1. Introduction
According to
Brás et al. (
2023b),
Le Coze and Antonsen (
2023), and
van Noordt and Tangi (
2023), companies are continuously exposed to a variety of risks that have the potential to disrupt operations, negatively impact profitability, or even jeopardize the organization’s existence in today’s fast-paced and interconnected business world. Natural disasters, cyberattacks, supply chain interruptions, market volatility, and legislative changes are just a few of the many variables that fall under this broad category of risks. Across all industries, businesses now place a premium on reducing these risks and guaranteeing business continuity. When an organization experiences a disruption—a natural disaster, cyberattack, economic downturn, or other unanticipated event that can possibly affect company operations—business continuity refers to the organization’s capacity to continue providing key services and functions both during and after the interruption. According to
Biolcheva and Valchev (
2022),
Le Coze and Antonsen (
2023), and
Perera et al. (
2023), traditional risk assessment techniques frequently fail to adequately handle the complexity and unpredictability of these contemporary concerns. Although risk assessment is a core component of risk management, traditional methods frequently depend on preset models, expert judgment, and historical data. The way hazards are evolving and the speed with which corporate settings are changing may not be sufficiently considered via these strategies. This shortcoming is especially noticeable when confronted with novel and extraordinary threats, such as the COVID-19 pandemic, which exposed vulnerabilities in many companies’ continuity strategies (
Drydakis 2022;
Perera et al. 2023). Additionally, manual risk assessment procedures are labor-intensive, time-consuming, and subject to human biases. Identifying small trends and patterns that may signal possible threats could prove challenging for these businesses due to their inability to analyze the large amounts of data created by modern enterprises.
Rodríguez-Espíndola et al. (
2022) assert that there is a critical need for more sophisticated and data-driven approaches to risk assessment that can adjust to changing conditions and offer timely insights.
The current body of research on business continuity and risk assessment has recognized the significance of using new technologies, especially artificial intelligence (AI), to improve risk management’s efficacy (
Meena and Madan 2023;
Galaz et al. 2021). Even though the use of AI in risk assessment has been studied to some extent, further investigation is needed to fully grasp the specific features of AI technology that can make a big difference in predicting risk assessment for business continuity. The promise of AI in risk management is frequently summarized in current research, but a thorough examination of the many AI methods and components that are most useful in various corporate situations is lacking (
Meena and Madan 2023). For example, machine learning algorithms, natural language processing (NLP), data analytics, and predictive maintenance systems are just a few of the many technologies that fall under the umbrella of AI. However, the relative impact and effectiveness of these AI components in mitigating various types of risks and ensuring business continuity remain underexplored (
Soldatos et al. 2022;
Schuett 2023;
Božić 2023;
Biolcheva and Valchev 2022). Moreover, while recognition of the potential benefits of AI in risk assessment continues to grow, empirical studies quantifying the actual improvements in risk assessment accuracy, efficiency, and overall business continuity resulting from AI adoption are limited. Organizations need concrete evidence to guide their investments in AI technologies for risk management (
Zarghami and Dumrak 2021;
Yue et al. 2024;
Zohuri et al. 2022).
Organizations are continually seeking effective strategies to identify, assess, and mitigate risks that threaten their continuity. According to
Jackson et al. (
2023), AI has become a transformational force in tackling these issues. Machine learning, NLP, data analytics, predictive maintenance, and other skills are only a few of the many functions of AI technology. With new opportunities to increase readiness and resilience, each of these AI features is vital to the predictive risk assessment for business continuity. According to
Brintrup et al. (
2023) and
Raza (
2023), the application of AI-driven predictive maintenance greatly enhances operational continuity. AI is able to forecast when infrastructure and machinery will break down or require maintenance by evaluating sensor data and equipment performance parameters. Preventive measures reduce unscheduled downtime, which is a vital component of business continuity, particularly in the industrial and critical infrastructure industries (
Fan et al. 2019;
Jiang et al. 2017;
Ray 2023;
Kagiyama et al. 2019).
