjin–Hebei urban agglomeration. They develop the Urbanization and Eco-environment Coupler (UEC), a system dynamics model that simulates the non-linear relationships between various elements. By creating comprehensive scenarios combining different policies and technical support levels, they identify paths to sustainable regional development. Their results suggest that improving technical and political support is key to guaranteeing sustainable development rather than further restricting urbanization.

In our work, we examine scenario planning to achieve the SDG goals in a rapidly changing environment. Useful insights in this area are provided by Aguiar et al. [18], who propose a novel approach to co-design global target-seeking scenarios by capturing multiple and contrasting perspectives on pathways to achieving the SDGs. Recognizing that implementing the SDGs requires coordinated actions across local, national, regional, and global levels, they emphasize the importance of including diverse perspectives from various scales and geographic regions. Their methodology involves a multi-stakeholder process that captures global perspectives through a review of existing scenarios, gathers sub-global perspectives, and analyzes the convergences and divergences between these viewpoints. In their case study of the African Dialogue on The World in 2050, they uncover divergent themes—such as urbanization, population growth, agricultural practices, and the roles of different actors—that challenge the underlying assumptions of the existing global sustainability scenarios, highlighting the necessity of incorporating local and regional nuances into global scenario planning. Similarly, Wilts and Britz [19] underscore the critical need for detailed disaggregated data to effectively assess progress toward the SDGs. They develop an SDG indicator framework for dynamic Computable General Equilibrium models, incorporating 68 endogenous indicators related to 15 SDGs with household-level detail based on a micro-simulation. Applying this framework in a global analysis focusing on selected low- and lower-middle-income countries, they find significant sustainability gaps by 2030 and 2050, especially in the environmental domain. Their analysis reveals that trade-offs exist among and within SDGs, as well as across different socioeconomic pathways. Notably, they observe increasing inequality over time for several indicators, regardless of the average aggregate household developments, pointing to the need for targeted redistribution and compensation policies. Together, these studies emphasize that achieving the SDGs requires not only capturing diverse perspectives in scenario planning but also incorporating detailed, disaggregated data to understand distributional impacts and trade-offs. By integrating multiple scales of perspectives [18] and detailed socioeconomic data [19], policymakers and researchers can develop more nuanced and effective strategies for sustainable development that account for inequalities and the complex interplay between different SDGs.

Since our research focuses on the national level, Allen et al.’s [20] study that reviews and assesses quantitative models that have the potential to support national development planning for the SDGs is important for us. They develop a typology and inventory of 80 different models and highlight gaps in the model capabilities for analyzing all the SDG targets within a single framework. They suggest that combining top-down macro-framework models with bottom-up sectoral models provides a robust approach for analysis and decision-making, enabling policymakers to explore trade-offs and synergies among sectors.

Academic institutions that have links with industrial companies can also significantly contribute to sustainable development. In this field, Beynaghi et al. [21] focus on the role of universities in advancing sustainable development. They systematically analyze the implications of the sustainable development trends and the future directions that universities might take under a potential second decade of Education for Sustainable Development (2015–2024). By projecting the current trends into the future, they frame possible future orientations through three unique scenarios: a socially oriented university, an environmentally oriented university, and an economically oriented university. Each scenario entails fundamental changes affecting the university’s mission, focus areas, disciplines, view of Education for Sustainable Development, external partners, projects, geographical focus, and main functions involved. Their work provides conceptual and practical instruments for scholars, university leaders, and policymakers to consider strategically how these futures might be realized.

These studies collectively demonstrate the critical role of scenario planning in addressing sustainable development and disaster risk reduction. They highlight the importance of integrating disaster considerations into development planning, involving multiple stakeholders, and capturing diverse perspectives to create comprehensive and effective scenarios.

2.3. Challenges and Limitations in Scenario Planning

Developing sustainable development scenarios that adequately account for disaster impacts is a demanding process. In our view, several key challenges emerge when an organization attempts to create scenarios for sustainable development:

• Achieving a clear description of the long-term goals that the company must fulfill within the planned period.
• Collecting and assessing data on influential factors (drivers), determining their impact magnitude and the trends of their change over time.
• Selecting viable scenario options and estimating the likelihood of their successful implementation.
• Producing detailed scenario narratives that are actionable, including both potential benefits and possible losses, to ensure their practical applicability.
• Coordinating the scenarios with interested groups, ensuring that diverse stakeholders—both internal and external—are engaged, informed, and supportive.

These steps collectively underscore the resource-intensive nature of scenario planning. Such complexity often limits organizations with fewer resources or less expertise from fully developing robust scenarios. The complexity of analyzing multiple SDG targets and variables is evident from studies like that of Allen et al. [20], who highlight that the existing models and approaches may not fully capture all the aspects of sustainable development within a single framework. Similarly, research by Nieto-Romero et al. [22] illustrates that information sharing and visioning alone do not guarantee change; scenarios must align with longer-term agendas, address differing barriers to action, and reconcile trade-offs among stakeholder groups. These findings reinforce the significance of considering stakeholder involvement and integrating scenarios into broader strategic processes—points that align closely with our emphasis on coordination and actionable scenario narratives.

Moreover, the methodological advancements discussed by Kiviluoto et al. [23] underscore the need for refined analytical techniques, such as specialized Delphi approaches, to systematically manage qualitative data and derive scenarios. This supports our assertion that a thorough understanding of influential drivers and the robust evaluation of scenarios’ feasibility are crucial. Ultimately, addressing these challenges—clear goal-setting, comprehensive data gathering, viable option selection, detailed scenario construction, and strong stakeholder coordination—can guide organizations toward more effective scenario planning, even in complex and resource-constrained contexts.

2.4. Challenges and Limitations in Scenario Planning (Old)

Despite the recognized benefits, developing sustainable development scenarios that adequately account for disaster impacts remains a labor-intensive and resource-demanding process. Organizations, particularly in developing countries, often lack the necessary resources and expertise to conduct thorough scenario analyses.

Allen et al. [20] emphasize that, while sophisticated models are available to support national development planning for the SDGs, gaps remain in their ability to analyze all the SDG targets and variables within a single modeling framework. They highlight the need for combining different modeling approaches to capture the complexity of sustainable development.

Nieto-Romero et al. [22] assess the opportunities and limitations of scenario planning in shaping a tangible agenda for sustainable development within a rural community in Transylvania. They find that, while scenario planning was useful for articulating a shared development trajectory, the actors perceived different barriers to action, and the trade-offs accepted for collaboration differed among the groups. Their study suggests that information sharing and visioning alone are not sufficient to catalyze change, emphasizing that scenarios should be integrated into longer-term agendas and strategies.

Kiviluoto et al. [23] conduct a Delphi study with 30 walking and cycling experts in Finland to explore transport experts’ views on the future and derive scenarios of walking and cycling within the broader context of the urban mobility system. They create five scenarios depicting walking and cycling in 2034, which can be used as a basis for strategic transport planning and policy. Their methodological elaboration of the disaggregative Delphi analysis systematizes the analysis of qualitative data, contributing to advancements in scenario planning methodologies.

These challenges highlight the need for innovative approaches and tools to make scenario planning more accessible and effective, especially for organizations with limited resources.

2.5. Innovative Approaches and the Role of Artificial Intelligence

Integrating innovative approaches, including artificial intelligence (AI), offers potential solutions to the challenges in scenario planning. According to existing research [24], AI tools can enhance predictive capabilities and facilitate complex data analysis; thus, we believe they could potentially help to streamline the scenario development process.

For example, advanced analytical frameworks have been applied in related domains to guide decision-making under complex conditions. Petrudi et al. [25] introduced a social sustainability innovation framework to assess suppliers during the COVID-19 pandemic, applying group grey best–worst and improved grey relational analysis methods. Their findings, highlighting “safety and health practices”, “remote working conditions”, and “localization” as the key criteria, demonstrate that robust analytical techniques can identify essential factors influencing organizational resilience and sustainability. Although not directly focused on disaster scenario planning, these methods underscore the value of sophisticated analyses to prioritize actions and enhance long-term preparedness.

Similarly, studies on the financial implications of disasters illustrate the importance of long-term perspectives. Montero et al. [26] analyze natural disasters’ impact on insurance company volatilities using a multi-event framework and GARCH-type models, providing policymakers and insurers with insights into volatility management and value-at-risk. Although their work centers on financial markets, it exemplifies how thorough quantitative analyses and model-based evaluations can inform strategic interventions that extend beyond immediate disaster effects and support informed scenario development.

While direct application of AI in scenario planning is not extensively covered in the provided literature, according to the authors of the current article, there is potential for AI tools, such as large language models and conversational agents, to facilitate communication, enhance data analysis, and support the generation of detailed scenarios. These technologies might help organizations to overcome resource constraints by automating aspects of the scenario development process and providing accessible platforms for stakeholder engagement. Especially, it is important because, according to existing research [27], there is a need to address the lack of control over governance that prioritizes financial performance and the company’s contribution to a sustainable future.

2.6. Integrating Disaster Risk into Sustainable Development Scenarios

The increasing frequency and severity of disasters necessitate the integration of disaster risk considerations into sustainable development scenarios. As highlighted by the studies discussed, scenario planning that incorporates disaster impacts can help organizations and policymakers to develop more robust and resilient strategies.

Kong et al. [28] propose a two-stage risk-neutral stochastic model to develop restoration resource deployment and allocation strategies for critical infrastructure systems under uncertain disaster scenarios. They apply a risk metric to assess the risk imposed by uncertainty and present a multi-objective optimization model for risk management. Their results demonstrate significant advantages in using risk-neutral strategies to improve system resilience and assist infrastructure operators in selecting effective emergency response strategies. One of the measures to reduce such risks is to increase transportation resilience as a component of city sustainability [29].

These works underscore the critical need for integrating disaster risk management into sustainable development planning and scenario analysis. By doing so, organizations can enhance their resilience, better prepare for uncertainties, and contribute to achieving the Sustainable Development Goals in a world where crises are increasingly frequent.

2.7. The Integration of Artificial Intelligence in Scenario Planning and Disaster Management

The integration of artificial intelligence (AI), particularly generative AI and conversational agents, presents a significant opportunity to enhance scenario planning and disaster management practices. Traditional scenario planning often relies heavily on expert judgment and may struggle to account for the complexity of future uncertainties [30]. The use of AI can address some of these limitations by automating data analysis.

Hao et al. [30] explore the potential integration of AI techniques into scenario planning, identifying applications in plan generation, scenario generation, and plan evaluation. They argue that AI can think creatively and unconventionally, sometimes generating scenarios beyond human imagination; to briefly justify this claim, we can refer to the famous example of the “Move 37” made by AlphaGo Lee. AI algorithms can analyze vast amounts of data to identify patterns and trends that may not be immediately apparent to human planners. This capability can significantly expand the scope and depth of scenario planning, enabling organizations to better prepare for adverse future scenarios.

Moreover, AI models such as deep learning and reinforcement learning excel at capturing implicit and dynamic interactions within complex systems [30]. In the context of disaster management, these models can simulate the impacts of various disaster scenarios on infrastructure, supply chains, and communities. If organizations successfully incorporate AI into scenario planning and achieve high-quality results, they can enhance their resilience by identifying vulnerabilities and testing the effectiveness of different strategies within a range of conditions.

Cheng et al. [31] demonstrate the practical application of AI in disaster management through a human–AI teaming workflow for estimating post-disaster debris volume and composition. Their approach leverages drones for rapid data collection and utilizes AI models alongside crowdsourcing to detect damaged buildings and assess the damage levels based on aerial imagery. By connecting building the damage states to the generated debris, they provide efficient and accurate debris estimations, reducing the time required from days to hours and minimizing the predictive uncertainty by up to 40%. This case study underscores how AI can support rapid decision-making and resource allocation in disaster response efforts.

Furthermore, the use of conversational AI agents, such as ChatGPT, holds promise for enhancing knowledge dissemination and decision support in technical fields. Hostetter et al. [32] evaluate the performance of ChatGPT and early version of Google’s Bard in handling fire-safety-related queries. They find that ChatGPT demonstrates relatively superior performance, providing detailed and correct responses to technical questions. While noting limitations such as potential biases and inaccuracies, the authors suggest that, as chatbot technology matures, it could revolutionize engineering practices by providing instant access to critical information.

These existing advancements by other authors highlight the potential of AI, particularly generative AI and conversational agents, to transform scenario planning and disaster management. By leveraging AI’s capabilities, organizations could potentially overcome resource constraints, improve the accuracy of their predictions, and enhance their capacity to prepare for and respond to disasters while pursuing the Sustainable Development Goals.

2.8. Challenges and Limitations of ChatGPT Use for Scenario Generation

Using AI tools like ChatGPT for scenario generation also presents challenges and limitations. Although it could potentially enable organizations with limited resources to identify priority areas and enhance competencies, effective application requires careful prompt engineering and ongoing verification of outputs. In our previous work [33], we argued that successful application relies on careful prompt construction and iterative refinement of outputs. This process ensures that the generated scenarios remain coherent, contextually relevant, and useful for addressing the complex challenges in sustainable development and disaster management. Moreover, according to Spaniol et al. [34], while AI can produce large volumes of scenarios at minimal costs, these outputs often lack the contextual richness and domain-specific insight that experienced scenario facilitators provide. This underscores the importance of iterative refinement and expert oversight. Following these practices should increase the efficiency of ChatGPT use in scenario generation. Without active human involvement to refine and adapt AI-generated narratives, the resulting scenarios risk oversimplifying strategic uncertainties and failing to inspire meaningful long-term planning. In other words, AI-driven scenario generation can support scenario planning processes, but it must be coupled with human expertise, ethical considerations, and a robust methodological framework to ensure high-quality, actionable outcomes.