One of the most important components of business continuity is incident response planning, which could be improved by AI. Security lapses, hacks, and other incidents could be quickly detected and handled by AI-powered incident response systems. The duration and effect of interruptions could be decreased by using these response systems to automate threat detection, evaluate attack patterns, and recommend response measures (
Al-rimy et al. 2019). By undertaking a thorough analysis of the capacity of various AI technology components in predictive risk assessment, this work sought to close the research gap. It aimed to offer empirical insights into the ways in which AI algorithms, natural language processing, data analytics, predictive maintenance, and incident response planning specifically improve risk assessment and, ultimately, guarantee business continuity. The goal of addressing these gaps is to provide useful advice to companies looking to use AI to successfully manage and mitigate risks.
1.1. Purpose of the Study
The primary purpose of this study was to examine the role of various aspects of AI technology in predictive risk assessment and how they contribute to ensuring business continuity.
1.2. Study Objectives
To examine the role of natural language processing (NLP) in automating risk assessment processes.
To analyze the influence of AI-powered data analytics in identifying emerging risks.
To examine the effectiveness of AI-driven predictive maintenance in reducing operational downtime.
To assess the integration of AI in incident response planning and its effect on minimizing business disruptions.
1.3. Research Hypotheses
Hypothesis 1. The integration of natural language processing (NLP) in risk assessment processes leads to more efficient and automated assessments, enhancing business continuity.
Hypothesis 2. AI-powered data analytics improve the identification of emerging risks, which enhances business continuity.
Hypothesis 3. AI-driven predictive maintenance reduces operational downtime and contributes to business continuity.
Hypothesis 4. The incorporation of AI in incident response planning minimizes business disruptions during crises, enhancing business continuity.
1.4. Significance of the Study
This study holds significance for businesses and organizations aiming to enhance their risk assessment processes and ensure business continuity. By understanding the role of AI in predictive risk assessment, organizations can make informed decisions regarding AI adoption. Additionally, this research contributes to the broader field of AI applications in business by providing empirical evidence of its impact on risk management and continuity planning.
3. Results
This section presents an interpretation and explanation of the findings of this study.
3.1. Demographic Characteristics
In terms of gender, the sample is predominantly male, constituting 60% of the respondents (
Table 1). Females represent 40% of the sample. This distribution reflects a gender imbalance in the sample, with a notable predominance of males. This could be indicative of the demographic trends in the field of AI or the specific sectors from which the sample was drawn. The age distribution of the respondents is skewed toward the middle-aged group.
The largest group, 50.6%, falls within the 36–45 years age bracket, indicating a mature and likely experienced cohort. The next significant group, 27.5%, is within the 25–35 years age bracket, representing the younger professionals in the field. Regarding educational qualifications, the overwhelming majority, 86.1%, hold a degree. This high percentage underscores the importance of advanced education in the field of AI, where diploma holders constitute 11.1%, while only a small fraction holds either a certificate or a master’s degree. The low representation of master’s degree holders could be due to various factors, including the recent growth of AI as a field of study or the sample’s specific professional contexts. In terms of experience with AI, the majority of respondents, 59.7%, have 2–10 years of experience, suggesting a significant presence of professionals who have a substantial but not extensive background in AI. Those with more than 10 years of experience account for 35.8%, indicating a significant proportion of highly experienced individuals in the field.
3.2. Descriptive Results
The results concerning the role of NLP in automating risk assessment processes are presented in
Table 2.
Table 2 shows that a high percentage (70.2%) agree or strongly agree, indicating that most respondents believe NLP has a positive impact on the speed of risk assessment processes. A substantial majority (79.2%) agree, suggesting that NLP is perceived to enhance the accuracy of risk identification. The absence of strongly agree responses might indicate some reservations or the need for further improvements. A total of 71.4% either agree or strongly agree, highlighting NLP’s effectiveness in handling unstructured data, a critical aspect of risk assessment. The majority (84.4%) trust NLP over manual analysis, indicating a high level of confidence in NLP’s capabilities in risk assessment. A majority (85.7%) agree or strongly agree that NLP is cost-effective, which is a vital factor for technology adoption in business. Most respondents (78.9%) find NLP tools user-friendly and easy to integrate, suggesting good usability and compatibility with existing systems. A combined 83.1% agree or strongly agree, indicating that NLP contributes positively to the consistency of risk assessments.
This study analyzed the influence of AI-powered data analytics in identifying emerging risks and the results are presented in
Table 3.
The results in
Table 3 show that a significant 70.1% of respondents (agree and strongly agree combined) believe that AI-powered data analytics enables quicker identification of emerging risks. This suggests that AI’s speed and efficiency in processing and analyzing large datasets are highly valued in a business environment where a rapid response to changing conditions is critical. Furthermore, 71.5% of respondents agree or strongly agree that AI enhances the accuracy of predicting potential risks. This reflects the advanced capabilities of AI in pattern recognition and predictive modeling, which are essential for accurate risk forecasting. A notable 64.3% (agree and strongly agree) feel that AI in data analytics has improved their organization’s responsiveness to unforeseen risks. This underscores AI’s role in enabling organizations to react swiftly and effectively to unexpected challenges, enhancing resilience and adaptability. A majority of 77.9% view AI-driven analytics tools as integral to their strategic risk management planning. This indicates a strong reliance on AI for strategic decision-making, underlining its importance in long-term risk management and business planning. An overwhelming 94.8% of respondents value the insights provided by AI-powered data analytics in their risk assessment process. This demonstrates the trust and reliance placed on AI’s analytical capabilities to guide risk assessment and management strategies. Also, 70.4% believe that AI data analytics has led to more comprehensive risk identification compared to traditional methods. This suggests that AI’s extensive data processing capabilities are crucial for identifying a broader range of potential risks, thus enhancing overall risk management. Finally, 93.5% agree or strongly agree that the use of AI supports a proactive approach to risk management. This reflects a significant shift from reactive to proactive risk management, facilitated by AI’s predictive capabilities.
This study further examined the effectiveness of AI-driven predictive maintenance in reducing operational downtime and the results are presented in
Table 4.
Table 4 shows that a significant majority (79.2%) of respondents agree (agree and strongly agree combined) that AI-driven predictive maintenance has notably reduced operational downtime. This high percentage underscores AI’s effectiveness in identifying potential equipment failures before they occur, thereby minimizing unexpected breakdowns and production halts. Furthermore, 76.8% of participants acknowledge the accuracy and timeliness of AI-generated predictive alerts. This reflects the advanced capabilities of AI systems in analyzing vast amounts of data to provide reliable predictions and alerts, enabling proactive maintenance actions. A substantial 82.5% of respondents agree that AI in maintenance leads to cost savings. This highlights AI’s role in preventing costly repairs and replacements by timely identifying issues that can be addressed before they escalate into major problems. Consequently, 79% of respondents agree that AI-driven maintenance strategies have enhanced the lifespan of critical equipment. AI’s ability to provide precise maintenance schedules and identify minor issues before they turn major contributes to prolonging equipment life and ensuring optimal performance. The responses vary more regarding efficiency in comparison to traditional maintenance approaches, with 61.1% agreeing that AI-based predictive maintenance is more efficient than traditional methods. This indicates a positive reception, yet also suggests room for improvement or a need for wider understanding and adoption of AI-based methods. Furthermore, 75.7% agree that implementing AI in maintenance has boosted overall operational efficiency. This reflects the broader impact of AI in optimizing not just equipment maintenance, but also streamlining various operational processes. An overwhelming 86.3% express satisfaction with AI’s role in their organization’s predictive maintenance. This high level of satisfaction demonstrates the perceived value and effectiveness of AI in this domain.
This study evaluated the integration of AI in incident response planning and its effect on minimizing business disruptions and the results are presented in
Table 5.