3. The Methodology of the Research

In this section, we present the methodology of the research, i.e., how we address the assessment of the effects of disasters on Sustainable Development Goals in companies. The main research data were obtained via a survey.

3.1. Analyzing Dependencies: Correlation and Mutual Information Analyses

To analyze the dependencies between variables in our dataset, we employed two complementary approaches: correlation analysis and mutual information (MI) analysis. Each method captures different aspects of the relationships between variables, enabling us to explore both linear and non-linear dependencies. Correlation coefficients are well-established statistical measures for examining linear relationships [35], while MI, rooted in information theory, quantifies the amount of shared information between variables without assuming linearity [36].

3.1.1. Correlation Analysis

Correlation analysis involves calculating the Pearson correlation coefficient, which measures the strength and direction of linear relationships between variables. The correlation coefficient r between two variables X and Y is defined as

$$r={\displaystyle \frac{\mathrm{Cov}(X,Y)}{{\sigma }_X{\sigma }_Y}},$$

where $\mathrm{Cov}(X,Y)$ is the covariance between X and Y, and ${\sigma }_X$ and ${\sigma }_Y$ are the standard deviations of X and Y, respectively.

A correlation matrix was constructed, with each element representing the correlation coefficient between two variables. The resulting matrix is symmetric ($r_{ij}=r_{ji}$) and provides a straightforward way to assess pairwise linear dependencies. Variables with high absolute correlation values ($\left|r\right|>\mathrm{threshold}$) indicate a strong linear relationship. This analysis is particularly useful for identifying variables that co-vary in a straightforward, proportional manner.

The correlation matrix was visualized as a heatmap, where the color intensity corresponds to the strength of the correlation.

3.1.2. Mutual Information with Null Model Thresholding

Mutual information (MI) quantifies the amount of shared information between two variables, capturing both linear and non-linear dependencies [36]. For discrete variables, MI is computed using

$$\mathrm{MI}(X,Y)=\sum _{x\in \mathcal{X}}\sum _{y\in \mathcal{Y}}p(x,y)log\left({\displaystyle \frac{p(x,y)}{p\left(x\right)p\left(y\right)}}\right),$$

where $p(x,y)$ is the joint probability mass function of X and Y, and $p\left(x\right)$ and $p\left(y\right)$ are their marginal probability mass functions.

Unlike correlation, MI does not assume linearity, making it sensitive to a broader range of relationships. The resulting MI matrix is symmetric ($\mathrm{MI}(X,Y)=\mathrm{MI}(Y,X)$) and provides a general measure of dependency. However, interpreting MI values can be challenging as the scale of MI depends on the entropy of the variables.

To address the lack of clear interpretability, we implemented a null model approach to establish a significance threshold for MI values. This approach is less commonly used but provides a robust statistical basis for interpreting MI.

Null Model Approach

The null model approach generates a baseline distribution of MI values under the assumption of independence. For each pair of variables, one variable (X) is randomly permuted while the other (Y) is kept unchanged. This process simulates the absence of any true dependency between the variables. The steps are as follows:

• For each pair of variables X and Y, permute X 100 times, creating random pairings with fixed Y.
• Compute the MI for each permutation using the formula for mutual information.
• Aggregate these MI values to form a null distribution.
• Define the significance threshold as the 99th percentile of the null distribution. MI values above this threshold are considered to be statistically significant.

By applying this approach to every pair of variables in the dataset, we derived pair-specific thresholds that reflect the unique statistical properties of the data. The MI matrix was visualized as a heatmap, with significant dependencies highlighted.

3.1.3. Comparison of Approaches

While correlation analysis is well-suited for identifying linear dependencies, mutual information (MI) regression provides a more general framework for detecting both linear and non-linear relationships. Together, these methods provide a comprehensive view of the relationships between variables, facilitating a deeper understanding of the underlying structure in the data.

An advantage of correlation analysis is its ability to indicate the direction of linear relationships through the sign of the correlation coefficients. Negative values in the correlation matrix reveal inverse relationships between variables, enabling more nuanced insights. For instance, a negative correlation coefficient between two variables indicates that, as one variable increases, the other tends to decrease. This directional information is valuable for understanding trade-offs or compensatory mechanisms within the system.

In contrast, MI is a non-negative measure that quantifies the amount of shared information between variables but does not convey the direction of the relationship. While MI effectively captures both linear and non-linear dependencies, it treats positive and negative associations equivalently. Therefore, combining correlation analysis with MI enables us to identify not only the presence and strength of dependencies but also their directionality.

Both the correlation and MI matrices were visualized as heatmaps. Hierarchical clustering using Ward’s method [37] was applied to reorder the variables based on the MI values, enabling patterns of dependency to emerge visually. The inclusion of negative correlation values in the heatmap adds depth to the analysis by highlighting inverse relationships, which might be overlooked if only MI values were considered.

3.2. Factors for Evaluation

Table 1. Factors and corresponding references.

No.	Parameter	The Authors Who Also Studied Similar Parameters
B1.1	Creativity of company employees	[38]
B1.2	Entrepreneurship of company employees	[38]
B2.1–B2.17	Sustainable Development Goals (SDGs)	[15,16,39]
B3.1	Risk management and increasing disaster resilience	[28,40,41,42,43,44]
B3.2	Rapid response and liquidation of disaster consequences	[28,42]
B3.3	The use of artificial intelligence in disaster planning and management	[40,41,42,43]
B3.4	Attracting and mobilizing stakeholders	[38,45]
B3.5	Securing supply chains	[28]
B3.6	Ensuring sustainability in the event of disasters	[42,46,47]
B3.7	Temporary accommodation of residents	[38,46,48]
B3.8	Data storage and protection	[44]
C1.1	Disconnection of electricity	[28,45,47,49]
C1.2	Water supply interruption	[28,45,47,49]
C1.3	Interruption of food supply	[50]
C1.4	Closed roads	[45,49]
C1.5	Disconnection of communication	[48]
C1.6	Destruction in the company	[38]
C1.7	Fire in the company	[40]
C1.8	A flood in the company	[40,49]
C1.9	Absence of heating and ventilation	[51]
C1.10	Problems with work organization due to the pandemic	[38,52]
C1.11	Problems with suppliers	[38,45]
C1.12	Problems with customers	[38,45,52]
C1.13	Injuries to employees	[45,47]
C1.14	Deaths of employees	[45,47]
C1.15	Information system violations due to hacking and viruses	[53]
C1.16	Psychological stress of employees	[45,47]
C1.17	Legal disputes	[54]
C1.18	Damage to reputation	[55]
C1.19	Financial losses	[38,47,54]
C1.20	Human resource issues	[45,47,49]

summarizes the key factors used for evaluation in this research. These factors have been selected based on their common use and relevance in studies by other authors, ensuring that they align with widely accepted parameters in the scientific community. Each factor is listed alongside references to studies where similar parameters were analyzed, highlighting their importance and application in various contexts. This comprehensive overview serves as the basis for the evaluation in this research.

3.3. Methodology for Scenario Generation

To demonstrate the potential of generative AI in scenario planning, we utilized OpenAI’s ChatGPT, a conversational AI model based on the GPT-4 architecture, to generate scenarios related to sustainable development and disaster management. The objective was to demonstrate how AI-generated scenarios can be employed in organizational planning and disaster preparedness efforts.

3.3.1. Prompt Engineering Approach

Prompt engineering is the process of crafting input prompts to guide AI models like ChatGPT to produce desired outputs. Effective prompt engineering is critical to ensure that the generated scenarios are relevant, coherent, and of high quality [56]. Although we do not pretend to be novel here, in our approach, we applied prompt engineering techniques that enabled us to obtain the desired quality of the output. This involved the following:

• Defining Clear Objectives: Explicitly stating the purpose of the scenario generation, focusing on specific themes such as Sustainable Development Goals (SDGs), disaster risk management, and organizational resilience.
• Providing Contextual Information: Including relevant background information in the prompts, such as the organizational context, industry sector, and specific challenges faced. This helps ChatGPT to tailor the scenarios to our needs; in our prompts, we included the knowledge extracted from conducted survey.
• Specifying Desired Output Structure: Guiding the AI to produce scenarios with a clear structure, including elements like timeframes, key actors, and potential outcomes. This ensures that the scenarios are comprehensive and actionable.
• Incorporating Key Variables and Constraints: Highlighting critical factors and uncertainties to be considered in the scenarios, such as technological advancements, climate change impacts, and regulatory environments.
• Iterative Refinement: Engaging in an iterative process, reviewing the AI’s outputs, and refining the prompts as needed to enhance clarity and relevance.

3.3.2. Approach to AI-Generated Scenarios

To increase the quality and relevance of the AI-generated scenarios, we took the following measures:

• Ethical Considerations: Followed ethical guidelines, ensuring that the prompts and generated content respected confidentiality, avoided biases, and complied with applicable policies [57].
• Preliminary Review: Conducted a preliminary review of the AI-generated scenarios for plausibility and alignment with the prompts. This involved checking for logical consistency and relevance to the specified context.
• Documentation: Documented the prompt engineering process, including iterations and refinements, to maintain transparency and replicability. However, regarding the current state of GenAI technology, the replicability property is quite limited.

By employing all these techniques, we aimed to ensure that the scenario generation process via ChatGPT yielded results meeting our quality expectations. More details will be provided at the end of the next section presenting the results.

4. Results

The study involved 30 respondents from Lithuanian companies. The companies’ requests were selected focusing primarily on large businesses. The survey respondents who were selected as specialists are familiar with making strategic decisions within the company or have an opinion on strategic issues. Part of the survey was distributed via email. Around one-third of the candidates responded to the requests. The other data were collected from participants during university-organized events for manufacturing and/or engineering companies. The majority of the respondents were engineers and designers (37%), followed by directors, department heads, and managers (33%). The rest were scientists (7%), as well as salespeople, marketing staff, and employees from other specialties (23%). Most of the respondents (90%) had higher education qualifications. We provide mean values along with standard deviation values for all the responses in Appendix C.

Regarding the size of the companies, half of the respondents (50%) were from large companies, 23% from small- and medium-sized companies (SMEs), and 27% from micro-companies. The primary activities of the surveyed companies were manufacturing (60%), while the rest specialized in construction, transport, sales, science, and fishing. Therefore, the results of our study are more reflective of large companies, particularly in the manufacturing sector.

When asked whether they had encountered issues regarding sustainable development and disaster management, 37% of the respondents indicated that they had, 47% said they had not, and the remaining 16% had no opinion on the matter. Similarly, 37% of the respondents indicated that their company develops sustainable development and disaster response scenarios (i.e., there are official documents declaring this within the company), 33% said their company does not, and the rest had no opinion.

According to the respondents’ answers, it is rare for companies to proactively engage in sustainable development and disaster prevention initiatives on their own. While external stakeholders like the state, suppliers, consumers, and other interested parties often participate in these processes, 57% of the respondents indicated that, in practice, only the company itself is involved in addressing issues of sustainable development and disaster management.

Regarding disaster preparedness, representatives of large companies rated their companies higher (3.5 points out of 5 possible) compared to representatives of small- and medium-sized companies (2.9 points) and micro-companies (2.8 points). The responses were rated on a scale from 1 (very bad) to 5 (very good).

The conducted studies showed that respondents from large companies assess their organizations’ preparedness for disasters better than representatives of small- and medium-sized or micro-companies. Some differences are also visible (see Figure 1) in the assessment of the SDGs in the context of disasters. For example, the representatives of micro-companies assessed the importance of gender equality, climate action, industry, innovation, and infrastructure nearly one point lower. The majority of the respondents from all the companies surveyed agreed that one of the most important SDGs is clean water and sanitation. They similarly assessed good health and well-being, the absence of hunger, life on land and below water, and responsible consumption as important factors.

The average ratings of the suitability of possible scenario types for disaster management were similar in large-, small-, and medium-sized companies. In micro-companies, these ratings were somewhat lower. “Rapid response and liquidation of disaster consequences” was rated as the most important scenario. Other highly evaluated scenarios included “securing supply chains”, “attracting and mobilizing stakeholders”, and “temporary accommodation of residents” (see Figure 2).

In the surveyed companies, the respondents mostly experienced negative impacts of disasters, such as disconnection of electricity, disconnection of communication, problems with suppliers, problems with customers, challenges with work organization due to the pandemic, financial losses, human resource issues, and psychological stress among employees. These impacts were assessed on average between 2.3 and 2.7 points on a scale from 1 to 5. Other impacts of disasters occurred even less frequently (see Figure 3).

4.1. Dependence Between Score Variables

Figure 4 shows the heatmap of the correlations between the scores for various questions. Only those questions with at least one correlation coefficient greater than 0.7 are included here, while the full heatmap is provided in Appendix B (Figure A1).