According to
Table 5’s results, the majority of respondents (76.9%) agree, with a minority (11.8%) strongly agreeing that the use of AI in incident response planning has significantly decreased business disruptions. Given the high degree of agreement, it appears that AI tools can effectively reduce the impact of accidents on company operations, resulting in an environment that enables businesses to be more resilient. Regarding AI in incident response helping with quicker recovery, opinions are divided; notable numbers of 38.7% strongly agree and 21.8% agree that AI helps with quicker recovery from incidents. Nonetheless, a sizable 37.7% are indifferent, suggesting some ambiguity or no discernible influence in this field. This suggests that although AI has advantages, its ability to expedite recovery may not be consistently evident in all contexts or industries. With another 27.9% in agreement, more than half of the respondents (51.7%) strongly think that AI helps in precisely forecasting the consequences of probable incidents. In order to effectively manage risks and plan for business continuity, proactive risk management requires a strong conviction about the accuracy of AI’s predictive powers. Here, the answers are more divided: 44.2% agree and 16.9% strongly agree that the integration of AI has enhanced management and coordination. Nonetheless, a total of 23.4% (5.2% strongly disagree and 18.2% disagree) voice doubt, indicating that variables like organizational preparedness, incident complexity, and the quality of implementation may have an impact on how beneficial AI is in this area. A balanced opinion is shown in the responses, with 29.9% agreeing and 11.7% strongly agreeing that plans enhanced by AI are more thorough and efficient. Nonetheless, a noteworthy 18.2% disagree and 36.4% neither agree nor disagree, indicating a split view on the superiority of AI-enhanced plans over conventional techniques. This may be the result of organizational context differences or variances in the quality of AI integration. The use of AI in crisis response planning has boosted corporate resilience according to a substantial majority (71.7%), with 9.7% strongly agreeing. This high degree of agreement emphasizes how AI may strengthen company operations’ overall resilience to disturbances. Similarly, 10.6% strongly agree and the majority (67.8%) agree that they are confident in AI-integrated plans’ capacity to manage upcoming business challenges. There is a widespread sense of confidence regarding AI’s ability to handle issues in the future, but there are still some misgivings, as evidenced by the 9.1% of respondents who disagree and the 9.2% who are neutral.
This study also identified the different outcomes of business continuity, and the results are presented in
Figure 2.
Figure 2 shows that the majority of the respondents (28.8%) indicated that the most significant outcome of effective business continuity is the continued improvement of revenue generation. This suggests that businesses that focus on maintaining uninterrupted operations are more likely to experience sustained or increasing revenue streams. This was followed by 21.6% of respondents who highlighted improved agility and adaptability abilities as key outcomes. This reflects the importance of being able to quickly adjust to changing circumstances, such as market shifts or emergencies, which is a crucial aspect of maintaining business continuity. Agile and adaptable businesses are better positioned to navigate unpredictable situations, thereby securing their operations and future. Furthermore, 16.3% of respondents chose improved risk management to achieve effective business continuity. Effective business continuity planning inherently involves identifying and mitigating various risks, thus enhancing the overall risk management capabilities of the organization. This could include risks related to financial, operational, technological, and reputational factors. Better employee safety and morale was cited by 11.3% of the respondents. This confirms the human element in business continuity, highlighting that safeguarding employees and maintaining high morale is not just an ethical obligation but also a business strategy that can lead to more resilient and effective operations. An improved competitive advantage was noted by 9.7% of respondents. This suggests that companies that are able to maintain continuous operations, especially in the face of disruptions that affect competitors, can gain an edge in the market. A smaller proportion of respondents, 7.6%, indicated improved regulatory compliance as a significant outcome. This is likely due to the fact that many business continuity strategies align with regulatory requirements, ensuring that companies are not only resilient but also compliant with legal and industry standards. The smallest portion of respondents (4.7%) mentioned other benefits, such as improved customer confidence and improved reputation. These are critical but often indirect benefits of business continuity, where consistent and reliable operations lead to increased trust among customers and stakeholders, thereby enhancing the company’s reputation in the market.
3.3. Regression Analysis
Table 6 presents the results of a multiple regression analysis that aimed to assess the cumulative impact of various AI technologies on business continuity.
From the regression results, the R square value is 0.584, and the adjusted R square is 0.501. This indicates that approximately 58.4% of the variance in business continuity can be explained by the independent variables in the model. The adjusted R square value, which accounts for the number of predictors in the model, is slightly lower but still significant. This suggests that the model has a good fit. Furthermore, the F statistic (63.01) and its associated significance value (0.001) indicate that the overall regression model is statistically significant.
The constant (intercept) value of 61.35 with a standard error of 8.13 and a significant T-value of 6.14 (p = 0.003) suggests that when all of the independent variables are at zero, the expected value for business continuity is notably high. This indicates other factors not covered in the model could also be influencing business continuity.
NLP shows a small positive unstandardized coefficient (0.114) and a standardized coefficient of 0.146, with a T-value of 1.104 (p = 0.002). This suggests that while NLP in automating risk assessment processes has a positive effect on business continuity, its impact is relatively moderate. While NLP does contribute to more efficient and automated risk assessments, its impact on business continuity is not as strong as some other factors. Hypothesis one is accepted but with a caveat that NLP’s influence is significant yet not dominant.