As shown in Figure 4, one cluster identified via hierarchical clustering (using Ward’s method) includes the questions from group C: financial losses, destruction in the company, death of employees, psychological stress of employees, legal disputes, and damage to reputation. This indicates a tendency among the respondents to evaluate these questions similarly, either increasing or decreasing their scores collectively. Notably, “psychological stress of employees” was the least correlated with the other items in this cluster.

Additionally, the fourth column in the heatmap shows a negative correlation between the scores from C1.16 psychological stress of employees and the scores from group B, which includes the SDGs’ importance scores. This suggests that those respondents who reported more frequent occurrences of psychological stress tended to rate sustainability factors as less important during disasters. This finding highlights the potential significance of stress-reducing measures in supporting sustainability-related initiatives. We hypothesize that psychological challenges may reduce employees’ motivation to focus on long-term goals, such as achieving SDGs.

Figure 5 presents the heatmap of the mutual information (MI) scores. The questions were filtered using the null model approach described in Section 3.1.2. The significance threshold for the 0.99 percentile was estimated as 0.467, and only questions with at least one MI score exceeding this value are shown.

Unlike linear correlation coefficients, MI highlights logical dependencies, such as the relationships between assessments of equality-related SDGs (e.g., B2.10 and B2.5) or between assessments of “Life below water” and “Life on land” SDGs (B2.14 and B2.15, respectively). Questions B3.1 and B3.5 are strongly related to each other in both the correlation and MI matrices. We observed that the score for “Securing supply chains” (B3.5) is a very good predictor, showing high MI scores with B3.1, B3.4, and B3.8.

There could be a potential informational overlap when we consider questions B3.5 and B3.1 as the mutual information between B3.5 and B3.1 is very high. Therefore, we could assume that we might leave out either question B3.5 or B3.1 in the survey without significant loss of information since they provide similar insights. However, since the mutual information between B3.1 and B3.8 is much lower (slightly above 0.99 percentile significance threshold 4.67) than between 3.5 and 3.8, the question score from B3.1 offers additional valuable information. This suggests that we should retain question B3.1 in the survey as it captures unique aspects that are not fully covered by B3.5 or B3.8.

4.2. Scenario Generation Using ChatGPT

As discussed in Section 3.3.2, in this section, we will showcase the scenario generation using state-of-the-art generative AI technology, more specifically ChatGPT by OpenAI (Sanfrancisco, CA, USA), the latest version, o1-preview, which is based on ChatGPT 4.0.

4.2.1. Information Provided in Scenario Prompt Generation

While generating scenarios using conversational AI, to achieve our subjective assessment regarding high-quality scenarios, it was essential to include specific information in the prompts [58]:

• Clear Scenario Focus: Define the main topic or challenge the scenario should address. For example, “Develop a scenario where a manufacturing company adapts to increasing climate-related disasters over the next decade”.
• Contextual Background: Provide background information about the organization, industry, or region. This may include size, location, key stakeholders, and current strategies.
• Timeframe: Specify the temporal scope of the scenario, e.g., “over the next 10 years” or “by 2030”.
• Key Drivers and Uncertainties: Identify critical factors that influence the scenario, such as technological developments, economic trends, policy changes, or environmental challenges.
• Desired Level of Detail: Indicate the expected depth and complexity of the scenario. For example, request a detailed narrative that includes specific actions, consequences, and stakeholder responses.
• Constraints and Assumptions: Mention any constraints or assumptions to be considered, such as resource limitations, regulatory frameworks, or social dynamics.
• Output Format: Specify if the scenario should be presented in a particular format, such as a narrative, bullet points, or a structured outline.

An example of a prompt fulfilling the requirements above might be the following:

“Imagine a scenario where a large manufacturing company in Lithuania faces an increasing frequency of natural disasters due to climate change over the next decade. The company aims to enhance its disaster preparedness and sustainability practices while maintaining profitability, despite resource limitations and stringent environmental regulations. The company must also address stakeholder concerns, including those of employees, local communities, and government agencies. Provide a detailed description of how the company adapts its operations, engages with stakeholders, and leverages artificial intelligence—specifically machine learning and predictive analytics—in disaster planning and management. Include potential challenges, ethical considerations, and outcomes. Present the scenario in a structured format with clear headings for each section, such as Introduction, Adaptive Strategies, Stakeholder Engagement, AI Implementation, Challenges, Ethical Considerations, and Outcomes.”

Here is a breakdown of the prompt:

• Clear Scenario Focus:
  • “Imagine a scenario where a large manufacturing company in Lithuania faces an increasing frequency of natural disasters due to climate change over the next decade.”
• Contextual Background:
  • “A large manufacturing company in Lithuania…”
• Timeframe:
  • “…over the next decade.”
• Key Drivers and Uncertainties:
  • “Increasing frequency of natural disasters due to climate change.”
• Desired Level of Detail:
  • “Provide a detailed description of how the company adapts its operations, engages with stakeholders, and leverages artificial intelligence—specifically machine learning and predictive analytics—in disaster planning and management. Include potential challenges, ethical considerations, and outcomes.”
• Constraints and Assumptions:
  • “…while maintaining profitability, despite resource limitations and stringent environmental regulations.”
• Output Format:
  • “Present the scenario in a structured format with clear headings for each section, such as Introduction, Adaptive Strategies, Stakeholder Engagement, AI Implementation, Challenges, Ethical Considerations, and Outcomes.”

4.2.2. Potential for Assessing AI-Generated Scenarios

Assessing the quality of AI-generated scenarios is important for understanding their utility in organizational planning. While assessment was beyond the scope of our current research, we acknowledge that future studies could benefit from evaluating AI-generated scenarios based on established criteria for high-quality scenarios, such as plausibility, relevance, internal consistency, differentiation, and comprehensiveness [59].

The key points for assessing AI-generated scenarios include the following:

• Plausibility: Determining whether the scenario is believable and grounded in logical assumptions.
• Relevance: Ensuring that the scenario addresses the specific issues and objectives relevant to the organization.
• Internal Consistency: Checking for logical coherence within the scenario without contradictions.
• Differentiation: Comparing multiple scenarios to ensure that they explore distinct pathways and outcomes.
• Actionability: Evaluating whether the scenario provides practical insights that can inform strategic decision-making.

Future research could involve experts in sustainable development and disaster management to rigorously assess AI-generated scenarios using these criteria. This would contribute to validating the effectiveness of AI in scenario planning and identifying areas for improvement.

4.2.3. Implications for Scenario Planning Using Conversational AI

The use of conversational AI like ChatGPT in scenario planning offers significant benefits, including rapid generation of diverse scenarios, enhanced creativity, and the ability to incorporate complex variables. However, the quality of the output is highly dependent on the quality of the input prompts.