The AI-powered data analytics variable shows a significant positive impact on business continuity, with an unstandardized coefficient of 0.341 and a high standardized coefficient of 0.650. The T-value of 5.03 (p = 0.001) indicates a strong positive relationship, suggesting that using AI for data analytics in identifying emerging risks is highly beneficial for business continuity. Therefore, hypothesis two is strongly accepted, indicating a critical role of AI in risk identification.
AI-driven predictive maintenance has an unstandardized coefficient of 0.174 and a lower standardized coefficient of 0.046, with a T-value of 1.17 (p = 0.011). AI-driven predictive maintenance does contribute to reducing operational downtime and thus aids in business continuity. However, its impact is comparatively less pronounced. Hypothesis three is accepted but indicates a lesser impact than in the other AI applications.
Integration of AI in Incident response planning shows the highest impact with an unstandardized coefficient of 0.361 and a standardized coefficient of 0.370. The T-value of 11.03 (p = 0.000) is highly significant. This suggests that integrating AI into incident response planning is critically beneficial for maintaining business continuity. Therefore, hypothesis four is strongly accepted, highlighting the critical role of AI in effective incident response planning for business continuity.
4. Materials and Methods
4.1. Study Design
This study adopted a quantitative research design, employing a cross-sectional approach to examine the role of AI technology in predictive risk assessment for business continuity. This design was selected because it was efficient in collecting a lot of data in a short amount of time, which made it easier to compare and analyze the effects of various AI technologies on business continuity. Because the survey was cross-sectional, it provided an overview of the thoughts and experiences of technology specialists at a particular moment in time.
4.2. Target Population
This research’s population of interest was categorized as technology specialists with knowledge of AI applications across a range of Greek sectors. This group of experts was chosen as an essential source of data due to their in-depth understanding of and expertise with AI technology (3.5% of persons with tertiary education and employed in science and technology) (
Eurostat 2022). Because Greece has a distinct economic and technical environment that may have an impact on the uptake and application of AI technologies, focusing on Greek industries gave this research a regional character that deepens it.
4.3. Sample Size
This study selected a sample size of 360 technology experts. The Krejcie and Morgan table, a commonly used technique for estimating sample sizes in research, was used to arrive at this figure (
Krejcie and Morgan 1970). It was determined that the selected sample size would adequately give a representative overview of the viewpoints and experiences of Greek IT professionals with relation to AI applications in the business continuity and predictive risk assessment. Considering the time and resources available for the investigation, a sample size of 360 individuals was deemed enough to strike a compromise between statistical power and practicality.
4.4. Sampling Technique
A stratified random sampling technique was employed to ensure a representative sample of the target population. To apply this approach, the population was divided into discrete industry-based groupings, or strata, from which participants were chosen at random. More broadly applicable conclusions were made possible by the method’s guarantee that the sample fairly represented the variety found among Greece’s technology experts.
4.5. Data Collection
A structured questionnaire was employed to gather quantitative information on the viewpoints and experiences of the participants regarding the application of AI technology in risk assessment and business continuity. With items ranging from “Strongly Disagree” to “Strongly Agree”, the questionnaire used a Likert scale approach. Because of its capacity to gauge participants’ thoughts or attitudes about certain claims about AI technology and its uses in a range of commercial settings, this format was selected. The questionnaire was mostly distributed online, using email lists, industry forums, and professional networks to reach the target group of technology professionals in Greece’s many industries. A larger audience, responder convenience, and a greater response rate were all guaranteed by online dissemination. It also made the process of gathering replies faster, which is crucial for preserving the data’s relevance in a rapidly evolving field like AI. To ensure a high response rate and quality of responses, ethical considerations were adhered to whereby participants were informed about the importance of the study and its potential impact on understanding AI’s role in business continuity. Clear instructions were provided on how to complete the questionnaire, and participants were assured of the confidentiality of their responses to encourage candidness in their answers. Follow-up reminders were also sent to participants who did not respond initially, to maximize the response rate.
4.6. Measurement of Variables
The primary variables of interest in this study were the perceived effectiveness and impact of different AI technologies (like NLP, AI-powered data analytics, AI-driven predictive maintenance, and AI integration in incident response planning) on business continuity. These variables were measured using a Likert scale, providing a quantitative measure of participants’ attitudes and perceptions. A Likert scale allowed for a nuanced analysis of the degree to which participants agreed or disagreed with statements regarding the effectiveness of AI technologies in various aspects of business continuity.
4.7. Data Analysis
The questionnaires underwent preliminary processing after being collected, which involved verifying their uniformity and completeness. To preserve the integrity of the data, any incomplete or inconsistently filled-out questionnaires were either corrected (where feasible) or left out of the analysis. This first screening made sure that the data used for the subsequent study were correct and comprehensive. The data collected through the questionnaires were analyzed using SPSS (Statistical Package for the Social Sciences). The analysis primarily involved regression analysis to investigate the relationships between the different AI technologies and their impact on business continuity. Regression analysis was chosen (Equation (1)) base on its ability to determine the strength and direction of these relationships, providing insights into which AI technologies have the most significant impact on predictive risk assessment and business continuity (
Kalfas et al. 2023;
Kalogiannidis et al. 2023b).
where
Y = Business continuity
β0 = Constant (coefficient of intercept)
= Natural language processing (NLP) in automating risk assessment processes
= AI-powered data analytics in identifying emerging risks
= AI-driven predictive maintenance in reducing operational downtime
= Integration of AI in incident response planning
= Represents the error term in the multiple regression model
The different hypotheses of this study were tested based on the obtained regression results at the 0.05 level of significance.
5. Discussion
This study’s findings indicate a strong perception of NLP as a positive force in speeding up and enhancing the accuracy of risk assessment processes. The high percentage of respondents agreeing that NLP significantly speeds up risk assessment and leads to more accurate identification of risks (
van Noordt and Tangi 2023;
Brás et al. 2023b;
Mohamed 2023) underscores the efficiency and effectiveness of NLP in handling unstructured data. This is in line with the insights provided by
Drydakis (
2022) and
A. Kumar et al. (
2023), who noted the capability of NLP in automating and streamlining risk assessment. This study also shows that when compared to conventional approaches, NLP results in a more accurate identification of dangers. Because NLP algorithms are skilled at understanding the subtleties of human language, they may extract insights and clues that could be missed by manual analysis. This increased precision is essential for spotting emerging hazards that might have serious consequences in addition to evident ones.
van Noordt and Tangi (
2023), for example, pointed out how NLP’s capacity to sift through enormous datasets might reveal hidden correlations and patterns, boosting the scope and depth of risk assessment. The ability of NLP technology to comprehend unstructured data is revolutionary for risk assessment. The majority of the data created in the current digital era is unstructured data, which might range from text documents to audio recordings. Over 80% of an organization’s data are unstructured, as
Kesa (
2023) noted, and NLP’s ability to interpret these data effectively is priceless. This enables more sophisticated and informed decision-making in addition to giving firms a more complete picture of potential threats.
This study also shows that findings from NLP are more trusted compared to those from manual analysis. This confidence is proof of the dependability and sophistication of contemporary NLP systems. According to
Biolcheva and Valchev (
2022), the accuracy and dependability of NLP have been greatly improved by advances in AI and machine learning, making NLP a reliable tool in the risk assessment toolbox. Furthermore, these results imply that NLP is an affordable option for automating risk assessment procedures. In a time when companies are constantly searching for budget-friendly alternatives, NLP offers a workable option that lowers the time and personnel costs related to manual analysis while simultaneously improving the overall caliber of risk assessment. Budget restrictions are a major factor for small and medium-sized firms (SMEs) in Greece, as
Soldatos et al. (
2022) has noted. This makes this feature of NLP especially essential in their environment. It has been discovered that incorporating NLP into risk assessment procedures increases consistency. While human error and biases are common in manual risk assessments, natural language processing (NLP) provides a standardized method of data analysis.
Perera et al. (
2023) also pointed out that maintaining consistency is essential to guaranteeing the validity of risk assessments and their ability to be repeated over time. Regression research, however, suggests that NLP has a moderate influence on business continuity, suggesting that while NLP is helpful, it is not the only factor that determines business continuity.