Effective prompt engineering is crucial for guiding the AI to produce valuable scenarios. By providing clear objectives, detailed context, and specific instructions, organizations can leverage AI to support strategic planning and enhance their preparedness for future uncertainties.

While our study demonstrates the potential of AI in generating scenarios, assessing the quality and utility of these scenarios remains an important area for future research. By exploring assessment methodologies and involving domain experts, organizations can better understand how to integrate AI-generated scenarios into their planning processes.

4.3. ChatGPT-Generated Scenarios

In this section, we provide the ChatGPT-generated scenarios according to the prompts in Appendix A. In the conversation, we also demonstrate how the data of our study findings were reflected in the generated scenarios by ChatGPT.

4.3.1. Pessimistic Scenario (2025–2035)

Company Profile

Baltic Car Makers is a large Lithuanian manufacturing company specializing in automotive parts production. As a key player in the national economy, it employs a significant number of professionals, including engineers, designers, and managers.

Initial Challenges

Despite its size, the company has limited prior engagement in sustainable development and disaster management practices. With climate change accelerating, natural disasters become more frequent, directly impacting operations. The existing vulnerabilities include the following:

• Frequent electricity and communication disruptions.
• Supply chain issues with both suppliers and customers.
• Organizational challenges during pandemics.
• Psychological stress among employees.

Impact of Possible Disasters

Between 2025 and 2035, Lithuania experiences an increase in severe natural disasters:

• High Probability: Floods, storms, and heatwaves become more common due to climate change.
• Size and Extent: Disasters affect large regions, disrupting national infrastructure.
• Effects on the Company:

  –

  Operations are frequently halted.

  –

  Supply chains are severely disrupted.

  –

  Physical infrastructure suffers significant damage.

  –

  Employee well-being deteriorates due to constant crises.

Actions to Increase Organizational Resilience

Baltic Car Makers makes minimal efforts to enhance disaster preparedness:

• Internal Efforts: Reluctance to invest in sustainability due to financial constraints.
• Lack of Initiative: Waits for external stakeholders to lead, but such support is limited.
• Failure to Collaborate: Does not actively seek partnerships or government assistance.

Stakeholder Actions

Stakeholder engagement is weak:

• Employees: High stress levels lead to decreased productivity and increased turnover.
• Suppliers and Customers: Face their own challenges, causing further supply chain breakdowns.
• Government Agencies: Provide minimal support due to resource limitations.

Integration of Artificial Intelligence

The company hesitates to invest in AI technologies:

• Lack of Adoption: No implementation of AI for disaster planning or supply chain management.
• Technological Lag: Competitors adopt AI, leaving Baltic Car Makers behind.

Addressing Sustainable Development Goals

Minimal progress is made on SDGs:

• Important SDGs Neglected: Little action on clean water (SDG 6), good health (SDG 3), or responsible consumption (SDG 12).
• Less-Prioritized SDGs Ignored: Gender equality (SDG 5), climate action (SDG 13), and innovation (SDG 9) remain unaddressed.

Resilience Enhancements

The company fails to strengthen its resilience:

• Infrastructure: No significant upgrades; facilities remain vulnerable.
• Policies and Training: Lacks disaster preparedness policies; no employee training.

Overcoming Barriers

Financial and operational barriers persist:

• Financial Constraints: No efforts to secure funding or reallocate resources.
• Negative Impacts Unmitigated: Disasters continue to cause significant operational losses.

Outcomes and Impact

Negative outcomes prevail:

• Disaster Preparedness Ratings: Remain low; company is ill-prepared for crises.
• Operational Continuity: Frequent disruptions lead to declining productivity and profitability.
• Competitive Advantage: Loses market share to better-prepared competitors.

Future Outlook

The long-term outlook is bleak:

• Survival: Company struggles to survive amidst mounting challenges.
• Reputation: Damaged due to poor crisis management and sustainability neglect.
• Missed Opportunities: Fails to capitalize on advancements in sustainable practices.

4.3.2. Most Likely Scenario (2025–2035)

Company Profile

Baltic Car Makers maintains its position as a leading automotive parts manufacturer in Lithuania, contributing significantly to the economy and employment.

Initial Challenges

Recognizing the increasing frequency of natural disasters, the company begins to engage in sustainable development and disaster management practices, albeit cautiously.

Impact of Possible Disasters

Natural disasters occur with moderate frequency:

• Medium Probability: Occasional floods and storms impact operations.
• Effects on the Company:

  –

  Operations are temporarily disrupted.

  –

  Supply chains experience delays but not complete breakdowns.

  –

  Employee well-being is affected but managed.

Actions to Increase Organizational Resilience

The company takes moderate steps:

• Internal Efforts:

  –

  Forms a basic disaster response team.

  –

  Allocates limited funds for infrastructure improvements.

• Collaboration:

  –

  Engages with suppliers to strengthen supply chains.

  –

  Participates in industry forums on disaster preparedness.

Stakeholder Actions

Stakeholder engagement improves:

• Employees: Receive some training; morale stabilizes.
• Suppliers and Customers: Work together to mitigate disruptions.
• Government Agencies: Provide occasional support, although bureaucratic hurdles persist.

Integration of Artificial Intelligence

Partial adoption of AI technologies:

• Implementation:

  –

  Introduces AI for basic supply chain management.

  –

  Uses predictive analytics for maintenance scheduling.

• Benefits: Some operational efficiencies are realized.

Addressing Sustainable Development Goals

Moderate progress is made:

• Important SDGs:

  –

  Implements water-saving measures (SDG 6).

  –

  Enhances workplace safety (SDG 3).

• Less-Prioritized SDGs:

  –

  Begins to explore climate action initiatives (SDG 13).

  –

  Invests modestly in innovation (SDG 9).

Resilience Enhancements

Strengthens resilience to a moderate extent:

• Infrastructure: Upgrades critical systems to withstand common disasters.
• Policies and Training: Implements basic disaster preparedness policies.

Overcoming Barriers

Some barriers are addressed:

• Financial Constraints: Secures modest funding through partnerships.
• Negative Impacts: Reduces operational disruptions through improved planning.

Outcomes and Impact

Mixed outcomes are observed:

• Disaster Preparedness Ratings: Improve to an average level.
• Operational Continuity: Stability is achieved, although not optimal.
• Competitive Advantage: Maintains market position but does not significantly excel.

Future Outlook

A cautious optimism prevails:

• Sustainability: Recognizes the need for ongoing efforts.
• Leadership Position: Viewed as a responsible company, although not a leader.
• Lessons Learned: Understands the importance of proactive measures.

4.3.3. Optimistic Scenario (2025–2035)

Company Profile

Baltic Car Makers emerges as a leader in sustainable manufacturing and disaster preparedness, setting industry benchmarks in Lithuania and beyond.