This study’s focus on AI-powered data analytics in identifying emerging risks reveals a critical area where artificial intelligence significantly contributes to predictive risk assessment and business continuity. AI-powered data analytics harnesses machine learning algorithms and advanced data processing to uncover valuable insights from extensive datasets, enabling organizations to foresee potential risks and adapt strategies proactively. One of the standout findings is the role of AI-powered data analytics in expediting the identification of emerging risks. Traditional risk assessment methods are often constrained by their reliance on historical data and predefined risk factors, which may not capture the evolving nature of the business landscape where new risks can surface rapidly. AI-driven analytics, however, excels in detecting subtle patterns and anomalies in data that could indicate the emergence of previously unrecognized risks. This capability is highlighted in
PwC-GMIS (
2020) report, which underscores the widespread adoption and the significant improvement in analytics capabilities brought about by AI in various organizations.
The advanced capabilities of AI in pattern recognition and predictive modeling are crucial for accurate risk forecasting. AI algorithms can analyze large datasets to identify trends that human analysts might overlook. This aspect is particularly important given the dynamic nature of risk factors in modern business environments. Studies like those by
Galaz et al. (
2021) and
Meena and Madan (
2023) have demonstrated the substantial impact of AI in predicting operational risks, affirming its indispensable role in enhancing risk management strategies (
Meena and Madan 2023;
Galaz et al. 2021). These findings resonate with the study’s observation of a strong positive relationship between AI-powered data analytics and business continuity, as indicated by the high standardized coefficients in the regression analysis. AI algorithms can continuously analyze data streams, allowing organizations to respond promptly to changing conditions. This feature is crucial in areas like supply chain risk management, where factors like weather patterns, geopolitical events, and supplier performance need constant monitoring. The real-time monitoring capability of AI is also echoed in the 2020 report by PwC-GMIS, emphasizing AI’s role in enhancing organizational responsiveness to unforeseen risks (
PwC-GMIS 2020). This study’s findings corroborate the research by
van Noordt and Tangi (
2023), indicating that AI-powered data analytics leads to more comprehensive risk identification compared to traditional methods (
van Noordt and Tangi 2023). This comprehensive approach to risk identification, enabled by AI, is vital for organizations to develop a thorough understanding of the potential threats they face.
The effectiveness of AI-driven predictive maintenance in reducing operational downtime is well-recognized among respondents (
Arpilleda 2023;
Chen et al. 2021). This finding corroborates the reports by the International Telecommunication Union (
ITU 2022) and
Claudino et al. (
2019), which highlight the cost savings and operational reliability achieved through predictive maintenance. However, the regression analysis suggests that its impact, while positive, is less pronounced compared to other AI applications. The integration of AI in incident response planning shows a high impact on minimizing business disruptions (
Charles et al. 2023;
Tan et al. 2022). This is consistent with
OECD’s (
2022) findings on rising cyber threats and the need for AI-driven incident response systems. The regression analysis strongly supports the critical role of AI in effective incident response planning for business continuity, underlining its significant contribution in maintaining operational resilience during crises.
This study’s findings provide valuable insights into how different AI technologies can be strategically leveraged for predictive risk assessment and business continuity. While each AI aspect contributes uniquely, the integration of AI in incident response planning stands out as particularly impactful. These insights are essential for businesses in Greece and beyond, as they navigate an increasingly complex and risk-prone business environment. This study, therefore, not only fills the identified research gap but also offers practical guidance for organizations aiming to harness AI for effective risk management and business continuity.
6. Conclusions
This study presented a comprehensive analysis of the role of AI in predictive risk assessment for the business continuity, with a particular focus on the Greek business landscape. The results show how different AI technologies may significantly improve risk management and guarantee the ongoing operations of businesses. NLP has proven to be a rather successful tool for automating risk assessment procedures. NLP has a generally mild effect on business continuity, even though it greatly expedites the risk assessment process and produces more accurate risk identification compared to older techniques. This shows that, even while NLP may help automate and streamline risk assessment procedures, it is only one piece of the puzzle when it comes to guaranteeing business continuity. On the other hand, emerging risk identification is more significantly impacted by AI-powered data analytics. AI’s capacity to analyze massive amounts of data quickly and spot patterns has proven to be quite helpful in quickly identifying new threats. This capacity enables an organizational response to unanticipated hazards and increases the accuracy of risk prediction. This study provides compelling evidence for the application of AI in data analytics as a vital instrument for strategic risk management and predictive risk assessment.