Initial Challenges

Proactively addresses sustainable development and disaster management, transforming challenges into opportunities.

Impact of Possible Disasters

Natural disasters have minimal impact due to effective strategies:

• High Probability: Disasters occur but are mitigated effectively.
• Effects on the Company:

  –

  Operations remain uninterrupted.

  –

  Supply chains are resilient and adaptive.

  –

  Employee well-being is prioritized and maintained.

Actions to Increase Organizational Resilience

Significant initiatives are undertaken:

• Internal Efforts:

  –

  Establishes a Sustainability and Resilience Department.

  –

  Integrates SDGs into corporate strategy.

• Investment:

  –

  Allocates 10% of annual revenue to sustainability initiatives.

  –

  Invests in renewable energy, reducing dependence on external grids.

• Collaboration:

  –

  Partners with universities for research on sustainable materials.

  –

  Engages with international organizations for best practices.

Stakeholder Actions

Strong engagement leads to collective success:

• Employees: Highly engaged; benefit from wellness programs and training.
• Suppliers and Customers: Integrated into disaster preparedness plans.
• Government Agencies: Provide support and recognize the company’s leadership.

Integration of Artificial Intelligence

Advanced AI technologies are fully implemented:

• Implementation:

  –

  Uses AI for predictive analytics and early warning systems.

  –

  Optimizes supply chains and real-time decision-making during disasters.

• Benefits: Achieves high efficiency and swift response to incidents.

Addressing Sustainable Development Goals

Significant progress across all the SDGs:

• SDG 6 (Clean Water): Implements water recycling in manufacturing.
• SDG 3 (Good Health): Offers comprehensive health programs.
• SDG 12 (Responsible Consumption): Adopts circular economy principles.
• SDG 5 (Gender Equality): Increases female representation in leadership.
• SDG 13 (Climate Action): Achieves carbon neutrality by 2030.
• SDG 9 (Industry, Innovation): Invests in cutting-edge technologies.

Resilience Enhancements

Company’s resilience is greatly enhanced:

• Infrastructure: Builds disaster-resistant facilities with renewable energy sources.
• Policies and Training: Establishes comprehensive policies; employees are well-prepared.

Overcoming Barriers

Effectively overcomes all barriers:

• Financial Constraints:

  –

  Secures funding through green bonds and EU grants.

  –

  Leverages investments and partnerships.

• Negative Impacts:

  –

  Implements robust cybersecurity measures.

  –

  Develops alternative supply chain strategies.

Outcomes and Impact

Positive outcomes are achieved:

• Disaster Preparedness Ratings: Ranks in the top 5% in Europe.
• Operational Continuity: Maintains uninterrupted operations.
• Competitive Advantage: Increases market share by 25%.
• Community Impact: Supports local communities through sustainable projects.

Future Outlook

The company is well-positioned for the future:

• Leadership: Receives international recognition for sustainability.
• Innovation: Continues to innovate with sustainable materials.
• Growth: Expands into new markets and sectors.

5. Discussion

The results of our survey provide valuable insights into how organizations perceive and prioritize various factors related to sustainable development and disaster risk management. In this section, we discuss our findings in relation to the existing literature, highlighting areas of convergence and divergence.

5.1. Entrepreneurship and Company Size

Our analysis did not reveal a correlation between the entrepreneurship of company employees and the size of the company (the coefficient is 0.167). Meanwhile, the correlation between company size and employee creativity was visible, 0.527. This suggests that smaller companies should foster more employee creativity compared to larger organizations. One possible explanation is that smaller companies offer more flexibility and opportunities for employees to engage in innovative activities, whereas larger organizations may have more resources for entrepreneurial initiatives.

This finding aligns with the observations of Sætra [60], who argues that businesses often overlook critical sustainability dimensions such as social equity and inclusive economic growth in their strategies. They emphasize the need for companies to adopt more comprehensive approaches to sustainability that promote innovation and engage employees in the development of sustainable futures. In larger companies, the tendency to adhere to established procedures may inhibit employee entrepreneurship, whereas smaller firms may be more agile and open to innovative ideas.

5.2. Risk Management and Disaster Resilience

We found strong positive correlations between risk management and increasing disaster resilience and several factors: the use of artificial intelligence in disaster planning and management, attracting and mobilizing stakeholders, securing supply chains, and ensuring sustainability in the event of disasters. This indicates that organizations that prioritize risk management are also more likely to adopt advanced technologies, engage stakeholders, secure their supply chains, and focus on sustainability during disasters.

This finding is consistent with the literature emphasizing the integration of AI and stakeholder engagement in enhancing disaster resilience. For instance, Heo et al. [61] discuss the feasibility of decarbonizing mega-scale industrial parks using AI-driven models to integrate renewable energy sources and improve sustainability. Their study demonstrates how AI can assist in planning and managing energy systems to mitigate climate change impacts.

Similarly, Rohde et al. [62] present the Sustainability Criteria and Indicators for Artificial Intelligence Systems (SCAIS) Framework, highlighting the importance of considering the multidimensional impacts of AI on sustainability. They advocate for a holistic approach to AI implementation that addresses the environmental, social, and economic dimensions, which aligns with our finding that AI use correlates with improved disaster resilience.

Moreover, Bag et al. [63] examine how multinational company (MNE) customer pressures influence suppliers’ compliance and commitment to climate change adaptation (CCA) and disaster risk reduction (DRR) goals. They find that MNE pressures can motivate suppliers to develop dynamic capabilities and enhance sustainability performance, underscoring the role of stakeholder engagement in disaster resilience.

5.3. Prioritization of Sustainable Development Goals

Our respondents rated clean water and sanitation, zero hunger, and good health and well-being as very important factors. In contrast, factors related to climate control and gender equality were evaluated as less important, with gender equality receiving a significantly lower score than the other factors.

This prioritization reflects a focus on immediate and tangible needs over broader and longer-term goals. Sætra [60] critiques the current business practices for emphasizing technical aspects of sustainability while overlooking the critical dimensions, such as social equity and inclusive growth. The low importance assigned to gender equality in our survey suggests that organizations may not fully recognize its significance in achieving sustainable development.

Additionally, Bag et al. [63] highlight that only MNE customer pressures effectively motivate suppliers to comply with the CCA and DRR goals, implying that, without external pressure, organizations may not prioritize climate action. This aligns with our finding that climate control factors were considered to be less important by the respondents.

Cumming et al. [64] discuss the need for AI-ethics-led sustainability frameworks that address issues like fairness, justice, and inclusiveness. The low prioritization of gender equality in our survey indicates a gap in addressing these ethical considerations within organizations.