This study discovered that, despite having a less noticeable effect than other AI applications, AI-driven predictive maintenance helps to reduce operational downtime. AI-powered predictive maintenance lowers the cost of equipment replacement and repair while extending the life of crucial equipment, both of which are essential for business continuity. However, the efficacy of AI varies in this field and is probably affected by the particular environment in which it is used.
One especially important use for business continuity that has evolved is the incorporation of AI into incident response planning. AI plays a critical role in event detection, management, and response, particularly with regard to cybersecurity concerns. This study discovered that incident response plans with AI enhancements are more thorough and efficient than conventional plans, boosting firms’ resistance to interruptions. These results were further supported by the regression analysis, which showed that AI applications, especially in data analytics and incident response planning, significantly improved business continuity. The regression analysis’s high R square value highlights the significant amount of business continuity variance that these AI solutions can account for.
This study’s overall findings emphasize how AI is revolutionizing risk assessment and business continuity planning. It highlights how important it is for companies, particularly those in Greece, to incorporate AI technology into their risk management plans. This integration will better equip them to anticipate and handle risks as well as bounce back from setbacks with agility, which will provide them with a competitive advantage in the market. These results also imply that although AI technologies have a lot to offer, how well they are implemented and the particular corporate environment in which they are used will determine how beneficial they are. Thus, companies have to develop a comprehensive strategy, including several AI technologies in a way that suits their unique requirements and difficulties. Furthermore, in order to stay up to date with the changing business environment and developing dangers, these technologies must be continuously evaluated and adjusted.
6.1. Implications of This Study
This study highlights how crucial NLP is to the automation of risk assessment procedures. Although NLP has little effect on business continuity, it greatly expedites the risk assessment procedure, which results in quicker and more accurate detection of possible hazards. Businesses may use NLP to handle enormous volumes of unstructured data quickly by incorporating it into their risk assessment frameworks. Risk assessments become more regular and dependable as a result of this automation, which also increases productivity and lowers the possibility of human error. It is crucial to remember that, even considering NLP’s advantages, a complete risk management strategy should incorporate other AI technologies as well.
One of the most important aspects of business continuity is the impact of AI-powered data analytics in spotting new threats. Based on this study, it can be concluded that AI-driven data analytics greatly increases the speed and accuracy of risk prediction. This ability is crucial in a constantly changing corporate environment. Businesses should leverage AI to process and analyze large datasets, enabling them to identify subtle trends and emerging risks that traditional methods might miss. This proactive approach to risk management is crucial for staying ahead of potential disruptions and ensuring business agility.
AI-driven predictive maintenance’s role in reducing operational downtime, although less pronounced than other AI applications, is still notable. Predictive maintenance minimizes the frequency and impact of equipment failures, thereby ensuring uninterrupted operations. This aspect is particularly relevant for industries reliant on machinery and equipment. By adopting AI-driven predictive maintenance, businesses can extend the lifespan of their assets, reduce maintenance costs, and enhance their overall operational efficiency.
6.2. Areas for Future Research and Limitations of This Study
Future research may focus on more sophisticated AI methods like deep learning, even though this paper covers AI algorithms and machine learning models in great detail. Investigations into how deep learning, with its advanced pattern recognition skills, may offer even more detailed insights into risk assessment and management and may be necessary. Future research should also address the ethical and regulatory implications of using AI in risk assessment. This includes exploring data privacy concerns, the potential for bias in AI algorithms, and how different regulatory frameworks impact the adoption of AI technologies.
The present research primarily emphasizes the use of AI technology in predicting risk assessment. Nevertheless, there is a lack of emphasis on business continuity management (BCM) as a means to mitigate the effects of artificial intelligence. Subsequent study should prioritize the development of inventive systems that can effectively tackle both business continuity management and the business consequences of AI. Merging BCM with risk assessment may cause confusion among readers due to their distinct disciplinary domains.
Furthermore, this research had methodological constraints. Although it examined the whole nation of Greece, the information used was only gathered from Greece, which might have compromised the generalizability of the findings. To overcome this constraint, we selected Greek IT experts as a representative sample and extrapolated their findings to include other regions worldwide, rather than only focusing on Greece.