5.4. Common Disaster Experiences

The most frequent disaster cases reported by our respondents were disconnection of electricity, disconnection of communication, problems with customers, and psychological stress of employees. These findings highlight the operational and human resource challenges that organizations face during disasters.

Kim et al. [65] emphasize the importance of cultivating the disaster safety industry to enhance disaster resilience and competitiveness. They note that government intervention is often required to promote the disaster safety industry, suggesting that organizations may struggle to address these challenges independently.

Furthermore, Jiang et al. [66] propose an agent-based model to assess the economic ripple effects of disasters while considering firms’ adaptive behaviors. Their findings demonstrate that firms’ adaptive behaviors can significantly reduce disaster losses. The frequent occurrence of operational disruptions and employee stress reported in our survey underscores the need for organizations to develop adaptive strategies and enhance their resilience.

5.5. Implications for Scenario Development and Organizational Resilience

Our findings have important implications for developing sustainable development scenarios that account for disaster consequences. The correlations between risk management practices and factors such as AI use, stakeholder engagement, and supply chain security suggest that integrating these elements into scenario planning can enhance organizational resilience.

The low prioritization of climate control and gender equality indicates potential blind spots in organizational strategies. As Sætra [60] and Cumming et al. [64] suggest, a more comprehensive approach to sustainability that includes social and ethical dimensions is necessary for achieving long-term sustainability goals.

The frequent occurrence of operational disruptions highlights the importance of addressing infrastructure vulnerabilities and human factors in disaster preparedness. Organizations may benefit from investing in robust communication and power systems as well as employee well-being programs to mitigate the impact of disasters.

5.6. Implications of AI Integration for Organizational Resilience and Sustainability

The integration of AI into scenario planning and disaster management aligns with the literature emphasizing its potential to enhance organizational resilience. Hao et al. [30] highlight how AI can automate plan generation, simulate complex scenarios, and evaluate the performance of different plans under varying conditions. By utilizing AI-driven tools, organizations can streamline the development of sustainable development scenarios that account for disaster impacts without the extensive resources traditionally required.

However, user perceptions of AI tools can vary depending on the use scenario. Yan et al. [67] explore differences in the user perceptions of ChatGPT across various scenarios, identifying factors such as information quality, perceived risk, attitude, and policy support. They find that users’ perceptions significantly differ based on the context in which they use AI tools, influencing their attitudes and support for policies regulating AI. This suggests that, while AI has the potential to improve disaster management practices, organizations must consider user acceptance and address concerns related to information quality and risks.

Moreover, the integration of AI into organizational processes has implications for workforce dynamics and organizational adaptation. Farrow [68] examine the implications of different ratios of human and AI intelligence in future organizational operating models. Their findings reveal concerns about potential job displacement and loss of human interaction, as well as opportunities for enhancing efficiency and problem-solving capabilities. Organizations must balance the benefits of automation and efficiency with the need to foster a culture of innovation and maintain employee engagement.

Additionally, ethical considerations play a crucial role in the successful implementation of AI in organizational contexts. Cumming et al. [64] emphasize the importance of AI-ethics-led sustainability frameworks, identifying key principles such as beneficence, non-maleficence, justice, explainability, autonomy, privacy, and bias mitigation. They argue that integrating ethical considerations into AI deployment is essential for building trust and ensuring that AI contributes positively to sustainability goals. Our findings, which show low prioritization in terms of those factors related to climate control and gender equality, may reflect a gap in addressing ethical considerations within organizations.

In conclusion, the integration of AI, generative AI, and conversational agents into scenario planning and disaster management holds significant potential for enhancing organizational resilience and sustainability. Organizations should consider investing in AI capabilities that augment human expertise, promote ethical practices, and foster a culture of innovation. By doing so, they can leverage the strengths of both AI and human intelligence to navigate uncertainties, enhance decision-making, and contribute to achieving the Sustainable Development Goals.

5.7. Limitations and Future Research

Our study has some limitations. The correlations observed are based on self-reported data from survey respondents, which may be subject to biases. Additionally, the sample may not be representative of all organizations, limiting the generalizability of the findings.

Future research could explore the reasons behind the low prioritization of climate control and gender equality, perhaps through qualitative methods such as interviews or focus groups. Investigating the barriers to integrating these factors into organizational strategies could inform the development of targeted interventions.

Moreover, further studies could examine the effectiveness of different risk management practices in enhancing disaster resilience, particularly the role of AI and stakeholder engagement. Longitudinal studies could assess the impact of these practices over time and in different contexts.

6. Conclusions

Increasing an organization’s resilience to disasters through sustainable development scenarios enables better adaptation to complex environmental conditions. The use of artificial intelligence may enrich and accelerate scenario creation, but ensuring professionalism remains essential, including ethical considerations, critical evaluation, external expert involvement, and thorough documentation.

Our research on Lithuanian companies revealed several key findings:

• Engagement in Sustainability and Disaster Prevention: Only 37% of the respondents reported encountering sustainable development and disaster management issues, and a similar proportion developed related scenarios. While external stakeholders (the state, suppliers, and consumers) are often involved, 57% indicated that companies ultimately address these challenges independently.
• Preparedness vs. Rapid Response: Companies prioritize rapid response and liquidation of disaster consequences (4.5/5) over risk management and increasing resilience (3.8/5). This suggests an emphasis on immediate action rather than long-term preparedness.
• Psychological Stress and SDGs: The correlation and mutual information analyses uncovered that employee psychological stress negatively correlates with the importance assigned to SDGs during disasters. Thus, stress-reducing measures are crucial for remaining focused on sustainability.
• Mutual Information Insights: The MI analysis complemented the correlation analysis by detecting non-linear dependencies and identifying potential survey question overlaps. For example, “Securing supply chains” (B3.5) emerged as a strong predictor.

The use of artificial intelligence in creating typical (pessimistic, most likely, and optimistic) scenarios for sustainable development, including disaster impact assessments and resilience strategies, could potentially enable resource-limited organizations to identify priority areas for competence building. When performing scenario generation, it remains important to consider ethics, logical consistency, contextual relevance, and to document prompt engineering. Following these practices should increase the efficiency of ChatGPT use in scenario generation. In this research, we provided detailed prompt engineering recommendations and examples, demonstrating how to generate scenarios using GenAI.

The research might be useful for researchers and decision-makers. Although only Lithuanian companies were studied, the insights developed from this research could be used on a global scale considering the more diverse situations regarding organizations’ resilience to disasters and the probability and scale of the disasters themselves. In conclusion, as the world grapples with an increasingly complex array of crises, this study provides insights for integrating disaster risk management into sustainable development strategies. The findings underscore the importance of building resilience at the organizational level and reimagining “normality” in the face of continuous disruptions, thereby contributing to the broader efforts to achieve the SDGs in a world where crises have become the new normal [69].